INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(88027, 2016, 'plos', 'Cytofkit: A Bioconductor Package for an Integrated Mass Cytometry Data Analysis Pipeline', 'Chen, Hao|Lau, Mai Chan|Wong, Michael Thomas|Newell, Evan W|Poidinger, Michael|Chen, Jinmiao', 'Agency for Science, Technology and Research (A*STAR), Singapore Immunology Network (SIgN), Singapore.|Agency for Science, Technology and Research (A*STAR), Singapore Immunology Network (SIgN), Singapore.|Agency for Science, Technology and Research (A*STAR), Singapore Immunology Network (SIgN), Singapore.|Agency for Science, Technology and Research (A*STAR), Singapore Immunology Network (SIgN), Singapore.|Agency for Science, Technology and Research (A*STAR), Singapore Immunology Network (SIgN), Singapore.|Agency for Science, Technology and Research (A*STAR), Singapore Immunology Network (SIgN), Singapore.', 'Singapore|Singapore|Singapore|Singapore|Singapore|Singapore', 94, '-', 'Journal Article', '10.1371/journal.pcbi.1005112', '27662185', 'Single-cell mass cytometry significantly increases the dimensionality of cytometry analysis as compared to fluorescence flow cytometry, providing unprecedented resolution of cellular diversity in tissues. However, analysis and interpretation of these high-dimensional data poses a significant technical challenge. Here, we present cytofkit, a new Bioconductor package, which integrates both state-of-the-art bioinformatics methods and in-house novel algorithms to offer a comprehensive toolset for mass cytometry data analysis. Cytofkit provides functions for data pre-processing, data visualization through linear or non-linear dimensionality reduction, automatic identification of cell subsets, and inference of the relatedness between cell subsets. This pipeline also provides a graphical user interface (GUI) for ease of use, as well as a shiny application (APP) for interactive visualization of cell subpopulations and progression profiles of key markers. Applied to a CD14-CD19- PBMCs dataset, cytofkit accurately identified different subsets of lymphocytes, applied to a human CD4+ T cell dataset, cytofkit uncovered multiple subtypes of TFH cells spanning blood and tonsils. Cytofkit is implemented in R, licensed under the Artistic license 2.0, and freely available from the Bioconductor website, https://bioconductor.org/packages/cytofkit/. Cytofkit is also applicable for flow cytometry data analysis.'),
(88028, 2016, 'plos', 'Template-Based Modeling of Protein-RNA Interactions', 'Zheng, Jinfang|Kundrotas, Petras J|Vakser, Ilya A|Liu, Shiyong', 'School of Physics and Key Laboratory of Molecular Biophysics of the Ministry of Education, Huazhong University of Science and Technology, Wuhan, Hubei, China.|Center for Computational Biology and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas, United States of America.|Center for Computational Biology and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas, United States of America.|School of Physics and Key Laboratory of Molecular Biophysics of the Ministry of Education, Huazhong University of Science and Technology, Wuhan, Hubei, China.', 'China|United States|United States|China', 3, 'Amino Acid Sequence|Cluster Analysis|Computational Biology|Humans|Models, Molecular|RNA|RNA-Binding Proteins|Sequence Alignment|Software', 'Journal Article', '10.1371/journal.pcbi.1005120', '27662342', 'Protein-RNA complexes formed by specific recognition between RNA and RNA-binding proteins play an important role in biological processes. More than a thousand of such proteins in human are curated and many novel RNA-binding proteins are to be discovered. Due to limitations of experimental approaches, computational techniques are needed for characterization of protein-RNA interactions. Although much progress has been made, adequate methodologies reliably providing atomic resolution structural details are still lacking. Although protein-RNA free docking approaches proved to be useful, in general, the template-based approaches provide higher quality of predictions. Templates are key to building a high quality model. Sequence/structure relationships were studied based on a representative set of binary protein-RNA complexes from PDB. Several approaches were tested for pairwise target/template alignment. The analysis revealed a transition point between random and correct binding modes. The results showed that structural alignment is better than sequence alignment in identifying good templates, suitable for generating protein-RNA complexes close to the native structure, and outperforms free docking, successfully predicting complexes where the free docking fails, including cases of significant conformational change upon binding. A template-based protein-RNA interaction modeling protocol PRIME was developed and benchmarked on a representative set of complexes.'),
(88029, 2016, 'plos', 'Modeling the Effects of Morphine on Simian Immunodeficiency Virus Dynamics', 'Vaidya, Naveen K|Ribeiro, Ruy M|Perelson, Alan S|Kumar, Anil', 'Department of Mathematics and Statistics, University of Missouri-Kansas City, Missouri, United States of America.|Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.|Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.|Division of Pharmacology, School of Pharmacy, University of Missouri-Kansas City, Missouri, United States of America.', 'United States|Mexico|Mexico|United States', 2, '-', 'Journal Article', '10.1371/journal.pcbi.1005127', '27668463', 'Complications of HIV-1 infection in individuals who utilize drugs of abuse is a significant problem, because these drugs have been associated with higher virus replication and accelerated disease progression as well as severe neuropathogenesis. To gain further insight it is important to quantify the effects of drugs of abuse on HIV-1 infection dynamics. Here, we develop a mathematical model that incorporates experimentally observed effects of morphine on inducing HIV-1 co-receptor expression. For comparison we also considered viral dynamic models with cytolytic or noncytolytic effector cell responses. Based on the small sample size Akaike information criterion, these models were inferior to the new model based on changes in co-receptor expression. The model with morphine affecting co-receptor expression agrees well with the experimental data from simian immunodeficiency virus infections in morphine-addicted macaques. Our results show that morphine promotes a target cell subpopulation switch from a lower level of susceptibility to a state that is about 2-orders of magnitude higher in susceptibility to SIV infection. As a result, the proportion of target cells with higher susceptibility remains extremely high in morphine conditioning. Such a morphine-induced population switch not only has adverse effects on the replication rate, but also results in a higher steady state viral load and larger CD4 count drops. Moreover, morphine conditioning may pose extra obstacles to controlling viral load during antiretroviral therapy, such as pre-exposure prophylaxis and post infection treatments. This study provides, for the first time, a viral dynamics model, viral dynamics parameters, and related analytical and simulation results for SIV dynamics under drugs of abuse.'),
(88030, 2016, 'plos', 'A Novel Drug-Mouse Phenotypic Similarity Method Detects Molecular Determinants of Drug Effects', 'Prinz, Jeanette|Vogt, Ingo|Adornetto, Gianluca|Campillos, Mónica', 'Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany.|Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany.|Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany.|Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany.', 'Germany|Germany|Germany|Germany', 5, '-', 'Journal Article', '10.1371/journal.pcbi.1005111', '27673331', 'The molecular mechanisms that translate drug treatment into beneficial and unwanted effects are largely unknown. We present here a novel approach to detect gene-drug and gene-side effect associations based on the phenotypic similarity of drugs and single gene perturbations in mice that account for the polypharmacological property of drugs. We scored the phenotypic similarity of human side effect profiles of 1,667 small molecules and biologicals to profiles of phenotypic traits of 5,384 mouse genes. The benchmarking with known relationships revealed a strong enrichment of physical and indirect drug-target connections, causative drug target-side effect links as well as gene-drug links involved in pharmacogenetic associations among phenotypically similar gene-drug pairs. The validation by in vitro assays and the experimental verification of an unknown connection between oxandrolone and prokineticin receptor 2 reinforces the ability of this method to provide new molecular insights underlying drug treatment. Thus, this approach may aid in the proposal of novel and personalized treatments.'),
(88031, 2016, 'plos', 'Data-Driven Metabolic Pathway Compositions Enhance Cancer Survival Prediction', 'Auslander, Noam|Wagner, Allon|Oberhardt, Matthew|Ruppin, Eytan', 'Center for Bioinformatics and Computational Biology and the Department of Computer Science, University of Maryland, College Park, Maryland, United States of America.|Department of Electrical Engineering and Computer Science and the Center for Computational Biology, University of California, Berkeley, Berkeley, California, United States of America.|Center for Bioinformatics and Computational Biology and the Department of Computer Science, University of Maryland, College Park, Maryland, United States of America.|Center for Bioinformatics and Computational Biology and the Department of Computer Science, University of Maryland, College Park, Maryland, United States of America.', 'United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1005125', '27673682', 'Altered cellular metabolism is an important characteristic and driver of cancer. Surprisingly, however, we find here that aggregating individual gene expression using canonical metabolic pathways fails to enhance the classification of noncancerous vs. cancerous tissues and the prediction of cancer patient survival. This supports the notion that metabolic alterations in cancer rewire cellular metabolism through unconventional pathways. Here we present MCF (Metabolic classifier and feature generator), which incorporates gene expression measurements into a human metabolic network to infer new cancer-mediated pathway compositions that enhance cancer vs. adjacent noncancerous tissue classification across five different cancer types. MCF outperforms standard classifiers based on individual gene expression and on canonical human curated metabolic pathways. It successfully builds robust classifiers integrating different datasets of the same cancer type. Reassuringly, the MCF pathways identified lead to metabolites known to be associated with the pertaining specific cancer types. Aggregating gene expression through MCF pathways leads to markedly better predictions of breast cancer patients survival in an independent cohort than using the canonical human metabolic pathways (C-index = 0.69 vs. 0.52, respectively). Notably, the survival predictive power of individual MCF pathways strongly correlates with their power in predicting cancer vs. noncancerous samples. The more predictive composite pathways identified via MCF are hence more likely to capture key metabolic alterations occurring in cancer than the canonical pathways characterizing healthy human metabolism.'),
(88032, 2016, 'plos', 'Decreases in Gap Junction Coupling Recovers Ca2+ and Insulin Secretion in Neonatal Diabetes Mellitus, Dependent on Beta Cell Heterogeneity and Noise', 'Notary, Aleena M|Westacott, Matthew J|Hraha, Thomas H|Pozzoli, Marina|Benninger, Richard K P', 'Department of Bioengineering, University of Colorado, Anschutz Medical campus, Aurora, Colorado, United States of America.|Department of Bioengineering, University of Colorado, Anschutz Medical campus, Aurora, Colorado, United States of America.|Department of Bioengineering, University of Colorado, Anschutz Medical campus, Aurora, Colorado, United States of America.|Department of Bioengineering, University of Colorado, Anschutz Medical campus, Aurora, Colorado, United States of America.|Department of Bioengineering, University of Colorado, Anschutz Medical campus, Aurora, Colorado, United States of America.', 'United States|United States|United States|United States|United States', 8, '-', 'Journal Article', '10.1371/journal.pcbi.1005116', '27681078', 'Diabetes is caused by dysfunction to xcexb2-cells in the islets of Langerhans, disrupting insulin secretion and glucose homeostasis. Gap junction-mediated electrical coupling between xcexb2-cells in the islet plays a major role in coordinating a pulsatile secretory response at elevated glucose and suppressing insulin secretion at basal glucose. Previously, we demonstrated that a critical number of inexcitable cells can rapidly suppress the overall islet response, as a result of gap junction coupling. This was demonstrated in a murine model of Neonatal Diabetes Mellitus (NDM) involving expression of ATP-insensitive KATP channels, and by a multi-cellular computational model of islet electrical activity. Here we examined the mechanisms by which gap junction coupling contributes to islet dysfunction in NDM. We first verified the computational model against [Ca2+] and insulin secretion measurements in islets expressing ATP-insensitive KATP channels under different levels of gap junction coupling. We then applied this model to predict how different KATP channel mutations found in NDM suppress [Ca2+], and the role of gap junction coupling in this suppression. We further extended the model to account for stochastic noise and insulin secretion dynamics. We found experimentally and in the islet model that reductions in gap junction coupling allow progressively greater glucose-stimulated [Ca2+] and insulin secretion following expression of ATP-insensitive KATP channels. The model demonstrated good correspondence between suppression of [Ca2+] and clinical presentation of different NDM mutations. Significant recoveries in [Ca2+] and insulin secretion were predicted for many mutations upon reductions in gap junction coupling, where stochastic noise played a significant role in the recoveries. These findings provide new understanding how the islet functions as a multicellular system and for the role of gap junction channels in exacerbating the effects of decreased cellular excitability. They further suggest novel therapeutic options for NDM and other monogenic forms of diabetes.'),
(88033, 2016, 'plos', 'SCOTTI: Efficient Reconstruction of Transmission within Outbreaks with the Structured Coalescent', 'De Maio, Nicola|Wu, Chieh-Hsi|Wilson, Daniel J', 'Institute for Emerging Infections, Oxford Martin School, University of Oxford, Oxford, United Kingdom.|Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom.|Institute for Emerging Infections, Oxford Martin School, University of Oxford, Oxford, United Kingdom.', 'United Kingdom|United Kingdom|United Kingdom', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1005130', '27681228', 'Exploiting pathogen genomes to reconstruct transmission represents a powerful tool in the fight against infectious disease. However, their interpretation rests on a number of simplifying assumptions that regularly ignore important complexities of real data, in particular within-host evolution and non-sampled patients. Here we propose a new approach to transmission inference called SCOTTI (Structured COalescent Transmission Tree Inference). This method is based on a statistical framework that models each host as a distinct population, and transmissions between hosts as migration events. Our computationally efficient implementation of this model enables the inference of host-to-host transmission while accommodating within-host evolution and non-sampled hosts. SCOTTI is distributed as an open source package for the phylogenetic software BEAST2. We show that SCOTTI can generally infer transmission events even in the presence of considerable within-host variation, can account for the uncertainty associated with the possible presence of non-sampled hosts, and can efficiently use data from multiple samples of the same host, although there is some reduction in accuracy when samples are collected very close to the infection time. We illustrate the features of our approach by investigating transmission from genetic and epidemiological data in a Foot and Mouth Disease Virus (FMDV) veterinary outbreak in England and a Klebsiella pneumoniae outbreak in a Nepali neonatal unit. Transmission histories inferred with SCOTTI will be important in devising effective measures to prevent and halt transmission.'),
(88034, 2016, 'plos', 'Exome Sequencing and Prediction of Long-Term Kidney Allograft Function', 'Mesnard, Laurent|Muthukumar, Thangamani|Burbach, Maren|Li, Carol|Shang, Huimin|Dadhania, Darshana|Lee, John R|Sharma, Vijay K|Xiang, Jenny|Suberbielle, Caroline|Carmagnat, Maryvonnick|Ouali, Nacera|Rondeau, Eric|Friedewald, John J|Abecassis, Michael M|Suthanthiran, Manikkam|Campagne, Fabien', 'The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, New York, United States of America, Department of Physiology and Biophysics, The Weill Cornell Medical College, New York, New York, United States of America.|Division of Nephrology and Hypertension, Weill Cornell Medical College, New York, New York, United States of America.|Division of Nephrology and Hypertension, Weill Cornell Medical College, New York, New York, United States of America.|Division of Nephrology and Hypertension, Weill Cornell Medical College, New York, New York, United States of America.|Genomics Core Facility, Weill Cornell Medical College, New York, New York, United States of America.|Division of Nephrology and Hypertension, Weill Cornell Medical College, New York, New York, United States of America.|Division of Nephrology and Hypertension, Weill Cornell Medical College, New York, New York, United States of America.|Division of Nephrology and Hypertension, Weill Cornell Medical College, New York, New York, United States of America.|Genomics Core Facility, Weill Cornell Medical College, New York, New York, United States of America.|Laboratoire dhistocompatibilitxc3xa9 Hxc3xb4pital Saint Louis APHP, Paris, France.|Laboratoire dhistocompatibilitxc3xa9 Hxc3xb4pital Saint Louis APHP, Paris, France.|INSERM UMR1155 et Service des Urgences Nxc3xa9phrologiques et Transplantation Rxc3xa9nale, APHP, Hxc3xb4pital Tenon, Paris, France.|INSERM UMR1155 et Service des Urgences Nxc3xa9phrologiques et Transplantation Rxc3xa9nale, APHP, Hxc3xb4pital Tenon, Paris, France.|Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America.|Comprehensive Transplant Center, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America.|Division of Nephrology and Hypertension, Weill Cornell Medical College, New York, New York, United States of America.|The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, New York, United States of America, Department of Physiology and Biophysics, The Weill Cornell Medical College, New York, New York, United States of America.', 'United States|United States|United States|United States|United States|United States|United States|Un', 17, '-', 'Journal Article', '10.1371/journal.pcbi.1005088', '27684477', 'Current strategies to improve graft outcome following kidney transplantation consider information at the human leukocyte antigen (HLA) loci. Cell surface antigens, in addition to HLA, may serve as the stimuli as well as the targets for the anti-allograft immune response and influence long-term graft outcomes. We therefore performed exome sequencing of DNA from kidney graft recipients and their living donors and estimated all possible cell surface antigens mismatches for a given donor/recipient pair by computing the number of amino acid mismatches in trans-membrane proteins. We designated this tally as the allogenomics mismatch score (AMS). We examined the association between the AMS and post-transplant estimated glomerular filtration rate (eGFR) using mixed models, considering transplants from three independent cohorts (a total of 53 donor-recipient pairs, 106 exomes, and 239 eGFR measurements). We found that the AMS has a significant effect on eGFR (mixed model, effect size across the entire range of the score: -19.4 [-37.7, -1.1], P = 0.0042, xcfx872 = 8.1919, d.f. = 1) that is independent of the HLA-A, B, DR matching, donor age, and time post-transplantation. The AMS effect is consistent across the three independent cohorts studied and similar to the strong effect size of donor age. Taken together, these results show that the AMS, a novel tool to quantify amino acid mismatches in trans-membrane proteins in individual donor/recipient pair, is a strong, robust predictor of long-term graft function in kidney transplant recipients.'),
(88035, 2016, 'plos', 'Adaptive Remodeling of Achilles Tendon: A Multi-scale Computational Model', 'Young, Stuart R|Gardiner, Bruce|Mehdizadeh, Arash|Rubenson, Jonas|Umberger, Brian|Smith, David W', 'Faculty of Engineering, Computing and Mathematics, University of Western Australia, Crawley, Western Australia, Australia.|School of Engineering and Information Technology, Murdoch University, Murdoch, Western Australia, Australia.|Faculty of Engineering, Computing and Mathematics, University of Western Australia, Crawley, Western Australia, Australia.|Biomechanics Laboratory, Department of Kinesiology, Pennsylvania State University, University Park, Pennsylvania, United States of America.|Department of Kinesiology, University of Massachusetts, Amherst, Massachusetts, United States of America.|Faculty of Engineering, Computing and Mathematics, University of Western Australia, Crawley, Western Australia, Australia.', 'Australia|Australia|Australia|United States|United States|Australia', 9, '-', 'Journal Article', '10.1371/journal.pcbi.1005106', '27684554', 'While it is known that musculotendon units adapt to their load environments, there is only a limited understanding of tendon adaptation in vivo. Here we develop a computational model of tendon remodeling based on the premise that mechanical damage and tenocyte-mediated tendon damage and repair processes modify the distribution of its collagen fiber lengths. We explain how these processes enable the tendon to geometrically adapt to its load conditions. Based on known biological processes, mechanical and strain-dependent proteolytic fiber damage are incorporated into our tendon model. Using a stochastic model of fiber repair, it is assumed that mechanically damaged fibers are repaired longer, whereas proteolytically damaged fibers are repaired shorter, relative to their pre-damage length. To study adaptation of tendon properties to applied load, our model musculotendon unit is a simplified three-component Hill-type model of the human Achilles-soleus unit. Our model results demonstrate that the geometric equilibrium state of the Achilles tendon can coincide with minimization of the total metabolic cost of muscle activation. The proposed tendon model independently predicts rates of collagen fiber turnover that are in general agreement with in vivo experimental measurements. While the computational model here only represents a first step in a new approach to understanding the complex process of tendon remodeling in vivo, given these findings, it appears likely that the proposed framework may itself provide a useful theoretical foundation for developing valuable qualitative and quantitative insights into tendon physiology and pathology.'),
(88036, 2016, 'plos', 'The Edge of Stability: Response Times and Delta Oscillations in Balanced Networks', 'Gillary, Grant|Niebur, Ernst', 'Zanvyl Krieger Mind/Brain Institute, Baltimore, Maryland, United States of America.|Zanvyl Krieger Mind/Brain Institute, Baltimore, Maryland, United States of America.', 'United States|United States', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1005121', '27689361', 'The standard architecture of neocortex is a network with excitation and inhibition in closely maintained balance. These networks respond fast and with high precision to their inputs and they allow selective amplification of patterned signals. The stability of such networks is known to depend on balancing the strengths of positive and negative feedback. We here show that a second condition is required for stability which depends on the relative strengths and time courses of fast (AMPA) and slow (NMDA) currents in the excitatory projections. This condition also determines the response time of the network. We show that networks which respond quickly to an input are necessarily close to an oscillatory instability which resonates in the delta range. This instability explains the existence of neocortical delta oscillations and the emergence of absence epilepsy. Although cortical delta oscillations are a network-level phenomenon, we show that in non-pathological networks, individual neurons receive sufficient information to keep the network in the fast-response regime without sliding into the instability.'),
(88037, 2016, 'plos', 'Novel Covariance-Based Neutrality Test of Time-Series Data Reveals Asymmetries in Ecological and Economic Systems', 'Washburne, Alex D|Burby, Joshua W|Lacker, Daniel', 'Department of Biology, Duke University, Durham, North Carolina, United States of America.|Plasma Physics Lab, Department of Physics, Princeton University, Princeton, New Jersey, United States of America.|Department of Operations Research and Financial Engineering, Princeton University, Princeton, New Jersey, United States of America.', 'United States|Jersey|Jersey', 5, '-', 'Journal Article', '10.1371/journal.pcbi.1005124', '27689714', 'Systems as diverse as the interacting species in a community, alleles at a genetic locus, and companies in a market are characterized by competition (over resources, space, capital, etc) and adaptation. Neutral theory, built around the hypothesis that individual performance is independent of group membership, has found utility across the disciplines of ecology, population genetics, and economics, both because of the success of the neutral hypothesis in predicting system properties and because deviations from these predictions provide information about the underlying dynamics. However, most tests of neutrality are weak, based on static system properties such as species-abundance distributions or the number of singletons in a sample. Time-series data provide a window onto a systems dynamics, and should furnish tests of the neutral hypothesis that are more powerful to detect deviations from neutrality and more informative about to the type of competitive asymmetry that drives the deviation. Here, we present a neutrality test for time-series data. We apply this test to several microbial time-series and financial time-series and find that most of these systems are not neutral. Our test isolates the covariance structure of neutral competition, thus facilitating further exploration of the nature of asymmetry in the covariance structure of competitive systems. Much like neutrality tests from population genetics that use relative abundance distributions have enabled researchers to scan entire genomes for genes under selection, we anticipate our time-series test will be useful for quick significance tests of neutrality across a range of ecological, economic, and sociological systems for which time-series data are available. Future work can use our test to categorize and compare the dynamic fingerprints of particular competitive asymmetries (frequency dependence, volatility smiles, etc) to improve forecasting and management of complex adaptive systems.'),
(88038, 2016, 'plos', 'Correction: Direct Correlation between Motile Behavior and Protein Abundance in Single Cells', 'PLOS Computational Biology Staff', 'Department of Operations Research and Financial Engineering, Princeton University, Princeton, New Jersey, United States of America.', 'Jersey', 0, '-', 'Published Erratum', '10.1371/journal.pcbi.1005149', '27689803', '[This corrects the article DOI: 10.1371/journal.pcbi.1005041.].'),
(88039, 2016, 'plos', 'Nonlinear Hebbian Learning as a Unifying Principle in Receptive Field Formation', 'Brito, Carlos S N|Gerstner, Wulfram', 'School of Computer and Communication Sciences and School of Life Science, Brain Mind Institute, Ecole Polytechnique Federale de Lausanne, Lausanne EPFL, Switzerland.|School of Computer and Communication Sciences and School of Life Science, Brain Mind Institute, Ecole Polytechnique Federale de Lausanne, Lausanne EPFL, Switzerland.', 'Switzerland|Switzerland', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1005070', '27690349', 'The development of sensory receptive fields has been modeled in the past by a variety of models including normative models such as sparse coding or independent component analysis and bottom-up models such as spike-timing dependent plasticity or the Bienenstock-Cooper-Munro model of synaptic plasticity. Here we show that the above variety of approaches can all be unified into a single common principle, namely nonlinear Hebbian learning. When nonlinear Hebbian learning is applied to natural images, receptive field shapes were strongly constrained by the input statistics and preprocessing, but exhibited only modest variation across different choices of nonlinearities in neuron models or synaptic plasticity rules. Neither overcompleteness nor sparse network activity are necessary for the development of localized receptive fields. The analysis of alternative sensory modalities such as auditory models or V2 development lead to the same conclusions. In all examples, receptive fields can be predicted a priori by reformulating an abstract model as nonlinear Hebbian learning. Thus nonlinear Hebbian learning and natural statistics can account for many aspects of receptive field formation across models and sensory modalities.'),
(88040, 2016, 'plos', 'AlignerBoost: A Generalized Software Toolkit for Boosting Next-Gen Sequencing Mapping Accuracy Using a Bayesian-Based Mapping Quality Framework', 'Zheng, Qi|Grice, Elizabeth A', 'Department of Dermatology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.|Department of Dermatology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.', 'United States|United States', 0, 'Algorithms|Bayes Theorem|DNA|DNA Mutational Analysis|High-Throughput Nucleotide Sequencing|Machine Learning|Pattern Recognition, Automated|Polymorphism, Single Nucleotide|Sequence Alignment|Software', 'Journal Article', '10.1371/journal.pcbi.1005096', '27706155', 'Accurate mapping of next-generation sequencing (NGS) reads to reference genomes is crucial for almost all NGS applications and downstream analyses. Various repetitive elements in human and other higher eukaryotic genomes contribute in large part to ambiguously (non-uniquely) mapped reads. Most available NGS aligners attempt to address this by either removing all non-uniquely mapping reads, or reporting one random or best hit based on simple heuristics. Accurate estimation of the mapping quality of NGS reads is therefore critical albeit completely lacking at present. Here we developed a generalized software toolkit AlignerBoost, which utilizes a Bayesian-based framework to accurately estimate mapping quality of ambiguously mapped NGS reads. We tested AlignerBoost with both simulated and real DNA-seq and RNA-seq datasets at various thresholds. In most cases, but especially for reads falling within repetitive regions, AlignerBoost dramatically increases the mapping precision of modern NGS aligners without significantly compromising the sensitivity even without mapping quality filters. When using higher mapping quality cutoffs, AlignerBoost achieves a much lower false mapping rate while exhibiting comparable or higher sensitivity compared to the aligner default modes, therefore significantly boosting the detection power of NGS aligners even using extreme thresholds. AlignerBoost is also SNP-aware, and higher quality alignments can be achieved if provided with known SNPs. AlignerBoosts algorithm is computationally efficient, and can process one million alignments within 30 seconds on a typical desktop computer. AlignerBoost is implemented as a uniform Java application and is freely available at https://github.com/Grice-Lab/AlignerBoost.'),
(88041, 2016, 'plos', 'A Motor-Gradient and Clustering Model of the Centripetal Motility of MTOCs in Meiosis I of Mouse Oocytes', 'Khetan, Neha|Athale, Chaitanya A', 'Division of Biology, Indian Institute of Science Education and Research (IISER) Pune, Pune, Maharashtra, India.|Division of Biology, Indian Institute of Science Education and Research (IISER) Pune, Pune, Maharashtra, India.', 'India|India', 0, 'Animals|Cells, Cultured|Computer Simulation|Dyneins|Meiosis|Mice|Microtubule-Organizing Center|Microtubules|Models, Biological|Molecular Motor Proteins|Oocytes|Spindle Apparatus', 'Journal Article', '10.1371/journal.pcbi.1005102', '27706163', 'Asters nucleated by Microtubule (MT) organizing centers (MTOCs) converge on chromosomes during spindle assembly in mouse oocytes undergoing meiosis I. Time-lapse imaging suggests that this centripetal motion is driven by a biased search-and-capture mechanism. Here, we develop a model of a random walk in a drift field to test the nature of the bias and the spatio-temporal dynamics of the search process. The model is used to optimize the spatial field of drift in simulations, by comparison to experimental motility statistics. In a second step, this optimized gradient is used to determine the location of immobilized dynein motors and MT polymerization parameters, since these are hypothesized to generate the gradient of forces needed to move MTOCs. We compare these scenarios to self-organized mechanisms by which asters have been hypothesized to find the cell-center- MT pushing at the cell-boundary and clustering motor complexes. By minimizing the error between simulation outputs and experiments, we find a model of pulling by a gradient of dynein motors alone can drive the centripetal motility. Interestingly, models of passive MT based pushing at the cortex, clustering by cross-linking motors and MT-dynamic instability gradients alone, by themselves do not result in the observed motility. The model predicts the sensitivity of the results to motor density and stall force, but not MTs per aster. A hybrid model combining a chromatin-centered immobilized dynein gradient, diffusible minus-end directed clustering motors and pushing at the cell cortex, is required to comprehensively explain the available data. The model makes experimentally testable predictions of a spatial bias and self-organized mechanisms by which MT asters can find the center of a large cell.'),
(88042, 2016, 'plos', 'Stoichiometric Representation of Gene-Protein-Reaction Associations Leverages Constraint-Based Analysis from Reaction to Gene-Level Phenotype Prediction', 'Machado, Daniel|Herrgård, Markus J|Rocha, Isabel', 'Centre of Biological Engineering, University of Minho, Braga, Portugal.|The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Horsølm, Denmark.|Centre of Biological Engineering, University of Minho, Braga, Portugal.', 'Portugal|Denmark|Portugal', 0, 'Animals|Computer Simulation|Gene Expression Regulation|Genome|Humans|Metabolic Flux Analysis|Metabolome|Models, Biological|Phenotype|Protein Interaction Mapping|Proteome', 'Journal Article', '10.1371/journal.pcbi.1005140', '27711110', 'Genome-scale metabolic reconstructions are currently available for hundreds of organisms. Constraint-based modeling enables the analysis of the phenotypic landscape of these organisms, predicting the response to genetic and environmental perturbations. However, since constraint-based models can only describe the metabolic phenotype at the reaction level, understanding the mechanistic link between genotype and phenotype is still hampered by the complexity of gene-protein-reaction associations. We implement a model transformation that enables constraint-based methods to be applied at the gene level by explicitly accounting for the individual fluxes of enzymes (and subunits) encoded by each gene. We show how this can be applied to different kinds of constraint-based analysis: flux distribution prediction, gene essentiality analysis, random flux sampling, elementary mode analysis, transcriptomics data integration, and rational strain design. In each case we demonstrate how this approach can lead to improved phenotype predictions and a deeper understanding of the genotype-to-phenotype link. In particular, we show that a large fraction of reaction-based designs obtained by current strain design methods are not actually feasible, and show how our approach allows using the same methods to obtain feasible gene-based designs. We also show, by extensive comparison with experimental 13C-flux data, how simple reformulations of different simulation methods with gene-wise objective functions result in improved prediction accuracy. The model transformation proposed in this work enables existing constraint-based methods to be used at the gene level without modification. This automatically leverages phenotype analysis from reaction to gene level, improving the biological insight that can be obtained from genome-scale models.'),
(88043, 2016, 'plos', 'Mechano-chemical Interactions in Cardiac Sarcomere Contraction: A Computational Modeling Study', 'Dupuis, Lauren J|Lumens, Joost|Arts, Theo|Delhaas, Tammo', 'Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands.|Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands.|Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands.|Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands', 0, 'Animals|Calcium Signaling|Computer Simulation|Humans|Mechanotransduction, Cellular|Models, Cardiovascular|Molecular Motor Proteins|Myocardial Contraction|Myocytes, Cardiac|Sarcomeres', 'Journal Article', '10.1371/journal.pcbi.1005126', '27716775', 'We developed a model of cardiac sarcomere contraction to study the calcium-tension relationship in cardiac muscle. Calcium mediates cardiac contraction through its interactions with troponin (Tn) and subsequently tropomyosin molecules. Experimental studies have shown that a slight increase in intracellular calcium concentration leads to a rapid increase in sarcomeric tension. Though it is widely accepted that the rapid increase is not possible without the concept of cooperativity, the mechanism is debated. We use the hypothesis that there exists a base level of cooperativity intrinsic to the thin filament that is boosted by mechanical tension, i.e. a high level of mechanical tension in the thin filament impedes the unbinding of calcium from Tn. To test these hypotheses, we developed a computational model in which a set of three parameters and inputs of calcium concentration and sarcomere length result in output tension. Tension as simulated appeared in good agreement with experimentally measured tension. Our results support the hypothesis that high tension in the thin filament impedes Tn deactivation by increasing the energy required to detach calcium from the Tn. Given this hypothesis, the model predicted that the areas with highest tension, i.e. closest to the Z-disk end of the single overlap region, show the largest concentration of active Tns.'),
(88044, 2016, 'plos', 'Effect of Glycosylation on an Immunodominant Region in the V1V2 Variable Domain of the HIV-1 Envelope gp120 Protein', 'Tian, Jianhui|López, Cesar A|Derdeyn, Cynthia A|Jones, Morris S|Pinter, Abraham|Korber, Bette|Gnanakaran, S', 'Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.|Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.|Department of Pathology and Laboratory Medicine and Emory Vaccine Center, Emory University, Atlanta, Georgia, United States of America.|University of California Berkeley, School of Public Health, Berkeley, California, United States of America.|New Jersey Medical School, Rutgers University, Newark, New Jersey, United States of America.|Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.|Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.', 'Mexico|Mexico|Georgia|United States|Jersey|Mexico|Mexico', 9, 'Binding Sites|Glycosylation|HIV Envelope Protein gp120|Immunodominant Epitopes|Molecular Docking Simulation|Protein Binding|Protein Domains|Single-Chain Antibodies', 'Journal Article', '10.1371/journal.pcbi.1005094', '27716795', 'Heavy glycosylation of the envelope (Env) surface subunit, gp120, is a key adaptation of HIV-1, however, the precise effects of glycosylation on the folding, conformation and dynamics of this protein are poorly understood. Here we explore the patterns of HIV-1 Env gp120 glycosylation, and particularly the enrichment in glycosylation sites proximal to the disulfide linkages at the base of the surface-exposed variable domains. To dissect the influence of glycans on the conformation these regions, we focused on an antigenic peptide fragment from a disulfide bridge-bounded region spanning the V1 and V2 hyper-variable domains of HIV-1 gp120. We used replica exchange molecular dynamics (MD) simulations to investigate how glycosylation influences its conformation and stability. Simulations were performed with and without N-linked glycosylation at two sites that are highly conserved across HIV-1 isolates (N156 and N160), both are contacts for recognition by V1V2-targeted broadly neutralizing antibodies against HIV-1. Glycosylation stabilized the pre-existing conformations of this peptide construct, reduced its propensity to adopt other secondary structures, and provided resistance against thermal unfolding. Simulations performed in the context of the Env trimer also indicated that glycosylation reduces flexibility of the V1V2 region, and provided insight into glycan-glycan interactions in this region. These stabilizing effects were influenced by a combination of factors, including the presence of a disulfide bond between the Cysteines at 131 and 157, which increased the formation of beta-strands. Together, these results provide a mechanism for conservation of disulfide linkage proximal glycosylation adjacent to the variable domains of gp120 and begin to explain how this could be exploited to enhance the immunogenicity of those regions. These studies suggest that glycopeptide immunogens can be designed to stabilize the most relevant Env conformations to focus the immune response on key neutralizing epitopes.'),
(88045, 2016, 'plos', 'Influence of Rotational Nucleosome Positioning on Transcription Start Site Selection in Animal Promoters', 'Dreos, René|Ambrosini, Giovanna|Bucher, Philipp', 'Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland.|Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland.|Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland.', 'Switzerland|Switzerland|Switzerland', 7, 'Base Sequence|Binding Sites|Computer Simulation|DNA|Models, Genetic|Molecular Sequence Data|Nucleosomes|Promoter Regions, Genetic|RNA Polymerase II|Transcription Initiation Site|Transcription, Genetic|Transcriptional Activation', 'Journal Article', '10.1371/journal.pcbi.1005144', '27716823', 'The recruitment of RNA-Pol-II to the transcription start site (TSS) is an important step in gene regulation in all organisms. Core promoter elements (CPE) are conserved sequence motifs that guide Pol-II to the TSS by interacting with specific transcription factors (TFs). However, only a minority of animal promoters contains CPEs. It is still unknown how Pol-II selects the TSS in their absence. Here we present a comparative analysis of promoters sequence composition and chromatin architecture in five eukaryotic model organisms, which shows the presence of common and unique DNA-encoded features used to organize chromatin. Analysis of Pol-II initiation patterns uncovers that, in the absence of certain CPEs, there is a strong correlation between the spread of initiation and the intensity of the 10 bp periodic signal in the nearest downstream nucleosome. Moreover, promoters primary and secondary initiation sites show a characteristic 10 bp periodicity in the absence of CPEs. We also show that DNA natural variants in the region immediately downstream the TSS are able to affect both the nucleosome-DNA affinity and Pol-II initiation pattern. These findings support the notion that, in addition to CPEs mediated selection, sequence-induced nucleosome positioning could be a common and conserved mechanism of TSS selection in animals.'),
(88046, 2016, 'plos', 'Retrospective Parameter Estimation and Forecast of Respiratory Syncytial Virus in the United States', 'Reis, Julia|Shaman, Jeffrey', 'Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, United States of America.|Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, United States of America.', 'United States|United States', 10, 'Bayes Theorem|Computer Simulation|Disease Outbreaks|Forecasting|Humans|Incidence|Models, Statistical|Prognosis|Proportional Hazards Models|Reproducibility of Results|Respiratory Syncytial Virus Infections|Respiratory Syncytial Viruses|Risk Assessment|Sensitivity and Specificity|United States', 'Journal Article', '10.1371/journal.pcbi.1005133', '27716828', 'Recent studies have shown that systems combining mathematical modeling and Bayesian inference methods can be used to generate real-time forecasts of future infectious disease incidence. Here we develop such a system to study and forecast respiratory syncytial virus (RSV). RSV is the most common cause of acute lower respiratory infection and bronchiolitis. Advanced warning of the epidemic timing and volume of RSV patient surges has the potential to reduce well-documented delays of treatment in emergency departments. We use a susceptible-infectious-recovered (SIR) model in conjunction with an ensemble adjustment Kalman filter (EAKF) and ten years of regional U.S. specimen data provided by the Centers for Disease Control and Prevention. The data and EAKF are used to optimize the SIR model and i) estimate critical epidemiological parameters over the course of each outbreak and ii) generate retrospective forecasts. The basic reproductive number, R0, is estimated at 3.0 (standard deviation 0.6) across all seasons and locations. The peak magnitude of RSV outbreaks is forecast with nearly 70% accuracy (i.e. nearly 70% of forecasts within 25% of the actual peak), four weeks before the predicted peak. This work represents a first step in the development of a real-time RSV prediction system.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(88047, 2016, 'plos', 'Large-Scale Off-Target Identification Using Fast and Accurate Dual Regularized One-Class Collaborative Filtering and Its Application to Drug Repurposing', 'Lim, Hansaim|Poleksic, Aleksandar|Yao, Yuan|Tong, Hanghang|He, Di|Zhuang, Luke|Meng, Patrick|Xie, Lei', 'The Graduate Center, The City University of New York, New York, New York, United States.|Department of Computer Science, University of Northern Iowa, Cedar Falls, Iowa, United States.|Department of Computer Science and Technology, Nanjing University, Nanjing, Jiangsu, China.|School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona, United States.|The Graduate Center, The City University of New York, New York, New York, United States.|Academy for Information Technology, Union County Vocational-Technical Schools, Scotch Plains, New Jersey, United States.|High Technology High School, Lincroft, New Jersey, United States.|The Graduate Center, The City University of New York, New York, New York, United States.', 'United States|United States|China|United States|United States|Jersey|Jersey|United States', 25, 'Antineoplastic Agents|Drug Evaluation, Preclinical|Drug Repositioning|High-Throughput Screening Assays|Molecular Targeted Therapy|Protein Binding|Protein Interaction Mapping|Proteins', 'Journal Article', '10.1371/journal.pcbi.1005135', '27716836', 'Target-based screening is one of the major approaches in drug discovery. Besides the intended target, unexpected drug off-target interactions often occur, and many of them have not been recognized and characterized. The off-target interactions can be responsible for either therapeutic or side effects. Thus, identifying the genome-wide off-targets of lead compounds or existing drugs will be critical for designing effective and safe drugs, and providing new opportunities for drug repurposing. Although many computational methods have been developed to predict drug-target interactions, they are either less accurate than the one that we are proposing here or computationally too intensive, thereby limiting their capability for large-scale off-target identification. In addition, the performances of most machine learning based algorithms have been mainly evaluated to predict off-target interactions in the same gene family for hundreds of chemicals. It is not clear how these algorithms perform in terms of detecting off-targets across gene families on a proteome scale. Here, we are presenting a fast and accurate off-target prediction method, REMAP, which is based on a dual regularized one-class collaborative filtering algorithm, to explore continuous chemical space, protein space, and their interactome on a large scale. When tested in a reliable, extensive, and cross-gene family benchmark, REMAP outperforms the state-of-the-art methods. Furthermore, REMAP is highly scalable. It can screen a dataset of 200 thousands chemicals against 20 thousands proteins within 2 hours. Using the reconstructed genome-wide target profile as the fingerprint of a chemical compound, we predicted that seven FDA-approved drugs can be repurposed as novel anti-cancer therapies. The anti-cancer activity of six of them is supported by experimental evidences. Thus, REMAP is a valuable addition to the existing in silico toolbox for drug target identification, drug repurposing, phenotypic screening, and side effect prediction. The software and benchmark are available at https://github.com/hansaimlim/REMAP.'),
(88048, 2016, 'plos', 'SARAH Domain-Mediated MST2-RASSF Dimeric Interactions', 'Sxc3xa1nchez-Sanz, Goar|Tywoniuk, Bartxc5x82omiej|Matallanas, David|Romano, David|Nguyen, Lan K|Kholodenko, Boris N|Rosta, Edina|Kolch, Walter|Buchete, Nicolae-Viorel', 'School of Physics & Institute for Discovery, University College Dublin, Belfield, Dublin, Ireland.|School of Physics & Institute for Discovery, University College Dublin, Belfield, Dublin, Ireland.|Systems Biology Ireland, University College Dublin, Belfield, Dublin, Ireland.|Systems Biology Ireland, University College Dublin, Belfield, Dublin, Ireland.|Systems Biology Ireland, University College Dublin, Belfield, Dublin, Ireland.|Systems Biology Ireland, University College Dublin, Belfield, Dublin, Ireland.|Department of Chemistry, Kings College London, London, United Kingdom.|Systems Biology Ireland, University College Dublin, Belfield, Dublin, Ireland.|School of Physics & Institute for Discovery, University College Dublin, Belfield, Dublin, Ireland.', 'Ireland|Ireland|Ireland|Ireland|Ireland|Ireland|United Kingdom|Ireland|Ireland', 10, 'Binding Sites|Carrier Proteins|Cyclin-Dependent Kinase Inhibitor p15|Dimerization|Drosophila Proteins|Enzyme Activation|Molecular Docking Simulation|Protein Binding|Protein Conformation|Protein Domains', 'Journal Article', '10.1371/journal.pcbi.1005051', '27716844', 'RASSF enzymes act as key apoptosis activators and tumor suppressors, being downregulated in many human cancers, although their exact regulatory roles remain unknown. A key downstream event in the RASSF pathway is the regulation of MST kinases, which are main effectors of RASSF-induced apoptosis. The regulation of MST1/2 includes both homo- and heterodimerization, mediated by helical SARAH domains, though the underlying molecular interaction mechanism is unclear. Here, we study the interactions between RASSF1A, RASSF5, and MST2 SARAH domains by using both atomistic molecular simulation techniques and experiments. We construct and study models of MST2 homodimers and MST2-RASSF SARAH heterodimers, and we identify the factors that control their high molecular stability. In addition, we also analyze both computationally and experimentally the interactions of MST2 SARAH domains with a series of synthetic peptides particularly designed to bind to it, and hope that our approach can be used to address some of the challenging problems in designing new anti-cancer drugs.'),
(88049, 2016, 'plos', 'A New Take on John Maynard Smiths Concept of Protein Space for Understanding Molecular Evolution', 'Ogbunugafor, C Brandon|Hartl, Daniel L', 'Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts, United States of America.|Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts, United States of America.', 'United States|United States', 3, 'Algorithms|Animals|Evolution, Molecular|Genetics|Humans|Models, Genetic|Proteins|Teaching', 'Journal Article', '10.1371/journal.pcbi.1005046', '27736867', 'Much of the public lacks a proper understanding of Darwinian evolution, a problem that can be addressed with new learning and teaching approaches to be implemented both inside the classroom and in less formal settings. Few analogies have been as successful in communicating the basics of molecular evolution as John Maynard Smiths protein space analogy (1970), in which he compared protein evolution to the transition between the terms WORD and GENE, changing one letter at a time to yield a different, meaningful word (in his example, the preferred path was WORD xe2x86x92 WORE xe2x86x92 GORE xe2x86x92 GONE xe2x86x92 GENE). Using freely available computer science tools (Google Books Ngram Viewer), we offer an update to Maynard Smiths analogy and explain how it might be developed into an exploratory and pedagogical device for understanding the basics of molecular evolution and, more specifically, the adaptive landscape concept. We explain how the device works through several examples and provide resources that might facilitate its use in multiple settings, ranging from public engagement activities to formal instruction in evolution, population genetics, and computational biology.'),
(88050, 2016, 'plos', 'Identifying Anatomical Origins of Coexisting Oscillations in the Cortical Microcircuit', 'Bos, Hannah|Diesmann, Markus|Helias, Moritz', 'Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA BRAIN Institute I, Jxc3xbclich Research Centre, Jxc3xbclich, Germany.|Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA BRAIN Institute I, Jxc3xbclich Research Centre, Jxc3xbclich, Germany.|Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA BRAIN Institute I, Jxc3xbclich Research Centre, Jxc3xbclich, Germany.', 'Germany|Germany|Germany', 16, 'Animals|Biological Clocks|Computer Simulation|Connectome|Feedback, Physiological|Humans|Models, Anatomic|Models, Neurological|Models, Statistical|Nerve Net|Synaptic Transmission|Visual Cortex', 'Journal Article', '10.1371/journal.pcbi.1005132', '27736873', 'Oscillations are omnipresent in neural population signals, like multi-unit recordings, EEG/MEG, and the local field potential. They have been linked to the population firing rate of neurons, with individual neurons firing in a close-to-irregular fashion at low rates. Using a combination of mean-field and linear response theory we predict the spectra generated in a layered microcircuit model of V1, composed of leaky integrate-and-fire neurons and based on connectivity compiled from anatomical and electrophysiological studies. The model exhibits low- and high-xcexb3 oscillations visible in all populations. Since locally generated frequencies are imposed onto other populations, the origin of the oscillations cannot be deduced from the spectra. We develop an universally applicable systematic approach that identifies the anatomical circuits underlying the generation of oscillations in a given network. Based on a theoretical reduction of the dynamics, we derive a sensitivity measure resulting in a frequency-dependent connectivity map that reveals connections crucial for the peak amplitude and frequency of the observed oscillations and identifies the minimal circuit generating a given frequency. The low-xcexb3 peak turns out to be generated in a sub-circuit located in layer 2/3 and 4, while the high-xcexb3 peak emerges from the inter-neurons in layer 4. Connections within and onto layer 5 are found to regulate slow rate fluctuations. We further demonstrate how small perturbations of the crucial connections have significant impact on the population spectra, while the impairment of other connections leaves the dynamics on the population level unaltered. The study uncovers connections where mechanisms controlling the spectra of the cortical microcircuit are most effective.'),
(88051, 2016, 'plos', 'Eco-evolutionary Model of Rapid Phenotypic Diversification in Species-Rich Communities', 'Villa Martín, Paula|Hidalgo, Jorge|Rubio de Casas, Rafael|Muñoz, Miguel A', 'Departamento de Electromagnetismo y Física de la Materia and Instituto Carlos I de Física Teórica y Computacional, Universidad de Granada, Granada, Spain.|Departamento de Electromagnetismo y Física de la Materia and Instituto Carlos I de Física Teórica y Computacional, Universidad de Granada, Granada, Spain.|Estación Experimental de Zonas Áridas, EEZA-CSIC, Almería, Spain.|Departamento de Electromagnetismo y Física de la Materia and Instituto Carlos I de Física Teórica y Computacional, Universidad de Granada, Granada, Spain.', 'Spain|Spain|Spain|Spain', 7, 'Biological Evolution|Computer Simulation|Ecosystem|Genetic Speciation|Genetic Variation|Genetics, Population|Models, Genetic|Phenotype|Plants|Species Specificity', 'Journal Article', '10.1371/journal.pcbi.1005139', '27736874', 'Evolutionary and ecosystem dynamics are often treated as different processes -operating at separate timescales- even if evidence reveals that rapid evolutionary changes can feed back into ecological interactions. A recent long-term field experiment has explicitly shown that communities of competing plant species can experience very fast phenotypic diversification, and that this gives rise to enhanced complementarity in resource exploitation and to enlarged ecosystem-level productivity. Here, we build on progress made in recent years in the integration of eco-evolutionary dynamics, and present a computational approach aimed at describing these empirical findings in detail. In particular we model a community of organisms of different but similar species evolving in time through mechanisms of birth, competition, sexual reproduction, descent with modification, and death. Based on simple rules, this model provides a rationalization for the emergence of rapid phenotypic diversification in species-rich communities. Furthermore, it also leads to non-trivial predictions about long-term phenotypic change and ecological interactions. Our results illustrate that the presence of highly specialized, non-competing species leads to very stable communities and reveals that phenotypically equivalent species occupying the same niche may emerge and coexist for very long times. Thus, the framework presented here provides a simple approach -complementing existing theories, but specifically devised to account for the specificities of the recent empirical findings for plant communities- to explain the collective emergence of diversification at a community level, and paves the way to further scrutinize the intimate entanglement of ecological and evolutionary processes, especially in species-rich communities.'),
(88052, 2016, 'plos', 'Forgetting in Reinforcement Learning Links Sustained Dopamine Signals to Motivation', 'Kato, Ayaka|Morita, Kenji', 'Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo, Japan.|Physical and Health Education, Graduate School of Education, The University of Tokyo, Tokyo, Japan.', 'Japan|Japan', 12, 'Computer Simulation|Corpus Striatum|Decision Making|Dopamine|Dopaminergic Neurons|Humans|Mental Recall|Models, Neurological|Motivation|Reinforcement (Psychology)', 'Journal Article', '10.1371/journal.pcbi.1005145', '27736881', 'It has been suggested that dopamine (DA) represents reward-prediction-error (RPE) defined in reinforcement learning and therefore DA responds to unpredicted but not predicted reward. However, recent studies have found DA response sustained towards predictable reward in tasks involving self-paced behavior, and suggested that this response represents a motivational signal. We have previously shown that RPE can sustain if there is decay/forgetting of learned-values, which can be implemented as decay of synaptic strengths storing learned-values. This account, however, did not explain the suggested link between tonic/sustained DA and motivation. In the present work, we explored the motivational effects of the value-decay in self-paced approach behavior, modeled as a series of Go or No-Go selections towards a goal. Through simulations, we found that the value-decay can enhance motivation, specifically, facilitate fast goal-reaching, albeit counterintuitively. Mathematical analyses revealed that underlying potential mechanisms are twofold: (1) decay-induced sustained RPE creates a gradient of Go values towards a goal, and (2) value-contrasts between Go and No-Go are generated because while chosen values are continually updated, unchosen values simply decay. Our model provides potential explanations for the key experimental findings that suggest DAs roles in motivation: (i) slowdown of behavior by post-training blockade of DA signaling, (ii) observations that DA blockade severely impairs effortful actions to obtain rewards while largely sparing seeking of easily obtainable rewards, and (iii) relationships between the reward amount, the level of motivation reflected in the speed of behavior, and the average level of DA. These results indicate that reinforcement learning with value-decay, or forgetting, provides a parsimonious mechanistic account for the DAs roles in value-learning and motivation. Our results also suggest that when biological systems for value-learning are active even though learning has apparently converged, the systems might be in a state of dynamic equilibrium, where learning and forgetting are balanced.'),
(88053, 2016, 'plos', 'Functional Connectivitys Degenerate View of Brain Computation', 'Marrelec, Guillaume|Messé, Arnaud|Giron, Alain|Rudrauf, David', 'Sorbonne Universités, UPMC Univ Paris 06, CNRS, INSERM, Laboratoire dimagerie biomédicale (LIB), Paris, France.|Department of Computational Neuroscience, University Medical Center Eppendorf, Hamburg University, Hamburg, Germany.|Sorbonne Universités, UPMC Univ Paris 06, CNRS, INSERM, Laboratoire dimagerie biomédicale (LIB), Paris, France.|Grenoble Institute of Neuroscience, INSERM-UJF-CHU, Grenoble, France.', 'France|Germany|France|France', 17, 'Brain|Cognition|Computer Simulation|Connectome|Female|Humans|Male|Models, Neurological|Models, Statistical|Nerve Net|Young Adult', 'Journal Article', '10.1371/journal.pcbi.1005031', '27736900', 'Brain computation relies on effective interactions between ensembles of neurons. In neuroimaging, measures of functional connectivity (FC) aim at statistically quantifying such interactions, often to study normal or pathological cognition. Their capacity to reflect a meaningful variety of patterns as expected from neural computation in relation to cognitive processes remains debated. The relative weights of time-varying local neurophysiological dynamics versus static structural connectivity (SC) in the generation of FC as measured remains unsettled. Empirical evidence features mixed results: from little to significant FC variability and correlation with cognitive functions, within and between participants. We used a unified approach combining multivariate analysis, bootstrap and computational modeling to characterize the potential variety of patterns of FC and SC both qualitatively and quantitatively. Empirical data and simulations from generative models with different dynamical behaviors demonstrated, largely irrespective of FC metrics, that a linear subspace with dimension one or two could explain much of the variability across patterns of FC. On the contrary, the variability across BOLD time-courses could not be reduced to such a small subspace. FC appeared to strongly reflect SC and to be partly governed by a Gaussian process. The main differences between simulated and empirical data related to limitations of DWI-based SC estimation (and SC itself could then be estimated from FC). Above and beyond the limited dynamical range of the BOLD signal itself, measures of FC may offer a degenerate representation of brain interactions, with limited access to the underlying complexity. They feature an invariant common core, reflecting the channel capacity of the network as conditioned by SC, with a limited, though perhaps meaningful residual variability.'),
(88054, 2016, 'plos', 'Mechanistic Modelling and Bayesian Inference Elucidates the Variable Dynamics of Double-Strand Break Repair', 'Woods, Mae L|Barnes, Chris P', 'Department of Cell and Developmental Biology, University College London, London, England.|Department of Cell and Developmental Biology, University College London, London, England.', '-|-', 4, 'Bayes Theorem|Computer Simulation|DNA|DNA Breaks, Double-Stranded|DNA Repair|Models, Chemical|Models, Genetic|Models, Molecular|Models, Statistical|Radiation Dosage|Radiation, Ionizing', 'Journal Article', '10.1371/journal.pcbi.1005131', '27741226', 'DNA double-strand breaks are lesions that form during metabolism, DNA replication and exposure to mutagens. When a double-strand break occurs one of a number of repair mechanisms is recruited, all of which have differing propensities for mutational events. Despite DNA repair being of crucial importance, the relative contribution of these mechanisms and their regulatory interactions remain to be fully elucidated. Understanding these mutational processes will have a profound impact on our knowledge of genomic instability, with implications across health, disease and evolution. Here we present a new method to model the combined activation of non-homologous end joining, single strand annealing and alternative end joining, following exposure to ionising radiation. We use Bayesian statistics to integrate eight biological data sets of double-strand break repair curves under varying genetic knockouts and confirm that our model is predictive by re-simulating and comparing to additional data. Analysis of the model suggests that there are at least three disjoint modes of repair, which we assign as fast, slow and intermediate. Our results show that when multiple data sets are combined, the rate for intermediate repair is variable amongst genetic knockouts. Further analysis suggests that the ratio between slow and intermediate repair depends on the presence or absence of DNA-PKcs and Ku70, which implies that non-homologous end joining and alternative end joining are not independent. Finally, we consider the proportion of double-strand breaks within each mechanism as a time series and predict activity as a function of repair rate. We outline how our insights can be directly tested using imaging and sequencing techniques and conclude that there is evidence of variable dynamics in alternative repair pathways. Our approach is an important step towards providing a unifying theoretical framework for the dynamics of DNA repair processes.'),
(88055, 2016, 'plos', 'Theta-Gamma Coding Meets Communication-through-Coherence: Neuronal Oscillatory Multiplexing Theories Reconciled', 'McLelland, Douglas|VanRullen, Rufin', 'CerCo, Université de Toulouse Paul Sabatier, CNRS, France.|CerCo, Université de Toulouse Paul Sabatier, CNRS, France.', 'France|France', 19, 'Animals|Attention|Biological Clocks|Computer Simulation|Feedback, Physiological|Gamma Rhythm|Humans|Models, Neurological|Neurons|Theta Rhythm|Visual Cortex|Visual Perception', 'Journal Article', '10.1371/journal.pcbi.1005162', '27741229', 'Several theories have been advanced to explain how cross-frequency coupling, the interaction of neuronal oscillations at different frequencies, could enable item multiplexing in neural systems. The communication-through-coherence theory proposes that phase-matching of gamma oscillations between areas enables selective processing of a single item at a time, and a later refinement of the theory includes a theta-frequency oscillation that provides a periodic reset of the system. Alternatively, the theta-gamma neural code theory proposes that a sequence of items is processed, one per gamma cycle, and that this sequence is repeated or updated across theta cycles. In short, both theories serve to segregate representations via the temporal domain, but differ on the number of objects concurrently represented. In this study, we set out to test whether each of these theories is actually physiologically plausible, by implementing them within a single model inspired by physiological data. Using a spiking network model of visual processing, we show that each of these theories is physiologically plausible and computationally useful. Both theories were implemented within a single network architecture, with two areas connected in a feedforward manner, and gamma oscillations generated by feedback inhibition within areas. Simply increasing the amplitude of global inhibition in the lower area, equivalent to an increase in the spatial scope of the gamma oscillation, yielded a switch from one mode to the other. Thus, these different processing modes may co-exist in the brain, enabling dynamic switching between exploratory and selective modes of attention.'),
(88056, 2016, 'plos', 'How Do Efficient Coding Strategies Depend on Origins of Noise in Neural Circuits?', 'Brinkman, Braden A W|Weber, Alison I|Rieke, Fred|Shea-Brown, Eric', 'Department of Applied Mathematics, University of Washington, Seattle, Washington, United States of America.|Department of Applied Mathematics, University of Washington, Seattle, Washington, United States of America.|Department of Physiology and Biophysics, University of Washington, Seattle, Washington, United States of America.|Department of Applied Mathematics, University of Washington, Seattle, Washington, United States of America.', 'United States|United States|United States|United States', 0, 'Animals|Computer Simulation|Humans|Information Storage and Retrieval|Models, Neurological|Models, Statistical|Nerve Net|Sensory Receptor Cells|Signal-To-Noise Ratio|Synaptic Transmission', 'Journal Article', '10.1371/journal.pcbi.1005150', '27741248', 'Neural circuits reliably encode and transmit signals despite the presence of noise at multiple stages of processing. The efficient coding hypothesis, a guiding principle in computational neuroscience, suggests that a neuron or population of neurons allocates its limited range of responses as efficiently as possible to best encode inputs while mitigating the effects of noise. Previous work on this question relies on specific assumptions about where noise enters a circuit, limiting the generality of the resulting conclusions. Here we systematically investigate how noise introduced at different stages of neural processing impacts optimal coding strategies. Using simulations and a flexible analytical approach, we show how these strategies depend on the strength of each noise source, revealing under what conditions the different noise sources have competing or complementary effects. We draw two primary conclusions: (1) differences in encoding strategies between sensory systems-or even adaptational changes in encoding properties within a given system-may be produced by changes in the structure or location of neural noise, and (2) characterization of both circuit nonlinearities as well as noise are necessary to evaluate whether a circuit is performing efficiently.'),
(88057, 2016, 'plos', 'Genetic Redundancies Enhance Information Transfer in Noisy Regulatory Circuits', 'Rodrigo, Guillermo|Poyatos, Juan F', 'Instituto de Biología Molecular y Celular de Plantas, CSIC-UPV, Valencia, Spain.|Logic of Genomic Systems Laboratory, CNB-CSIC, Madrid, Spain.', 'Spain|Spain', 0, 'Adaptation, Physiological|Animals|Computer Simulation|Feedback, Physiological|Gene Expression Regulation|Humans|Information Storage and Retrieval|Models, Genetic|Proteome|Signal Transduction|Signal-To-Noise Ratio', 'Journal Article', '10.1371/journal.pcbi.1005156', '27741249', 'Cellular decision making is based on regulatory circuits that associate signal thresholds to specific physiological actions. This transmission of information is subjected to molecular noise what can decrease its fidelity. Here, we show instead how such intrinsic noise enhances information transfer in the presence of multiple circuit copies. The result is due to the contribution of noise to the generation of autonomous responses by each copy, which are altogether associated with a common decision. Moreover, factors that correlate the responses of the redundant units (extrinsic noise or regulatory cross-talk) contribute to reduce fidelity, while those that further uncouple them (heterogeneity within the copies) can lead to stronger information gain. Overall, our study emphasizes how the interplay of signal thresholding, redundancy, and noise influences the accuracy of cellular decision making. Understanding this interplay provides a basis to explain collective cell signaling mechanisms, and to engineer robust decisions with noisy genetic circuits.'),
(88058, 2016, 'plos', 'A Parsimonious Model of the Rabbit Action Potential Elucidates the Minimal Physiological Requirements for Alternans and Spiral Wave Breakup', 'Gray, Richard A|Pathmanathan, Pras', 'Division of Biomedical Physics, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, Maryland, United States of America.|Division of Biomedical Physics, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, Maryland, United States of America.', 'United States|United States', 15, 'Action Potentials|Animals|Arrhythmias, Cardiac|Biological Clocks|Cell Membrane|Computer Simulation|Heart Conduction System|Ion Channel Gating|Membrane Potentials|Models, Cardiovascular|Models, Neurological|Muscle Cells|Potassium|Potassium Channels|Rabbits', 'Journal Article', '10.1371/journal.pcbi.1005087', '27749895', 'Elucidating the underlying mechanisms of fatal cardiac arrhythmias requires a tight integration of electrophysiological experiments, models, and theory. Existing models of transmembrane action potential (AP) are complex (resulting in over parameterization) and varied (leading to dissimilar predictions). Thus, simpler models are needed to elucidate the minimal physiological requirements to reproduce significant observable phenomena using as few parameters as possible. Moreover, models have been derived from experimental studies from a variety of species under a range of environmental conditions (for example, all existing rabbit AP models incorporate a formulation of the rapid sodium current, INa, based on 30 year old data from chick embryo cell aggregates). Here we develop a simple parsimonious rabbit AP model that is mathematically identifiable (i.e., not over parameterized) by combining a novel Hodgkin-Huxley formulation of INa with a phenomenological model of repolarization similar to the voltage dependent, time-independent rectifying outward potassium current (IK). The model was calibrated using the following experimental data sets measured from the same species (rabbit) under physiological conditions: dynamic current-voltage (I-V) relationships during the AP upstroke, rapid recovery of AP excitability during the relative refractory period, and steady-state INa inactivation via voltage clamp. Simulations reproduced several important emergent phenomena including cellular alternans at rates > 250 bpm as observed in rabbit myocytes, reentrant spiral waves as observed on the surface of the rabbit heart, and spiral wave breakup. Model variants were studied which elucidated the minimal requirements for alternans and spiral wave break up, namely the kinetics of INa inactivation and the non-linear rectification of IK.The simplicity of the model, and the fact that its parameters have physiological meaning, make it ideal for engendering generalizable mechanistic insight and should provide a solid building-block to generate more detailed ionic models to represent complex rabbit electrophysiology.'),
(88059, 2016, 'plos', 'Correction: A Network Biology Approach Identifies Molecular Cross-Talk between Normal Prostate Epithelial and Prostate Carcinoma Cells', 'PLOS Computational Biology Staff', 'Division of Biomedical Physics, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, Maryland, United States of America.', 'United States', 1, '-', 'Journal Article', '10.1371/journal.pcbi.1005172', '27749896', '[This corrects the article DOI: 10.1371/journal.pcbi.1004884.].'),
(88060, 2016, 'plos', 'Symbiotic Cell Differentiation and Cooperative Growth in Multicellular Aggregates', 'Yamagishi, Jumpei F|Saito, Nen|Kaneko, Kunihiko', 'College of Arts and Sciences, The University of Tokyo, Meguro-ku, Tokyo, Japan.|Graduate School of Arts and Sciences, The University of Tokyo, Meguro-ku, Tokyo, Japan.|Graduate School of Arts and Sciences, The University of Tokyo, Meguro-ku, Tokyo, Japan.', 'Japan|Japan|Japan', 1, 'Cell Communication|Cell Differentiation|Computer Simulation|Microbial Interactions|Models, Biological|Quorum Sensing|Signal Transduction|Spheroids, Cellular|Symbiosis', 'Journal Article', '10.1371/journal.pcbi.1005042', '27749898', 'As cells grow and divide under a given environment, they become crowded and resources are limited, as seen in bacterial biofilms and multicellular aggregates. These cells often show strong interactions through exchanging chemicals, as evident in quorum sensing, to achieve mutualism and division of labor. Here, to achieve stable division of labor, three characteristics are required. First, isogenous cells differentiate into several types. Second, this aggregate of distinct cell types shows better growth than that of isolated cells without interaction and differentiation, by achieving division of labor. Third, this cell aggregate is robust with respect to the number distribution of differentiated cell types. Indeed, theoretical studies have thus far considered how such cooperation is achieved when the ability of cell differentiation is presumed. Here, we address how cells acquire the ability of cell differentiation and division of labor simultaneously, which is also connected with the robustness of a cell society. For this purpose, we developed a dynamical-systems model of cells consisting of chemical components with intracellular catalytic reaction dynamics. The reactions convert external nutrients into internal components for cellular growth, and the divided cells interact through chemical diffusion. We found that cells sharing an identical catalytic network spontaneously differentiate via induction from cell-cell interactions, and then achieve division of labor, enabling a higher growth rate than that in the unicellular case. This symbiotic differentiation emerged for a class of reaction networks under the condition of nutrient limitation and strong cell-cell interactions. Then, robustness in the cell type distribution was achieved, while instability of collective growth could emerge even among the cooperative cells when the internal reserves of products were dominant. The present mechanism is simple and general as a natural consequence of interacting cells with limited resources, and is consistent with the observed behaviors and forms of several aggregates of unicellular organisms.'),
(88061, 2016, 'plos', 'Likelihood-Based Inference of B Cell Clonal Families', 'Ralph, Duncan K|Matsen, Frederick A', 'Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.|Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.', 'United States|United States', 33, 'B-Lymphocytes|Clone Cells|Computer Simulation|Gene Rearrangement, B-Lymphocyte|High-Throughput Nucleotide Sequencing|Models, Genetic|Models, Immunological|Models, Statistical|Receptors, Antigen, B-Cell|Sequence Analysis, DNA', 'Journal Article', '10.1371/journal.pcbi.1005086', '27749910', 'The human immune system depends on a highly diverse collection of antibody-making B cells. B cell receptor sequence diversity is generated by a random recombination process called rearrangement forming progenitor B cells, then a Darwinian process of lineage diversification and selection called affinity maturation. The resulting receptors can be sequenced in high throughput for research and diagnostics. Such a collection of sequences contains a mixture of various lineages, each of which may be quite numerous, or may consist of only a single member. As a step to understanding the process and result of this diversification, one may wish to reconstruct lineage membership, i.e. to cluster sampled sequences according to which came from the same rearrangement events. We call this clustering problem clonal family inference. In this paper we describe and validate a likelihood-based framework for clonal family inference based on a multi-hidden Markov Model (multi-HMM) framework for B cell receptor sequences. We describe an agglomerative algorithm to find a maximum likelihood clustering, two approximate algorithms with various trade-offs of speed versus accuracy, and a third, fast algorithm for finding specific lineages. We show that under simulation these algorithms greatly improve upon existing clonal family inference methods, and that they also give significantly different clusters than previous methods when applied to two real data sets.'),
(88062, 2016, 'plos', 'rasbhari: Optimizing Spaced Seeds for Database Searching, Read Mapping and Alignment-Free Sequence Comparison', 'Hahn, Lars|Leimeister, Chris-André|Ounit, Rachid|Lonardi, Stefano|Morgenstern, Burkhard', 'University of Göttingen, Department of Bioinformatics, Göttingen, Germany.|University of Göttingen, Department of Bioinformatics, Göttingen, Germany.|University of California, Riverside, Department of Computer Science and Engineering, Riverside, California, United States of America.|University of California, Riverside, Department of Computer Science and Engineering, Riverside, California, United States of America.|University of Göttingen, Department of Bioinformatics, Göttingen, Germany.', 'Germany|Germany|United States|United States|Germany', 0, 'Algorithms|DNA|DNA Mutational Analysis|Data Mining|Database Management Systems|Databases, Genetic|Machine Learning|Pattern Recognition, Automated|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1371/journal.pcbi.1005107', '27760124', 'Many algorithms for sequence analysis rely on word matching or word statistics. Often, these approaches can be improved if binary patterns representing match and dont-care positions are used as a filter, such that only those positions of words are considered that correspond to the match positions of the patterns. The performance of these approaches, however, depends on the underlying patterns. Herein, we show that the overlap complexity of a pattern set that was introduced by Ilie and Ilie is closely related to the variance of the number of matches between two evolutionarily related sequences with respect to this pattern set. We propose a modified hill-climbing algorithm to optimize pattern sets for database searching, read mapping and alignment-free sequence comparison of nucleic-acid sequences, our implementation of this algorithm is called rasbhari. Depending on the application at hand, rasbhari can either minimize the overlap complexity of pattern sets, maximize their sensitivity in database searching or minimize the variance of the number of pattern-based matches in alignment-free sequence comparison. We show that, for database searching, rasbhari generates pattern sets with slightly higher sensitivity than existing approaches. In our Spaced Words approach to alignment-free sequence comparison, pattern sets calculated with rasbhari led to more accurate estimates of phylogenetic distances than the randomly generated pattern sets that we previously used. Finally, we used rasbhari to generate patterns for short read classification with CLARK-S. Here too, the sensitivity of the results could be improved, compared to the default patterns of the program. We integrated rasbhari into Spaced Words, the source code of rasbhari is freely available at http://rasbhari.gobics.de/.'),
(88063, 2016, 'plos', 'Unsupervised Learning in an Ensemble of Spiking Neural Networks Mediated by ITDP', 'Shim, Yoonsik|Philippides, Andrew|Staras, Kevin|Husbands, Phil', 'Centre for Computational Neuroscience and Robotics, University of Sussex, Falmer, Brighton, United Kingdom.|Centre for Computational Neuroscience and Robotics, University of Sussex, Falmer, Brighton, United Kingdom.|Neuroscience, School of Life Sciences, University of Sussex, Falmer, Brighton, United Kingdom.|Centre for Computational Neuroscience and Robotics, University of Sussex, Falmer, Brighton, United Kingdom.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 8, 'Action Potentials|Animals|Biological Clocks|Computer Simulation|Humans|Models, Neurological|Nerve Net|Neural Networks (Computer)|Neuronal Plasticity|Neurons|Pattern Recognition, Automated|Pattern Recognition, Physiological|Unsupervised Machine Learning', 'Journal Article', '10.1371/journal.pcbi.1005137', '27760125', 'We propose a biologically plausible architecture for unsupervised ensemble learning in a population of spiking neural network classifiers. A mixture of experts type organisation is shown to be effective, with the individual classifier outputs combined via a gating network whose operation is driven by input timing dependent plasticity (ITDP). The ITDP gating mechanism is based on recent experimental findings. An abstract, analytically tractable model of the ITDP driven ensemble architecture is derived from a logical model based on the probabilities of neural firing events. A detailed analysis of this model provides insights that allow it to be extended into a full, biologically plausible, computational implementation of the architecture which is demonstrated on a visual classification task. The extended model makes use of a style of spiking network, first introduced as a model of cortical microcircuits, that is capable of Bayesian inference, effectively performing expectation maximization. The unsupervised ensemble learning mechanism, based around such spiking expectation maximization (SEM) networks whose combined outputs are mediated by ITDP, is shown to perform the visual classification task well and to generalize to unseen data. The combined ensemble performance is significantly better than that of the individual classifiers, validating the ensemble architecture and learning mechanisms. The properties of the full model are analysed in the light of extensive experiments with the classification task, including an investigation into the influence of different input feature selection schemes and a comparison with a hierarchical STDP based ensemble architecture.'),
(88064, 2016, 'plos', 'DREISS: Using State-Space Models to Infer the Dynamics of Gene Expression Driven by External and Internal Regulatory Networks', 'Wang, Daifeng|He, Fei|Maslov, Sergei|Gerstein, Mark', 'Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, United States of America.|Biology Department, Brookhaven National Laboratory, Upton, New York, United States of America.|Biology Department, Brookhaven National Laboratory, Upton, New York, United States of America.|Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America.', 'United States|United States|United States|United States', 0, 'Algorithms|Animals|Computer Simulation|Feedback, Physiological|Gene Expression Regulation|Gene Regulatory Networks|Humans|Models, Biological|Proteome|Software', 'Journal Article', '10.1371/journal.pcbi.1005146', '27760135', 'Gene expression is controlled by the combinatorial effects of regulatory factors from different biological subsystems such as general transcription factors (TFs), cellular growth factors and microRNAs. A subsystems gene expression may be controlled by its internal regulatory factors, exclusively, or by external subsystems, or by both. It is thus useful to distinguish the degree to which a subsystem is regulated internally or externally-e.g., how non-conserved, species-specific TFs affect the expression of conserved, cross-species genes during evolution. We developed a computational method (DREISS, dreiss.gerteinlab.org) for analyzing the Dynamics of gene expression driven by Regulatory networks, both External and Internal based on State Space models. Given a subsystem, the state and control in the model refer to its own (internal) and another subsystems (external) gene expression levels. The state at a given time is determined by the state and control at a previous time. Because typical time-series data do not have enough samples to fully estimate the models parameters, DREISS uses dimensionality reduction, and identifies canonical temporal expression trajectories (e.g., degradation, growth and oscillation) representing the regulatory effects emanating from various subsystems. To demonstrate capabilities of DREISS, we study the regulatory effects of evolutionarily conserved vs. divergent TFs across distant species. In particular, we applied DREISS to the time-series gene expression datasets of C. elegans and D. melanogaster during their embryonic development. We analyzed the expression dynamics of the conserved, orthologous genes (orthologs), seeing the degree to which these can be accounted for by orthologous (internal) versus species-specific (external) TFs. We found that between two species, the orthologs have matched, internally driven expression patterns but very different externally driven ones. This is particularly true for genes with evolutionarily ancient functions (e.g. the ribosomal proteins), in contrast to those with more recently evolved functions (e.g., cell-cell communication). This suggests that despite striking morphological differences, some fundamental embryonic-developmental processes are still controlled by ancient regulatory systems.'),
(88065, 2016, 'plos', 'Optimized Treatment Schedules for Chronic Myeloid Leukemia', 'He, Qie|Zhu, Junfeng|Dingli, David|Foo, Jasmine|Leder, Kevin Zox', 'Department of Industrial and Systems Engineering, University of Minnesota, Minneapolis, MN, USA.|Department of Industrial and Systems Engineering, University of Minnesota, Minneapolis, MN, USA.|Department of Hematology, Mayo Clinic, Rochester, MN, USA.|Department of Mathematics, University of Minnesota, Minneapolis, MN.|Department of Industrial and Systems Engineering, University of Minnesota, Minneapolis, MN, USA.', 'United States|United States|United States|United States|United States', 0, 'Antineoplastic Agents|Antineoplastic Combined Chemotherapy Protocols|Decision Support Systems, Clinical|Drug Administration Schedule|Drug Monitoring|Drug Therapy, Computer-Assisted|Humans|Leukemia, Myelogenous, Chronic, BCR-ABL Positive|Treatment Outcome', 'Journal Article', '10.1371/journal.pcbi.1005129', '27764087', 'Over the past decade, several targeted therapies (e.g. imatinib, dasatinib, nilotinib) have been developed to treat Chronic Myeloid Leukemia (CML). Despite an initial response to therapy, drug resistance remains a problem for some CML patients. Recent studies have shown that resistance mutations that preexist treatment can be detected in a substantial number of patients, and that this may be associated with eventual treatment failure. One proposed method to extend treatment efficacy is to use a combination of multiple targeted therapies. However, the design of such combination therapies (timing, sequence, etc.) remains an open challenge. In this work we mathematically model the dynamics of CML response to combination therapy and analyze the impact of combination treatment schedules on treatment efficacy in patients with preexisting resistance. We then propose an optimization problem to find the best schedule of multiple therapies based on the evolution of CML according to our ordinary differential equation model. This resulting optimization problem is nontrivial due to the presence of ordinary different equation constraints and integer variables. Our model also incorporates drug toxicity constraints by tracking the dynamics of patient neutrophil counts in response to therapy. We determine optimal combination strategies that maximize time until treatment failure on hypothetical patients, using parameters estimated from clinical data in the literature.'),
(88066, 2016, 'plos', 'Ten Simple Rules for Digital Data Storage', 'Hart, Edmund M|Barmby, Pauline|LeBauer, David|Michonneau, François|Mount, Sarah|Mulrooney, Patrick|Poisot, Timothée|Woo, Kara H|Zimmerman, Naupaka B|Hollister, Jeffrey W', 'University of Vermont, Department of Biology, Burlington, Vermont, United States of America.|University of Western Ontario, Department of Physics and Astronomy, London, Canada.|University of Illinois at Urbana-Champaign, National Center for Supercomputing Applications and Institute for Genomic Biology, Urbana, Illinois, United States of America.|University of Florida, iDigBio, Florida Museum of Natural History, Gainesville, Florida, United States of America.|Kings College London, Department of Informatics, London, United Kingdom.|University of California at San Diego, San Diego Supercomputer Center, San Diego, California, United States of America.|Université de Montréal, Département de Sciences Biologiques, Montreal, Canada.|Washington State University, Center for Environmental Research, Education, and Outreach, Pullman, Washington, United States of America.|University of Arizona, School of Plant Sciences, Tucson, Arizona, United States of America.|US Environmental Protection Agency, Atlantic Ecology Division, Narragansett, Rhode Island, United States of America.', 'United States|Canada|United States|United States|United Kingdom|United States|Canada|United States|U', 23, 'Databases, Factual|Humans|Information Storage and Retrieval', 'Editorial', '10.1371/journal.pcbi.1005097', '27764088', '-'),
(88067, 2016, 'plos', 'Ten Simple Rules for Developing a MOOC', 'Manallack, David T|Yuriev, Elizabeth', 'Faculty of Pharmacy and Pharmaceutical Sciences, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia.|Faculty of Pharmacy and Pharmaceutical Sciences, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia.', 'Australia|Australia', 8, 'Computer-Assisted Instruction|Education, Distance|Humans|Internet', 'Editorial', '10.1371/journal.pcbi.1005061', '27764089', '-');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(88068, 2016, 'plos', 'Population Density Modulates Drug Inhibition and Gives Rise to Potential Bistability of Treatment Outcomes for Bacterial Infections', 'Karslake, Jason|Maltas, Jeff|Brumm, Peter|Wood, Kevin B', 'Department of Biophysics, University of Michigan, Ann Arbor, MI.|Department of Biophysics, University of Michigan, Ann Arbor, MI.|Department of Biophysics, University of Michigan, Ann Arbor, MI.|Department of Biophysics, University of Michigan, Ann Arbor, MI.', '-|-|-|-', 0, 'Anti-Bacterial Agents|Bacterial Load|Computer Simulation|Dose-Response Relationship, Drug|Drug Resistance, Bacterial|Enterococcus faecalis|Gram-Positive Bacterial Infections|Humans|Microbial Sensitivity Tests|Models, Biological', 'Journal Article', '10.1371/journal.pcbi.1005098', '27764095', 'The inoculum effect (IE) is an increase in the minimum inhibitory concentration (MIC) of an antibiotic as a function of the initial size of a microbial population. The IE has been observed in a wide range of bacteria, implying that antibiotic efficacy may depend on population density. Such density dependence could have dramatic effects on bacterial population dynamics and potential treatment strategies, but explicit measures of per capita growth as a function of density are generally not available. Instead, the IE measures MIC as a function of initial population size, and population density changes by many orders of magnitude on the timescale of the experiment. Therefore, the functional relationship between population density and antibiotic inhibition is generally not known, leaving many questions about the impact of the IE on different treatment strategies unanswered. To address these questions, here we directly measured real-time per capita growth of Enterococcus faecalis populations exposed to antibiotic at fixed population densities using multiplexed computer-automated culture devices. We show that density-dependent growth inhibition is pervasive for commonly used antibiotics, with some drugs showing increased inhibition and others decreased inhibition at high densities. For several drugs, the density dependence is mediated by changes in extracellular pH, a community-level phenomenon not previously linked with the IE. Using a simple mathematical model, we demonstrate how this density dependence can modulate population dynamics in constant drug environments. Then, we illustrate how time-dependent dosing strategies can mitigate the negative effects of density-dependence. Finally, we show that these density effects lead to bistable treatment outcomes for a wide range of antibiotic concentrations in a pharmacological model of antibiotic treatment. As a result, infections exceeding a critical density often survive otherwise effective treatments.'),
(88069, 2016, 'plos', 'Dynamic Nucleosome Movement Provides Structural Information of Topological Chromatin Domains in Living Human Cells', 'Shinkai, Soya|Nozaki, Tadasu|Maeshima, Kazuhiro|Togashi, Yuichi', 'Research Center for the Mathematics on Chromatin Live Dynamics (RcMcD), Hiroshima University, Higashi-Hiroshima, Japan.|Biological Macromolecules Laboratory, Structural Biology Center, National Institute of Genetics, Mishima, Japan.|Biological Macromolecules Laboratory, Structural Biology Center, National Institute of Genetics, Mishima, Japan.|Research Center for the Mathematics on Chromatin Live Dynamics (RcMcD), Hiroshima University, Higashi-Hiroshima, Japan.', 'Japan|Japan|Japan|Japan', 0, 'Binding Sites|Chromatin|Chromatin Assembly and Disassembly|Computer Simulation|HeLa Cells|Humans|Models, Chemical|Models, Genetic|Models, Molecular|Molecular Conformation|Molecular Imaging|Motion|Nucleosomes', 'Journal Article', '10.1371/journal.pcbi.1005136', '27764097', 'The mammalian genome is organized into submegabase-sized chromatin domains (CDs) including topologically associating domains, which have been identified using chromosome conformation capture-based methods. Single-nucleosome imaging in living mammalian cells has revealed subdiffusively dynamic nucleosome movement. It is unclear how single nucleosomes within CDs fluctuate and how the CD structure reflects the nucleosome movement. Here, we present a polymer model wherein CDs are characterized by fractal dimensions and the nucleosome fibers fluctuate in a viscoelastic medium with memory. We analytically show that the mean-squared displacement (MSD) of nucleosome fluctuations within CDs is subdiffusive. The diffusion coefficient and the subdiffusive exponent depend on the structural information of CDs. This analytical result enabled us to extract information from the single-nucleosome imaging data for HeLa cells. Our observation that the MSD is lower at the nuclear periphery region than the interior region indicates that CDs in the heterochromatin-rich nuclear periphery region are more compact than those in the euchromatin-rich interior region with respect to the fractal dimensions as well as the size. Finally, we evaluated that the average size of CDs is in the range of 100-500 nm and that the relaxation time of nucleosome movement within CDs is a few seconds. Our results provide physical and dynamic insights into the genome architecture in living cells.'),
(88070, 2016, 'plos', 'The Spatial Dynamics of Predators and the Benefits and Costs of Sharing Information', 'Barbier, Matthieu|Watson, James R', 'Centre for Biodiversity Theory and Modelling, National Centre for Scientific Research(CNRS), France.|Stockholm Resilience Centre, Stockholm University, Sweden.', 'France|Sweden', 11, 'Animals|Computer Simulation|Cooperative Behavior|Ecosystem|Food Chain|Game Theory|Humans|Information Dissemination|Models, Biological|Population Dynamics|Predatory Behavior', 'Journal Article', '10.1371/journal.pcbi.1005147', '27764098', 'Predators of all kinds, be they lions hunting in the Serengeti or fishermen searching for their catch, display various collective strategies. A common strategy is to share information about the location of prey. However, depending on the spatial characteristics and mobility of predators and prey, information sharing can either improve or hinder individual success. Here, our goal is to investigate the interacting effects of space and information sharing on predation efficiency, represented by the expected rate at which prey are found and consumed. We derive a feeding functional response that accounts for both spatio-temporal heterogeneity and communication, and validate this mathematical analysis with a computational agent-based model. This agent-based model has an explicit yet minimal representation of space, as well as information sharing about the location of prey. The analytical model simplifies predator behavior into a few discrete states and one essential trade-off, between the individual benefit of acquiring information and the cost of creating spatial and temporal correlation between predators. Despite the absence of an explicit spatial dimension in these equations, they quantitatively predict the predator consumption rates measured in the agent-based simulations across the explored parameter space. Together, the mathematical analysis and agent-based simulations identify the conditions for when there is a benefit to sharing information, and also when there is a cost.'),
(88071, 2016, 'plos', 'Intrinsic Noise Profoundly Alters the Dynamics and Steady State of Morphogen-Controlled Bistable Genetic Switches', 'Perez-Carrasco, Ruben|Guerrero, Pilar|Briscoe, James|Page, Karen M', 'Department of Mathematics, University College London, Gower Street, London WC1E 6BT, UK.|Department of Mathematics, University College London, Gower Street, London WC1E 6BT, UK.|The Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UK.|Department of Mathematics, University College London, Gower Street, London WC1E 6BT, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 22, 'Adaptation, Physiological|Animals|Computer Simulation|Gene Expression Regulation, Developmental|Genes, Switch|Homeostasis|Humans|Intercellular Signaling Peptides and Proteins|Models, Genetic|Models, Statistical|Morphogenesis|Signal-To-Noise Ratio', 'Journal Article', '10.1371/journal.pcbi.1005154', '27768683', 'During tissue development, patterns of gene expression determine the spatial arrangement of cell types. In many cases, gradients of secreted signalling molecules-morphogens-guide this process by controlling downstream transcriptional networks. A mechanism commonly used in these networks to convert the continuous information provided by the gradient into discrete transitions between adjacent cell types is the genetic toggle switch, composed of cross-repressing transcriptional determinants. Previous analyses have emphasised the steady state output of these mechanisms. Here, we explore the dynamics of the toggle switch and use exact numerical simulations of the kinetic reactions, the corresponding Chemical Langevin Equation, and Minimum Action Path theory to establish a framework for studying the effect of gene expression noise on patterning time and boundary position. This provides insight into the time scale, gene expression trajectories and directionality of stochastic switching events between cell states. Taking gene expression noise into account predicts that the final boundary position of a morphogen-induced toggle switch, although robust to changes in the details of the noise, is distinct from that of the deterministic system. Moreover, the dramatic increase in patterning time close to the boundary predicted from the deterministic case is substantially reduced. The resulting stochastic switching introduces differences in patterning time along the morphogen gradient that result in a patterning wave propagating away from the morphogen source with a velocity determined by the intrinsic noise. The wave sharpens and slows as it advances and may never reach steady state in a biologically relevant time. This could explain experimentally observed dynamics of pattern formation. Together the analysis reveals the importance of dynamical transients for understanding morphogen-driven transcriptional networks and indicates that gene expression noise can qualitatively alter developmental patterning.'),
(88072, 2016, 'plos', 'Long-Range Signaling in MutS and MSH Homologs via Switching of Dynamic Communication Pathways', 'Wang, Beibei|Francis, Joshua|Sharma, Monika|Law, Sean M|Predeus, Alexander V|Feig, Michael', 'Department of Biochemistry & Molecular Biology, Michigan State University, East Lansing, MI, United States.|Department of Biochemistry & Molecular Biology, Michigan State University, East Lansing, MI, United States.|Department of Biochemistry & Molecular Biology, Michigan State University, East Lansing, MI, United States.|Department of Biochemistry & Molecular Biology, Michigan State University, East Lansing, MI, United States.|Department of Biochemistry & Molecular Biology, Michigan State University, East Lansing, MI, United States.|Department of Biochemistry & Molecular Biology, Michigan State University, East Lansing, MI, United States.', 'United States|United States|United States|United States|United States|United States', 5, 'Binding Sites|Cell Communication|DNA|DNA Repair Enzymes|Gene Expression Regulation|MutS DNA Mismatch-Binding Protein|Protein Binding|Protein Conformation|Signal Transduction|Structure-Activity Relationship', 'Journal Article', '10.1371/journal.pcbi.1005159', '27768684', 'Allostery is conformation regulation by propagating a signal from one site to another distal site. This study focuses on the long-range communication in DNA mismatch repair proteins MutS and its homologs where intramolecular signaling has to travel over 70 Å to couple lesion detection to ATPase activity and eventual downstream repair. Using dynamic network analysis based on extensive molecular dynamics simulations, multiple preserved communication pathways were identified that would allow such long-range signaling. The pathways appear to depend on the nucleotides bound to the ATPase domain as well as the type of DNA substrate consistent with previously proposed functional cycles of mismatch recognition and repair initiation by MutS and homologs. A mechanism is proposed where pathways are switched without major conformational rearrangements allowing for efficient long-range signaling and allostery.'),
(88073, 2016, 'plos', 'PreTIS: A Tool to Predict Non-canonical 5 UTR Translational Initiation Sites in Human and Mouse', 'Reuter, Kerstin|Biehl, Alexander|Koch, Laurena|Helms, Volkhard', 'Center for Bioinformatics, Saarland University, Saarbrücken, Germany.|Center for Bioinformatics, Saarland University, Saarbrücken, Germany.|Center for Bioinformatics, Saarland University, Saarbrücken, Germany.|Center for Bioinformatics, Saarland University, Saarbrücken, Germany.', 'Germany|Germany|Germany|Germany', 12, '5 Untranslated Regions|Algorithms|Animals|Base Sequence|HEK293 Cells|Humans|Mice|Molecular Sequence Data|RNA, Messenger|Sequence Alignment|Sequence Analysis, RNA|Software|Species Specificity|Transcription Initiation Site', 'Journal Article', '10.1371/journal.pcbi.1005170', '27768687', 'Translation of mRNA sequences into proteins typically starts at an AUG triplet. In rare cases, translation may also start at alternative non-AUG codons located in the annotated 5 UTR which leads to an increased regulatory complexity. Since ribosome profiling detects translational start sites at the nucleotide level, the properties of these start sites can then be used for the statistical evaluation of functional open reading frames. We developed a linear regression approach to predict in-frame and out-of-frame translational start sites within the 5 UTR from mRNA sequence information together with their translation initiation confidence. Predicted start codons comprise AUG as well as near-cognate codons. The underlying datasets are based on published translational start sites for human HEK293 and mouse embryonic stem cells that were derived by the original authors from ribosome profiling data. The average prediction accuracy of true vs. false start sites for HEK293 cells was 80%. When applied to mouse mRNA sequences, the same model predicted translation initiation sites observed in mouse ES cells with an accuracy of 76%. Moreover, we illustrate the effect of in silico mutations in the flanking sequence context of a start site on the predicted initiation confidence. Our new webservice PreTIS visualizes alternative start sites and their respective ORFs and predicts their ability to initiate translation. Solely, the mRNA sequence is required as input. PreTIS is accessible at http://service.bioinformatik.uni-saarland.de/pretis.'),
(88074, 2016, 'plos', 'Viral RNA Degradation and Diffusion Act as a Bottleneck for the Influenza A Virus Infection Efficiency', 'Schelker, Max|Mair, Caroline Maria|Jolmes, Fabian|Welke, Robert-William|Klipp, Edda|Herrmann, Andreas|Flöttmann, Max|Sieben, Christian', 'Theoretical Biophysics, Humboldt-Universität zu Berlin, Berlin, Germany.|Molecular Biophysics, Humboldt-Universität zu Berlin, Berlin, Germany.|Molecular Biophysics, Humboldt-Universität zu Berlin, Berlin, Germany.|Molecular Biophysics, Humboldt-Universität zu Berlin, Berlin, Germany.|Theoretical Biophysics, Humboldt-Universität zu Berlin, Berlin, Germany.|Molecular Biophysics, Humboldt-Universität zu Berlin, Berlin, Germany.|Theoretical Biophysics, Humboldt-Universität zu Berlin, Berlin, Germany.|Molecular Biophysics, Humboldt-Universität zu Berlin, Berlin, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 6, 'Computer Simulation|Diffusion|Endocytosis|Hemagglutinins|Humans|Influenza A virus|Influenza, Human|Models, Biological|RNA, Viral|Virus Internalization', 'Journal Article', '10.1371/journal.pcbi.1005075', '27780209', 'After endocytic uptake, influenza viruses transit early endosomal compartments and eventually reach late endosomes. There, the viral glycoprotein hemagglutinin (HA) triggers fusion between endosomal and viral membrane, a critical step that leads to release of the viral segmented genome destined to reach the cell nucleus. Endosomal maturation is a complex process involving acidification of the endosomal lumen as well as endosome motility along microtubules. While the pH drop is clearly critical for the conformational change and membrane fusion activity of HA, the effect of intracellular transport dynamics on the progress of infection remains largely unclear. In this study, we developed a comprehensive mathematical model accounting for the first steps of influenza virus infection. We calibrated our model with experimental data and challenged its predictions using recombinant viruses with altered pH sensitivity of HA. We identified the time point of virus-endosome fusion and thereby the diffusion distance of the released viral genome to the nucleus as a critical bottleneck for efficient virus infection. Further, we concluded and supported experimentally that the viral RNA is subjected to cytosolic degradation strongly limiting the probability of a successful genome import into the nucleus.'),
(88075, 2016, 'plos', 'Apparent Motion Suppresses Responses in Early Visual Cortex: A Population Code Model', 'Van Humbeeck, Nathalie|Putzeys, Tom|Wagemans, Johan', 'Laboratory of Experimental Psychology, Department of Brain & Cognition, University of Leuven, Leuven, Belgium.|Laboratory of Experimental Psychology, Department of Brain & Cognition, University of Leuven, Leuven, Belgium.|Laboratory of Experimental Psychology, Department of Brain & Cognition, University of Leuven, Leuven, Belgium.', 'Belgium|Belgium|Belgium', 0, 'Animals|Computer Simulation|Female|Humans|Illusions|Male|Models, Neurological|Motion Perception|Nerve Net|Neural Inhibition|Perceptual Masking|Photic Stimulation|Visual Cortex|Young Adult', 'Journal Article', '10.1371/journal.pcbi.1005155', '27783622', 'Two stimuli alternately presented at different locations can evoke a percept of a stimulus continuously moving between the two locations. The neural mechanism underlying this apparent motion (AM) is thought to be increased activation of primary visual cortex (V1) neurons tuned to locations along the AM path, although evidence remains inconclusive. AM masking, which refers to the reduced detectability of stimuli along the AM path, has been taken as evidence for AM-related V1 activation. AM-induced neural responses are thought to interfere with responses to physical stimuli along the path and as such impair the perception of these stimuli. However, AM masking can also be explained by predictive coding models, predicting that responses to stimuli presented on the AM path are suppressed when they match the spatio-temporal prediction of a stimulus moving along the path. In the present study, we find that AM has a distinct effect on the detection of target gratings, limiting the maximum performance at high contrast levels. This masking is strongest when the target orientation is identical to the orientation of the inducers. We developed a V1-like population code model of early visual processing, based on a standard contrast normalization model. We find that AM-related activation in early visual cortex is too small to either cause masking or to be perceived as motion. Our model instead predicts strong suppression of early sensory responses during AM, consistent with the theoretical framework of predictive coding.'),
(88076, 2016, 'plos', 'Glucose Oscillations Can Activate an Endogenous Oscillator in Pancreatic Islets', 'McKenna, Joseph P|Dhumpa, Raghuram|Mukhitov, Nikita|Roper, Michael G|Bertram, Richard', 'Department of Mathematics, Florida State University, Tallahassee, Florida, United States of America.|Department of Chemistry and Biochemistry, Florida State University, Tallahassee, Florida, United States of America.|Department of Chemistry and Biochemistry, Florida State University, Tallahassee, Florida, United States of America.|Department of Chemistry and Biochemistry and Program in Molecular Biophysics, Florida State University, Tallahassee, Florida, United States of America.|Department of Mathematics and Programs in Neuroscience and Molecular Biophysics, Florida State University, Tallahassee, Florida, United States of America.', 'United States|United States|United States|United States|United States', 8, 'Animals|Biological Clocks|Calcium Signaling|Cells, Cultured|Computer Simulation|Feedback, Physiological|Glucose|Glycolysis|Humans|Insulin|Insulin Secretion|Islets of Langerhans|Models, Biological', 'Journal Article', '10.1371/journal.pcbi.1005143', '27788129', 'Pancreatic islets manage elevations in blood glucose level by secreting insulin into the bloodstream in a pulsatile manner. Pulsatile insulin secretion is governed by islet oscillations such as bursting electrical activity and periodic Ca2+ entry in xcexb2-cells. In this report, we demonstrate that although islet oscillations are lost by fixing a glucose stimulus at a high concentration, they may be recovered by subsequently converting the glucose stimulus to a sinusoidal wave. We predict with mathematical modeling that the sinusoidal glucose signals ability to recover islet oscillations depends on its amplitude and period, and we confirm our predictions by conducting experiments with islets using a microfluidics platform. Our results suggest a mechanism whereby oscillatory blood glucose levels recruit non-oscillating islets to enhance pulsatile insulin output from the pancreas. Our results also provide support for the main hypothesis of the Dual Oscillator Model, that a glycolytic oscillator endogenous to islet xcexb2-cells drives pulsatile insulin secretion.'),
(88077, 2016, 'plos', 'High-Accuracy HLA Type Inference from Whole-Genome Sequencing Data Using Population Reference Graphs', 'Dilthey, Alexander T|Gourraud, Pierre-Antoine|Mentzer, Alexander J|Cereb, Nezih|Iqbal, Zamin|McVean, Gil', 'Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom.|Neurology Department, UCSF, San Francisco, United States of America.|Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom.|Histogenetics, Ossining, United States of America.|Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom.|Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom.', 'United Kingdom|United States|United Kingdom|United States|United Kingdom|United Kingdom', 0, 'Algorithms|Chromosome Mapping|Genetics, Population|Genome, Human|Hemochromatosis Protein|High-Throughput Nucleotide Sequencing|Humans|Reference Values', 'Journal Article', '10.1371/journal.pcbi.1005151', '27792722', 'Genetic variation at the Human Leucocyte Antigen (HLA) genes is associated with many autoimmune and infectious disease phenotypes, is an important element of the immunological distinction between self and non-self, and shapes immune epitope repertoires. Determining the allelic state of the HLA genes (HLA typing) as a by-product of standard whole-genome sequencing data would therefore be highly desirable and enable the immunogenetic characterization of samples in currently ongoing population sequencing projects. Extensive hyperpolymorphism and sequence similarity between the HLA genes, however, pose problems for accurate read mapping and make HLA type inference from whole-genome sequencing data a challenging problem. We describe how to address these challenges in a Population Reference Graph (PRG) framework. First, we construct a PRG for 46 (mostly HLA) genes and pseudogenes, their genomic context and their characterized sequence variants, integrating a database of over 10,000 known allele sequences. Second, we present a sequence-to-PRG paired-end read mapping algorithm that enables accurate read mapping for the HLA genes. Third, we infer the most likely pair of underlying alleles at G group resolution from the IMGT/HLA database at each locus, employing a simple likelihood framework. We show that HLA*PRG, our algorithm, outperforms existing methods by a wide margin. We evaluate HLA*PRG on six classical class I and class II HLA genes (HLA-A, -B, -C, -DQA1, -DQB1, -DRB1) and on a set of 14 samples (3 samples with 2 x 100bp, 11 samples with 2 x 250bp Illumina HiSeq data). Of 158 alleles tested, we correctly infer 157 alleles (99.4%). We also identify and re-type two erroneous alleles in the original validation data. We conclude that HLA*PRG for the first time achieves accuracies comparable to gold-standard reference methods from standard whole-genome sequencing data, though high computational demands (currently ~30-250 CPU hours per sample) remain a significant challenge to practical application.'),
(88078, 2016, 'plos', 'Probabilistic Learning by Rodent Grid Cells', 'Cheung, Allen', 'The University of Queensland, Queensland Brain Institute, Upland Road, St. Lucia, Queensland, Australia.', 'Australia', 0, 'Animals|Computer Simulation|Grid Cells|Hippocampus|Maze Learning|Models, Neurological|Models, Statistical|Nerve Net|Neuronal Plasticity|Rats|Spatial Learning|Synaptic Transmission', 'Journal Article', '10.1371/journal.pcbi.1005165', '27792723', 'Mounting evidence shows mammalian brains are probabilistic computers, but the specific cells involved remain elusive. Parallel research suggests that grid cells of the mammalian hippocampal formation are fundamental to spatial cognition but their diverse response properties still defy explanation. No plausible model exists which explains stable grids in darkness for twenty minutes or longer, despite being one of the first results ever published on grid cells. Similarly, no current explanation can tie together grid fragmentation and grid rescaling, which show very different forms of flexibility in grid responses when the environment is varied. Other properties such as attractor dynamics and grid anisotropy seem to be at odds with one another unless additional properties are assumed such as a varying velocity gain. Modelling efforts have largely ignored the breadth of response patterns, while also failing to account for the disastrous effects of sensory noise during spatial learning and recall, especially in darkness. Here, published electrophysiological evidence from a range of experiments are reinterpreted using a novel probabilistic learning model, which shows that grid cell responses are accurately predicted by a probabilistic learning process. Diverse response properties of probabilistic grid cells are statistically indistinguishable from rat grid cells across key manipulations. A simple coherent set of probabilistic computations explains stable grid fields in darkness, partial grid rescaling in resized arenas, low-dimensional attractor grid cell dynamics, and grid fragmentation in hairpin mazes. The same computations also reconcile oscillatory dynamics at the single cell level with attractor dynamics at the cell ensemble level. Additionally, a clear functional role for boundary cells is proposed for spatial learning. These findings provide a parsimonious and unified explanation of grid cell function, and implicate grid cells as an accessible neuronal population readout of a set of probabilistic spatial computations.'),
(88079, 2016, 'plos', 'Temperature-Dependent Model of Multi-step Transcription Initiation in Escherichia coli Based on Live Single-Cell Measurements', 'Oliveira, Samuel M D|Häkkinen, Antti|Lloyd-Price, Jason|Tran, Huy|Kandavalli, Vinodh|Ribeiro, Andre S', 'Laboratory of Biosystem Dynamics, Department of Signal Processing, Tampere University of Technology, Tampere, Finland.|Laboratory of Biosystem Dynamics, Department of Signal Processing, Tampere University of Technology, Tampere, Finland.|Laboratory of Biosystem Dynamics, Department of Signal Processing, Tampere University of Technology, Tampere, Finland.|Laboratory of Biosystem Dynamics, Department of Signal Processing, Tampere University of Technology, Tampere, Finland.|Laboratory of Biosystem Dynamics, Department of Signal Processing, Tampere University of Technology, Tampere, Finland.|Laboratory of Biosystem Dynamics, Department of Signal Processing, Tampere University of Technology, Tampere, Finland.', 'Finland|Finland|Finland|Finland|Finland|Finland', 16, 'Computer Simulation|Escherichia coli|Escherichia coli Proteins|Models, Biological|Models, Statistical|Promoter Regions, Genetic|Temperature|Transcription Initiation Site|Transcription, Genetic|Transcriptional Activation', 'Journal Article', '10.1371/journal.pcbi.1005174', '27792724', 'Transcription kinetics is limited by its initiation steps, which differ between promoters and with intra- and extracellular conditions. Regulation of these steps allows tuning both the rate and stochasticity of RNA production. We used time-lapse, single-RNA microscopy measurements in live Escherichia coli to study how the rate-limiting steps in initiation of the Plac/ara-1 promoter change with temperature and induction scheme. For this, we compared detailed stochastic models fit to the empirical data in maximum likelihood sense using statistical methods. Using this analysis, we found that temperature affects the rate limiting steps unequally, as nonlinear changes in the closed complex formation suffice to explain the differences in transcription dynamics between conditions. Meanwhile, a similar analysis of the PtetA promoter revealed that it has a different rate limiting step configuration, with temperature regulating different steps. Finally, we used the derived models to explore a possible cause for why the identified steps are preferred as the main cause for behavior modifications with temperature: we find that transcription dynamics is either insensitive or responds reciprocally to changes in the other steps. Our results suggests that different promoters employ different rate limiting step patterns that control not only their rate and variability, but also their sensitivity to environmental changes.'),
(88080, 2016, 'plos', 'MD/DPD Multiscale Framework for Predicting Morphology and Stresses of Red Blood Cells in Health and Disease', 'Chang, Hung-Yu|Li, Xuejin|Li, He|Karniadakis, George Em', 'Division of Applied Mathematics, Brown University, Providence, Rhode Island, United States of America.|Division of Applied Mathematics, Brown University, Providence, Rhode Island, United States of America.|Division of Applied Mathematics, Brown University, Providence, Rhode Island, United States of America.|Division of Applied Mathematics, Brown University, Providence, Rhode Island, United States of America.', 'United States|United States|United States|United States', 0, 'Animals|Compressive Strength|Computer Simulation|Elastic Modulus|Erythrocyte Deformability|Erythrocytes|Erythrocytes, Abnormal|Hardness|Hematologic Diseases|Humans|Models, Cardiovascular|Stress, Mechanical|Viscosity', 'Journal Article', '10.1371/journal.pcbi.1005173', '27792725', 'Healthy red blood cells (RBCs) have remarkable deformability, squeezing through narrow capillaries as small as 3 microns in diameter without any damage. However, in many hematological disorders the spectrin network and lipid bilayer of diseased RBCs may be significantly altered, leading to impaired functionality including loss of deformability. We employ a two-component whole-cell multiscale model to quantify the biomechanical characteristics of the healthy and diseased RBCs, including Plasmodium falciparum-infected RBCs (Pf-RBCs) and defective RBCs in hereditary disorders, such as spherocytosis and elliptocytosis. In particular, we develop a two-step multiscale framework based on coarse-grained molecular dynamics (CGMD) and dissipative particle dynamics (DPD) to predict the static and dynamic responses of RBCs subject to tensile forcing, using experimental information only on the structural defects in the lipid bilayer, cytoskeleton, and their interaction. We first employ CGMD on a small RBC patch to compute the shear modulus, bending stiffness, and network parameters, which are subsequently used as input to a whole-cell DPD model to predict the RBC shape and corresponding stress field. For Pf-RBCs at trophozoite and schizont stages, the presence of cytoadherent knobs elevates the shear response in the lipid bilayer and stiffens the RBC membrane. For RBCs in spherocytosis and elliptocytosis, the bilayer-cytoskeleton interaction is weakened, resulting in substantial increase of the tensile stress in the lipid bilayer. Furthermore, we investigate the transient behavior of stretching deformation and shape relaxation of the normal and defective RBCs. Different from the normal RBCs possessing high elasticity, our simulations reveal that the defective RBCs respond irreversibly, i.e., they lose their ability to recover the normal biconcave shape in successive loading cycles of stretching and relaxation. Our findings provide fundamental insights into the microstructure and biomechanics of RBCs, and demonstrate that the two-step multiscale framework presented here can be used effectively for in silico studies of hematological disorders based on first principles and patient-specific experimental input at the protein level.'),
(88081, 2016, 'plos', 'Structural Identifiability of Dynamic Systems Biology Models', 'Villaverde, Alejandro F|Barreiro, Antonio|Papachristodoulou, Antonis', 'Department of Engineering Science, University of Oxford, Oxford, United Kingdom.|Department of Systems & Control Engineering, University of Vigo, Vigo, Spain.|Department of Engineering Science, University of Oxford, Oxford, United Kingdom.', 'United Kingdom|Spain|United Kingdom', 61, 'Algorithms|Animals|Computer Simulation|Humans|Models, Biological|Nonlinear Dynamics|Programming Languages|Software|Systems Biology', 'Journal Article', '10.1371/journal.pcbi.1005153', '27792726', 'A powerful way of gaining insight into biological systems is by creating a nonlinear differential equation model, which usually contains many unknown parameters. Such a model is called structurally identifiable if it is possible to determine the values of its parameters from measurements of the model outputs. Structural identifiability is a prerequisite for parameter estimation, and should be assessed before exploiting a model. However, this analysis is seldom performed due to the high computational cost involved in the necessary symbolic calculations, which quickly becomes prohibitive as the problem size increases. In this paper we show how to analyse the structural identifiability of a very general class of nonlinear models by extending methods originally developed for studying observability. We present results about models whose identifiability had not been previously determined, report unidentifiabilities that had not been found before, and show how to modify those unidentifiable models to make them identifiable. This method helps prevent problems caused by lack of identifiability analysis, which can compromise the success of tasks such as experiment design, parameter estimation, and model-based optimization. The procedure is called STRIKE-GOLDD (STRuctural Identifiability taKen as Extended-Generalized Observability with Lie Derivatives and Decomposition), and it is implemented in a MATLAB toolbox which is available as open source software. The broad applicability of this approach facilitates the analysis of the increasingly complex models used in systems biology and other areas.'),
(88082, 2016, 'plos', 'Catecholaminergic Regulation of Learning Rate in a Dynamic Environment', 'Jepma, Marieke|Murphy, Peter R|Nassar, Matthew R|Rangel-Gomez, Mauricio|Meeter, Martijn|Nieuwenhuis, Sander', 'Cognitive Psychology Unit, Institute of Psychology, Leiden University, and Leiden Institute for Brain and Cognition, Leiden University, Leiden, the Netherlands.|Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.|Department of Cognitive, Linguistic & Psychological Sciences, Brown University, Providence, RI, United States of America.|Department of Psychology, University of California, Berkeley, United States of America.|Department of Education Sciences, Vrije Universiteit Amsterdam, The Netherlands.|Cognitive Psychology Unit, Institute of Psychology, Leiden University, and Leiden Institute for Brain and Cognition, Leiden University, Leiden, the Netherlands.', 'Netherlands|Germany|United States|United States|Netherlands|Netherlands', 0, 'Adaptation, Physiological|Adolescent|Adult|Brain Mapping|Catecholamines|Dopamine|Double-Blind Method|Ecosystem|Female|Humans|Learning|Male|Neuronal Plasticity|Neurotransmitter Agents|Norepinephrine|Parietal Lobe|Young Adult', 'Journal Article', '10.1371/journal.pcbi.1005171', '27792728', 'Adaptive behavior in a changing world requires flexibly adapting ones rate of learning to the rate of environmental change. Recent studies have examined the computational mechanisms by which various environmental factors determine the impact of new outcomes on existing beliefs (i.e., the learning rate). However, the brain mechanisms, and in particular the neuromodulators, involved in this process are still largely unknown. The brain-wide neurophysiological effects of the catecholamines norepinephrine and dopamine on stimulus-evoked cortical responses suggest that the catecholamine systems are well positioned to regulate learning about environmental change, but more direct evidence for a role of this system is scant. Here, we report evidence from a study employing pharmacology, scalp electrophysiology and computational modeling (N = 32) that suggests an important role for catecholamines in learning rate regulation. We found that the P3 component of the EEG-an electrophysiological index of outcome-evoked phasic catecholamine release in the cortex-predicted learning rate, and formally mediated the effect of prediction-error magnitude on learning rate. P3 amplitude also mediated the effects of two computational variables-capturing the unexpectedness of an outcome and the uncertainty of a preexisting belief-on learning rate. Furthermore, a pharmacological manipulation of catecholamine activity affected learning rate following unanticipated task changes, in a way that depended on participants baseline learning rate. Our findings provide converging evidence for a causal role of the human catecholamine systems in learning-rate regulation as a function of environmental change.'),
(88083, 2016, 'plos', 'Topology, Cross-Frequency, and Same-Frequency Band Interactions Shape the Generation of Phase-Amplitude Coupling in a Neural Mass Model of a Cortical Column', 'Sotero, Roberto C', 'Hotchkiss Brain Institute, Department of Radiology, University of Calgary, Calgary, AB, Canada.', 'Canada', 0, 'Biological Clocks|Brain Waves|Computer Simulation|Humans|Male|Models, Neurological|Nerve Net|Nonlinear Dynamics|Synaptic Transmission', 'Journal Article', '10.1371/journal.pcbi.1005180', '27802274', 'Phase-amplitude coupling (PAC), a type of cross-frequency coupling (CFC) where the phase of a low-frequency rhythm modulates the amplitude of a higher frequency, is becoming an important indicator of information transmission in the brain. However, the neurobiological mechanisms underlying its generation remain undetermined. A realistic, yet tractable computational model of the phenomenon is thus needed. Here we analyze a neural mass model of a cortical column, comprising fourteen neuronal populations distributed across four layers (L2/3, L4, L5 and L6). A control analysis showed that the conditional transfer entropy (cTE) measure is able to correctly estimate the flow of information between neuronal populations. Then, we computed cTE from the phases to the amplitudes of the oscillations generated in the cortical column. This approach provides information regarding directionality by distinguishing PAC from APC (amplitude-phase coupling), i.e. the information transfer from amplitudes to phases, and can be used to estimate other types of CFC such as amplitude-amplitude coupling (AAC) and phase-phase coupling (PPC). While experiments often only focus on one or two PAC combinations (e.g., theta-gamma or alpha-gamma), we found that a cortical column can simultaneously generate almost all possible PAC combinations, depending on connectivity parameters, time constants, and external inputs. PAC interactions with and without an anatomical equivalent (direct and indirect interactions, respectively) were analyzed. We found that the strength of PAC between two populations was strongly correlated with the strength of the effective connections between the populations and, on average, did not depend on whether the PAC connection was direct or indirect. When considering a cortical column circuit as a complex network, we found that neuronal populations making indirect PAC connections had, on average, higher local clustering coefficient, efficiency, and betweenness centrality than populations making direct connections and populations not involved in PAC connections. This suggests that their interactions were more effective when transmitting information. Since approximately 60% of the obtained interactions represented indirect connections, our results highlight the importance of the topology of cortical circuits for the generation of the PAC phenomenon. Finally, our results demonstrated that indirect PAC interactions can be explained by a cascade of direct CFC and same-frequency band interactions, suggesting that PAC analysis of experimental data should be accompanied by the estimation of other types of frequency interactions for an integrative understanding of the phenomenon.'),
(88084, 2016, 'plos', 'WORMHOLE: Novel Least Diverged Ortholog Prediction through Machine Learning', 'Sutphin, George L|Mahoney, J Matthew|Sheppard, Keith|Walton, David O|Korstanje, Ron', 'The Jackson Laboratory, Bar Harbor, ME, United States of America.|Department of Neurological Sciences, University of Vermont College of Medicine, Burlington, VT, United States of America.|The Jackson Laboratory, Bar Harbor, ME, United States of America.|The Jackson Laboratory, Bar Harbor, ME, United States of America.|The Jackson Laboratory, Bar Harbor, ME, United States of America.', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Animals|Evolution, Molecular|Genetic Speciation|Genetic Variation|High-Throughput Nucleotide Sequencing|Humans|Machine Learning|Pattern Recognition, Automated|Proteins|Sequence Homology, Amino Acid|Software', 'Journal Article', '10.1371/journal.pcbi.1005182', '27812085', 'The rapid advancement of technology in genomics and targeted genetic manipulation has made comparative biology an increasingly prominent strategy to model human disease processes. Predicting orthology relationships between species is a vital component of comparative biology. Dozens of strategies for predicting orthologs have been developed using combinations of gene and protein sequence, phylogenetic history, and functional interaction with progressively increasing accuracy. A relatively new class of orthology prediction strategies combines aspects of multiple methods into meta-tools, resulting in improved prediction performance. Here we present WORMHOLE, a novel ortholog prediction meta-tool that applies machine learning to integrate 17 distinct ortholog prediction algorithms to identify novel least diverged orthologs (LDOs) between 6 eukaryotic species-humans, mice, zebrafish, fruit flies, nematodes, and budding yeast. Machine learning allows WORMHOLE to intelligently incorporate predictions from a wide-spectrum of strategies in order to form aggregate predictions of LDOs with high confidence. In this study we demonstrate the performance of WORMHOLE across each combination of query and target species. We show that WORMHOLE is particularly adept at improving LDO prediction performance between distantly related species, expanding the pool of LDOs while maintaining low evolutionary distance and a high level of functional relatedness between genes in LDO pairs. We present extensive validation, including cross-validated prediction of PANTHER LDOs and evaluation of evolutionary divergence and functional similarity, and discuss future applications of machine learning in ortholog prediction. A WORMHOLE web tool has been developed and is available at http://wormhole.jax.org/.'),
(88085, 2016, 'plos', 'Interrogating Emergent Transport Properties for Molecular Motor Ensembles: A Semi-analytical Approach', 'Bhaban, Shreyas|Materassi, Donatello|Li, Mingang|Hays, Thomas|Salapaka, Murti', 'Department of Electrical Engineering, University of Minnesota Twin Cities, Minneapolis, Minnesota, United States of America.|Department of Electrical Engineering and Computer Science, University of Tennessee Knoxville, Knoxville, Tennessee, United States of America.|Department of Genetics, Cell Biology, and Development, University of Minnesota Twin Cities, Minneapolis, Minnesota, United States of America.|Department of Genetics, Cell Biology, and Development, University of Minnesota Twin Cities, Minneapolis, Minnesota, United States of America.|Department of Electrical Engineering, University of Minnesota Twin Cities, Minneapolis, Minnesota, United States of America.', 'United States|United States|United States|United States|United States', 5, 'Binding Sites|Biological Transport, Active|Computer Simulation|Energy Transfer|Microtubules|Models, Biological|Models, Chemical|Models, Statistical|Molecular Motor Proteins|Motion|Protein Binding', 'Journal Article', '10.1371/journal.pcbi.1005152', '27812098', 'Intracellular transport is an essential function in eucaryotic cells, facilitated by motor proteins-proteins converting chemical energy into kinetic energy. It is understood that motor proteins work in teams enabling unidirectional and bidirectional transport of intracellular cargo over long distances. Disruptions of the underlying transport mechanisms, often caused by mutations that alter single motor characteristics, are known to cause neurodegenerative diseases. For example, phosphorylation of kinesin motor domain at the serine residue is implicated in Huntingtons disease, with a recent study of phosphorylated and phosphomimetic serine residues indicating lowered single motor stalling forces. In this article we report the effects of mutations of this nature on transport properties of cargo carried by multiple wild-type and mutant motors. Results indicate that mutants with altered stall forces might determine the average velocity and run-length even when they are outnumbered by wild type motors in the ensemble. It is shown that mutants gain a competitive advantage and lead to an increase in the expected run-length when the load on the cargo is in the vicinity of the mutants stalling force or a multiple of its stalling force. A separate contribution of this article is the development of a semi-analytic method to analyze transport of cargo by multiple motors of multiple types. The technique determines transition rates between various relative configurations of motors carrying the cargo using the transition rates between various absolute configurations. This enables a computation of biologically relevant quantities like average velocity and run-length without resorting to Monte Carlo simulations. It can also be used to introduce alterations of various single motor parameters to model a mutation and to deduce effects of such alterations on the transport of a common cargo by multiple motors. Our method is easily implementable and we provide a software package for general use.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(88086, 2016, 'plos', 'The Protein Cost of Metabolic Fluxes: Prediction from Enzymatic Rate Laws and Cost Minimization', 'Noor, Elad|Flamholz, Avi|Bar-Even, Arren|Davidi, Dan|Milo, Ron|Liebermeister, Wolfram', 'Institute of Molecular Systems Biology, Eidgenössische Technische Hochschule, Zürich, Switzerland.|Department of Molecular and Cellular Biology, University of California, Berkeley, Berkeley, California, United States of America.|Max Planck Institute for Molecular Plant Physiology, Golm, Germany.|Department of Plant Sciences, The Weizmann Institute of Science, Rehovot, Israel.|Department of Plant Sciences, The Weizmann Institute of Science, Rehovot, Israel.|Institute of Biochemistry, Charité Universitätsmedizin Berlin, Berlin, Germany.', 'Switzerland|United States|Germany|Israel|Israel|Germany', 0, 'Bacterial Proteins|Computer Simulation|Energy Metabolism|Enzyme Activation|Enzymes|Escherichia coli|Metabolic Flux Analysis|Models, Biological', 'Journal Article', '10.1371/journal.pcbi.1005167', '27812109', 'Bacterial growth depends crucially on metabolic fluxes, which are limited by the cells capacity to maintain metabolic enzymes. The necessary enzyme amount per unit flux is a major determinant of metabolic strategies both in evolution and bioengineering. It depends on enzyme parameters (such as kcat and KM constants), but also on metabolite concentrations. Moreover, similar amounts of different enzymes might incur different costs for the cell, depending on enzyme-specific properties such as protein size and half-life. Here, we developed enzyme cost minimization (ECM), a scalable method for computing enzyme amounts that support a given metabolic flux at a minimal protein cost. The complex interplay of enzyme and metabolite concentrations, e.g. through thermodynamic driving forces and enzyme saturation, would make it hard to solve this optimization problem directly. By treating enzyme cost as a function of metabolite levels, we formulated ECM as a numerically tractable, convex optimization problem. Its tiered approach allows for building models at different levels of detail, depending on the amount of available data. Validating our method with measured metabolite and protein levels in E. coli central metabolism, we found typical prediction fold errors of 4.1 and 2.6, respectively, for the two kinds of data. This result from the cost-optimized metabolic state is significantly better than randomly sampled metabolite profiles, supporting the hypothesis that enzyme cost is important for the fitness of E. coli. ECM can be used to predict enzyme levels and protein cost in natural and engineered pathways, and could be a valuable computational tool to assist metabolic engineering projects. Furthermore, it establishes a direct connection between protein cost and thermodynamics, and provides a physically plausible and computationally tractable way to include enzyme kinetics into constraint-based metabolic models, where kinetics have usually been ignored or oversimplified.'),
(88087, 2016, 'plos', 'Dynamic Cholesterol-Conditioned Dimerization of the G Protein Coupled Chemokine Receptor Type 4', 'Pluhackova, Kristyna|Gahbauer, Stefan|Kranz, Franziska|Wassenaar, Tsjerk A|Böckmann, Rainer A', 'Computational Biology, Department of Biology, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany.|Computational Biology, Department of Biology, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany.|Computational Biology, Department of Biology, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany.|Computational Biology, Department of Biology, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany.|Computational Biology, Department of Biology, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany.', 'Germany|Germany|Germany|Germany|Germany', 43, 'Binding Sites|Cholesterol|Computer Simulation|Kinetics|Lipid Bilayers|Models, Chemical|Molecular Dynamics Simulation|Protein Binding|Protein Conformation|Protein Multimerization|Receptors, CXCR4|Receptors, G-Protein-Coupled|Structure-Activity Relationship', 'Journal Article', '10.1371/journal.pcbi.1005169', '27812115', 'G protein coupled receptors (GPCRs) allow for the transmission of signals across biological membranes. For a number of GPCRs, this signaling was shown to be coupled to prior dimerization of the receptor. The chemokine receptor type 4 (CXCR4) was reported before to form dimers and their functionality was shown to depend on membrane cholesterol. Here, we address the dimerization pattern of CXCR4 in pure phospholipid bilayers and in cholesterol-rich membranes. Using ensembles of molecular dynamics simulations, we show that CXCR4 dimerizes promiscuously in phospholipid membranes. Addition of cholesterol dramatically affects the dimerization pattern: cholesterol binding largely abolishes the preferred dimer motif observed for pure phospholipid bilayers formed mainly by transmembrane helices 1 and 7 (TM1/TM5-7) at the dimer interface. In turn, the symmetric TM3,4/TM3,4 interface is enabled first by intercalating cholesterol molecules. These data provide a molecular basis for the modulation of GPCR activity by its lipid environment.'),
(88088, 2016, 'plos', 'Neural Population Dynamics during Reaching Are Better Explained by a Dynamical System than Representational Tuning', 'Michaels, Jonathan A|Dann, Benjamin|Scherberger, Hansjörg', 'German Primate Center, Göttingen, Germany.|German Primate Center, Göttingen, Germany.|German Primate Center, Göttingen, Germany.', 'Germany|Germany|Germany', 41, 'Animals|Arm|Computer Simulation|Humans|Models, Neurological|Motor Cortex|Movement|Nerve Net|Neurons|Psychomotor Performance|Synaptic Transmission', 'Journal Article', '10.1371/journal.pcbi.1005175', '27814352', 'Recent models of movement generation in motor cortex have sought to explain neural activity not as a function of movement parameters, known as representational models, but as a dynamical system acting at the level of the population. Despite evidence supporting this framework, the evaluation of representational models and their integration with dynamical systems is incomplete in the literature. Using a representational velocity-tuning based simulation of center-out reaching, we show that incorporating variable latency offsets between neural activity and kinematics is sufficient to generate rotational dynamics at the level of neural populations, a phenomenon observed in motor cortex. However, we developed a covariance-matched permutation test (CMPT) that reassigns neural data between task conditions independently for each neuron while maintaining overall neuron-to-neuron relationships, revealing that rotations based on the representational model did not uniquely depend on the underlying condition structure. In contrast, rotations based on either a dynamical model or motor cortex data depend on this relationship, providing evidence that the dynamical model more readily explains motor cortex activity. Importantly, implementing a recurrent neural network we demonstrate that both representational tuning properties and rotational dynamics emerge, providing evidence that a dynamical system can reproduce previous findings of representational tuning. Finally, using motor cortex data in combination with the CMPT, we show that results based on small numbers of neurons or conditions should be interpreted cautiously, potentially informing future experimental design. Together, our findings reinforce the view that representational models lack the explanatory power to describe complex aspects of single neuron and population level activity.'),
(88089, 2016, 'plos', 'Tensor Analysis Reveals Distinct Population Structure that Parallels the Different Computational Roles of Areas M1 and V1', 'Seely, Jeffrey S|Kaufman, Matthew T|Ryu, Stephen I|Shenoy, Krishna V|Cunningham, John P|Churchland, Mark M', 'Department of Neuroscience, Columbia University Medical Center, New York, NY, United States of America.|Neurosciences Program,Stanford University, Stanford, CA, United States of America.|Department of Electrical Engineering, Stanford University, Stanford, CA, United States of America.|Neurosciences Program,Stanford University, Stanford, CA, United States of America.|Grossman Center for the Statistics of Mind, Columbia University Medical Center, New York, NY, United States of America.|Department of Neuroscience, Columbia University Medical Center, New York, NY, United States of America.', 'United States|United States|United States|United States|United States|United States', 8, 'Animals|Brain Mapping|Computer Simulation|Diffusion Tensor Imaging|Haplorhini|Models, Neurological|Motor Cortex|Movement|Nerve Net|Psychomotor Performance|Reproducibility of Results|Sensitivity and Specificity|Visual Cortex|Visual Perception', 'Comparative Study', '10.1371/journal.pcbi.1005164', '27814353', 'Cortical firing rates frequently display elaborate and heterogeneous temporal structure. One often wishes to compute quantitative summaries of such structure-a basic example is the frequency spectrum-and compare with model-based predictions. The advent of large-scale population recordings affords the opportunity to do so in new ways, with the hope of distinguishing between potential explanations for why responses vary with time. We introduce a method that assesses a basic but previously unexplored form of population-level structure: when data contain responses across multiple neurons, conditions, and times, they are naturally expressed as a third-order tensor. We examined tensor structure for multiple datasets from primary visual cortex (V1) and primary motor cortex (M1). All V1 datasets were simplest (there were relatively few degrees of freedom) along the neuron mode, while all M1 datasets were simplest along the condition mode. These differences could not be inferred from surface-level response features. Formal considerations suggest why tensor structure might differ across modes. For idealized linear models, structure is simplest across the neuron mode when responses reflect external variables, and simplest across the condition mode when responses reflect population dynamics. This same pattern was present for existing models that seek to explain motor cortex responses. Critically, only dynamical models displayed tensor structure that agreed with the empirical M1 data. These results illustrate that tensor structure is a basic feature of the data. For M1 the tensor structure was compatible with only a subset of existing models.'),
(88090, 2016, 'plos', 'Using Matrix and Tensor Factorizations for the Single-Trial Analysis of Population Spike Trains', 'Onken, Arno|Liu, Jian K|Karunasekara, P P Chamanthi R|Delis, Ioannis|Gollisch, Tim|Panzeri, Stefano', 'Neural Computation Laboratory, Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di Tecnologia, Rovereto, Italy.|Department of Ophthalmology, University Medical Center Goettingen, Goettingen, Germany.|Neural Computation Laboratory, Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di Tecnologia, Rovereto, Italy.|Department of Biomedical Engineering, Columbia University, New York, New York, United States of America.|Department of Ophthalmology, University Medical Center Goettingen, Goettingen, Germany.|Neural Computation Laboratory, Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di Tecnologia, Rovereto, Italy.', 'Italy|Germany|Italy|United States|Germany|Italy', 12, 'Action Potentials|Algorithms|Animals|Computer Simulation|Factor Analysis, Statistical|Humans|Models, Neurological|Models, Statistical|Nerve Net|Retinal Ganglion Cells|Urodela', 'Journal Article', '10.1371/journal.pcbi.1005189', '27814363', 'Advances in neuronal recording techniques are leading to ever larger numbers of simultaneously monitored neurons. This poses the important analytical challenge of how to capture compactly all sensory information that neural population codes carry in their spatial dimension (differences in stimulus tuning across neurons at different locations), in their temporal dimension (temporal neural response variations), or in their combination (temporally coordinated neural population firing). Here we investigate the utility of tensor factorizations of population spike trains along space and time. These factorizations decompose a dataset of single-trial population spike trains into spatial firing patterns (combinations of neurons firing together), temporal firing patterns (temporal activation of these groups of neurons) and trial-dependent activation coefficients (strength of recruitment of such neural patterns on each trial). We validated various factorization methods on simulated data and on populations of ganglion cells simultaneously recorded in the salamander retina. We found that single-trial tensor space-by-time decompositions provided low-dimensional data-robust representations of spike trains that capture efficiently both their spatial and temporal information about sensory stimuli. Tensor decompositions with orthogonality constraints were the most efficient in extracting sensory information, whereas non-negative tensor decompositions worked well even on non-independent and overlapping spike patterns, and retrieved informative firing patterns expressed by the same population in response to novel stimuli. Our method showed that populations of retinal ganglion cells carried information in their spike timing on the ten-milliseconds-scale about spatial details of natural images. This information could not be recovered from the spike counts of these cells. First-spike latencies carried the majority of information provided by the whole spike train about fine-scale image features, and supplied almost as much information about coarse natural image features as firing rates. Together, these results highlight the importance of spike timing, and particularly of first-spike latencies, in retinal coding.'),
(88091, 2016, 'plos', 'Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments', 'Van Valen, David A|Kudo, Takamasa|Lane, Keara M|Macklin, Derek N|Quach, Nicolas T|DeFelice, Mialy M|Maayan, Inbal|Tanouchi, Yu|Ashley, Euan A|Covert, Markus W', 'Department of Bioengineering, Stanford University, Stanford, California, United States of America.|Department of Chemical and Systems Biology, Stanford University, Stanford, California, United States of America.|Department of Bioengineering, Stanford University, Stanford, California, United States of America.|Department of Bioengineering, Stanford University, Stanford, California, United States of America.|Department of Bioengineering, Stanford University, Stanford, California, United States of America.|Department of Bioengineering, Stanford University, Stanford, California, United States of America.|Department of Bioengineering, Stanford University, Stanford, California, United States of America.|Department of Bioengineering, Stanford University, Stanford, California, United States of America.|Department of Genetics, Stanford University, Stanford, California, United States of America.|Department of Bioengineering, Stanford University, Stanford, California, United States of America.', 'United States|United States|United States|United States|United States|United States|United States|Un', 0, 'Cell Tracking|Image Enhancement|Image Interpretation, Computer-Assisted|Intravital Microscopy|Machine Learning|Neural Networks (Computer)|Pattern Recognition, Automated|Reproducibility of Results|Sensitivity and Specificity', 'Journal Article', '10.1371/journal.pcbi.1005177', '27814364', 'Live-cell imaging has opened an exciting window into the role cellular heterogeneity plays in dynamic, living systems. A major critical challenge for this class of experiments is the problem of image segmentation, or determining which parts of a microscope image correspond to which individual cells. Current approaches require many hours of manual curation and depend on approaches that are difficult to share between labs. They are also unable to robustly segment the cytoplasms of mammalian cells. Here, we show that deep convolutional neural networks, a supervised machine learning method, can solve this challenge for multiple cell types across the domains of life. We demonstrate that this approach can robustly segment fluorescent images of cell nuclei as well as phase images of the cytoplasms of individual bacterial and mammalian cells from phase contrast images without the need for a fluorescent cytoplasmic marker. These networks also enable the simultaneous segmentation and identification of different mammalian cell types grown in co-culture. A quantitative comparison with prior methods demonstrates that convolutional neural networks have improved accuracy and lead to a significant reduction in curation time. We relay our experience in designing and optimizing deep convolutional neural networks for this task and outline several design rules that we found led to robust performance. We conclude that deep convolutional neural networks are an accurate method that require less curation time, are generalizable to a multiplicity of cell types, from bacteria to mammalian cells, and expand live-cell imaging capabilities to include multi-cell type systems.'),
(88092, 2016, 'plos', 'Effect of Ionic Diffusion on Extracellular Potentials in Neural Tissue', 'Halnes, Geir|Mxc3xa4ki-Marttunen, Tuomo|Keller, Daniel|Pettersen, Klas H|Andreassen, Ole A|Einevoll, Gaute T', 'Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences, xc3x85s, Norway.|NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway.|Blue Brain Project, xc3x89cole Polytechnique Fxc3xa9dxc3xa9rale de Lausanne (EPFL), Lausanne, Switzerland.|Letten Centre and GliaLab, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway.|NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway.|Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences, xc3x85s, Norway.', 'Norway|Norway|Switzerland|Norway|Norway|Norway', 22, 'Animals|Computer Simulation|Diffusion|Electromagnetic Fields|Extracellular Fluid|Humans|Ion Channel Gating|Ions|Membrane Potentials|Models, Chemical|Models, Neurological|Nerve Net|Neurons', 'Journal Article', '10.1371/journal.pcbi.1005193', '27820827', 'Recorded potentials in the extracellular space (ECS) of the brain is a standard measure of population activity in neural tissue. Computational models that simulate the relationship between the ECS potential and its underlying neurophysiological processes are commonly used in the interpretation of such measurements. Standard methods, such as volume-conductor theory and current-source density theory, assume that diffusion has a negligible effect on the ECS potential, at least in the range of frequencies picked up by most recording systems. This assumption remains to be verified. We here present a hybrid simulation framework that accounts for diffusive effects on the ECS potential. The framework uses (1) the NEURON simulator to compute the activity and ionic output currents from multicompartmental neuron models, and (2) the electrodiffusive Kirchhoff-Nernst-Planck framework to simulate the resulting dynamics of the potential and ion concentrations in the ECS, accounting for the effect of electrical migration as well as diffusion. Using this framework, we explore the effect that ECS diffusion has on the electrical potential surrounding a small population of 10 pyramidal neurons. The neural model was tuned so that simulations over xe2x88xbc100 seconds of biological time led to shifts in ECS concentrations by a few millimolars, similar to what has been seen in experiments. By comparing simulations where ECS diffusion was absent with simulations where ECS diffusion was included, we made the following key findings: (i) ECS diffusion shifted the local potential by up to xe2x88xbc0.2 mV. (ii) The power spectral density (PSD) of the diffusion-evoked potential shifts followed a 1/f2 power law. (iii) Diffusion effects dominated the PSD of the ECS potential for frequencies up to several hertz. In scenarios with large, but physiologically realistic ECS concentration gradients, diffusion was thus found to affect the ECS potential well within the frequency range picked up in experimental recordings.'),
(88093, 2016, 'plos', 'Co-operation between Polymerases and Nucleotide Synthetases in the RNA World', 'Kim, Ye Eun|Higgs, Paul G', 'Origins Institute and Department of Physics and Astronomy McMaster University, Hamilton, Ontario, Canada.|Origins Institute and Department of Physics and Astronomy McMaster University, Hamilton, Ontario, Canada.', 'Canada|Canada', 17, 'DNA-Directed RNA Polymerases|Enzyme Activation|Evolution, Molecular|Models, Chemical|Models, Genetic|Models, Statistical|Polynucleotide Ligases|RNA, Catalytic', 'Journal Article', '10.1371/journal.pcbi.1005161', '27820829', 'It is believed that life passed through an RNA World stage in which replication was sustained by catalytic RNAs (ribozymes). The two most obvious types of ribozymes are a polymerase, which uses a neighbouring strand as a template to make a complementary sequence to the template, and a nucleotide synthetase, which synthesizes monomers for use by the polymerase. When a chemical source of monomers is available, the polymerase can survive on its own. When the chemical supply of monomers is too low, nucleotide production by the synthetase is essential and the two ribozymes can only survive when they are together. Here we consider a computational model to investigate conditions under which coexistence and cooperation of these two types of ribozymes is possible. The model considers six types of strands: the two functional sequences, the complementary strands to these sequences (which are required as templates), and non-functional mutants of the two sequences (which act as parasites). Strands are distributed on a two-dimensional lattice. Polymerases replicate strands on neighbouring sites and synthetases produce monomers that diffuse in the local neighbourhood. We show that coexistence of unlinked polymerases and synthetases is possible in this spatial model under conditions in which neither sequence could survive alone, hence, there is a selective force for increasing complexity. Coexistence is dependent on the relative lengths of the two functional strands, the strand diffusion rate, the monomer diffusion rate, and the rate of deleterious mutations. The sensitivity of this two-ribozyme system suggests that evolution of a system of many types of ribozymes would be difficult in a purely spatial model with unlinked genes. We therefore speculate that linkage of genes onto mini-chromosomes and encapsulation of strands in protocells would have been important fairly early in the history of life as a means of enabling more complex systems to evolve.'),
(88094, 2016, 'plos', 'Ten Simple Rules for Developing Public Biological Databases', 'Helmy, Mohamed|Crits-Christoph, Alexander|Bader, Gary D', 'The Donnelly Centre, University of Toronto, Toronto, ON, Canada.|Department of Biophysics, Johns Hopkins University, Baltimore, MD, United States of America.|The Donnelly Centre, University of Toronto, Toronto, ON, Canada.', 'Canada|United States|Canada', 0, 'Biology|Databases, Factual|Internet', 'Editorial', '10.1371/journal.pcbi.1005128', '27832061', '-'),
(88095, 2016, 'plos', 'MinePath: Mining for Phenotype Differential Sub-paths in Molecular Pathways', 'Koumakis, Lefteris|Kanterakis, Alexandros|Kartsaki, Evgenia|Chatzimina, Maria|Zervakis, Michalis|Tsiknakis, Manolis|Vassou, Despoina|Kafetzopoulos, Dimitris|Marias, Kostas|Moustakis, Vassilis|Potamias, George', 'Computational BioMedicine Laboratory (CBML), Institute of Computers Science (ICS), Foundation for Research and Technology-Hellas (FORTH), Heraklion, Crete, Greece.|Computational BioMedicine Laboratory (CBML), Institute of Computers Science (ICS), Foundation for Research and Technology-Hellas (FORTH), Heraklion, Crete, Greece.|Computational BioMedicine Laboratory (CBML), Institute of Computers Science (ICS), Foundation for Research and Technology-Hellas (FORTH), Heraklion, Crete, Greece.|Computational BioMedicine Laboratory (CBML), Institute of Computers Science (ICS), Foundation for Research and Technology-Hellas (FORTH), Heraklion, Crete, Greece.|School of Electrical and Computer Engineering, Technical University of Crete, Greece.|Computational BioMedicine Laboratory (CBML), Institute of Computers Science (ICS), Foundation for Research and Technology-Hellas (FORTH), Heraklion, Crete, Greece.|Institute of Molecular Biology & Biotechnology, FORTH, Heraklion, Crete, Greece.|Institute of Molecular Biology & Biotechnology, FORTH, Heraklion, Crete, Greece.|Computational BioMedicine Laboratory (CBML), Institute of Computers Science (ICS), Foundation for Research and Technology-Hellas (FORTH), Heraklion, Crete, Greece.|School of Production Engineering & Management, Technical University of Crete, Greece.|Computational BioMedicine Laboratory (CBML), Institute of Computers Science (ICS), Foundation for Research and Technology-Hellas (FORTH), Heraklion, Crete, Greece.', 'Greece|Greece|Greece|Greece|Greece|Greece|Greece|Greece|Greece|Greece|Greece', 0, 'Algorithms|Computer Simulation|Data Mining|Databases, Genetic|Gene Expression Profiling|Models, Biological|Proteome|Signal Transduction|Software', 'Journal Article', '10.1371/journal.pcbi.1005187', '27832067', 'Pathway analysis methodologies couple traditional gene expression analysis with knowledge encoded in established molecular pathway networks, offering a promising approach towards the biological interpretation of phenotype differentiating genes. Early pathway analysis methodologies, named as gene set analysis (GSA), view pathways just as plain lists of genes without taking into account either the underlying pathway network topology or the involved gene regulatory relations. These approaches, even if they achieve computational efficiency and simplicity, consider pathways that involve the same genes as equivalent in terms of their gene enrichment characteristics. Most recent pathway analysis approaches take into account the underlying gene regulatory relations by examining their consistency with gene expression profiles and computing a score for each profile. Even with this approach, assessing and scoring single-relations limits the ability to reveal key gene regulation mechanisms hidden in longer pathway sub-paths. We introduce MinePath, a pathway analysis methodology that addresses and overcomes the aforementioned problems. MinePath facilitates the decomposition of pathways into their constituent sub-paths. Decomposition leads to the transformation of single-relations to complex regulation sub-paths. Regulation sub-paths are then matched with gene expression sample profiles in order to evaluate their functional status and to assess phenotype differential power. Assessment of differential power supports the identification of the most discriminant profiles. In addition, MinePath assess the significance of the pathways as a whole, ranking them by their p-values. Comparison results with state-of-the-art pathway analysis systems are indicative for the soundness and reliability of the MinePath approach. In contrast with many pathway analysis tools, MinePath is a web-based system (www.minepath.org) offering dynamic and rich pathway visualization functionality, with the unique characteristic to color regulatory relations between genes and reveal their phenotype inclination. This unique characteristic makes MinePath a valuable tool for in silico molecular biology experimentation as it serves the biomedical researchers exploratory needs to reveal and interpret the regulatory mechanisms that underlie and putatively govern the expression of target phenotypes.'),
(88096, 2016, 'plos', 'High-Frequency Resonance in the Gerbil Medial Superior Olive', 'Mikiel-Hunter, Jason|Kotak, Vibhakar|Rinzel, John', 'Center for Neural Science, New York University, New York, United States of America.|Center for Neural Science, New York University, New York, United States of America.|Center for Neural Science, New York University, New York, United States of America.', 'United States|United States|United States', 11, 'Acoustic Stimulation|Action Potentials|Animals|Auditory Threshold|Computer Simulation|Excitatory Postsynaptic Potentials|Gerbillinae|Ion Channel Gating|Models, Neurological|Nerve Net|Pitch Perception|Potassium|Potassium Channels|Sensory Thresholds|Superior Olivary Complex|Synaptic Transmission', 'Journal Article', '10.1371/journal.pcbi.1005166', '27832077', 'A high-frequency, subthreshold resonance in the guinea pig medial superior olive (MSO) was recently linked to the efficient extraction of spatial cues from the fine structure of acoustic stimuli. We report here that MSO neurons in gerbil also have resonant properties and, based on our whole-cell recordings and computational modeling, that a low-voltage-gated potassium current, IKLT, underlies the resonance. We show that resonance was lost following dynamic clamp replacement of IKLT with a leak conductance and in the model when voltage-gating of IKLT was suppressed. Resonance was characterized using small amplitude sinusoidal stimuli to generate impedance curves as typically done for linear systems analysis. Extending our study into the nonlinear, voltage-dependent regime, we increased stimulus amplitude and found, experimentally and in simulations, that the subthreshold resonant frequency (242Hz for weak stimuli) increased continuously to the resonant frequency for spiking (285Hz). The spike resonance of these phasic-firing (type III excitable) MSO neurons and of the model is of particular interest also because previous studies of resonance typically involved neurons/models (type II excitable, such as the standard Hodgkin-Huxley model) that can fire tonically for steady inputs. To probe more directly how these resonances relate to MSO neurons as slope-detectors, we presented periodic trains of brief, fast-rising excitatory post-synaptic potentials (EPSCs) to the model. While weak subthreshold EPSC trains were essentially low-pass filtered, resonance emerged as EPSC amplitude increased. Interestingly, for spike-evoking EPSC trains, the threshold amplitude at spike resonant frequency (317Hz) was lower than the single ESPC threshold. Our finding of a frequency-dependent threshold for repetitive brief EPSC stimuli and preferred frequency for spiking calls for further consideration of both subthreshold and suprathreshold resonance to fast and precise temporal processing in the MSO.'),
(88097, 2016, 'plos', 'Induced Fit in Protein Multimerization: The HFBI Case', 'Riccardi, Laura|Mereghetti, Paolo', 'Laboratory of Molecular Modeling and Drug Discovery, Istituto Italiano di Tecnologia, Genoa, Italy.|Center for Nanotechnology Innovation @NEST, Istituto Italiano di Tecnologia, Pisa, Italy.', 'Italy|Italy', 0, 'Binding Sites|Computer Simulation|Fungal Proteins|Hydrophobic and Hydrophilic Interactions|Models, Chemical|Models, Molecular|Protein Binding|Protein Conformation|Protein Multimerization|Surface Properties|Trichoderma', 'Journal Article', '10.1371/journal.pcbi.1005202', '27832079', 'Hydrophobins, produced by filamentous fungi, are small amphipathic proteins whose biological functions rely on their unique surface-activity properties. Understanding the mechanistic details of the multimerization process is of primary importance to clarify the interfacial activity of hydrophobins. We used free energy calculations to study the role of a flexible xcexb2-hairpin in the multimerization process in hydrophobin II from Trichoderma reesei (HFBI). We characterized how the displacement of this xcexb2-hairpin controls the stability of the monomers/dimers/tetramers in solution. The regulation of the oligomerization equilibrium of HFBI will necessarily affect its interfacial properties, fundamental for its biological function and for technological applications. Moreover, we propose possible routes for the multimerization process of HFBI in solution. This is the first case where a mechanism by which a flexible loop flanking a rigid patch controls the protein-protein binding equilibrium, already known for proteins with charged binding hot-spots, is described within a hydrophobic patch.'),
(88098, 2016, 'plos', 'Machine Learning for Characterization of Insect Vector Feeding', 'Willett, Denis S|George, Justin|Willett, Nora S|Stelinski, Lukasz L|Lapointe, Stephen L', 'USDA-ARS, Chemistry Unit, Center for Medical, Agricultural, and Veterinary Entomology, Gainesville, FL, USA.|USDA-ARS, Subtropical Insects and Horticultural Research Unit, United States Horticultural Research Laboratory, Fort Pierce, Florida, USA.|Department of Computer Science, Princeton University, Princeton, NJ, USA.|University of Florida, Entomology and Nematology Department, Citrus Research and Education Center, University of Florida, Lake ALfred, FL, USA.|USDA-ARS, Subtropical Insects and Horticultural Research Unit, United States Horticultural Research Laboratory, Fort Pierce, Florida, USA.', 'United States|United States|United States|United States|United States', 12, 'Animals|Citrus|Computer Simulation|Feeding Behavior|Insect Vectors|Machine Learning|Models, Biological|Pattern Recognition, Automated|Plethysmography, Impedance|Sucking Behavior', 'Journal Article', '10.1371/journal.pcbi.1005158', '27832081', 'Insects that feed by ingesting plant and animal fluids cause devastating damage to humans, livestock, and agriculture worldwide, primarily by transmitting pathogens of plants and animals. The feeding processes required for successful pathogen transmission by sucking insects can be recorded by monitoring voltage changes across an insect-food source feeding circuit. The output from such monitoring has traditionally been examined manually, a slow and onerous process. We taught a computer program to automatically classify previously described insect feeding patterns involved in transmission of the pathogen causing citrus greening disease. We also show how such analysis contributes to discovery of previously unrecognized feeding states and can be used to characterize plant resistance mechanisms. This advance greatly reduces the time and effort required to analyze insect feeding, and should facilitate developing, screening, and testing of novel intervention strategies to disrupt pathogen transmission affecting agriculture, livestock and human health.'),
(88099, 2016, 'plos', 'Combined Changes in Chloride Regulation and Neuronal Excitability Enable Primary Afferent Depolarization to Elicit Spiking without Compromising its Inhibitory Effects', 'Takkala, Petri|Zhu, Yi|Prescott, Steven A', 'Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, Ontario, Canada.|Center for Pain Research, Department of Anesthesiology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.|Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, Ontario, Canada.', 'Canada|United States|Canada', 6, 'Action Potentials|Afferent Pathways|Animals|Cells, Cultured|Chlorine|Computer Simulation|Male|Membrane Potentials|Models, Neurological|Neural Inhibition|Neurons, Afferent|Presynaptic Terminals|Rats|Rats, Sprague-Dawley|Receptors, GABA', 'Journal Article', '10.1371/journal.pcbi.1005215', '27835641', 'The central terminals of primary afferent fibers experience depolarization upon activation of GABAA receptors (GABAAR) because their intracellular chloride concentration is maintained above electrochemical equilibrium. Primary afferent depolarization (PAD) normally mediates inhibition via sodium channel inactivation and shunting but can evoke spikes under certain conditions. Antidromic (centrifugal) conduction of these spikes may contribute to neurogenic inflammation while orthodromic (centripetal) conduction could contribute to pain in the case of nociceptive fibers. PAD-induced spiking is assumed to override presynaptic inhibition. Using computer simulations and dynamic clamp experiments, we sought to identify which biophysical changes are required to enable PAD-induced spiking and whether those changes necessarily compromise PAD-mediated inhibition. According to computational modeling, a depolarizing shift in GABA reversal potential (EGABA) and increased intrinsic excitability (manifest as altered spike initiation properties) were necessary for PAD-induced spiking, whereas increased GABAAR conductance density (xe1xb8xa1GABA) had mixed effects. We tested our predictions experimentally by using dynamic clamp to insert virtual GABAAR conductances with different EGABA and kinetics into acutely dissociated dorsal root ganglion (DRG) neuron somata. Comparable experiments in central axon terminals are prohibitively difficult but the biophysical requirements for PAD-induced spiking are arguably similar in soma and axon. Neurons from naxc3xafve (i.e. uninjured) rats were compared before and after pharmacological manipulation of intrinsic excitability, and against neurons from nerve-injured rats. Experimental data confirmed that, in most neurons, both predicted changes were necessary to yield PAD-induced spiking. Importantly, such changes did not prevent PAD from inhibiting other spiking or from blocking spike propagation. In fact, since the high value of xe1xb8xa1GABA required for PAD-induced spiking still mediates strong inhibition, we conclude that PAD-induced spiking does not represent failure of presynaptic inhibition. Instead, diminished PAD caused by reduction of xe1xb8xa1GABA poses a greater risk to presynaptic inhibition and the sensory processing that relies upon it.'),
(88100, 2016, 'plos', 'The Environment Shapes the Inner Vestibule of LeuT', 'Sohail, Azmat|Jayaraman, Kumaresan|Venkatesan, Santhoshkannan|Gotfryd, Kamil|Daerr, Markus|Gether, Ulrik|Loland, Claus J|Wanner, Klaus T|Freissmuth, Michael|Sitte, Harald H|Sandtner, Walter|Stockner, Thomas', 'Medical University of Vienna, Center for Physiology and Pharmacology, Institute of Pharmacology, Vienna, Austria.|Medical University of Vienna, Center for Physiology and Pharmacology, Institute of Pharmacology, Vienna, Austria.|Medical University of Vienna, Center for Physiology and Pharmacology, Institute of Pharmacology, Vienna, Austria.|University of Copenhagen, Faculty of Health and Medical Sciences Denmark, Department of Neuroscience and Pharmacology, Copenhagen, Denmark.|Ludwig Maximilians University Munich, Department of Pharmacy, Center of Drug Research, Munich, Germany.|University of Copenhagen, Faculty of Health and Medical Sciences Denmark, Department of Neuroscience and Pharmacology, Copenhagen, Denmark.|University of Copenhagen, Faculty of Health and Medical Sciences Denmark, Department of Neuroscience and Pharmacology, Copenhagen, Denmark.|Ludwig Maximilians University Munich, Department of Pharmacy, Center of Drug Research, Munich, Germany.|Medical University of Vienna, Center for Physiology and Pharmacology, Institute of Pharmacology, Vienna, Austria.|Medical University of Vienna, Center for Physiology and Pharmacology, Institute of Pharmacology, Vienna, Austria.|Medical University of Vienna, Center for Physiology and Pharmacology, Institute of Pharmacology, Vienna, Austria.|Medical University of Vienna, Center for Physiology and Pharmacology, Institute of Pharmacology, Vienna, Austria.', 'Austria|Austria|Austria|Denmark|Germany|Denmark|Denmark|Germany|Austria|Austria|Austria|Austria', 0, 'Amino Acid Transport Systems|Bacterial Proteins|Lipid Bilayers|Models, Chemical|Molecular Dynamics Simulation|Neurotransmitter Transport Proteins|Protein Conformation|Protein Domains|Structure-Activity Relationship', 'Journal Article', '10.1371/journal.pcbi.1005197', '27835643', 'Human neurotransmitter transporters are found in the nervous system terminating synaptic signals by rapid removal of neurotransmitter molecules from the synaptic cleft. The homologous transporter LeuT, found in Aquifex aeolicus, was crystallized in different conformations. Here, we investigated the inward-open state of LeuT. We compared LeuT in membranes and micelles using molecular dynamics simulations and lanthanide-based resonance energy transfer (LRET). Simulations of micelle-solubilized LeuT revealed a stable and widely open inward-facing conformation. However, this conformation was unstable in a membrane environment. The helix dipole and the charged amino acid of the first transmembrane helix (TM1A) partitioned out of the hydrophobic membrane core. Free energy calculations showed that movement of TM1A by 0.30 nm was driven by a free energy difference of ~15 kJ/mol. Distance measurements by LRET showed TM1A movements, consistent with the simulations, confirming a substantially different inward-open conformation in lipid bilayer from that inferred from the crystal structure.'),
(88101, 2016, 'plos', 'NullSeq: A Tool for Generating Random Coding Sequences with Desired Amino Acid and GC Contents', 'Liu, Sophia S|Hockenberry, Adam J|Lancichinetti, Andrea|Jewett, Michael C|Amaral, Luís A N', 'Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois, United States of America.|Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois, United States of America.|Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois, United States of America.|Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois, United States of America.|Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois, United States of America.', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Base Composition|Protein Engineering|Proteins|Sequence Analysis, DNA|Sequence Analysis, Protein|Software', 'Journal Article', '10.1371/journal.pcbi.1005184', '27835644', 'The existence of over- and under-represented sequence motifs in genomes provides evidence of selective evolutionary pressures on biological mechanisms such as transcription, translation, ligand-substrate binding, and host immunity. In order to accurately identify motifs and other genome-scale patterns of interest, it is essential to be able to generate accurate null models that are appropriate for the sequences under study. While many tools have been developed to create random nucleotide sequences, protein coding sequences are subject to a unique set of constraints that complicates the process of generating appropriate null models. There are currently no tools available that allow users to create random coding sequences with specified amino acid composition and GC content for the purpose of hypothesis testing. Using the principle of maximum entropy, we developed a method that generates unbiased random sequences with pre-specified amino acid and GC content, which we have developed into a python package. Our method is the simplest way to obtain maximally unbiased random sequences that are subject to GC usage and primary amino acid sequence constraints. Furthermore, this approach can easily be expanded to create unbiased random sequences that incorporate more complicated constraints such as individual nucleotide usage or even di-nucleotide frequencies. The ability to generate correctly specified null models will allow researchers to accurately identify sequence motifs which will lead to a better understanding of biological processes as well as more effective engineering of biological systems.'),
(88102, 2016, 'plos', 'PoCos: Population Covering Locus Sets for Risk Assessment in Complex Diseases', 'Ayati, Marzieh|Koyutürk, Mehmet', 'Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, Ohio, United States of America.|Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, Ohio, United States of America.', 'United States|United States', 1, 'Algorithms|Genetic Association Studies|Genetic Markers|Genetic Predisposition to Disease|Humans|Prevalence|Quantitative Trait Loci|Reproducibility of Results|Risk Assessment|Sensitivity and Specificity', 'Journal Article', '10.1371/journal.pcbi.1005195', '27835645', 'Susceptibility loci identified by GWAS generally account for a limited fraction of heritability. Predictive models based on identified loci also have modest success in risk assessment and therefore are of limited practical use. Many methods have been developed to overcome these limitations by incorporating prior biological knowledge. However, most of the information utilized by these methods is at the level of genes, limiting analyses to variants that are in or proximate to coding regions. We propose a new method that integrates protein protein interaction (PPI) as well as expression quantitative trait loci (eQTL) data to identify sets of functionally related loci that are collectively associated with a trait of interest. We call such sets of loci population covering locus sets (PoCos). The contributions of the proposed approach are three-fold: 1) We consider all possible genotype models for each locus, thereby enabling identification of combinatorial relationships between multiple loci. 2) We develop a framework for the integration of PPI and eQTL into a heterogenous network model, enabling efficient identification of functionally related variants that are associated with the disease. 3) We develop a novel method to integrate the genotypes of multiple loci in a PoCo into a representative genotype to be used in risk assessment. We test the proposed framework in the context of risk assessment for seven complex diseases, type 1 diabetes (T1D), type 2 diabetes (T2D), psoriasis (PS), bipolar disorder (BD), coronary artery disease (CAD), hypertension (HT), and multiple sclerosis (MS). Our results show that the proposed method significantly outperforms individual variant based risk assessment models as well as the state-of-the-art polygenic score. We also show that incorporation of eQTL data improves the performance of identified POCOs in risk assessment. We also assess the biological relevance of PoCos for three diseases that have similar biological mechanisms and identify novel candidate genes. The resulting software is publicly available at http://compbio.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(88103, 2016, 'plos', 'A Statistical Framework to Identify Deviation from Time Linearity in Epigenetic Aging', 'Snir, Sagi|vonHoldt, Bridgett M|Pellegrini, Matteo', 'Department of Evolutionary Biology, University of Haifa, Haifa, Israel.|Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, United States of America.|Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, Los Angeles, California, United States of America.', 'Israel|Jersey|United States', 6, 'Adolescent|Adult|Aged|Aged, 80 and over|Aging|Animals|Biological Clocks|Child|Child, Preschool|Computer Simulation|CpG Islands|DNA|DNA Methylation|Data Interpretation, Statistical|Epigenesis, Genetic|Female|Humans|Infant|Infant, Newborn|Male|Middle Aged|Models, Genetic|Models, Statistical|Sequence Analysis, DNA|Young Adult', 'Journal Article', '10.1371/journal.pcbi.1005183', '27835646', 'In multiple studies DNA methylation has proven to be an accurate biomarker of age. To develop these biomarkers, the methylation of multiple CpG sites is typically linearly combined to predict chronological age. By contrast, in this study we apply the Universal PaceMaker (UPM) model to investigate changes in DNA methylation during aging. The UPM was initially developed to study rate acceleration/deceleration in sequence evolution. Rather than identifying which linear combinations of sites predicts age, the UPM models the rates of change of multiple CpG sites, as well as their starting methylation levels, and estimates the age of each individual to optimize the model fit. We refer to the estimated age as the epigenetic age, which is in contrast to the known chronological age of each individual. We construct a statistical framework and devise an algorithm to determine whether a genomic pacemaker is in effect (i.e rates of change vary with age). The decision is made by comparing two competing likelihood based models, the molecular clock (MC) and UPM. For the molecular clock model, we use the known chronological age of each individual and fit the methylation rates at multiple sites, and express the problem as a linear least squares and solve it in polynomial time. For the UPM case, the search space is larger as we are fitting both the epigenetic age of each individual as well as the rates for each site, yet we succeed to reduce the problem to the space of individuals and polynomial in the more significant space-the methylated sites. We first tested our algorithm on simulated data to elucidate the factors affecting the identification of the pacemaker model. We find that, provided with enough data, our algorithm is capable of identifying a pacemaker even when a weak signal is present in the data. Based on these results, we applied our method to DNA methylation data from human blood from individuals of various ages. Although the improvement in variance across sites between the UPM and MC was small, the results suggest that the existence of a pacemaker is highly significant. The PaceMaker results also suggest a decay in the rate of change in DNA methylation with age.'),
(88104, 2016, 'plos', 'Network Receptive Field Modeling Reveals Extensive Integration and Multi-feature Selectivity in Auditory Cortical Neurons', 'Harper, Nicol S|Schoppe, Oliver|Willmore, Ben D B|Cui, Zhanfeng|Schnupp, Jan W H|King, Andrew J', 'Dept. of Physiology, Anatomy and Genetics (DPAG), Sherrington Building, University of Oxford, United Kingdom.|Dept. of Physiology, Anatomy and Genetics (DPAG), Sherrington Building, University of Oxford, United Kingdom.|Dept. of Physiology, Anatomy and Genetics (DPAG), Sherrington Building, University of Oxford, United Kingdom.|Institute of Biomedical Engineering, Department of Engineering Science, Old Road Campus Research Building, University of Oxford, Headington, United Kingdom.|Department of Biomedical Science, City University of Hong Kong, Kowloon Tong, Hong Kong.|Dept. of Physiology, Anatomy and Genetics (DPAG), Sherrington Building, University of Oxford, United Kingdom.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|Hong Kong|United Kingdom', 18, 'Acoustic Stimulation|Animals|Auditory Cortex|Auditory Perception|Computer Simulation|Humans|Models, Neurological|Nerve Net|Neural Inhibition|Nonlinear Dynamics|Sensory Receptor Cells|Systems Integration', 'Journal Article', '10.1371/journal.pcbi.1005113', '27835647', 'Cortical sensory neurons are commonly characterized using the receptive field, the linear dependence of their response on the stimulus. In primary auditory cortex neurons can be characterized by their spectrotemporal receptive fields, the spectral and temporal features of a sound that linearly drive a neuron. However, receptive fields do not capture the fact that the response of a cortical neuron results from the complex nonlinear network in which it is embedded. By fitting a nonlinear feedforward network model (a network receptive field) to cortical responses to natural sounds, we reveal that primary auditory cortical neurons are sensitive over a substantially larger spectrotemporal domain than is seen in their standard spectrotemporal receptive fields. Furthermore, the network receptive field, a parsimonious network consisting of 1-7 sub-receptive fields that interact nonlinearly, consistently better predicts neural responses to auditory stimuli than the standard receptive fields. The network receptive field reveals separate excitatory and inhibitory sub-fields with different nonlinear properties, and interaction of the sub-fields gives rise to important operations such as gain control and conjunctive feature detection. The conjunctive effects, where neurons respond only if several specific features are present together, enable increased selectivity for particular complex spectrotemporal structures, and may constitute an important stage in sound recognition. In conclusion, we demonstrate that fitting auditory cortical neural responses with feedforward network models expands on simple linear receptive field models in a manner that yields substantially improved predictive power and reveals key nonlinear aspects of cortical processing, while remaining easy to interpret in a physiological context.'),
(88105, 2016, 'plos', 'Quantifying Differences and Similarities in Whole-Brain White Matter Architecture Using Local Connectome Fingerprints', 'Yeh, Fang-Cheng|Vettel, Jean M|Singh, Aarti|Poczos, Barnabas|Grafton, Scott T|Erickson, Kirk I|Tseng, Wen-Yih I|Verstynen, Timothy D', 'Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America.|U.S. Army Research Laboratory, Aberdeen Proving Ground, Aberdeen, Maryland, United States of America.|Department of Machine Learning, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.|Department of Machine Learning, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.|Department of Psychological and Brain Sciences, University of California, Santa Barbara, Santa Barbara, California, United States of America.|Department of Psychology, University of Pittsburgh, Pittsburgh, Pennsylvania United States of America.|Institute of Medical Device and Imaging and Molecular Imaging Center, National Taiwan University, Taipei, Taiwan.|Department of Psychology and Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.', 'United States|United States|United States|United States|United States|United States|-|United States', 36, 'Adult|Algorithms|Brain|Connectome|Diffusion Tensor Imaging|Female|Humans|Image Enhancement|Image Interpretation, Computer-Assisted|Male|Pattern Recognition, Automated|Reproducibility of Results|Sensitivity and Specificity|Subtraction Technique|White Matter|Young Adult', 'Journal Article', '10.1371/journal.pcbi.1005203', '27846212', 'Quantifying differences or similarities in connectomes has been a challenge due to the immense complexity of global brain networks. Here we introduce a noninvasive method that uses diffusion MRI to characterize whole-brain white matter architecture as a single local connectome fingerprint that allows for a direct comparison between structural connectomes. In four independently acquired data sets with repeated scans (total N = 213), we show that the local connectome fingerprint is highly specific to an individual, allowing for an accurate self-versus-others classification that achieved 100% accuracy across 17,398 identification tests. The estimated classification error was approximately one thousand times smaller than fingerprints derived from diffusivity-based measures or region-to-region connectivity patterns for repeat scans acquired within 3 months. The local connectome fingerprint also revealed neuroplasticity within an individual reflected as a decreasing trend in self-similarity across time, whereas this change was not observed in the diffusivity measures. Moreover, the local connectome fingerprint can be used as a phenotypic marker, revealing 12.51% similarity between monozygotic twins, 5.14% between dizygotic twins, and 4.51% between none-twin siblings, relative to differences between unrelated subjects. This novel approach opens a new door for probing the influence of pathological, genetic, social, or environmental factors on the unique configuration of the human connectome.'),
(88106, 2016, 'plos', 'Kinetic Modeling of ABCG2 Transporter Heterogeneity: A Quantitative, Single-Cell Analysis of the Side Population Assay', 'Prasanphanich, Adam F|White, Douglas E|Gran, Margaret A|Kemp, Melissa L', 'The Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University, Atlanta, Georgia, United States of America.|The Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University, Atlanta, Georgia, United States of America.|The Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University, Atlanta, Georgia, United States of America.|The Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University, Atlanta, Georgia, United States of America.', 'Georgia|Georgia|Georgia|Georgia', 7, 'ATP Binding Cassette Transporter, Subfamily G, Member 2|Biological Assay|Flow Cytometry|Kinetics|Metabolic Clearance Rate|Microscopy, Fluorescence|Models, Biological|Neoplasm Proteins|Side-Population Cells', 'Journal Article', '10.1371/journal.pcbi.1005188', '27851764', 'The side population (SP) assay, a technique used in cancer and stem cell research, assesses the activity of ABC transporters on Hoechst staining in the presence and absence of transporter inhibition, identifying SP and non-SP cell (NSP) subpopulations by differential staining intensity. The interpretation of the assay is complicated because the transporter-mediated mechanisms fail to account for cell-to-cell variability within a population or adequately control the direct role of transporter activity on staining intensity. We hypothesized that differences in dye kinetics at the single-cell level, such as ABCG2 transporter-mediated efflux and DNA binding, are responsible for the differential cell staining that demarcates SP/NSP identity. We report changes in A549 phenotype during time in culture and with TGFxcexb2 treatment that correlate with SP size. Clonal expansion of individually sorted cells re-established both SP and NSPs, indicating that SP membership is dynamic. To assess the validity of a purely kinetics-based interpretation of SP/NSP identity, we developed a computational approach that simulated cell staining within a heterogeneous cell population, this exercise allowed for the direct inference of the role of transporter activity and inhibition on cell staining. Our simulated SP assay yielded appropriate SP responses for kinetic scenarios in which high transporter activity existed in a portion of the cells and little differential staining occurred in the majority of the population. With our approach for single-cell analysis, we observed SP and NSP cells at both ends of a transporter activity continuum, demonstrating that features of transporter activity as well as DNA content are determinants of SP/NSP identity.'),
(88107, 2016, 'plos', 'Markov Networks of Collateral Resistance: National Antimicrobial Resistance Monitoring System Surveillance Results from Escherichia coli Isolates, 2004-2012', 'Love, William J|Zawack, Kelson A|Booth, James G|Grxd3xa7hn, Yrjo T|Lanzas, Cristina', 'Department of Population Health and Pathobiology, College of Veterinary Medicine, North Carolina State University, Raleigh, North Carolina, United States of America.|Department of Biological Statistics & Computational Biology, College of Agriculture and Life Sciences, Cornell University, Ithaca, New York, United States of America.|Department of Biological Statistics & Computational Biology, College of Agriculture and Life Sciences, Cornell University, Ithaca, New York, United States of America.|Department of Population Medicine and Diagnostic Sciences, College of Veterinary Medicine, Cornell University, Ithaca, New York, United States of America.|Department of Population Health and Pathobiology, College of Veterinary Medicine, North Carolina State University, Raleigh, North Carolina, United States of America.', 'United States|United States|United States|United States|United States', 10, 'Anti-Bacterial Agents|Bacterial Infections|Computer Simulation|Drug Resistance, Bacterial|Escherichia coli|Humans|Markov Chains|Models, Statistical|Population Surveillance|Prevalence|Reproducibility of Results|Risk Assessment|Sensitivity and Specificity|Treatment Outcome', 'Journal Article', '10.1371/journal.pcbi.1005160', '27851767', 'Surveillance of antimicrobial resistance (AMR) is an important component of public health. Antimicrobial drug use generates selective pressure that may lead to resistance against to the administered drug, and may also select for collateral resistances to other drugs. Analysis of AMR surveillance data has focused on resistance to individual drugs but joint distributions of resistance in bacterial populations are infrequently analyzed and reported. New methods are needed to characterize and communicate joint resistance distributions. Markov networks are a class of graphical models that define connections, or edges, between pairs of variables with non-zero partial correlations and are used here to describe AMR resistance relationships. The graphical least absolute shrinkage and selection operator is used to estimate sparse Markov networks from AMR surveillance data. The method is demonstrated using a subset of Escherichia coli isolates collected by the National Antimicrobial Resistance Monitoring System between 2004 and 2012 which included AMR results for 16 drugs from 14418 isolates. Of the 119 possible unique edges, 33 unique edges were identified at least once during the study period and graphical density ranged from 16.2% to 24.8%. Two frequent dense subgraphs were noted, one containing the five xcexb2-lactam drugs and the other containing both sulfonamides, three aminoglycosides, and tetracycline. Density did not appear to change over time (p = 0.71). Unweighted modularity did not appear to change over time (p = 0.18), but a significant decreasing trend was noted in the modularity of the weighted networks (p < 0.005) indicating relationships between drugs of different classes tended to increase in strength and frequency over time compared to relationships between drugs of the same class. The current method provides a novel method to study the joint resistance distribution, but additional work is required to unite the underlying biological and genetic characteristics of the isolates with the current results derived from phenotypic data.'),
(88108, 2016, 'plos', 'Drivers of Inter-individual Variation in Dengue Viral Load Dynamics', 'Ben-Shachar, Rotem|Schmidler, Scott|Koelle, Katia', 'Program in Computational Biology and Bioinformatics, Duke University, Durham, North Carolina, United States of America.|Program in Computational Biology and Bioinformatics, Duke University, Durham, North Carolina, United States of America.|Department of Biology, Duke University, Durham, North Carolina, United States of America.', 'United States|United States|United States', 14, 'Adolescent|Adult|Computer Simulation|Dengue|Dengue Virus|Female|Humans|Male|Middle Aged|Models, Statistical|Prevalence|Reproducibility of Results|Risk Factors|Sensitivity and Specificity|Species Specificity|Vietnam|Viral Load|Young Adult', 'Journal Article', '10.1371/journal.pcbi.1005194', '27855153', 'Dengue is a vector-borne viral disease of humans that endemically circulates in many tropical and subtropical regions worldwide. Infection with dengue can result in a range of disease outcomes. A considerable amount of research has sought to improve our understanding of this variation in disease outcomes and to identify predictors of severe disease. Contributing to this research, patterns of viral load in dengue infected patients have been quantified, with analyses indicating that peak viral load levels, rates of viral load decline, and time to peak viremia are useful predictors of severe disease. Here, we take a complementary approach to understanding patterns of clinical manifestation and inter-individual variation in viral load dynamics. Specifically, we statistically fit mathematical within-host models of dengue to individual-level viral load data to test virological and immunological hypotheses explaining inter-individual variation in dengue viral load. We choose between alternative models using model selection criteria to determine which hypotheses are best supported by the data. We first show that the cellular immune response plays an important role in regulating viral load in secondary dengue infections. We then provide statistical support for the process of antibody-dependent enhancement (but not original antigenic sin) in the development of severe disease in secondary dengue infections. Finally, we show statistical support for serotype-specific differences in viral infectivity rates, with infectivity rates of dengue serotypes 2 and 3 exceeding those of serotype 1. These results contribute to our understanding of dengue viral load patterns and their relationship to the development of severe dengue disease. They further have implications for understanding how dengue transmissibility may depend on the immune status of infected individuals and the identity of the infecting serotype.'),
(88109, 2016, 'plos', 'Error-Robust Modes of the Retinal Population Code', 'Prentice, Jason S|Marre, Olivier|Ioffe, Mark L|Loback, Adrianna R|Tkaxc4x8dik, Gaxc5xa1per|Berry, Michael J', 'Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States of America.|Institut de la Vision, UMRS 968 UPMC, INSERM, Paris, France.|Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States of America.|Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States of America.|Institute of Science and Technology Austria, Austria.|Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States of America.', 'United States|France|United States|United States|Austria|United States', 13, 'Action Potentials|Cells, Cultured|Computer Simulation|Humans|Models, Neurological|Models, Statistical|Nerve Net|Retinal Ganglion Cells|Synaptic Transmission|Vision, Ocular', 'Journal Article', '10.1371/journal.pcbi.1005148', '27855154', 'Across the nervous system, certain population spiking patterns are observed far more frequently than others. A hypothesis about this structure is that these collective activity patterns function as population codewords-collective modes-carrying information distinct from that of any single cell. We investigate this phenomenon in recordings of xe2x88xbc150 retinal ganglion cells, the retinas output. We develop a novel statistical model that decomposes the population response into modes, it predicts the distribution of spiking activity in the ganglion cell population with high accuracy. We found that the modes represent localized features of the visual stimulus that are distinct from the features represented by single neurons. Modes form clusters of activity states that are readily discriminated from one another. When we repeated the same visual stimulus, we found that the same mode was robustly elicited. These results suggest that retinal ganglion cells collective signaling is endowed with a form of error-correcting code-a principle that may hold in brain areas beyond retina.'),
(88110, 2016, 'plos', 'Forecasting Influenza Outbreaks in Boroughs and Neighborhoods of New York City', 'Yang, Wan|Olson, Donald R|Shaman, Jeffrey', 'Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, United States of America.|New York City Department of Health and Mental Hygiene, New York, New York, United States of America.|Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, United States of America.', 'United States|United States|United States', 20, 'Computer Simulation|Data Interpretation, Statistical|Disease Outbreaks|Female|Forecasting|Humans|Incidence|Influenza, Human|Male|Models, Statistical|New York City|Population Surveillance|Proportional Hazards Models|Reproducibility of Results|Residence Characteristics|Risk Assessment|Sensitivity and Specificity|Spatio-Temporal Analysis|Urban Population', 'Journal Article', '10.1371/journal.pcbi.1005201', '27855155', 'The ideal spatial scale, or granularity, at which infectious disease incidence should be monitored and forecast has been little explored. By identifying the optimal granularity for a given disease and host population, and matching surveillance and prediction efforts to this scale, response to emergent and recurrent outbreaks can be improved. Here we explore how granularity and representation of spatial structure affect influenza forecast accuracy within New York City. We develop network models at the borough and neighborhood levels, and use them in conjunction with surveillance data and a data assimilation method to forecast influenza activity. These forecasts are compared to an alternate system that predicts influenza for each borough or neighborhood in isolation. At the borough scale, influenza epidemics are highly synchronous despite substantial differences in intensity, and inclusion of network connectivity among boroughs generally improves forecast accuracy. At the neighborhood scale, we observe much greater spatial heterogeneity among influenza outbreaks including substantial differences in local outbreak timing and structure, however, inclusion of the network model structure generally degrades forecast accuracy. One notable exception is that local outbreak onset, particularly when signal is modest, is better predicted with the network model. These findings suggest that observation and forecast at sub-municipal scales within New York City provides richer, more discriminant information on influenza incidence, particularly at the neighborhood scale where greater heterogeneity exists, and that the spatial spread of influenza among localities can be forecast.'),
(88111, 2016, 'plos', 'An Integrative Approach to Computational Modelling of the Gene Regulatory Network Controlling Clostridium botulinum Type A1 Toxin Production', 'Ihekwaba, Adaoha E C|Mura, Ivan|Walshaw, John|Peck, Michael W|Barker, Gary C', 'Gut Health and Food Safety, Institute of Food Research, Norwich Research Park, Colney, Norwich, United Kingdom.|Department of Industrial Engineering, Universidad de los Andes, Bogotá, Colombia.|Gut Health and Food Safety, Institute of Food Research, Norwich Research Park, Colney, Norwich, United Kingdom.|Gut Health and Food Safety, Institute of Food Research, Norwich Research Park, Colney, Norwich, United Kingdom.|Gut Health and Food Safety, Institute of Food Research, Norwich Research Park, Colney, Norwich, United Kingdom.', 'United Kingdom|Colombia|United Kingdom|United Kingdom|United Kingdom', 5, 'Bacterial Proteins|Botulinum Toxins, Type A|Clostridium botulinum type A|Computer Simulation|Gene Expression Regulation, Bacterial|Gene Regulatory Networks|Models, Biological|Species Specificity|Systems Integration', 'Journal Article', '10.1371/journal.pcbi.1005205', '27855161', 'Clostridium botulinum produces botulinum neurotoxins (BoNTs), highly potent substances responsible for botulism. Currently, mathematical models of C. botulinum growth and toxigenesis are largely aimed at risk assessment and do not include explicit genetic information beyond group level but integrate many component processes, such as signalling, membrane permeability and metabolic activity. In this paper we present a scheme for modelling neurotoxin production in C. botulinum Group I type A1, based on the integration of diverse information coming from experimental results available in the literature. Experiments show that production of BoNTs depends on the growth-phase and is under the control of positive and negative regulatory elements at the intracellular level. Toxins are released as large protein complexes and are associated with non-toxic components. Here, we systematically review and integrate those regulatory elements previously described in the literature for C. botulinum Group I type A1 into a population dynamics model, to build the very first computational model of toxin production at the molecular level. We conduct a validation of our model against several items of published experimental data for different wild type and mutant strains of C. botulinum Group I type A1. The result of this process underscores the potential of mathematical modelling at the cellular level, as a means of creating opportunities in developing new strategies that could be used to prevent botulism, and potentially contribute to improved methods for the production of toxin that is used for therapeutics.'),
(88112, 2016, 'plos', 'Antioxidant Properties of Kynurenines: Density Functional Theory Calculations', 'Zhuravlev, Aleksandr V|Zakharov, Gennady A|Shchegolev, Boris F|Savvateeva-Popova, Elena V', 'Neurogenetics Laboratory, Pavlov Institute of Physiology RAS, St. Petersburg, Russia.|Neurogenetics Laboratory, Pavlov Institute of Physiology RAS, St. Petersburg, Russia.|Neurogenetics Laboratory, Pavlov Institute of Physiology RAS, St. Petersburg, Russia.|Neurogenetics Laboratory, Pavlov Institute of Physiology RAS, St. Petersburg, Russia.', '-|-|-|-', 19, 'Antioxidants|Computer Simulation|Hydrogen Bonding|Hydroxyl Radical|Kynurenine|Models, Chemical|Models, Molecular|Oxidation-Reduction|Oxygen|Reactive Oxygen Species', 'Journal Article', '10.1371/journal.pcbi.1005213', '27861556', 'Kynurenines, the main products of tryptophan catabolism, possess both prooxidant and anioxidant effects. Having multiple neuroactive properties, kynurenines are implicated in the development of neurological and cognitive disorders, such as Alzheimers, Parkinsons, and Huntingtons diseases. Autoxidation of 3-hydroxykynurenine (3HOK) and its derivatives, 3-hydroxyanthranilic acid (3HAA) and xanthommatin (XAN), leads to the hyperproduction of reactive oxygen species (ROS) which damage cell structures. At the same time, 3HOK and 3HAA have been shown to be powerful ROS scavengers. Their ability to quench free radicals is believed to result from the presence of the aromatic hydroxyl group which is able to easily abstract an electron and H-atom. In this study, the redox properties for kynurenines and several natural and synthetic antioxidants have been calculated at different levels of density functional theory in the gas phase and water solution. Hydroxyl bond dissociation enthalpy (BDE) and ionization potential (IP) for 3HOK and 3HAA appear to be lower than for xanthurenic acid (XAA), several phenolic antioxidants, and ascorbic acid. BDE and IP for the compounds with aromatic hydroxyl group are lower than for their precursors without hydroxyl group. The reaction rate for H donation to *O-atom of phenoxyl radical (Ph-O*) and methyl peroxy radical (Met-OO*) decreases in the following rankings: 3HOK ~ 3HAA > XAAOXO > XAAENOL. The enthalpy absolute value for Met-OO* addition to the aromatic ring of the antioxidant radical increases in the following rankings: 3HAA* < 3HOK* < XAAOXO* < XAAENOL*. Thus, the high free radical scavenging activity of 3HAA and 3HOK can be explained by the easiness of H-atom abstraction and transfer to O-atom of the free radical, rather than by Met-OO* addition to the kynurenine radical.'),
(88113, 2016, 'plos', 'Identification of Conserved Moieties in Metabolic Networks by Graph Theoretical Analysis of Atom Transition Networks', 'Haraldsdóttir, Hulda S|Fleming, Ronan M T', 'Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg.|Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg.', 'Luxembourg|Luxembourg', 11, 'Computer Simulation|Conserved Sequence|Metabolic Networks and Pathways|Models, Biological|Models, Chemical|Proteome|Sequence Homology, Amino Acid', 'Journal Article', '10.1371/journal.pcbi.1004999', '27870845', 'Conserved moieties are groups of atoms that remain intact in all reactions of a metabolic network. Identification of conserved moieties gives insight into the structure and function of metabolic networks and facilitates metabolic modelling. All moiety conservation relations can be represented as nonnegative integer vectors in the left null space of the stoichiometric matrix corresponding to a biochemical network. Algorithms exist to compute such vectors based only on reaction stoichiometry but their computational complexity has limited their application to relatively small metabolic networks. Moreover, the vectors returned by existing algorithms do not, in general, represent conservation of a specific moiety with a defined atomic structure. Here, we show that identification of conserved moieties requires data on reaction atom mappings in addition to stoichiometry. We present a novel method to identify conserved moieties in metabolic networks by graph theoretical analysis of their underlying atom transition networks. Our method returns the exact group of atoms belonging to each conserved moiety as well as the corresponding vector in the left null space of the stoichiometric matrix. It can be implemented as a pipeline of polynomial time algorithms. Our implementation completes in under five minutes on a metabolic network with more than 4,000 mass balanced reactions. The scalability of the method enables extension of existing applications for moiety conservation relations to genome-scale metabolic networks. We also give examples of new applications made possible by elucidating the atomic structure of conserved moieties.'),
(88114, 2016, 'plos', 'Improved Measures of Integrated Information', 'Tegmark, Max', 'Department of Physics & MIT Kavli Institute, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.', 'United States', 28, 'Animals|Brain|Brain Mapping|Cognition|Computer Simulation|Consciousness|Humans|Information Storage and Retrieval|Machine Learning|Models, Neurological|Systems Integration', 'Journal Article', '10.1371/journal.pcbi.1005123', '27870846', 'Although there is growing interest in measuring integrated information in computational and cognitive systems, current methods for doing so in practice are computationally unfeasible. Existing and novel integration measures are investigated and classified by various desirable properties. A simple taxonomy of xcexa6-measures is presented where they are each characterized by their choice of factorization method (5 options), choice of probability distributions to compare (3 xc3x97 4 options) and choice of measure for comparing probability distributions (7 options). When requiring the xcexa6-measures to satisfy a minimum of attractive properties, these hundreds of options reduce to a mere handful, some of which turn out to be identical. Useful exact and approximate formulas are derived that can be applied to real-world data from laboratory experiments without posing unreasonable computational demands.'),
(88115, 2016, 'plos', 'Creating High-Resolution Multiscale Maps of Human Tissue Using Multi-beam SEM', 'Pereira, André F|Hageman, Daniel J|Garbowski, Tomasz|Riedesel, Christof|Knothe, Ulf|Zeidler, Dirk|Knothe Tate, Melissa L', 'Graduate School of Biomedical Engineering, University of New South Wales, Sydney, Australia.|Graduate School of Biomedical Engineering, University of New South Wales, Sydney, Australia.|Carl Zeiss Microscopy GmbH, Oberkochen, Germany.|Carl Zeiss Microscopy GmbH, Oberkochen, Germany.|Orthopaedic and Rheumatologic Institute, Cleveland Clinic, Cleveland, Ohio, United States of America.|Carl Zeiss Microscopy GmbH, Oberkochen, Germany.|Graduate School of Biomedical Engineering, University of New South Wales, Sydney, Australia.', 'Australia|Australia|Germany|Germany|United States|Germany|Australia', 6, 'Computer Graphics|Femur Neck|Humans|Image Enhancement|Image Interpretation, Computer-Assisted|Microscopy, Electron, Scanning|Reproducibility of Results|Sensitivity and Specificity|Software|User-Computer Interface', 'Journal Article', '10.1371/journal.pcbi.1005217', '27870847', 'Multi-beam scanning electron microscopy (mSEM) enables high-throughput, nano-resolution imaging of macroscopic tissue samples, providing an unprecedented means for structure-function characterization of biological tissues and their cellular inhabitants, seamlessly across multiple length scales. Here we describe computational methods to reconstruct and navigate a multitude of high-resolution mSEM images of the human hip. We calculated cross-correlation shift vectors between overlapping images and used a mass-spring-damper model for optimal global registration. We utilized the Google Maps API to create an interactive map and provide open access to our reconstructed mSEM datasets to both the public and scientific communities via our website www.mechbio.org. The nano- to macro-scale map reveals the tissues biological and material constituents. Living inhabitants of the hip bone (e.g. osteocytes) are visible in their local extracellular matrix milieu (comprising collagen and mineral) and embedded in bones structural tissue architecture, i.e. the osteonal structures in which layers of mineralized tissue are organized in lamellae around a central blood vessel. Multi-beam SEM and our presented methodology enable an unprecedented, comprehensive understanding of health and disease from the molecular to organ length scale.'),
(88116, 2016, 'plos', 'A Biophysical Systems Approach to Identifying the Pathways of Acute and Chronic Doxorubicin Mitochondrial Cardiotoxicity', 'de Oliveira, Bernardo L|Niederer, Steven', 'Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, Kings College London, London, United Kingdom.|Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, Kings College London, London, United Kingdom.', 'United Kingdom|United Kingdom', 12, 'Acute Disease|Animals|Antibiotics, Antineoplastic|Cardiotoxins|Chronic Disease|Citric Acid Cycle|Computer Simulation|DNA Damage|DNA Repair|DNA, Mitochondrial|Dose-Response Relationship, Drug|Doxorubicin|Electron Transport Chain Complex Proteins|Humans|Mitochondria, Heart|Models, Biological|Reactive Oxygen Species', 'Journal Article', '10.1371/journal.pcbi.1005214', '27870850', 'The clinical use of the anthracycline doxorubicin is limited by its cardiotoxicity which is associated with mitochondrial dysfunction. Redox cycling, mitochondrial DNA damage and electron transport chain inhibition have been identified as potential mechanisms of toxicity. However, the relative roles of each of these proposed mechanisms are still not fully understood. The purpose of this study is to identify which of these pathways independently or in combination are responsible for doxorubicin toxicity. A state of the art mathematical model of the mitochondria including the citric acid cycle, electron transport chain and ROS production and scavenging systems was extended by incorporating a novel representation for mitochondrial DNA damage and repair. In silico experiments were performed to quantify the contributions of each of the toxicity mechanisms to mitochondrial dysfunction during the acute and chronic stages of toxicity. Simulations predict that redox cycling has a minor role in doxorubicin cardiotoxicity. Electron transport chain inhibition is the main pathway for acute toxicity for supratherapeutic doses, being lethal at mitochondrial concentrations higher than 200xcexbcM. Direct mitochondrial DNA damage is the principal pathway of chronic cardiotoxicity for therapeutic doses, leading to a progressive and irreversible long term mitochondrial dysfunction.'),
(88117, 2016, 'plos', 'Order Under Uncertainty: Robust Differential Expression Analysis Using Probabilistic Models for Pseudotime Inference', 'Campbell, Kieran R|Yau, Christopher', 'Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom.|Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom.', 'United Kingdom|United Kingdom', 32, '-', 'Journal Article', '10.1371/journal.pcbi.1005212', '27870852', 'Single cell gene expression profiling can be used to quantify transcriptional dynamics in temporal processes, such as cell differentiation, using computational methods to label each cell with a pseudotime where true time series experimentation is too difficult to perform. However, owing to the high variability in gene expression between individual cells, there is an inherent uncertainty in the precise temporal ordering of the cells. Pre-existing methods for pseudotime estimation have predominantly given point estimates precluding a rigorous analysis of the implications of uncertainty. We use probabilistic modelling techniques to quantify pseudotime uncertainty and propagate this into downstream differential expression analysis. We demonstrate that reliance on a point estimate of pseudotime can lead to inflated false discovery rates and that probabilistic approaches provide greater robustness and measures of the temporal resolution that can be obtained from pseudotime inference.'),
(88118, 2016, 'plos', 'Malaria Elimination Campaigns in the Lake Kariba Region of Zambia: A Spatial Dynamical Model', 'Nikolov, Milen|Bever, Caitlin A|Upfill-Brown, Alexander|Hamainza, Busiku|Miller, John M|Eckhoff, Philip A|Wenger, Edward A|Gerardin, Jaline', 'Institute for Disease Modeling, Bellevue, WA, United States.|Institute for Disease Modeling, Bellevue, WA, United States.|Institute for Disease Modeling, Bellevue, WA, United States.|National Malaria Control Centre, Lusaka, Zambia.|PATH Malaria Control and Elimination Program in Africa (MACEPA), Lusaka, Zambia.|Institute for Disease Modeling, Bellevue, WA, United States.|Institute for Disease Modeling, Bellevue, WA, United States.|Institute for Disease Modeling, Bellevue, WA, United States.', 'United States|United States|United States|Zambia|Zambia|United States|United States|United States', 16, 'Antimalarials|Computer Simulation|Disease Eradication|Female|Health Promotion|Humans|Malaria|Male|Models, Statistical|Mosquito Control|Outcome Assessment (Health Care)|Population Surveillance|Prevalence|Risk Factors|Spatio-Temporal Analysis|Zambia', 'Journal Article', '10.1371/journal.pcbi.1005192', '27880764', 'As more regions approach malaria elimination, understanding how different interventions interact to reduce transmission becomes critical. The Lake Kariba area of Southern Province, Zambia, is part of a multi-country elimination effort and presents a particular challenge as it is an interconnected region of variable transmission intensities. In 2012-13, six rounds of mass test-and-treat drug campaigns were carried out in the Lake Kariba region. A spatial dynamical model of malaria transmission in the Lake Kariba area, with transmission and climate modeled at the village scale, was calibrated to the 2012-13 prevalence survey data, with case management rates, insecticide-treated net usage, and drug campaign coverage informed by surveillance. The model captured the spatio-temporal trends of decline and rebound in malaria prevalence in 2012-13 at the village scale. Various interventions implemented between 2016-22 were simulated to compare their effects on reducing regional transmission and achieving and maintaining elimination through 2030. Simulations predict that elimination requires sustaining high coverage with vector control over several years. When vector control measures are well-implemented, targeted mass drug campaigns in high-burden areas further increase the likelihood of elimination, although drug campaigns cannot compensate for insufficient vector control. If infections are regularly imported from outside the region into highly receptive areas, vector control must be maintained within the region until importations cease. Elimination in the Lake Kariba region is possible, although human movement both within and from outside the region risk damaging the success of elimination programs.'),
(88119, 2016, 'plos', 'Real-Time Control of an Articulatory-Based Speech Synthesizer for Brain Computer Interfaces', 'Bocquelet, Florent|Hueber, Thomas|Girin, Laurent|Savariaux, Christophe|Yvert, Blaise', 'INSERM, BrainTech Laboratory U1205, Grenoble, France.|CNRS, GIPSA-Lab, Saint-Martin-dHères, France.|Univ. Grenoble Alpes, GIPSA-Lab, Saint-Martin-dHères, France.|CNRS, GIPSA-Lab, Saint-Martin-dHères, France.|INSERM, BrainTech Laboratory U1205, Grenoble, France.', 'France|France|France|France|France', 28, 'Biofeedback, Psychology|Brain-Computer Interfaces|Communication Aids for Disabled|Computer Systems|Humans|Neural Networks (Computer)|Phonetics|Sound Spectrography|Speech Acoustics|Speech Intelligibility|Speech Production Measurement', 'Journal Article', '10.1371/journal.pcbi.1005119', '27880768', 'Restoring natural speech in paralyzed and aphasic people could be achieved using a Brain-Computer Interface (BCI) controlling a speech synthesizer in real-time. To reach this goal, a prerequisite is to develop a speech synthesizer producing intelligible speech in real-time with a reasonable number of control parameters. We present here an articulatory-based speech synthesizer that can be controlled in real-time for future BCI applications. This synthesizer converts movements of the main speech articulators (tongue, jaw, velum, and lips) into intelligible speech. The articulatory-to-acoustic mapping is performed using a deep neural network (DNN) trained on electromagnetic articulography (EMA) data recorded on a reference speaker synchronously with the produced speech signal. This DNN is then used in both offline and online modes to map the position of sensors glued on different speech articulators into acoustic parameters that are further converted into an audio signal using a vocoder. In offline mode, highly intelligible speech could be obtained as assessed by perceptual evaluation performed by 12 listeners. Then, to anticipate future BCI applications, we further assessed the real-time control of the synthesizer by both the reference speaker and new speakers, in a closed-loop paradigm using EMA data recorded in real time. A short calibration period was used to compensate for differences in sensor positions and articulatory differences between new speakers and the reference speaker. We found that real-time synthesis of vowels and consonants was possible with good intelligibility. In conclusion, these results open to future speech BCI applications using such articulatory-based speech synthesizer.'),
(88120, 2016, 'plos', 'Individual Differences in Dynamic Functional Brain Connectivity across the Human Lifespan', 'Davison, Elizabeth N|Turner, Benjamin O|Schlesinger, Kimberly J|Miller, Michael B|Grafton, Scott T|Bassett, Danielle S|Carlson, Jean M', 'Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, New Jersey, United States of America.|Department of Psychological & Brain Sciences, University of California, Santa Barbara, Santa Barbara, California, United States of America.|Department of Physics, University of California, Santa Barbara, Santa Barbara, California, United States of America.|Department of Psychological & Brain Sciences, University of California, Santa Barbara, Santa Barbara, California, United States of America.|Department of Psychological & Brain Sciences, University of California, Santa Barbara, Santa Barbara, California, United States of America.|Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.|Department of Physics, University of California, Santa Barbara, Santa Barbara, California, United States of America.', 'Jersey|United States|United States|United States|United States|United States|United States', 16, 'Adaptation, Physiological|Adolescent|Adult|Aged|Brain|Cognition|Computer Simulation|Connectome|Female|Humans|Longevity|Male|Middle Aged|Models, Neurological|Nerve Net|Neural Pathways|Neuronal Plasticity|Reproducibility of Results|Sensitivity and Specificity|Young Adult', 'Journal Article', '10.1371/journal.pcbi.1005178', '27880785', 'Individual differences in brain functional networks may be related to complex personal identifiers, including health, age, and ability. Dynamic network theory has been used to identify properties of dynamic brain function from fMRI data, but the majority of analyses and findings remain at the level of the group. Here, we apply hypergraph analysis, a method from dynamic network theory, to quantify individual differences in brain functional dynamics. Using a summary metric derived from the hypergraph formalism-hypergraph cardinality-we investigate individual variations in two separate, complementary data sets. The first data set (multi-task) consists of 77 individuals engaging in four consecutive cognitive tasks. We observe that hypergraph cardinality exhibits variation across individuals while remaining consistent within individuals between tasks, moreover, the analysis of one of the memory tasks revealed a marginally significant correspondence between hypergraph cardinality and age. This finding motivated a similar analysis of the second data set (age-memory), in which 95 individuals, aged 18-75, performed a memory task with a similar structure to the multi-task memory task. With the increased age range in the age-memory data set, the correlation between hypergraph cardinality and age correspondence becomes significant. We discuss these results in the context of the well-known finding linking age with network structure, and suggest that hypergraph analysis should serve as a useful tool in furthering our understanding of the dynamic network structure of the brain.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(88121, 2016, 'plos', 'Computational Discovery of Putative Leads for Drug Repositioning through Drug-Target Interaction Prediction', 'Coelho, Edgar D|Arrais, Joel P|Oliveira, José Luís', 'Department of Electronics, Telecommunications and Informatics (DETI), Institute of Electronics and Telematics Engineering of Aveiro (IEETA), University of Aveiro, Aveiro, Portugal.|Department of Informatics Engineering (DEI), Centre for Informatics and Systems of the University of Coimbra (CISUC), University of Coimbra, Coimbra, Portugal.|Department of Electronics, Telecommunications and Informatics (DETI), Institute of Electronics and Telematics Engineering of Aveiro (IEETA), University of Aveiro, Aveiro, Portugal.', 'Portugal|Portugal|Portugal', 16, 'Anti-Bacterial Agents|Bacterial Proteins|Computer Simulation|Drug Discovery|Drug Evaluation, Preclinical|Drug Repositioning|Models, Chemical|Protein Interaction Mapping', 'Journal Article', '10.1371/journal.pcbi.1005219', '27893735', 'De novo experimental drug discovery is an expensive and time-consuming task. It requires the identification of drug-target interactions (DTIs) towards targets of biological interest, either to inhibit or enhance a specific molecular function. Dedicated computational models for protein simulation and DTI prediction are crucial for speed and to reduce the costs associated with DTI identification. In this paper we present a computational pipeline that enables the discovery of putative leads for drug repositioning that can be applied to any microbial proteome, as long as the interactome of interest is at least partially known. Network metrics calculated for the interactome of the bacterial organism of interest were used to identify putative drug-targets. Then, a random forest classification model for DTI prediction was constructed using known DTI data from publicly available databases, resulting in an area under the ROC curve of 0.91 for classification of out-of-sampling data. A drug-target network was created by combining 3,081 unique ligands and the expected ten best drug targets. This network was used to predict new DTIs and to calculate the probability of the positive class, allowing the scoring of the predicted instances. Molecular docking experiments were performed on the best scoring DTI pairs and the results were compared with those of the same ligands with their original targets. The results obtained suggest that the proposed pipeline can be used in the identification of new leads for drug repositioning. The proposed classification model is available at http://bioinformatics.ua.pt/software/dtipred/.'),
(88122, 2016, 'plos', 'Macromolecular Crowding Regulates the Gene Expression Profile by Limiting Diffusion', 'Golkaram, Mahdi|Hellander, Stefan|Drawert, Brian|Petzold, Linda R', 'Department of Mechanical Engineering, University of California, Santa Barbara, California, United States of America.|Department of Computer Science, University of California, Santa Barbara, California, United States of America.|Department of Computer Science, University of California, Santa Barbara, California, United States of America.|Department of Mechanical Engineering, University of California, Santa Barbara, California, United States of America.', 'United States|United States|United States|United States', 13, 'Animals|Computer Simulation|Diffusion|Humans|Macromolecular Substances|MicroRNAs|Models, Chemical|Models, Genetic|Models, Statistical|Protein Biosynthesis|Stochastic Processes|Transcription Factors|Transcriptional Activation|Transcriptome', 'Journal Article', '10.1371/journal.pcbi.1005122', '27893768', 'We seek to elucidate the role of macromolecular crowding in transcription and translation. It is well known that stochasticity in gene expression can lead to differential gene expression and heterogeneity in a cell population. Recent experimental observations by Tan et al. have improved our understanding of the functional role of macromolecular crowding. It can be inferred from their observations that macromolecular crowding can lead to robustness in gene expression, resulting in a more homogeneous cell population. We introduce a spatial stochastic model to provide insight into this process. Our results show that macromolecular crowding reduces noise (as measured by the kurtosis of the mRNA distribution) in a cell population by limiting the diffusion of transcription factors (i.e. removing the unstable intermediate states), and that crowding by large molecules reduces noise more efficiently than crowding by small molecules. Finally, our simulation results provide evidence that the local variation in chromatin density as well as the total volume exclusion of the chromatin in the nucleus can induce a homogenous cell population.'),
(88123, 2016, 'plos', 'Extending Integrate-and-Fire Model Neurons to Account for the Effects of Weak Electric Fields and Input Filtering Mediated by the Dendrite', 'Aspart, Florian|Ladenbauer, Josef|Obermayer, Klaus', 'Department of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, Berlin, Germany.|Department of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, Berlin, Germany.|Department of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, Berlin, Germany.', 'Germany|Germany|Germany', 9, 'Action Potentials|Animals|Computer Simulation|Dendrites|Electromagnetic Fields|Humans|Membrane Potentials|Models, Neurological|Neurons|Radiation Dosage|Synaptic Transmission', 'Journal Article', '10.1371/journal.pcbi.1005206', '27893786', 'Transcranial brain stimulation and evidence of ephaptic coupling have recently sparked strong interests in understanding the effects of weak electric fields on the dynamics of brain networks and of coupled populations of neurons. The collective dynamics of large neuronal populations can be efficiently studied using single-compartment (point) model neurons of the integrate-and-fire (IF) type as their elements. These models, however, lack the dendritic morphology required to biophysically describe the effect of an extracellular electric field on the neuronal membrane voltage. Here, we extend the IF point neuron models to accurately reflect morphology dependent electric field effects extracted from a canonical spatial ball-and-stick (BS) neuron model. Even in the absence of an extracellular field, neuronal morphology by itself strongly affects the cellular response properties. We, therefore, derive additional components for leaky and nonlinear IF neuron models to reproduce the subthreshold voltage and spiking dynamics of the BS model exposed to both fluctuating somatic and dendritic inputs and an extracellular electric field. We show that an oscillatory electric field causes spike rate resonance, or equivalently, pronounced spike to field coherence. Its resonance frequency depends on the location of the synaptic background inputs. For somatic inputs the resonance appears in the beta and gamma frequency range, whereas for distal dendritic inputs it is shifted to even higher frequencies. Irrespective of an external electric field, the presence of a dendritic cable attenuates the subthreshold response at the soma to slowly-varying somatic inputs while implementing a low-pass filter for distal dendritic inputs. Our point neuron model extension is straightforward to implement and is computationally much more efficient compared to the original BS model. It is well suited for studying the dynamics of large populations of neurons with heterogeneous dendritic morphology with (and without) the influence of weak external electric fields.'),
(88124, 2016, 'plos', 'Kinase Inhibition Leads to Hormesis in a Dual Phosphorylation-Dephosphorylation Cycle', 'Rashkov, Peter|Barrett, Ian P|Beardmore, Robert E|Bendtsen, Claus|Gudelj, Ivana', 'School of Biosciences, University of Exeter, Exeter, United Kingdom.|Discovery Sciences, Innovative Medicines and Early Development, AstraZeneca, Cambridge, United Kingdom.|School of Biosciences, University of Exeter, Exeter, United Kingdom.|Discovery Sciences, Innovative Medicines and Early Development, AstraZeneca, Cambridge, United Kingdom.|School of Biosciences, University of Exeter, Exeter, United Kingdom.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 3, 'Animals|Cell Physiological Phenomena|Computer Simulation|Hormesis|Humans|Models, Biological|Models, Chemical|Phosphorylation|Protein Kinase Inhibitors|Protein Kinases', 'Journal Article', '10.1371/journal.pcbi.1005216', '27898662', 'Many antimicrobial and anti-tumour drugs elicit hormetic responses characterised by low-dose stimulation and high-dose inhibition. While this can have profound consequences for human health, with low drug concentrations actually stimulating pathogen or tumour growth, the mechanistic understanding behind such responses is still lacking. We propose a novel, simple but general mechanism that could give rise to hormesis in systems where an inhibitor acts on an enzyme. At its core is one of the basic building blocks in intracellular signalling, the dual phosphorylation-dephosphorylation motif, found in diverse regulatory processes including control of cell proliferation and programmed cell death. Our analytically-derived conditions for observing hormesis provide clues as to why this mechanism has not been previously identified. Current mathematical models regularly make simplifying assumptions that lack empirical support but inadvertently preclude the observation of hormesis. In addition, due to the inherent population heterogeneities, the presence of hormesis is likely to be masked in empirical population-level studies. Therefore, examining hormetic responses at single-cell level coupled with improved mathematical models could substantially enhance detection and mechanistic understanding of hormesis.'),
(88125, 2016, 'plos', 'As Simple As Possible, but Not Simpler: Exploring the Fidelity of Coarse-Grained Protein Models for Simulated Force Spectroscopy', 'Habibi, Mona|Rottler, Jxc3xb6rg|Plotkin, Steven S', 'Department of Physics & Astronomy, University of British Columbia, Vancouver, British Columbia, Canada.|Department of Physics & Astronomy, University of British Columbia, Vancouver, British Columbia, Canada.|Department of Physics & Astronomy, University of British Columbia, Vancouver, British Columbia, Canada.', 'Canada|Canada|Canada', 11, 'Computer Simulation|Elastic Modulus|Micromanipulation|Microscopy, Atomic Force|Models, Chemical|Models, Molecular|Protein Conformation|Protein Unfolding|Proteins|Stress, Mechanical', 'Journal Article', '10.1371/journal.pcbi.1005211', '27898663', 'Mechanical unfolding of a single domain of loop-truncated superoxide dismutase protein has been simulated via force spectroscopy techniques with both all-atom (AA) models and several coarse-grained models having different levels of resolution: A Gxc5x8d model containing all heavy atoms in the protein (HA-Gxc5x8d), the associative memory, water mediated, structure and energy model (AWSEM) which has 3 interaction sites per amino acid, and a Gxc5x8d model containing only one interaction site per amino acid at the Cxcexb1 position (Cxcexb1-Gxc5x8d). To systematically compare results across models, the scales of time, energy, and force had to be suitably renormalized in each model. Surprisingly, the HA-Gxc5x8d model gives the softest protein, exhibiting much smaller force peaks than all other models after the above renormalization. Clustering to render a structural taxonomy as the protein unfolds showed that the AA, HA-Gxc5x8d, and Cxcexb1-Gxc5x8d models exhibit a single pathway for early unfolding, which eventually bifurcates repeatedly to multiple branches only after the protein is about half-unfolded. The AWSEM model shows a single dominant unfolding pathway over the whole range of unfolding, in contrast to all other models. TM alignment, clustering analysis, and native contact maps show that the AWSEM pathway has however the most structural similarity to the AA model at high nativeness, but the least structural similarity to the AA model at low nativeness. In comparison to the AA model, the sequence of native contact breakage is best predicted by the HA-Gxc5x8d model. All models consistently predict a similar unfolding mechanism for early force-induced unfolding events, but diverge in their predictions for late stage unfolding events when the protein is more significantly disordered.'),
(88126, 2016, 'plos', 'Do Vascular Networks Branch Optimally or Randomly across Spatial Scales?', 'Tekin, Elif|Hunt, David|Newberry, Mitchell G|Savage, Van M', 'Department of Biomathematics, University of California, Los Angeles, David Geffen School of Medicine, Los Angeles, California, United States of America.|Department of Biomathematics, University of California, Los Angeles, David Geffen School of Medicine, Los Angeles, California, United States of America.|Department of Biology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.|Department of Biomathematics, University of California, Los Angeles, David Geffen School of Medicine, Los Angeles, California, United States of America.', 'United States|United States|United States|United States', 14, 'Animals|Blood Vessels|Body Patterning|Humans|Mice|Models, Anatomic|Models, Cardiovascular|Models, Statistical', 'Journal Article', '10.1371/journal.pcbi.1005223', '27902691', 'Modern models that derive allometric relationships between metabolic rate and body mass are based on the architectural design of the cardiovascular system and presume sibling vessels are symmetric in terms of radius, length, flow rate, and pressure. Here, we study the cardiovascular structure of the human head and torso and of a mouse lung based on three-dimensional images processed via our software Angicart. In contrast to modern allometric theories, we find systematic patterns of asymmetry in vascular branching, potentially explaining previously documented mismatches between predictions (power-law or concave curvature) and observed empirical data (convex curvature) for the allometric scaling of metabolic rate. To examine why these systematic asymmetries in vascular branching might arise, we construct a mathematical framework to derive predictions based on local, junction-level optimality principles that have been proposed to be favored in the course of natural selection and development. The two most commonly used principles are material-cost optimizations (construction materials or blood volume) and optimization of efficient flow via minimization of power loss. We show that material-cost optimization solutions match with distributions for asymmetric branching across the whole network but do not match well for individual junctions. Consequently, we also explore random branching that is constrained at scales that range from local (junction-level) to global (whole network). We find that material-cost optimizations are the strongest predictor of vascular branching in the human head and torso, whereas locally or intermediately constrained random branching is comparable to material-cost optimizations for the mouse lung. These differences could be attributable to developmentally-programmed local branching for larger vessels and constrained random branching for smaller vessels.'),
(88127, 2016, 'plos', 'Correction: Multi-level Modeling of Light-Induced Stomatal Opening Offers New Insights into Its Regulation by Drought', 'PLOS Computational Biology Staff', 'Department of Biomathematics, University of California, Los Angeles, David Geffen School of Medicine, Los Angeles, California, United States of America.', 'United States', 0, '-', 'Published Erratum', '10.1371/journal.pcbi.1005181', '27902692', '[This corrects the article DOI: 10.1371/journal.pcbi.1003930.].'),
(88128, 2016, 'plos', 'Text Mining Genotype-Phenotype Relationships from Biomedical Literature for Database Curation and Precision Medicine', 'Singhal, Ayush|Simmons, Michael|Lu, Zhiyong', 'National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, Maryland, United States of America.|National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, Maryland, United States of America.|National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, Maryland, United States of America.', 'United States|United States|United States', 42, 'Data Mining|Database Management Systems|Databases, Genetic|Genetic Predisposition to Disease|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Natural Language Processing|Periodicals as Topic|Precision Medicine', 'Journal Article', '10.1371/journal.pcbi.1005017', '27902695', 'The practice of precision medicine will ultimately require databases of genes and mutations for healthcare providers to reference in order to understand the clinical implications of each patients genetic makeup. Although the highest quality databases require manual curation, text mining tools can facilitate the curation process, increasing accuracy, coverage, and productivity. However, to date there are no available text mining tools that offer high-accuracy performance for extracting such triplets from biomedical literature. In this paper we propose a high-performance machine learning approach to automate the extraction of disease-gene-variant triplets from biomedical literature. Our approach is unique because we identify the genes and protein products associated with each mutation from not just the local text content, but from a global context as well (from the Internet and from all literature in PubMed). Our approach also incorporates protein sequence validation and disease association using a novel text-mining-based machine learning approach. We extract disease-gene-variant triplets from all abstracts in PubMed related to a set of ten important diseases (breast cancer, prostate cancer, pancreatic cancer, lung cancer, acute myeloid leukemia, Alzheimers disease, hemochromatosis, age-related macular degeneration (AMD), diabetes mellitus, and cystic fibrosis). We then evaluate our approach in two ways: (1) a direct comparison with the state of the art using benchmark datasets, (2) a validation study comparing the results of our approach with entries in a popular human-curated database (UniProt) for each of the previously mentioned diseases. In the benchmark comparison, our full approach achieves a 28% improvement in F1-measure (from 0.62 to 0.79) over the state-of-the-art results. For the validation study with UniProt Knowledgebase (KB), we present a thorough analysis of the results and errors. Across all diseases, our approach returned 272 triplets (disease-gene-variant) that overlapped with entries in UniProt and 5,384 triplets without overlap in UniProt. Analysis of the overlapping triplets and of a stratified sample of the non-overlapping triplets revealed accuracies of 93% and 80% for the respective categories (cumulative accuracy, 77%). We conclude that our process represents an important and broadly applicable improvement to the state of the art for curation of disease-gene-variant relationships.'),
(88129, 2016, 'plos', 'Modeling Cellular Noise Underlying Heterogeneous Cell Responses in the Epidermal Growth Factor Signaling Pathway', 'Iwamoto, Kazunari|Shindo, Yuki|Takahashi, Koichi', 'Laboratory for Biochemical Simulation, RIKEN Quantitative Biology Center, Suita, Osaka, Japan.|Laboratory for Biochemical Simulation, RIKEN Quantitative Biology Center, Suita, Osaka, Japan.|Laboratory for Biochemical Simulation, RIKEN Quantitative Biology Center, Suita, Osaka, Japan.', 'Japan|Japan|Japan', 0, 'Animals|Computer Simulation|Epidermal Growth Factor|Extracellular Signal-Regulated MAP Kinases|Gene Expression Regulation|Humans|MAP Kinase Signaling System|Models, Biological|Models, Statistical|Nuclear Pore|Signal Transduction', 'Journal Article', '10.1371/journal.pcbi.1005222', '27902699', 'Cellular heterogeneity, which plays an essential role in biological phenomena, such as drug resistance and migration, is considered to arise from intrinsic (i.e., reaction kinetics) and extrinsic (i.e., protein variability) noise in the cell. However, the mechanistic effects of these types of noise to determine the heterogeneity of signal responses have not been elucidated. Here, we report that the output of epidermal growth factor (EGF) signaling activity is modulated by cellular noise, particularly by extrinsic noise of particular signaling components in the pathway. We developed a mathematical model of the EGF signaling pathway incorporating regulation between extracellular signal-regulated kinase (ERK) and nuclear pore complex (NPC), which is necessary for switch-like activation of the nuclear ERK response. As the threshold of switch-like behavior is more sensitive to perturbations than the graded response, the effect of biological noise is potentially critical for cell fate decision. Our simulation analysis indicated that extrinsic noise, but not intrinsic noise, contributes to cell-to-cell heterogeneity of nuclear ERK. In addition, we accurately estimated variations in abundance of the signal proteins between individual cells by direct comparison of experimental data with simulation results using Apparent Measurement Error (AME). AME was constant regardless of whether the protein levels varied in a correlated manner, while covariation among proteins influenced cell-to-cell heterogeneity of nuclear ERK, suppressing the variation. Simulations using the estimated protein abundances showed that each protein species has different effects on cell-to-cell variation in the nuclear ERK response. In particular, variability of EGF receptor, Ras, Raf, and MEK strongly influenced cellular heterogeneity, while others did not. Overall, our results indicated that cellular heterogeneity in response to EGF is strongly driven by extrinsic noise, and that such heterogeneity results from variability of particular protein species that function as sensitive nodes, which may contribute to the pathogenesis of human diseases.'),
(88130, 2016, 'plos', 'Detecting Mild Traumatic Brain Injury Using Resting State Magnetoencephalographic Connectivity', 'Vakorin, Vasily A|Doesburg, Sam M|da Costa, Leodante|Jetly, Rakesh|Pang, Elizabeth W|Taylor, Margot J', 'Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, British Columbia, Canada.|Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, British Columbia, Canada.|Department of Surgery, Division of Neurosurgery, Sunnybrook Hospital, University of Toronto, Toronto, Ontario, Canada.|Canadian Forces Health Services, Directorate of Mental Health, Ottawa, Ontario, Canada.|Neuroscience and Mental Health, Hospital for Sick Children Research Institute, Toronto, Ontario, Canada.|Department of Diagnostic Imaging, Hospital for Sick Children Research Institute, Toronto, Ontario, Canada.', 'Canada|Canada|Canada|Canada|Canada|Canada', 0, 'Adult|Brain|Brain Injuries, Traumatic|Brain Mapping|Cluster Analysis|Humans|Magnetoencephalography|Male|Signal Processing, Computer-Assisted|Young Adult', 'Journal Article', '10.1371/journal.pcbi.1004914', '27906973', 'Accurate means to detect mild traumatic brain injury (mTBI) using objective and quantitative measures remain elusive. Conventional imaging typically detects no abnormalities despite post-concussive symptoms. In the present study, we recorded resting state magnetoencephalograms (MEG) from adults with mTBI and controls. Atlas-guided reconstruction of resting state activity was performed for 90 cortical and subcortical regions, and calculation of inter-regional oscillatory phase synchrony at various frequencies was performed. We demonstrate that mTBI is associated with reduced network connectivity in the delta and gamma frequency range (>30 Hz), together with increased connectivity in the slower alpha band (8-12 Hz). A similar temporal pattern was associated with correlations between network connectivity and the length of time between the injury and the MEG scan. Using such resting state MEG network synchrony we were able to detect mTBI with 88% accuracy. Classification confidence was also correlated with clinical symptom severity scores. These results provide the first evidence that imaging of MEG network connectivity, in combination with machine learning, has the potential to accurately detect and determine the severity of mTBI.'),
(88131, 2016, 'plos', 'In Silico Knockout Studies of Xenophagic Capturing of Salmonella', 'Scheidel, Jennifer|Amstein, Leonie|Ackermann, Jörg|Dikic, Ivan|Koch, Ina', 'Molecular Bioinformatics, Institute of Computer Science, Johann Wolfgang Goethe-University Frankfurt am Main, Frankfurt am Main, Germany.|Molecular Bioinformatics, Institute of Computer Science, Johann Wolfgang Goethe-University Frankfurt am Main, Frankfurt am Main, Germany.|Molecular Bioinformatics, Institute of Computer Science, Johann Wolfgang Goethe-University Frankfurt am Main, Frankfurt am Main, Germany.|Institute of Biochemistry II, Johann Wolfgang Goethe-University Hospital Frankfurt am Main, Frankfurt am Main, Germany.|Molecular Bioinformatics, Institute of Computer Science, Johann Wolfgang Goethe-University Frankfurt am Main, Frankfurt am Main, Germany.', 'Germany|Germany|Germany|Germany|Germany', 8, 'Animals|Autophagy|Computer Simulation|Cytosol|Galectins|Intestinal Mucosa|Intestine, Small|Mammals|Models, Biological|Protein-Serine-Threonine Kinases|Salmonella typhimurium|Ubiquitin', 'Journal Article', '10.1371/journal.pcbi.1005200', '27906974', 'The degradation of cytosol-invading pathogens by autophagy, a process known as xenophagy, is an important mechanism of the innate immune system. Inside the host, Salmonella Typhimurium invades epithelial cells and resides within a specialized intracellular compartment, the Salmonella-containing vacuole. A fraction of these bacteria does not persist inside the vacuole and enters the host cytosol. Salmonella Typhimurium that invades the host cytosol becomes a target of the autophagy machinery for degradation. The xenophagy pathway has recently been discovered, and the exact molecular processes are not entirely characterized. Complete kinetic data for each molecular process is not available, so far. We developed a mathematical model of the xenophagy pathway to investigate this key defense mechanism. In this paper, we present a Petri net model of Salmonella xenophagy in epithelial cells. The model is based on functional information derived from literature data. It comprises the molecular mechanism of galectin-8-dependent and ubiquitin-dependent autophagy, including regulatory processes, like nutrient-dependent regulation of autophagy and TBK1-dependent activation of the autophagy receptor, OPTN. To model the activation of TBK1, we proposed a new mechanism of TBK1 activation, suggesting a spatial and temporal regulation of this process. Using standard Petri net analysis techniques, we found basic functional modules, which describe different pathways of the autophagic capture of Salmonella and reflect the basic dynamics of the system. To verify the model, we performed in silico knockout experiments. We introduced a new concept of knockout analysis to systematically compute and visualize the results, using an in silico knockout matrix. The results of the in silico knockout analyses were consistent with published experimental results and provide a basis for future investigations of the Salmonella xenophagy pathway.'),
(88132, 2016, 'plos', 'A Graph-Centric Approach for Metagenome-Guided Peptide and Protein Identification in Metaproteomics', 'Tang, Haixu|Li, Sujun|Ye, Yuzhen', 'School of Informatics and Computing, Indiana University, Bloomington, Indiana, United States of America.|School of Informatics and Computing, Indiana University, Bloomington, Indiana, United States of America.|School of Informatics and Computing, Indiana University, Bloomington, Indiana, United States of America.', 'India|India|India', 0, 'Algorithms|Humans|Metagenomics|Microbiota|Peptides|Proteins|Proteomics|Tandem Mass Spectrometry', 'Journal Article', '10.1371/journal.pcbi.1005224', '27918579', 'Metaproteomic studies adopt the common bottom-up proteomics approach to investigate the protein composition and the dynamics of protein expression in microbial communities. When matched metagenomic and/or metatranscriptomic data of the microbial communities are available, metaproteomic data analyses often employ a metagenome-guided approach, in which complete or fragmental protein-coding genes are first directly predicted from metagenomic (and/or metatranscriptomic) sequences or from their assemblies, and the resulting protein sequences are then used as the reference database for peptide/protein identification from MS/MS spectra. This approach is often limited because protein coding genes predicted from metagenomes are incomplete and fragmental. In this paper, we present a graph-centric approach to improving metagenome-guided peptide and protein identification in metaproteomics. Our method exploits the de Bruijn graph structure reported by metagenome assembly algorithms to generate a comprehensive database of protein sequences encoded in the community. We tested our method using several public metaproteomic datasets with matched metagenomic and metatranscriptomic sequencing data acquired from complex microbial communities in a biological wastewater treatment plant. The results showed that many more peptides and proteins can be identified when assembly graphs were utilized, improving the characterization of the proteins expressed in the microbial communities. The additional proteins we identified contribute to the characterization of important pathways such as those involved in degradation of chemical hazards. Our tools are released as open-source software on github at https://github.com/COL-IU/Graph2Pro.'),
(88133, 2016, 'plos', 'Different Evolutionary Paths to Complexity for Small and Large Populations of Digital Organisms', 'LaBar, Thomas|Adami, Christoph', 'Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, Michigan, United States of America.|Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, Michigan, United States of America.', 'United States|United States', 12, 'Computer Simulation|Evolution, Molecular|Genetic Drift|Genetics, Population|Genome|Models, Genetic', 'Journal Article', '10.1371/journal.pcbi.1005066', '27923053', 'A major aim of evolutionary biology is to explain the respective roles of adaptive versus non-adaptive changes in the evolution of complexity. While selection is certainly responsible for the spread and maintenance of complex phenotypes, this does not automatically imply that strong selection enhances the chance for the emergence of novel traits, that is, the origination of complexity. Population size is one parameter that alters the relative importance of adaptive and non-adaptive processes: as population size decreases, selection weakens and genetic drift grows in importance. Because of this relationship, many theories invoke a role for population size in the evolution of complexity. Such theories are difficult to test empirically because of the time required for the evolution of complexity in biological populations. Here, we used digital experimental evolution to test whether large or small asexual populations tend to evolve greater complexity. We find that both small and large-but not intermediate-sized-populations are favored to evolve larger genomes, which provides the opportunity for subsequent increases in phenotypic complexity. However, small and large populations followed different evolutionary paths towards these novel traits. Small populations evolved larger genomes by fixing slightly deleterious insertions, while large populations fixed rare beneficial insertions that increased genome size. These results demonstrate that genetic drift can lead to the evolution of complexity in small populations and that purifying selection is not powerful enough to prevent the evolution of complexity in large populations.'),
(88134, 2016, 'plos', 'Fused Regression for Multi-source Gene Regulatory Network Inference', 'Lam, Kari Y|Westrick, Zachary M|Müller, Christian L|Christiaen, Lionel|Bonneau, Richard', 'New York University, New York, New York, United States of America.|New York University, New York, New York, United States of America.|Simons Foundation, New York, New York, United States of America.|New York University, New York, New York, United States of America.|New York University, New York, New York, United States of America.', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Bacillus|Computational Biology|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation, Bacterial|Gene Regulatory Networks|Models, Genetic|Regression Analysis', 'Journal Article', '10.1371/journal.pcbi.1005157', '27923054', 'Understanding gene regulatory networks is critical to understanding cellular differentiation and response to external stimuli. Methods for global network inference have been developed and applied to a variety of species. Most approaches consider the problem of network inference independently in each species, despite evidence that gene regulation can be conserved even in distantly related species. Further, network inference is often confined to single data-types (single platforms) and single cell types. We introduce a method for multi-source network inference that allows simultaneous estimation of gene regulatory networks in multiple species or biological processes through the introduction of priors based on known gene relationships such as orthology incorporated using fused regression. This approach improves network inference performance even when orthology mapping and conservation are incomplete. We refine this method by presenting an algorithm that extracts the true conserved subnetwork from a larger set of potentially conserved interactions and demonstrate the utility of our method in cross species network inference. Last, we demonstrate our methods utility in learning from data collected on different experimental platforms.'),
(88135, 2016, 'plos', 'Correction: Separating Timing, Movement Conditions and Individual Differences in the Analysis of Human Movement', 'PLOS Computational Biology Staff', 'New York University, New York, New York, United States of America.', 'United States', 0, '-', 'Published Erratum', '10.1371/journal.pcbi.1005254', '27923059', '[This corrects the article DOI: 10.1371/journal.pcbi.1005092.].'),
(88136, 2016, 'plos', 'The Limitations of Model-Based Experimental Design and Parameter Estimation in Sloppy Systems', 'White, Andrew|Tolman, Malachi|Thames, Howard D|Withers, Hubert Rodney|Mason, Kathy A|Transtrum, Mark K', 'Department of Physics & Astronomy, Brigham Young University, Provo, Utah, United States of America.|Department of Physics & Astronomy, Brigham Young University, Provo, Utah, United States of America.|Department of Biostatistics, UT MD Anderson Cancer Center, Houston, Texas, United States of America.|Department of Experimental Radiation Oncology, UT MD Anderson Cancer Center, Houston, Texas, United States of America.|Department of Experimental Radiation Oncology, UT MD Anderson Cancer Center, Houston, Texas, United States of America.|Department of Physics & Astronomy, Brigham Young University, Provo, Utah, United States of America.', 'United States|United States|United States|United States|United States|United States', 18, 'Algorithms|Animals|DNA Repair|ErbB Receptors|Kinetics|Mice|Models, Biological|Research Design|Signal Transduction|Whole-Body Irradiation', 'Journal Article', '10.1371/journal.pcbi.1005227', '27923060', 'We explore the relationship among experimental design, parameter estimation, and systematic error in sloppy models. We show that the approximate nature of mathematical models poses challenges for experimental design in sloppy models. In many models of complex biological processes it is unknown what are the relevant physical mechanisms that must be included to explain system behaviors. As a consequence, models are often overly complex, with many practically unidentifiable parameters. Furthermore, which mechanisms are relevant/irrelevant vary among experiments. By selecting complementary experiments, experimental design may inadvertently make details that were ommitted from the model become relevant. When this occurs, the model will have a large systematic error and fail to give a good fit to the data. We use a simple hyper-model of model error to quantify a models discrepancy and apply it to two models of complex biological processes (EGFR signaling and DNA repair) with optimally selected experiments. We find that although parameters may be accurately estimated, the discrepancy in the model renders it less predictive than it was in the sloppy regime where systematic error is small. We introduce the concept of a sloppy system-a sequence of models of increasing complexity that become sloppy in the limit of microscopic accuracy. We explore the limits of accurate parameter estimation in sloppy systems and argue that identifying underlying mechanisms controlling system behavior is better approached by considering a hierarchy of models of varying detail rather than focusing on parameter estimation in a single model.'),
(88137, 2016, 'plos', 'The Kinematics of Plant Nutation Reveals a Simple Relation between Curvature and the Orientation of Differential Growth', 'Bastien, Renaud|Meroz, Yasmine', 'Department of Collective Behaviour, Max Planck Institute for Ornithology and Department of Biology, University of Konstanz, Konstanz, Germany.|John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, United States of America.', 'Germany|United States', 15, 'Biomechanical Phenomena|Computational Biology|Models, Biological|Movement|Plant Development', 'Journal Article', '10.1371/journal.pcbi.1005238', '27923062', 'Nutation is an oscillatory movement that plants display during their development. Despite its ubiquity among plants movements, the relation between the observed movement and the underlying biological mechanisms remains unclear. Here we show that the kinematics of the full organ in 3D give a simple picture of plant nutation, where the orientation of the curvature along the main axis of the organ aligns with the direction of maximal differential growth. Within this framework we reexamine the validity of widely used experimental measurements of the apical tip as markers of growth dynamics. We show that though this relation is correct under certain conditions, it does not generally hold, and is not sufficient to uncover the specific role of each mechanism. As an example we re-interpret previously measured experimental observations using our model.'),
(88138, 2016, 'plos', 'Control of Gene Expression by RNA Binding Protein Action on Alternative Translation Initiation Sites', 'Re, Angela|Waldron, Levi|Quattrone, Alessandro', 'Laboratory of Translational Genomics, Centre for Integrative Biology, University of Trento, Polo Scientifico e Tecnologico Fabio Ferrari, Trento, Italy.|City University of New York Graduate School of Public Health and Health Policy, New York, New York, United States of America.|Laboratory of Translational Genomics, Centre for Integrative Biology, University of Trento, Polo Scientifico e Tecnologico Fabio Ferrari, Trento, Italy.', 'Italy|United States|Italy', 0, '5 Untranslated Regions|Binding Sites|Computational Biology|Gene Expression Regulation|Humans|Models, Genetic|RNA-Binding Proteins|Sequence Analysis, RNA', 'Journal Article', '10.1371/journal.pcbi.1005198', '27923063', 'Transcript levels do not faithfully predict protein levels, due to post-transcriptional regulation of gene expression mediated by RNA binding proteins (RBPs) and non-coding RNAs. We developed a multivariate linear regression model integrating RBP levels and predicted RBP-mRNA regulatory interactions from matched transcript and protein datasets. RBPs significantly improved the accuracy in predicting protein abundance of a portion of the total modeled mRNAs in three panels of tissues and cells and for different methods employed in the detection of mRNA and protein. The presence of upstream translation initiation sites (uTISs) at the mRNA 5 untranslated regions was strongly associated with improvement in predictive accuracy. On the basis of these observations, we propose that the recently discovered widespread uTISs in the human genome can be a previously unappreciated substrate of translational control mediated by RBPs.'),
(88139, 2016, 'plos', 'Sparse Regression Based Structure Learning of Stochastic Reaction Networks from Single Cell Snapshot Time Series', 'Klimovskaia, Anna|Ganscha, Stefan|Claassen, Manfred', 'Institute for Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.|Institute for Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.|Institute for Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.', 'Switzerland|Switzerland|Switzerland', 0, 'Apoptosis|Computational Biology|Metabolic Networks and Pathways|Models, Biological|Regression Analysis|Signal Transduction|Single-Cell Analysis|Stochastic Processes|TNF-Related Apoptosis-Inducing Ligand', 'Journal Article', '10.1371/journal.pcbi.1005234', '27923064', 'Stochastic chemical reaction networks constitute a model class to quantitatively describe dynamics and cell-to-cell variability in biological systems. The topology of these networks typically is only partially characterized due to experimental limitations. Current approaches for refining network topology are based on the explicit enumeration of alternative topologies and are therefore restricted to small problem instances with almost complete knowledge. We propose the reactionet lasso, a computational procedure that derives a stepwise sparse regression approach on the basis of the Chemical Master Equation, enabling large-scale structure learning for reaction networks by implicitly accounting for billions of topology variants. We have assessed the structure learning capabilities of the reactionet lasso on synthetic data for the complete TRAIL induced apoptosis signaling cascade comprising 70 reactions. We find that the reactionet lasso is able to efficiently recover the structure of these reaction systems, ab initio, with high sensitivity and specificity. With only < 1% false discoveries, the reactionet lasso is able to recover 45% of all true reactions ab initio among > 6000 possible reactions and over 102000 network topologies. In conjunction with information rich single cell technologies such as single cell RNA sequencing or mass cytometry, the reactionet lasso will enable large-scale structure learning, particularly in areas with partial network structure knowledge, such as cancer biology, and thereby enable the detection of pathological alterations of reaction networks. We provide software to allow for wide applicability of the reactionet lasso.'),
(88140, 2016, 'plos', 'Scaling Properties of Dimensionality Reduction for Neural Populations and Network Models', 'Williamson, Ryan C|Cowley, Benjamin R|Litwin-Kumar, Ashok|Doiron, Brent|Kohn, Adam|Smith, Matthew A|Yu, Byron M', 'Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.|Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.|Center for Theoretical Neuroscience, Columbia University, New York City, New York, United States of America.|Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.|Dominick Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, New York, United States of America.|Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.|Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Action Potentials|Animals|Computational Biology|Macaca|Male|Models, Neurological|Nerve Net|Visual Cortex', 'Journal Article', '10.1371/journal.pcbi.1005141', '27926936', 'Recent studies have applied dimensionality reduction methods to understand how the multi-dimensional structure of neural population activity gives rise to brain function. It is unclear, however, how the results obtained from dimensionality reduction generalize to recordings with larger numbers of neurons and trials or how these results relate to the underlying network structure. We address these questions by applying factor analysis to recordings in the visual cortex of non-human primates and to spiking network models that self-generate irregular activity through a balance of excitation and inhibition. We compared the scaling trends of two key outputs of dimensionality reduction-shared dimensionality and percent shared variance-with neuron and trial count. We found that the scaling properties of networks with non-clustered and clustered connectivity differed, and that the in vivo recordings were more consistent with the clustered network. Furthermore, recordings from tens of neurons were sufficient to identify the dominant modes of shared variability that generalize to larger portions of the network. These findings can help guide the interpretation of dimensionality reduction outputs in regimes of limited neuron and trial sampling and help relate these outputs to the underlying network structure.'),
(88141, 2016, 'plos', 'Dopamine Neurons Change the Type of Excitability in Response to Stimuli', 'Morozova, Ekaterina O|Zakharov, Denis|Gutkin, Boris S|Lapish, Christopher C|Kuznetsov, Alexey', 'Department of Physics, Indiana University, Bloomington, Indiana, United States of America.|Institute of Applied Physics, Nizhny Novgorod, Russia.|Group of Neural Theory, INSERM U960 LNC, IEC, Ecole Normale Superieure PSL University, Paris.|Addiction Neuroscience Program, Indiana University - Purdue University, Indianapolis, Indiana, United States of America.|Department of Mathematical sciences, Indiana University - Purdue University, Indianapolis, Indiana, United States of America.', 'India|-|-|India|India', 0, 'Calcium|Computational Biology|Dopamine|Dopaminergic Neurons|Models, Neurological|N-Methylaspartate|Receptors, N-Methyl-D-Aspartate|Sodium|gamma-Aminobutyric Acid', 'Journal Article', '10.1371/journal.pcbi.1005233', '27930673', 'The dynamics of neuronal excitability determine the neurons response to stimuli, its synchronization and resonance properties and, ultimately, the computations it performs in the brain. We investigated the dynamical mechanisms underlying the excitability type of dopamine (DA) neurons, using a conductance-based biophysical model, and its regulation by intrinsic and synaptic currents. Calibrating the model to reproduce low frequency tonic firing results in N-methyl-D-aspartate (NMDA) excitation balanced by xcexb3-Aminobutyric acid (GABA)-mediated inhibition and leads to type I excitable behavior characterized by a continuous decrease in firing frequency in response to hyperpolarizing currents. Furthermore, we analyzed how excitability type of the DA neuron model is influenced by changes in the intrinsic current composition. A subthreshold sodium current is necessary for a continuous frequency decrease during application of a negative current, and the low-frequency balanced state during simultaneous activation of NMDA and GABA receptors. Blocking this current switches the neuron to type II characterized by the abrupt onset of repetitive firing. Enhancing the anomalous rectifier Ih current also switches the excitability to type II. Key characteristics of synaptic conductances that may be observed in vivo also change the type of excitability: a depolarized xcexb3-Aminobutyric acid receptor (GABAR) reversal potential or co-activation of xcexb1-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPARs) leads to an abrupt frequency drop to zero, which is typical for type II excitability. Coactivation of N-methyl-D-aspartate receptors (NMDARs) together with AMPARs and GABARs shifts the type I/II boundary toward more hyperpolarized GABAR reversal potentials. To better understand how altering each of the aforementioned currents leads to changes in excitability profile of DA neuron, we provide a thorough dynamical analysis. Collectively, these results imply that type I excitability in dopamine neurons might be important for low firing rates and fine-tuning basal dopamine levels, while switching excitability to type II during NMDAR and AMPAR activation may facilitate a transient increase in dopamine concentration, as type II neurons are more amenable to synchronization by mutual excitation.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(88142, 2016, 'plos', 'Spatiotemporal Analysis of the 2014 Ebola Epidemic in West Africa', 'Backer, Jantien A|Wallinga, Jacco', 'Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, The Netherlands.|Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, The Netherlands.', 'Netherlands|Netherlands', 18, 'Africa, Western|Epidemics|Hemorrhagic Fever, Ebola|Humans|Incidence|Models, Biological|Spatio-Temporal Analysis', 'Journal Article', '10.1371/journal.pcbi.1005210', '27930675', 'In 2014-2016, Guinea, Sierra Leone and Liberia in West Africa experienced the largest and longest Ebola epidemic since the discovery of the virus in 1976. During the epidemic, incidence data were collected and published at increasing resolution. To monitor the epidemic as it spread within and between districts, we develop an analysis method that exploits the full spatiotemporal resolution of the data by combining a local model for time-varying effective reproduction numbers with a gravity-type model for spatial dispersion of the infection. We test this method in simulations and apply it to the weekly incidences of confirmed and probable cases per district up to June 2015, as reported by the World Health Organization. Our results indicate that, of the newly infected cases, only a small percentage, between 4% and 10%, migrates to another district, and a minority of these migrants, between 0% and 23%, leave their country. The epidemics in the three countries are found to be similar in estimated effective reproduction numbers, and in the probability of importing infection into a district. The countries might have played different roles in cross-border transmissions, although a sensitivity analysis suggests that this could also be related to underreporting. The spatiotemporal analysis method can exploit available longitudinal incidence data at different geographical locations to monitor local epidemics, determine the extent of spatial spread, reveal the contribution of local and imported cases, and identify sources of introductions in uninfected areas. With good quality data on incidence, this data-driven method can help to effectively control emerging infections.'),
(88143, 2016, 'plos', 'Stochastic Simulation Service: Bridging the Gap between the Computational Expert and the Biologist', 'Drawert, Brian|Hellander, Andreas|Bales, Ben|Banerjee, Debjani|Bellesia, Giovanni|Daigle, Bernie J|Douglas, Geoffrey|Gu, Mengyuan|Gupta, Anand|Hellander, Stefan|Horuk, Chris|Nath, Dibyendu|Takkar, Aviral|Wu, Sheng|Lötstedt, Per|Krintz, Chandra|Petzold, Linda R', 'Department of Computer Science, University of California, Santa Barbara, Santa Barbara, California, United States of America.|Department of Information Technology, Division of Scientific Computing, Uppsala University, Uppsala, Sweden.|Department of Mechanical Engineering, University of California, Santa Barbara, Santa Barbara, California, United States of America.|Department of Computer Science, University of California, Santa Barbara, Santa Barbara, California, United States of America.|Department of Computer Science, University of California, Santa Barbara, Santa Barbara, California, United States of America.|Departments of Biological Sciences and Computer Science, The University of Memphis, Memphis, Tennessee, United States of America.|Department of Computer Science, University of California, Santa Barbara, Santa Barbara, California, United States of America.|Department of Computer Science, University of California, Santa Barbara, Santa Barbara, California, United States of America.|Department of Computer Science, University of California, Santa Barbara, Santa Barbara, California, United States of America.|Department of Computer Science, University of California, Santa Barbara, Santa Barbara, California, United States of America.|Department of Computer Science, University of California, Santa Barbara, Santa Barbara, California, United States of America.|Department of Computer Science, University of California, Santa Barbara, Santa Barbara, California, United States of America.|Department of Computer Science, University of California, Santa Barbara, Santa Barbara, California, United States of America.|Department of Computer Science, University of California, Santa Barbara, Santa Barbara, California, United States of America.|Department of Information Technology, Division of Scientific Computing, Uppsala University, Uppsala, Sweden.|Department of Computer Science, University of California, Santa Barbara, Santa Barbara, California, United States of America.|Department of Computer Science, University of California, Santa Barbara, Santa Barbara, California, United States of America.', 'United States|Sweden|United States|United States|United States|United States|United States|United St', 24, 'Computational Biology|Computer Simulation|Software|Stochastic Processes', 'Journal Article', '10.1371/journal.pcbi.1005220', '27930676', 'We present StochSS: Stochastic Simulation as a Service, an integrated development environment for modeling and simulation of both deterministic and discrete stochastic biochemical systems in up to three dimensions. An easy to use graphical user interface enables researchers to quickly develop and simulate a biological model on a desktop or laptop, which can then be expanded to incorporate increasing levels of complexity. StochSS features state-of-the-art simulation engines. As the demand for computational power increases, StochSS can seamlessly scale computing resources in the cloud. In addition, StochSS can be deployed as a multi-user software environment where collaborators share computational resources and exchange models via a public model repository. We demonstrate the capabilities and ease of use of StochSS with an example of model development and simulation at increasing levels of complexity.'),
(88144, 2016, 'plos', 'Beyond the Hypercube: Evolutionary Accessibility of Fitness Landscapes with Realistic Mutational Networks', 'Zagorski, Marcin|Burda, Zdzislaw|Waclaw, Bartlomiej', 'Institute of Science and Technology (IST) Austria, Klosterneuburg, Austria.|Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, Krakow, Poland.|School of Physics and Astronomy, The University of Edinburgh, Edinburgh, United Kingdom.', 'Austria|Poland|United Kingdom', 0, 'Computational Biology|Evolution, Molecular|Genetic Fitness|Genotype|Models, Genetic|Mutation', 'Journal Article', '10.1371/journal.pcbi.1005218', '27935934', 'Evolutionary pathways describe trajectories of biological evolution in the space of different variants of organisms (genotypes). The probability of existence and the number of evolutionary pathways that lead from a given genotype to a better-adapted genotype are important measures of accessibility of local fitness optima and the reproducibility of evolution. Both quantities have been studied in simple mathematical models where genotypes are represented as binary sequences of two types of basic units, and the network of permitted mutations between the genotypes is a hypercube graph. However, it is unclear how these results translate to the biologically relevant case in which genotypes are represented by sequences of more than two units, for example four nucleotides (DNA) or 20 amino acids (proteins), and the mutational graph is not the hypercube. Here we investigate accessibility of the best-adapted genotype in the general case of K > 2 units. Using computer generated and experimental fitness landscapes we show that accessibility of the global fitness maximum increases with K and can be much higher than for binary sequences. The increase in accessibility comes from the increase in the number of indirect trajectories exploited by evolution for higher K. As one of the consequences, the fraction of genotypes that are accessible increases by three orders of magnitude when the number of units K increases from 2 to 16 for landscapes of size N xe2x88xbc 106 genotypes. This suggests that evolution can follow many different trajectories on such landscapes and the reconstruction of evolutionary pathways from experimental data might be an extremely difficult task.'),
(88145, 2016, 'plos', 'Stimulus-Driven Population Activity Patterns in Macaque Primary Visual Cortex', 'Cowley, Benjamin R|Smith, Matthew A|Kohn, Adam|Yu, Byron M', 'Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.|Center for Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.|Dominick Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, New York, United States of America.|Center for Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.', 'United States|United States|United States|United States', 18, 'Animals|Computational Biology|Macaca mulatta|Male|Models, Neurological|Neurons|Photic Stimulation|Principal Component Analysis|Visual Cortex', 'Journal Article', '10.1371/journal.pcbi.1005185', '27935935', 'Dimensionality reduction has been applied in various brain areas to study the activity of populations of neurons. To interpret the outputs of dimensionality reduction, it is important to first understand its outputs for brain areas for which the relationship between the stimulus and neural response is well characterized. Here, we applied principal component analysis (PCA) to trial-averaged neural responses in macaque primary visual cortex (V1) to study two fundamental, population-level questions. First, we characterized how neural complexity relates to stimulus complexity, where complexity is measured using relative comparisons of dimensionality. Second, we assessed the extent to which responses to different stimuli occupy similar dimensions of the population activity space using a novel statistical method. For comparison, we performed the same dimensionality reduction analyses on the activity of a recently-proposed V1 receptive field model and a deep convolutional neural network. Our results show that the dimensionality of the population response changes systematically with alterations in the properties and complexity of the visual stimulus.'),
(88146, 2016, 'plos', 'Correction: Improved Metabolic Models for E coli and Mycoplasma genitalium from GlobalFit, an Algorithm That Simultaneously Matches Growth and Non-Growth Data Sets', 'PLOS Computational Biology Staff', 'Center for Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.', 'United States', 0, '-', 'Published Erratum', '10.1371/journal.pcbi.1005264', '27935945', '[This corrects the article DOI: 10.1371/journal.pcbi.1005036.].'),
(88147, 2016, 'plos', 'A Stochastic Model of the Yeast Cell Cycle Reveals Roles for Feedback Regulation in Limiting Cellular Variability', 'Barik, Debashis|Ball, David A|Peccoud, Jean|Tyson, John J', 'School of Chemistry, University of Hyderabad, Hyderabad, Telangana, India.|Virginia Bioinformatics Institute, Virginia Polytechnic Institute & State University, Blacksburg, Virginia, United States of America.|Virginia Bioinformatics Institute, Virginia Polytechnic Institute & State University, Blacksburg, Virginia, United States of America.|Virginia Bioinformatics Institute, Virginia Polytechnic Institute & State University, Blacksburg, Virginia, United States of America.', 'India|United States|United States|United States', 19, 'Cell Cycle|Cyclin-Dependent Kinases|Feedback, Physiological|Models, Biological|Mutation|Phenotype|Saccharomyces cerevisiae|Stochastic Processes', 'Journal Article', '10.1371/journal.pcbi.1005230', '27935947', 'The cell division cycle of eukaryotes is governed by a complex network of cyclin-dependent protein kinases (CDKs) and auxiliary proteins that govern CDK activities. The control system must function reliably in the context of molecular noise that is inevitable in tiny yeast cells, because mistakes in sequencing cell cycle events are detrimental or fatal to the cell or its progeny. To assess the effects of noise on cell cycle progression requires not only extensive, quantitative, experimental measurements of cellular heterogeneity but also comprehensive, accurate, mathematical models of stochastic fluctuations in the CDK control system. In this paper we provide a stochastic model of the budding yeast cell cycle that accurately accounts for the variable phenotypes of wild-type cells and more than 20 mutant yeast strains simulated in different growth conditions. We specifically tested the role of feedback regulations mediated by G1- and SG2M-phase cyclins to minimize the noise in cell cycle progression. Details of the model are informed and tested by quantitative measurements (by fluorescence in situ hybridization) of the joint distributions of mRNA populations in yeast cells. We use the model to predict the phenotypes of ~30 mutant yeast strains that have not yet been characterized experimentally.'),
(88148, 2016, 'plos', 'Alpha Helices Are More Robust to Mutations than Beta Strands', 'Abrusán, György|Marsh, Joseph A', 'MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road, Edinburgh EH4 2XU, United Kingdom.|MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road, Edinburgh EH4 2XU, United Kingdom.', 'United Kingdom|United Kingdom', 5, 'Amino Acid Sequence|Computational Biology|Humans|Models, Molecular|Mutation|Protein Conformation, alpha-Helical|Protein Conformation, beta-Strand|Sequence Analysis, Protein', 'Journal Article', '10.1371/journal.pcbi.1005242', '27935949', 'The rapidly increasing amount of data on human genetic variation has resulted in a growing demand to identify pathogenic mutations computationally, as their experimental validation is currently beyond reach. Here we show that alpha helices and beta strands differ significantly in their ability to tolerate mutations: helices can accumulate more mutations than strands without change, due to the higher numbers of inter-residue contacts in helices. This results in two patterns: a) the same number of mutations causes less structural change in helices than in strands, b) helices diverge more rapidly in sequence than strands within the same domains. Additionally, both helices and strands are significantly more robust than coils. Based on this observation we show that human missense mutations that change secondary structure are more likely to be pathogenic than those that do not. Moreover, inclusion of predicted secondary structure changes shows significant utility for improving upon state-of-the-art pathogenicity predictions.'),
(88149, 2016, 'plos', 'Utility and Limitations of Using Gene Expression Data to Identify Functional Associations', 'Uygun, Sahra|Peng, Cheng|Lehti-Shiu, Melissa D|Last, Robert L|Shiu, Shin-Han', 'Genetics Program, Michigan State University, East Lansing, Michigan, United States of America.|Department of Plant Biology, Michigan State University, East Lansing, Michigan, United States of America.|Department of Plant Biology, Michigan State University, East Lansing, Michigan, United States of America.|Department of Plant Biology, Michigan State University, East Lansing, Michigan, United States of America.|Genetics Program, Michigan State University, East Lansing, Michigan, United States of America.', 'United States|United States|United States|United States|United States', 17, 'Arabidopsis Proteins|Computational Biology|Databases, Genetic|Gene Expression Profiling|Molecular Sequence Annotation|Oligonucleotide Array Sequence Analysis|Proteins|Stress, Physiological', 'Journal Article', '10.1371/journal.pcbi.1005244', '27935950', 'Gene co-expression has been widely used to hypothesize gene function through guilt-by association. However, it is not clear to what degree co-expression is informative, whether it can be applied to genes involved in different biological processes, and how the type of dataset impacts inferences about gene functions. Here our goal is to assess the utility and limitations of using co-expression as a criterion to recover functional associations between genes. By determining the percentage of gene pairs in a metabolic pathway with significant expression correlation, we found that many genes in the same pathway do not have similar transcript profiles and the choice of dataset, annotation quality, gene function, expression similarity measure, and clustering approach significantly impacts the ability to recover functional associations between genes using Arabidopsis thaliana as an example. Some datasets are more informative in capturing coordinated expression profiles and larger data sets are not always better. In addition, to recover the maximum number of known pathways and identify candidate genes with similar functions, it is important to explore rather exhaustively multiple dataset combinations, similarity measures, clustering algorithms and parameters. Finally, we validated the biological relevance of co-expression cluster memberships with an independent phenomics dataset and found that genes that consistently cluster with leucine degradation genes tend to have similar leucine levels in mutants. This study provides a framework for obtaining gene functional associations by maximizing the information that can be obtained from gene expression datasets.'),
(88150, 2016, 'plos', 'Synchronization and Random Triggering of Lymphatic Vessel Contractions', 'Baish, James W|Kunert, Christian|Padera, Timothy P|Munn, Lance L', 'Department of Biomedical Engineering, Bucknell University, Lewisburg, Pennsylvania, United States of America.|Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America.|Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America.|Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America.', 'United States|United States|United States|United States', 0, 'Animals|Biological Transport|Calcium|Lymphatic Vessels|Mice|Models, Biological|Nitric Oxide', 'Journal Article', '10.1371/journal.pcbi.1005231', '27935958', 'The lymphatic system is responsible for transporting interstitial fluid back to the bloodstream, but unlike the cardiovascular system, lacks a centralized pump-the heart-to drive flow. Instead, each collecting lymphatic vessel can individually contract and dilate producing unidirectional flow enforced by intraluminal check valves. Due to the large number and spatial distribution of such pumps, high-level coordination would be unwieldy. This leads to the question of how each segment of lymphatic vessel responds to local signals that can contribute to the coordination of pumping on a network basis. Beginning with elementary fluid mechanics and known cellular behaviors, we show that two complementary oscillators emerge from i) mechanical stretch with calcium ion transport and ii) fluid shear stress induced nitric oxide production (NO). Using numerical simulation and linear stability analysis we show that the newly identified shear-NO oscillator shares similarities with the well-known Van der Pol oscillator, but has unique characteristics. Depending on the operating conditions, the shear-NO process may i) be inherently stable, ii) oscillate spontaneously in response to random disturbances or iii) synchronize with weak periodic stimuli. When the complementary shear-driven and stretch-driven oscillators interact, either may dominate, producing a rich family of behaviors similar to those observed in vivo.'),
(88151, 2016, 'plos', 'Feedback Loops of the Mammalian Circadian Clock Constitute Repressilator', 'Pett, J Patrick|Korenxc4x8dixc4x8d, Anja|Wesener, Felix|Kramer, Achim|Herzel, Hanspeter', 'Institute for Theoretical Biology, Humboldt-Universitxc3xa4t zu Berlin, Berlin, Germany.|Center for Functional Genomics and Biochips, Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia.|Institute for Theoretical Biology, Humboldt-Universitxc3xa4t zu Berlin, Berlin, Germany.|Laboratory of Chronobiology, Charitxc3xa9-Universitxc3xa4tsmedizin Berlin, Berlin, Germany.|Institute for Theoretical Biology, Charitxc3xa9-Universitxc3xa4tsmedizin Berlin, Berlin, Germany.', 'Germany|Slovenia|Germany|Germany|Germany', 28, 'Animals|Circadian Clocks|Computational Biology|Feedback, Physiological|Gene Regulatory Networks|Mice|Models, Genetic', 'Journal Article', '10.1371/journal.pcbi.1005266', '27942033', 'Mammals evolved an endogenous timing system to coordinate their physiology and behaviour to the 24h period of the solar day. While it is well accepted that circadian rhythms are generated by intracellular transcriptional feedback loops, it is still debated which network motifs are necessary and sufficient for generating self-sustained oscillations. Here, we systematically explore a data-based circadian oscillator model with multiple negative and positive feedback loops and identify a series of three subsequent inhibitions known as repressilator as a core element of the mammalian circadian oscillator. The central role of the repressilator motif is consistent with time-resolved ChIP-seq experiments of circadian clock transcription factors and loss of rhythmicity in core clock gene knockouts.'),
(88152, 2016, 'plos', 'Precision of Readout at the hunchback Gene: Analyzing Short Transcription Time Traces in Living Fly Embryos', 'Desponds, Jonathan|Tran, Huy|Ferraro, Teresa|Lucas, Tanguy|Perez Romero, Carmina|Guillou, Aurelien|Fradin, Cecile|Coppey, Mathieu|Dostatni, Nathalie|Walczak, Aleksandra M', 'Ecole Normale Superieure, PSL Research University, Paris, France.|Ecole Normale Superieure, PSL Research University, Paris, France.|Ecole Normale Superieure, PSL Research University, Paris, France.|Sorbonne Universités, UPMC Univ Paris 06, Paris, France.|McMaster University, Hamilton, Ontario, Canada.|Sorbonne Universités, UPMC Univ Paris 06, Paris, France.|McMaster University, Hamilton, Ontario, Canada.|Sorbonne Universités, UPMC Univ Paris 06, Paris, France.|Sorbonne Universités, UPMC Univ Paris 06, Paris, France.|Ecole Normale Superieure, PSL Research University, Paris, France.', 'France|France|France|France|Canada|France|Canada|France|France|France', 0, 'Animals|Cell Cycle|Computational Biology|DNA-Binding Proteins|Drosophila|Drosophila Proteins|Embryo, Nonmammalian|Embryonic Development|Models, Genetic|Time Factors|Transcription Factors|Transcription, Genetic', 'Journal Article', '10.1371/journal.pcbi.1005256', '27942043', 'The simultaneous expression of the hunchback gene in the numerous nuclei of the developing fly embryo gives us a unique opportunity to study how transcription is regulated in living organisms. A recently developed MS2-MCP technique for imaging nascent messenger RNA in living Drosophila embryos allows us to quantify the dynamics of the developmental transcription process. The initial measurement of the morphogens by the hunchback promoter takes place during very short cell cycles, not only giving each nucleus little time for a precise readout, but also resulting in short time traces of transcription. Additionally, the relationship between the measured signal and the promoter state depends on the molecular design of the reporting probe. We develop an analysis approach based on tailor made autocorrelation functions that overcomes the short trace problems and quantifies the dynamics of transcription initiation. Based on live imaging data, we identify signatures of bursty transcription initiation from the hunchback promoter. We show that the precision of the expression of the hunchback gene to measure its position along the anterior-posterior axis is low both at the boundary and in the anterior even at cycle 13, suggesting additional post-transcriptional averaging mechanisms to provide the precision observed in fixed embryos.'),
(88153, 2016, 'plos', 'Numerical Approach to Spatial Deterministic-Stochastic Models Arising in Cell Biology', 'Schaff, James C|Gao, Fei|Li, Ye|Novak, Igor L|Slepchenko, Boris M', 'Richard D. Berlin Center for Cell Analysis and Modeling, Department of Cell Biology, University of Connecticut Health Center, Farmington, Connecticut, United States of America.|Richard D. Berlin Center for Cell Analysis and Modeling, Department of Cell Biology, University of Connecticut Health Center, Farmington, Connecticut, United States of America.|Richard D. Berlin Center for Cell Analysis and Modeling, Department of Cell Biology, University of Connecticut Health Center, Farmington, Connecticut, United States of America.|Richard D. Berlin Center for Cell Analysis and Modeling, Department of Cell Biology, University of Connecticut Health Center, Farmington, Connecticut, United States of America.|Richard D. Berlin Center for Cell Analysis and Modeling, Department of Cell Biology, University of Connecticut Health Center, Farmington, Connecticut, United States of America.', 'United States|United States|United States|United States|United States', 12, 'Algorithms|Calcium|Cell Biology|Cell Polarity|Computational Biology|Computer Simulation|Models, Biological|Reproducibility of Results|Stochastic Processes', 'Journal Article', '10.1371/journal.pcbi.1005236', '27959915', 'Hybrid deterministic-stochastic methods provide an efficient alternative to a fully stochastic treatment of models which include components with disparate levels of stochasticity. However, general-purpose hybrid solvers for spatially resolved simulations of reaction-diffusion systems are not widely available. Here we describe fundamentals of a general-purpose spatial hybrid method. The method generates realizations of a spatially inhomogeneous hybrid system by appropriately integrating capabilities of a deterministic partial differential equation solver with a popular particle-based stochastic simulator, Smoldyn. Rigorous validation of the algorithm is detailed, using a simple model of calcium sparks as a testbed. The solver is then applied to a deterministic-stochastic model of spontaneous emergence of cell polarity. The approach is general enough to be implemented within biologist-friendly software frameworks such as Virtual Cell.'),
(88154, 2016, 'plos', 'Temporal Dynamics and Developmental Maturation of Salience, Default and Central-Executive Network Interactions Revealed by Variational Bayes Hidden Markov Modeling', 'Ryali, Srikanth|Supekar, Kaustubh|Chen, Tianwen|Kochalka, John|Cai, Weidong|Nicholas, Jonathan|Padmanabhan, Aarthi|Menon, Vinod', 'Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, United States of America.|Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, United States of America.|Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, United States of America.|Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, United States of America.|Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, United States of America.|Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, United States of America.|Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, United States of America.|Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, United States of America.', 'United States|United States|United States|United States|United States|United States|United States|Un', 30, 'Adult|Bayes Theorem|Brain|Child|Cognition|Computational Biology|Computer Simulation|Female|Humans|Magnetic Resonance Imaging|Male|Markov Chains|Models, Neurological|Time Factors|Young Adult', 'Journal Article', '10.1371/journal.pcbi.1005138', '27959921', 'Little is currently known about dynamic brain networks involved in high-level cognition and their ontological basis. Here we develop a novel Variational Bayesian Hidden Markov Model (VB-HMM) to investigate dynamic temporal properties of interactions between salience (SN), default mode (DMN), and central executive (CEN) networks-three brain systems that play a critical role in human cognition. In contrast to conventional models, VB-HMM revealed multiple short-lived states characterized by rapid switching and transient connectivity between SN, CEN, and DMN. Furthermore, the three static networks occurred in a segregated state only intermittently. Findings were replicated in two adult cohorts from the Human Connectome Project. VB-HMM further revealed immature dynamic interactions between SN, CEN, and DMN in children, characterized by higher mean lifetimes in individual states, reduced switching probability between states and less differentiated connectivity across states. Our computational techniques provide new insights into human brain network dynamics and its maturation with development.'),
(88155, 2016, 'plos', 'Impact of Lipid Composition and Receptor Conformation on the Spatio-temporal Organization of xcexbc-Opioid Receptors in a Multi-component Plasma Membrane Model', 'Marino, Kristen A|Prada-Gracia, Diego|Provasi, Davide|Filizola, Marta', 'Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, United States of America.|Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, United States of America.|Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, United States of America.|Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, United States of America.', 'United States|United States|United States|United States', 30, 'Animals|Cell Membrane|Lipid Metabolism|Lipids|Mice|Models, Molecular|Receptors, Opioid, mu', 'Journal Article', '10.1371/journal.pcbi.1005240', '27959924', 'The lipid composition of cell membranes has increasingly been recognized as playing an important role in the function of various membrane proteins, including G Protein-Coupled Receptors (GPCRs). For instance, experimental and computational evidence has pointed to lipids influencing receptor oligomerization directly, by physically interacting with the receptor, and/or indirectly, by altering the bulk properties of the membrane. While the exact role of oligomerization in the function of class A GPCRs such as the xcexbc-opioid receptor (MOR) is still unclear, insight as to how these receptors oligomerize and the relevance of the lipid environment to this phenomenon is crucial to our understanding of receptor function. To examine the effect of lipids and different MOR conformations on receptor oligomerization we carried out extensive coarse-grained molecular dynamics simulations of crystal structures of inactive and/or activated MOR embedded in an idealized mammalian plasma membrane composed of 63 lipid types asymmetrically distributed across the two leaflets. The results of these simulations point, for the first time, to specific direct and indirect effects of the lipids, as well as the receptor conformation, on the spatio-temporal organization of MOR in the plasma membrane. While sphingomyelin-rich, high-order lipid regions near certain transmembrane (TM) helices of MOR induce an effective long-range attractive force on individual protomers, both long-range lipid order and interface formation are found to be conformation dependent, with a larger number of different interfaces formed by inactive MOR compared to active MOR.'),
(88156, 2016, 'plos', 'Encoding in Balanced Networks: Revisiting Spike Patterns and Chaos in Stimulus-Driven Systems', 'Lajoie, Guillaume|Lin, Kevin K|Thivierge, Jean-Philippe|Shea-Brown, Eric', 'University of Washington Institute for Neuroengineering, University of Washington, Seattle, Washington, United States of America.|School of Mathematics, University of Arizona, Tucson, Arizona, United States of America.|School of Psychology, University of Ottawa, Ottawa, Ontario, Canada.|University of Washington Institute for Neuroengineering, University of Washington, Seattle, Washington, United States of America.', 'United States|United States|Canada|United States', 0, 'Action Potentials|Computational Biology|Models, Neurological|Neurons|Nonlinear Dynamics', 'Journal Article', '10.1371/journal.pcbi.1005258', '27973557', 'Highly connected recurrent neural networks often produce chaotic dynamics, meaning their precise activity is sensitive to small perturbations. What are the consequences of chaos for how such networks encode streams of temporal stimuli? On the one hand, chaos is a strong source of randomness, suggesting that small changes in stimuli will be obscured by intrinsically generated variability. On the other hand, recent work shows that the type of chaos that occurs in spiking networks can have a surprisingly low-dimensional structure, suggesting that there may be room for fine stimulus features to be precisely resolved. Here we show that strongly chaotic networks produce patterned spikes that reliably encode time-dependent stimuli: using a decoder sensitive to spike times on timescales of 10s of ms, one can easily distinguish responses to very similar inputs. Moreover, recurrence serves to distribute signals throughout chaotic networks so that small groups of cells can encode substantial information about signals arriving elsewhere. A conclusion is that the presence of strong chaos in recurrent networks need not exclude precise encoding of temporal stimuli via spike patterns.'),
(88157, 2016, 'plos', 'When Optimal Feedback Control Is Not Enough: Feedforward Strategies Are Required for Optimal Control with Active Sensing', 'Yeo, Sang-Hoon|Franklin, David W|Wolpert, Daniel M', 'Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, United Kingdom.|Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, United Kingdom.|Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, United Kingdom.', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Adult|Biomechanical Phenomena|Computational Biology|Feedback, Sensory|Female|Hand|Humans|Male|Motor Activity|Psychomotor Performance|Young Adult', 'Journal Article', '10.1371/journal.pcbi.1005190', '27973566', 'Movement planning is thought to be primarily determined by motor costs such as inaccuracy and effort. Solving for the optimal plan that minimizes these costs typically leads to specifying a time-varying feedback controller which both generates the movement and can optimally correct for errors that arise within a movement. However, the quality of the sensory feedback during a movement can depend substantially on the generated movement. We show that by incorporating such state-dependent sensory feedback, the optimal solution incorporates active sensing and is no longer a pure feedback process but includes a significant feedforward component. To examine whether people take into account such state-dependency in sensory feedback we asked people to make movements in which we controlled the reliability of sensory feedback. We made the visibility of the hand state-dependent, such that the visibility was proportional to the component of hand velocity in a particular direction. Subjects gradually adapted to such a sensory perturbation by making curved hand movements. In particular, they appeared to control the late visibility of the movement matching predictions of the optimal controller with state-dependent sensory noise. Our results show that trajectory planning is not only sensitive to motor costs but takes sensory costs into account and argues for optimal control of movement in which feedforward commands can play a significant role.'),
(88158, 2016, 'plos', 'Evolution at Sutures and Centers: Recombination Can Aid Adaptation of Spatially Structured Populations on Rugged Fitness Landscapes', 'Cooper, Jacob D|Kerr, Benjamin', 'Department of Biology, University of Washington, Seattle, Washington, United States of America.|Department of Biology, University of Washington, Seattle, Washington, United States of America.', 'United States|United States', 0, 'Computational Biology|Epistasis, Genetic|Evolution, Molecular|Genetic Fitness|Genotype|Models, Genetic|Recombination, Genetic|Reproduction', 'Journal Article', '10.1371/journal.pcbi.1005247', '27973606', 'Epistatic interactions among genes can give rise to rugged fitness landscapes, in which multiple peaks of high-fitness allele combinations are separated by valleys of low-fitness genotypes. How populations traverse rugged fitness landscapes is a long-standing question in evolutionary biology. Sexual reproduction may affect how a population moves within a rugged fitness landscape. Sex may generate new high-fitness genotypes by recombination, but it may also destroy high-fitness genotypes by shuffling the genes of a fit parent with a genetically distinct mate, creating low-fitness offspring. Either of these opposing aspects of sex require genotypic diversity in the population. Spatially structured populations may harbor more diversity than well-mixed populations, potentially amplifying both positive and negative effects of sex. On the other hand, spatial structure leads to clumping in which mating is more likely to occur between like types, diminishing the effects of recombination. In this study, we use computer simulations to investigate the combined effects of recombination and spatial structure on adaptation in rugged fitness landscapes. We find that spatially restricted mating and offspring dispersal may allow multiple genotypes inhabiting suboptimal peaks to coexist, and recombination at the sutures between the clusters of these genotypes can create genetically novel offspring. Sometimes such an offspring genotype inhabits a new peak on the fitness landscape. In such a case, spatially restricted mating allows this fledgling subpopulation to avoid recombination with distinct genotypes, as mates are more likely to be the same genotype. Such population centers can allow nascent peaks to establish despite recombination. Spatial structure may therefore allow an evolving population to enjoy the creative side of sexual recombination while avoiding its destructive side.'),
(88159, 2016, 'plos', 'Hierarchical Post-transcriptional Regulation of Colicin E2 Expression in Escherichia coli', 'Lechner, Matthias|Schwarz, Mathias|Opitz, Madeleine|Frey, Erwin', 'Arnold-Sommerfeld-Center for Theoretical Physics and Center for NanoScience, Ludwig-Maximilians-Universität, Munich, Germany.|Arnold-Sommerfeld-Center for Theoretical Physics and Center for NanoScience, Ludwig-Maximilians-Universität, Munich, Germany.|Arnold-Sommerfeld-Center for Theoretical Physics and Center for NanoScience, Ludwig-Maximilians-Universität, Munich, Germany.|Arnold-Sommerfeld-Center for Theoretical Physics and Center for NanoScience, Ludwig-Maximilians-Universität, Munich, Germany.', 'Germany|Germany|Germany|Germany', 8, 'Animals|Colicins|Dogs|Escherichia coli|Escherichia coli Proteins|Gene Expression Regulation, Bacterial|RNA, Bacterial', 'Journal Article', '10.1371/journal.pcbi.1005243', '27977665', 'Post-transcriptional regulation of gene expression plays a crucial role in many bacterial pathways. In particular, the translation of mRNA can be regulated by trans-acting, small, non-coding RNAs (sRNAs) or mRNA-binding proteins, each of which has been successfully treated theoretically using two-component models. An important system that includes a combination of these modes of post-transcriptional regulation is the Colicin E2 system. DNA damage, by triggering the SOS response, leads to the heterogeneous expression of the Colicin E2 operon including the cea gene encoding the toxin colicin E2, and the cel gene that codes for the induction of cell lysis and release of colicin. Although previous studies have uncovered the systems basic regulatory interactions, its dynamical behavior is still unknown. Here, we develop a simple, yet comprehensive, mathematical model of the colicin E2 regulatory network, and study its dynamics. Its post-transcriptional regulation can be reduced to three hierarchically ordered components: the mRNA including the cel gene, the mRNA-binding protein CsrA, and an effective sRNA that regulates CsrA. We demonstrate that the stationary state of this system exhibits a pronounced threshold in the abundance of free mRNA. As post-transcriptional regulation is known to be noisy, we performed a detailed stochastic analysis, and found fluctuations to be largest at production rates close to the threshold. The magnitude of fluctuations can be tuned by the rate of production of the sRNA. To study the dynamics in response to an SOS signal, we incorporated the LexA-RecA SOS response network into our model. We found that CsrA regulation filtered out short-lived activation peaks and caused a delay in lysis gene expression for prolonged SOS signals, which is also seen in experiments. Moreover, we showed that a stochastic SOS signal creates a broad lysis time distribution. Our model thus theoretically describes Colicin E2 expression dynamics in detail and reveals the importance of the specific regulatory components for the timing of toxin release.'),
(88160, 2016, 'plos', 'Lack of Ecological and Life History Context Can Create the Illusion of Social Interactions in Dictyostelium discoideum', 'Martínez-García, Ricardo|Tarnita, Corina E', 'Department of Ecology and Evolutionary Biology, Princeton University, Princeton NJ, United States of America.|Department of Ecology and Evolutionary Biology, Princeton University, Princeton NJ, United States of America.', 'United States|United States', 0, 'Dictyostelium|Ecology|Life Cycle Stages|Models, Biological|Social Behavior', 'Journal Article', '10.1371/journal.pcbi.1005246', '27977666', 'Studies of social microbes often focus on one fitness component (reproductive success within the social complex), with little information about or attention to other stages of the life cycle or the ecological context. This can lead to paradoxical results. The life cycle of the social amoeba Dictyostelium discoideum includes a multicellular stage in which not necessarily clonal amoebae aggregate upon starvation to form a possibly chimeric (genetically heterogeneous) fruiting body made of dead stalk cells and spores. The lab-measured reproductive skew in the spores of chimeras indicates strong social antagonism that should result in low genotypic diversity, which is inconsistent with observations from nature. Two studies have suggested that this inconsistency stems from the one-dimensional assessment of fitness (spore production) and that the solution lies in tradeoffs between multiple life-history traits, e.g.: spore size versus viability, and spore-formation (via aggregation) versus staying vegetative (as non-aggregated cells). We develop an ecologically-grounded, socially-neutral model (i.e. no social interactions between genotypes) for the life cycle of social amoebae in which we theoretically explore multiple non-social life-history traits, tradeoffs and tradeoff-implementing mechanisms. We find that spore production comes at the expense of time to complete aggregation, and, depending on the experimental setup, spore size and viability. Furthermore, experimental results regarding apparent social interactions within chimeric mixes can be qualitatively recapitulated under this neutral hypothesis, without needing to invoke social interactions. This allows for simple potential resolutions to the previously paradoxical results. We conclude that the complexities of life histories, including social behavior and multicellularity, can only be understood in the appropriate multidimensional ecological context, when considering all stages of the life cycle.'),
(88161, 2016, 'plos', 'Universal or Specific? A Modeling-Based Comparison of Broad-Spectrum Influenza Vaccines against Conventional, Strain-Matched Vaccines', 'Subramanian, Rahul|Graham, Andrea L|Grenfell, Bryan T|Arinaminpathy, Nimalan', 'Department of Ecology and Evolution, University of Chicago, Chicago, Illinois, United States of America.|Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, United States of America.|Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, United States of America.|Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom.', 'United States|Jersey|Jersey|United Kingdom', 10, 'Computational Biology|Humans|Influenza A virus|Influenza Vaccines|Influenza, Human|Models, Immunological|Models, Statistical|Pandemics|Vaccination', 'Journal Article', '10.1371/journal.pcbi.1005204', '27977667', 'Despite the availability of vaccines, influenza remains a major public health challenge. A key reason is the virus capacity for immune escape: ongoing evolution allows the continual circulation of seasonal influenza, while novel influenza viruses invade the human population to cause a pandemic every few decades. Current vaccines have to be updated continually to keep up to date with this antigenic change, but emerging universal vaccines-targeting more conserved components of the influenza virus-offer the potential to act across all influenza A strains and subtypes. Influenza vaccination programmes around the world are steadily increasing in their population coverage. In future, how might intensive, routine immunization with novel vaccines compare against similar mass programmes utilizing conventional vaccines? Specifically, how might novel and conventional vaccines compare, in terms of cumulative incidence and rates of antigenic evolution of seasonal influenza? What are their potential implications for the impact of pandemic emergence? Here we present a new mathematical model, capturing both transmission dynamics and antigenic evolution of influenza in a simple framework, to explore these questions. We find that, even when matched by per-dose efficacy, universal vaccines could dampen population-level transmission over several seasons to a greater extent than conventional vaccines. Moreover, by lowering opportunities for cross-protective immunity in the population, conventional vaccines could allow the increased spread of a novel pandemic strain. Conversely, universal vaccines could mitigate both seasonal and pandemic spread. However, where it is not possible to maintain annual, intensive vaccination coverage, the duration and breadth of immunity raised by universal vaccines are critical determinants of their performance relative to conventional vaccines. In future, conventional and novel vaccines are likely to play complementary roles in vaccination strategies against influenza: in this context, our results suggest important characteristics to monitor during the clinical development of emerging vaccine technologies.'),
(88162, 2016, 'plos', 'Role of Autoregulation and Relative Synthesis of Operon Partners in Alternative Sigma Factor Networks', 'Narula, Jatin|Tiwari, Abhinav|Igoshin, Oleg A', 'Department of Bioengineering, Rice University, Houston, Texas, United States of America.|Department of Bioengineering, Rice University, Houston, Texas, United States of America.|Department of Bioengineering, Rice University, Houston, Texas, United States of America.', 'United States|United States|United States', 1, 'Bacillus subtilis|Gene Expression Regulation, Bacterial|Models, Biological|Operon|Sigma Factor|Stress, Physiological', 'Journal Article', '10.1371/journal.pcbi.1005267', '27977677', 'Despite the central role of alternative sigma factors in bacterial stress response and virulence their regulation remains incompletely understood. Here we investigate one of the best-studied examples of alternative sigma factors: the xcfx83B network that controls the general stress response of Bacillus subtilis to uncover widely relevant general design principles that describe the structure-function relationship of alternative sigma factor regulatory networks. We show that the relative stoichiometry of the synthesis rates of xcfx83B, its anti-sigma factor RsbW and the anti-anti-sigma factor RsbV plays a critical role in shaping the network behavior by forcing the xcfx83B network to function as an ultrasensitive negative feedback loop. We further demonstrate how this negative feedback regulation insulates alternative sigma factor activity from competition with the housekeeping sigma factor for RNA polymerase and allows multiple stress sigma factors to function simultaneously with little competitive interference.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(88163, 2016, 'plos', 'An Extended Normalization Model of Attention Accounts for Feature-Based Attentional Enhancement of Both Response and Coherence Gain', 'Schwedhelm, Philipp|Krishna, B Suresh|Treue, Stefan', 'Cognitive Neuroscience Laboratory, German Primate Center - Leibniz Institute for Primate Research, Goettingen, Germany.|Cognitive Neuroscience Laboratory, German Primate Center - Leibniz Institute for Primate Research, Goettingen, Germany.|Cognitive Neuroscience Laboratory, German Primate Center - Leibniz Institute for Primate Research, Goettingen, Germany.', 'Germany|Germany|Germany', 6, 'Adolescent|Adult|Attention|Computer Simulation|Female|Humans|Male|Models, Neurological|Neurons|Photic Stimulation|Psychometrics|Psychomotor Performance|Reproducibility of Results|Young Adult', 'Journal Article', '10.1371/journal.pcbi.1005225', '27977679', 'Paying attention to a sensory feature improves its perception and impairs that of others. Recent work has shown that a Normalization Model of Attention (NMoA) can account for a wide range of physiological findings and the influence of different attentional manipulations on visual performance. A key prediction of the NMoA is that attention to a visual feature like an orientation or a motion direction will increase the response of neurons preferring the attended feature (response gain) rather than increase the sensory input strength of the attended stimulus (input gain). This effect of feature-based attention on neuronal responses should translate to similar patterns of improvement in behavioral performance, with psychometric functions showing response gain rather than input gain when attention is directed to the task-relevant feature. In contrast, we report here that when human subjects are cued to attend to one of two motion directions in a transparent motion display, attentional effects manifest as a combination of input and response gain. Further, the impact on input gain is greater when attention is directed towards a narrow range of motion directions than when it is directed towards a broad range. These results are captured by an extended NMoA, which either includes a stimulus-independent attentional contribution to normalization or utilizes direction-tuned normalization. The proposed extensions are consistent with the feature-similarity gain model of attention and the attentional modulation in extrastriate area MT, where neuronal responses are enhanced and suppressed by attention to preferred and non-preferred motion directions respectively.'),
(88164, 2016, 'plos', 'Modeling Contact Inhibition of Locomotion of Colliding Cells Migrating on Micropatterned Substrates', 'Kulawiak, Dirk Alexander|Camley, Brian A|Rappel, Wouter-Jan', 'Institut für Theoretische Physik, Technische Universität Berlin, Berlin, Germany.|Department of Physics, University of California, San Diego, San Diego, California, United States of America.|Department of Physics, University of California, San Diego, San Diego, California, United States of America.', 'Germany|United States|United States', 0, 'Cell Communication|Cell Movement|Cell Polarity|Contact Inhibition|Models, Biological|Surface Properties', 'Journal Article', '10.1371/journal.pcbi.1005239', '27984579', 'In cancer metastasis, embryonic development, and wound healing, cells can coordinate their motion, leading to collective motility. To characterize these cell-cell interactions, which include contact inhibition of locomotion (CIL), micropatterned substrates are often used to restrict cell migration to linear, quasi-one-dimensional paths. In these assays, collisions between polarized cells occur frequently with only a few possible outcomes, such as cells reversing direction, sticking to one another, or walking past one another. Using a computational phase field model of collective cell motility that includes the mechanics of cell shape and a minimal chemical model for CIL, we are able to reproduce all cases seen in two-cell collisions. A subtle balance between the internal cell polarization, CIL and cell-cell adhesion governs the collision outcome. We identify the parameters that control transitions between the different cases, including cell-cell adhesion, propulsion strength, and the rates of CIL. These parameters suggest hypotheses for why different cell types have different collision behavior and the effect of interventions that modulate collision outcomes. To reproduce the heterogeneity in cell-cell collision outcomes observed experimentally in neural crest cells, we must either carefully tune our parameters or assume that there is significant cell-to-cell variation in key parameters like cell-cell adhesion.'),
(88165, 2016, 'plos', 'Improved Prediction of Non-methylated Islands in Vertebrates Highlights Different Characteristic Sequence Patterns', 'Huska, Matthew|Vingron, Martin', 'Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany.|Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany.', 'Germany|Germany', 0, 'Animals|Chickens|Computational Biology|CpG Islands|DNA Methylation|Epigenesis, Genetic|Humans|Mice|Promoter Regions, Genetic|Sequence Analysis, DNA|Supervised Machine Learning|Support Vector Machine|Xenopus|Zebrafish', 'Journal Article', '10.1371/journal.pcbi.1005249', '27984582', 'Non-methylated islands (NMIs) of DNA are genomic regions that are important for gene regulation and development. A recent study of genome-wide non-methylation data in vertebrates by Long et al. (eLife 2013,2:e00348) has shown that many experimentally identified non-methylated regions do not overlap with classically defined CpG islands which are computationally predicted using simple DNA sequence features. This is especially true in cold-blooded vertebrates such as Danio rerio (zebrafish). In order to investigate how predictive DNA sequence is of a regions methylation status, we applied a supervised learning approach using a spectrum kernel support vector machine, to see if a more complex model and supervised learning can be used to improve non-methylated island prediction and to understand the sequence properties of these regions. We demonstrate that DNA sequence is highly predictive of methylation status, and that in contrast to existing CpG island prediction methods our method is able to provide more useful predictions of NMIs genome-wide in all vertebrate organisms that were studied. Our results also show that in cold-blooded vertebrates (Anolis carolinensis, Xenopus tropicalis and Danio rerio) where genome-wide classical CpG island predictions consist primarily of false positives, longer primarily AT-rich DNA sequence features are able to identify these regions much more accurately.'),
(88166, 2016, 'plos', 'Atrial Heterogeneity Generates Re-entrant Substrate during Atrial Fibrillation and Anti-arrhythmic Drug Action: Mechanistic Insights from Canine Atrial Models', 'Varela, Marta|Colman, Michael A|Hancox, Jules C|Aslanidi, Oleg V', 'Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, Kings College London, London, United Kingdom.|School of Biomedical Sciences, Faculty of Biological Sciences, University of Leeds, Leeds, United Kingdom.|School of Physiology, Pharmacology and Neuroscience, Biomedical Sciences Building, University of Bristol, Bristol, United Kingdom.|Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, Kings College London, London, United Kingdom.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Animals|Anti-Arrhythmia Agents|Arrhythmias, Cardiac|Biomechanical Phenomena|Dogs|Heart Atria|Models, Cardiovascular|Reproducibility of Results|Single-Cell Analysis', 'Journal Article', '10.1371/journal.pcbi.1005245', '27984585', 'Anti-arrhythmic drug therapy is a frontline treatment for atrial fibrillation (AF), but its success rates are highly variable. This is due to incomplete understanding of the mechanisms of action of specific drugs on the atrial substrate at different stages of AF progression. We aimed to elucidate the role of cellular, tissue and organ level atrial heterogeneities in the generation of a re-entrant substrate during AF progression, and their modulation by the acute action of selected anti-arrhythmic drugs. To explore the complex cell-to-organ mechanisms, a detailed biophysical models of the entire 3D canine atria was developed. The model incorporated atrial geometry and fibre orientation from high-resolution micro-computed tomography, region-specific atrial cell electrophysiology and the effects of progressive AF-induced remodelling. The actions of multi-channel class III anti-arrhythmic agents vernakalant and amiodarone were introduced in the model by inhibiting appropriate ionic channel currents according to experimentally reported concentration-response relationships. AF was initiated by applied ectopic pacing in the pulmonary veins, which led to the generation of localized sustained re-entrant waves (rotors), followed by progressive wave breakdown and rotor multiplication in both atria. The simulated AF scenarios were in agreement with observations in canine models and patients. The 3D atrial simulations revealed that a re-entrant substrate was typically provided by tissue regions of high heterogeneity of action potential duration (APD). Amiodarone increased atrial APD and reduced APD heterogeneity and was more effective in terminating AF than vernakalant, which increased both APD and APD dispersion. In summary, the initiation and sustenance of rotors in AF is linked to atrial APD heterogeneity and APD reduction due to progressive remodelling. Our results suggest that anti-arrhythmic strategies that increase atrial APD without increasing its dispersion are effective in terminating AF.'),
(88167, 2016, 'plos', 'Competing Mechanistic Hypotheses of Acetaminophen-Induced Hepatotoxicity Challenged by Virtual Experiments', 'Smith, Andrew K|Petersen, Brenden K|Ropella, Glen E P|Kennedy, Ryan C|Kaplowitz, Neil|Ookhtens, Murad|Hunt, C Anthony', 'Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, United States of America.|UCSF/UCB Joint Graduate Group in Bioengineering, University of California, Berkeley, Berkeley, CA, United States of America.|Tempus Dictum, Inc., Milwaukie, OR, United States of America.|Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, United States of America.|Division of Gastrointestinal and Liver Diseases, Department of Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States of America.|Division of Gastrointestinal and Liver Diseases, Department of Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States of America.|Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, United States of America.', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Acetaminophen|Animals|Chemical and Drug Induced Liver Injury|Computational Biology|Computer Simulation|Liver|Mice|Models, Biological', 'Journal Article', '10.1371/journal.pcbi.1005253', '27984590', 'Acetaminophen-induced liver injury in mice is a model for drug-induced liver injury in humans. A precondition for improved strategies to disrupt and/or reverse the damage is a credible explanatory mechanism for how toxicity phenomena emerge and converge to cause hepatic necrosis. The Target Phenomenon in mice is that necrosis begins adjacent to the lobules central vein (CV) and progresses outward. An explanatory mechanism remains elusive. Evidence supports that location dependent differences in NAPQI (the reactive metabolite) formation within hepatic lobules (NAPQI zonation) are necessary and sufficient prerequisites to account for that phenomenon. We call that the NZ-mechanism hypothesis. Challenging that hypothesis in mice is infeasible because 1) influential variables cannot be controlled, and 2) it would require sequential intracellular measurements at different lobular locations within the same mouse. Virtual hepatocytes use independently configured periportal-to-CV gradients to exhibit lobule-location dependent behaviors. Employing NZ-mechanism achieved quantitative validation targets for acetaminophen clearance and metabolism but failed to achieve the Target Phenomenon. We posited that, in order to do so, at least one additional feature must exhibit zonation by decreasing in the CV direction. We instantiated and explored two alternatives: 1) a glutathione depletion threshold diminishes in the CV direction, and 2) ability to repair mitochondrial damage diminishes in the CV direction. Inclusion of one or the other feature into NZ-mechanism failed to achieve the Target Phenomenon. However, inclusion of both features enabled successfully achieving the Target Phenomenon. The merged mechanism provides a multilevel, multiscale causal explanation of key temporal features of acetaminophen hepatotoxicity in mice. We discovered that variants of the merged mechanism provide plausible quantitative explanations for the considerable variation in 24-hour necrosis scores among 37 genetically diverse mouse strains following a single toxic acetaminophen dose.'),
(88168, 2016, 'plos', 'The Multilayer Connectome of Caenorhabditis elegans', 'Bentley, Barry|Branicky, Robyn|Barnes, Christopher L|Chew, Yee Lian|Yemini, Eviatar|Bullmore, Edward T|Vértes, Petra E|Schafer, William R', 'Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, United Kingdom.|Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, United Kingdom.|Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, United Kingdom.|Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, United Kingdom.|Department of Biological Sciences, Columbia University, New York, NY, United States of America.|Department of Psychiatry, University of Cambridge, Cambridge United Kingdom.|Department of Psychiatry, University of Cambridge, Cambridge United Kingdom.|Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, United Kingdom.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United States|United Kingdom|United King', 61, 'Animals|Biogenic Monoamines|Caenorhabditis elegans|Computational Biology|Connectome|Signal Transduction', 'Journal Article', '10.1371/journal.pcbi.1005283', '27984591', 'Connectomics has focused primarily on the mapping of synaptic links in the brain, yet it is well established that extrasynaptic volume transmission, especially via monoamines and neuropeptides, is also critical to brain function and occurs primarily outside the synaptic connectome. We have mapped the putative monoamine connections, as well as a subset of neuropeptide connections, in C. elegans based on new and published gene expression data. The monoamine and neuropeptide networks exhibit distinct topological properties, with the monoamine network displaying a highly disassortative star-like structure with a rich-club of interconnected broadcasting hubs, and the neuropeptide network showing a more recurrent, highly clustered topology. Despite the low degree of overlap between the extrasynaptic (or wireless) and synaptic (or wired) connectomes, we find highly significant multilink motifs of interaction, pinpointing locations in the network where aminergic and neuropeptide signalling modulate synaptic activity. Thus, the C. elegans connectome can be mapped as a multiplex network with synaptic, gap junction, and neuromodulator layers representing alternative modes of interaction between neurons. This provides a new topological plan for understanding how aminergic and peptidergic modulation of behaviour is achieved by specific motifs and loci of integration between hard-wired synaptic or junctional circuits and extrasynaptic signals wirelessly broadcast from a small number of modulatory neurons.'),
(88169, 2016, 'plos', 'Inferring Aggregated Functional Traits from Metagenomic Data Using Constrained Non-negative Matrix Factorization: Application to Fiber Degradation in the Human Gut Microbiota', 'Raguideau, Sébastien|Plancade, Sandra|Pons, Nicolas|Leclerc, Marion|Laroche, Béatrice', 'Institut Micalis, INRA, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France.|MaIAGE, INRA, Université Paris-Saclay, Jouy-en-Josas, France.|MGP, INRA, Université Paris-Saclay, Jouy-en-Josas, France.|Institut Micalis, INRA, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France.|MaIAGE, INRA, Université Paris-Saclay, Jouy-en-Josas, France.', 'France|France|France|France|France', 0, 'Algorithms|Bacteria|Carbohydrate Metabolism|Dietary Fiber|Feces|Fermentation|Gastrointestinal Microbiome|Humans|Metagenomics', 'Journal Article', '10.1371/journal.pcbi.1005252', '27984592', 'Whole Genome Shotgun (WGS) metagenomics is increasingly used to study the structure and functions of complex microbial ecosystems, both from the taxonomic and functional point of view. Gene inventories of otherwise uncultured microbial communities make the direct functional profiling of microbial communities possible. The concept of community aggregated trait has been adapted from environmental and plant functional ecology to the framework of microbial ecology. Community aggregated traits are quantified from WGS data by computing the abundance of relevant marker genes. They can be used to study key processes at the ecosystem level and correlate environmental factors and ecosystem functions. In this paper we propose a novel model based approach to infer combinations of aggregated traits characterizing specific ecosystemic metabolic processes. We formulate a model of these Combined Aggregated Functional Traits (CAFTs) accounting for a hierarchical structure of genes, which are associated on microbial genomes, further linked at the ecosystem level by complex co-occurrences or interactions. The model is completed with constraints specifically designed to exploit available genomic information, in order to favor biologically relevant CAFTs. The CAFTs structure, as well as their intensity in the ecosystem, is obtained by solving a constrained Non-negative Matrix Factorization (NMF) problem. We developed a multicriteria selection procedure for the number of CAFTs. We illustrated our method on the modelling of ecosystemic functional traits of fiber degradation by the human gut microbiota. We used 1408 samples of gene abundances from several high-throughput sequencing projects and found that four CAFTs only were needed to represent the fiber degradation potential. This data reduction highlighted biologically consistent functional patterns while providing a high quality preservation of the original data. Our method is generic and can be applied to other metabolic processes in the gut or in other ecosystems.'),
(88170, 2016, 'plos', 'Glycoside Hydrolases across Environmental Microbial Communities', 'Berlemont, Renaud|Martiny, Adam C', 'Dept. of Biological Sciences, California State University, Long Beach, California, United States of America.|Dept. of Earth System Science, University of California, Irvine, California, United States of America.', 'United States|United States', 0, 'Bacteria|Bacterial Proteins|Carbohydrate Metabolism|Environmental Microbiology|Glycoside Hydrolases|Metagenomics|Microbial Consortia|Substrate Specificity', 'Journal Article', '10.1371/journal.pcbi.1005300', '27992426', 'Across many environments microbial glycoside hydrolases support the enzymatic processing of carbohydrates, a critical function in many ecosystems. Little is known about how the microbial composition of a community and the potential for carbohydrate processing relate to each other. Here, using 1,934 metagenomic datasets, we linked changes in community composition to variation of potential for carbohydrate processing across environments. We were able to show that each ecosystem-type displays a specific potential for carbohydrate utilization. Most of this potential was associated with just 77 bacterial genera. The GH content in bacterial genera is best described by their taxonomic affiliation. Across metagenomes, fluctuations of the microbial community structure and GH potential for carbohydrate utilization were correlated. Our analysis reveals that both deterministic and stochastic processes contribute to the assembly of complex microbial communities.'),
(88171, 2016, 'plos', 'Systematic Approximations to Susceptible-Infectious-Susceptible Dynamics on Networks', 'Keeling, Matt J|House, Thomas|Cooper, Alison J|Pellis, Lorenzo', 'Zeeman Institute: SBIDER, University of Warwick, Coventry, United Kingdom.|Zeeman Institute: SBIDER, University of Warwick, Coventry, United Kingdom.|School of Engineering, University of Warwick, Coventry, United Kingdom.|Zeeman Institute: SBIDER, University of Warwick, Coventry, United Kingdom.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 9, 'Algorithms|Communicable Diseases|Computational Biology|Computer Simulation|Disease Susceptibility|Humans|Models, Biological|Prevalence|Stochastic Processes', 'Journal Article', '10.1371/journal.pcbi.1005296', '27997542', 'Network-based infectious disease models have been highly effective in elucidating the role of contact structure in the spread of infection. As such, pair- and neighbourhood-based approximation models have played a key role in linking findings from network simulations to standard (random-mixing) results. Recently, for SIR-type infections (that produce one epidemic in a closed population) on locally tree-like networks, these approximations have been shown to be exact. However, network models are ideally suited for Sexually Transmitted Infections (STIs) due to the greater level of detail available for sexual contact networks, and these diseases often possess SIS-type dynamics. Here, we consider the accuracy of three systematic approximations that can be applied to arbitrary disease dynamics, including SIS behaviour. We focus in particular on low degree networks, in which the small number of neighbours causes build-up of local correlations between the state of adjacent nodes that are challenging to capture. By examining how and when these approximation models converge to simulation results, we generate insights into the role of network structure in the infection dynamics of SIS-type infections.'),
(88172, 2016, 'plos', 'Zipfs Law Arises Naturally When There Are Underlying, Unobserved Variables', 'Aitchison, Laurence|Corradi, Nicola|Latham, Peter E', 'Gatsby Computational Neuroscience Unit, University College London, London, United Kingdom.|Weill Medical College, Cornell University, New York, New York, United States of America.|Gatsby Computational Neuroscience Unit, University College London, London, United Kingdom.', 'United Kingdom|United States|United Kingdom', 0, 'Action Potentials|Databases, Factual|Entropy|Language|Models, Neurological|Models, Theoretical', 'Journal Article', '10.1371/journal.pcbi.1005110', '27997544', 'Zipfs law, which states that the probability of an observation is inversely proportional to its rank, has been observed in many domains. While there are models that explain Zipfs law in each of them, those explanations are typically domain specific. Recently, methods from statistical physics were used to show that a fairly broad class of models does provide a general explanation of Zipfs law. This explanation rests on the observation that real world data is often generated from underlying causes, known as latent variables. Those latent variables mix together multiple models that do not obey Zipfs law, giving a model that does. Here we extend that work both theoretically and empirically. Theoretically, we provide a far simpler and more intuitive explanation of Zipfs law, which at the same time considerably extends the class of models to which this explanation can apply. Furthermore, we also give methods for verifying whether this explanation applies to a particular dataset. Empirically, these advances allowed us extend this explanation to important classes of data, including word frequencies (the first domain in which Zipfs law was discovered), data with variable sequence length, and multi-neuron spiking activity.'),
(88173, 2016, 'plos', 'Normal Modes Expose Active Sites in Enzymes', 'Glantz-Gashai, Yitav|Meirson, Tomer|Samson, Abraham O', 'Faculty of Medicine in the Galilee, Bar Ilan University, Safed, Israel.|Faculty of Medicine in the Galilee, Bar Ilan University, Safed, Israel.|Faculty of Medicine in the Galilee, Bar Ilan University, Safed, Israel.', 'Israel|Israel|Israel', 4, 'Catalytic Domain|Computational Biology|Databases, Protein|Enzymes|Models, Molecular|Solvents', 'Journal Article', '10.1371/journal.pcbi.1005293', '28002427', 'Accurate prediction of active sites is an important tool in bioinformatics. Here we present an improved structure based technique to expose active sites that is based on large changes of solvent accessibility accompanying normal mode dynamics. The technique which detects EXPOsure of active SITes through normal modEs is named EXPOSITE. The technique is trained using a small 133 enzyme dataset and tested using a large 845 enzyme dataset, both with known active site residues. EXPOSITE is also tested in a benchmark protein ligand dataset (PLD) comprising 48 proteins with and without bound ligands. EXPOSITE is shown to successfully locate the active site in most instances, and is found to be more accurate than other structure-based techniques. Interestingly, in several instances, the active site does not correspond to the largest pocket. EXPOSITE is advantageous due to its high precision and paves the way for structure based prediction of active site in enzymes.'),
(88174, 2016, 'plos', 'Inference of Functionally-Relevant N-acetyltransferase Residues Based on Statistical Correlations', 'Neuwald, Andrew F|Altschul, Stephen F', 'Institute for Genome Sciences and Department of Biochemistry & Molecular Biology, University of Maryland School of Medicine, BioPark II, Room 617, Baltimore, MD, United States of America.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, United States of America.', 'United States|United States', 0, 'Acetyltransferases|Amino Acid Sequence|Animals|Caenorhabditis elegans Proteins|Computational Biology|Humans|Markov Chains|Models, Molecular|Monte Carlo Method|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1371/journal.pcbi.1005294', '28002465', 'Over evolutionary time, members of a superfamily of homologous proteins sharing a common structural core diverge into subgroups filling various functional niches. At the sequence level, such divergence appears as correlations that arise from residue patterns distinct to each subgroup. Such a superfamily may be viewed as a population of sequences corresponding to a complex, high-dimensional probability distribution. Here we model this distribution as hierarchical interrelated hidden Markov models (hiHMMs), which describe these sequence correlations implicitly. By characterizing such correlations one may hope to obtain information regarding functionally-relevant properties that have thus far evaded detection. To do so, we infer a hiHMM distribution from sequence data using Bayes theorem and Markov chain Monte Carlo (MCMC) sampling, which is widely recognized as the most effective approach for characterizing a complex, high dimensional distribution. Other routines then map correlated residue patterns to available structures with a view to hypothesis generation. When applied to N-acetyltransferases, this reveals sequence and structural features indicative of functionally important, yet generally unknown biochemical properties. Even for sets of proteins for which nothing is known beyond unannotated sequences and structures, this can lead to helpful insights. We describe, for example, a putative coenzyme-A-induced-fit substrate binding mechanism mediated by arginine residue switching between salt bridge and xcfx80-xcfx80 stacking interactions. A suite of programs implementing this approach is available (psed.igs.umaryland.edu).'),
(88175, 2016, 'plos', 'The Effect of Gap Junctional Coupling on the Spatiotemporal Patterns of Ca2+ Signals and the Harmonization of Ca2+-Related Cellular Responses', 'Dougoud, Michaël|Vinckenbosch, Laura|Mazza, Christian|Schwaller, Beat|Pecze, László', 'Department of Mathematics, University of Fribourg, Fribourg, Switzerland.|Department of Mathematics, University of Fribourg, Fribourg, Switzerland.|Department of Mathematics, University of Fribourg, Fribourg, Switzerland.|Anatomy, Department of Medicine, University of Fribourg, Fribourg, Switzerland.|Anatomy, Department of Medicine, University of Fribourg, Fribourg, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 3, 'Animals|Calcium|Calcium Signaling|Cell Communication|Computer Simulation|Gap Junctions|Humans|Models, Biological|Signal Transduction', 'Journal Article', '10.1371/journal.pcbi.1005295', '28027293', 'Calcium ions (Ca2+) are important mediators of a great variety of cellular activities e.g. in response to an agonist activation of a receptor. The magnitude of a cellular response is often encoded by frequency modulation of Ca2+ oscillations and correlated with the stimulation intensity. The stimulation intensity highly depends on the sensitivity of a cell to a certain agonist. In some cases, it is essential that neighboring cells produce a similar and synchronized response to an agonist despite their different sensitivity. In order to decipher the presumed function of Ca2+ waves spreading among connecting cells, a mathematical model was developed. This model allows to numerically modifying the connectivity probability between neighboring cells, the permeability of gap junctions and the individual sensitivity of cells to an agonist. Here, we show numerically that strong gap junctional coupling between neighbors ensures an equilibrated response to agonist stimulation via formation of Ca2+ phase waves, i.e. a less sensitive neighbor will produce the same or similar Ca2+ signal as its highly sensitive neighbor. The most sensitive cells within an ensemble are the wave initiator cells. The Ca2+ wave in the cytoplasm is driven by a sensitization wave front in the endoplasmic reticulum. The wave velocity is proportional to the cellular sensitivity and to the strength of the coupling. The waves can form different patterns including circular rings and spirals. The observed pattern depends on the strength of noise, gap junctional permeability and the connectivity probability between neighboring cells. Our simulations reveal that one highly sensitive region gradually takes the lead within the entire noisy system by generating directed circular phase waves originating from this region.'),
(88176, 2016, 'plos', 'The Hamiltonian Brain: Efficient Probabilistic Inference with Excitatory-Inhibitory Neural Circuit Dynamics', 'Aitchison, Laurence|Lengyel, Máté', 'Gatsby Computational Neuroscience Unit, University College London, London, United Kingdom.|Computational & Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, United Kingdom.', 'United Kingdom|United Kingdom', 4, 'Animals|Computer Simulation|Feedback, Physiological|Humans|Models, Neurological|Models, Statistical|Nerve Net|Neural Inhibition|Synaptic Transmission|Visual Cortex|Visual Perception', 'Journal Article', '10.1371/journal.pcbi.1005186', '28027294', 'Probabilistic inference offers a principled framework for understanding both behaviour and cortical computation. However, two basic and ubiquitous properties of cortical responses seem difficult to reconcile with probabilistic inference: neural activity displays prominent oscillations in response to constant input, and large transient changes in response to stimulus onset. Indeed, cortical models of probabilistic inference have typically either concentrated on tuning curve or receptive field properties and remained agnostic as to the underlying circuit dynamics, or had simplistic dynamics that gave neither oscillations nor transients. Here we show that these dynamical behaviours may in fact be understood as hallmarks of the specific representation and algorithm that the cortex employs to perform probabilistic inference. We demonstrate that a particular family of probabilistic inference algorithms, Hamiltonian Monte Carlo (HMC), naturally maps onto the dynamics of excitatory-inhibitory neural networks. Specifically, we constructed a model of an excitatory-inhibitory circuit in primary visual cortex that performed HMC inference, and thus inherently gave rise to oscillations and transients. These oscillations were not mere epiphenomena but served an important functional role: speeding up inference by rapidly spanning a large volume of state space. Inference thus became an order of magnitude more efficient than in a non-oscillatory variant of the model. In addition, the network matched two specific properties of observed neural dynamics that would otherwise be difficult to account for using probabilistic inference. First, the frequency of oscillations as well as the magnitude of transients increased with the contrast of the image stimulus. Second, excitation and inhibition were balanced, and inhibition lagged excitation. These results suggest a new functional role for the separation of cortical populations into excitatory and inhibitory neurons, and for the neural oscillations that emerge in such excitatory-inhibitory networks: enhancing the efficiency of cortical computations.'),
(88177, 2016, 'plos', 'Inferential Structure Determination of Chromosomes from Single-Cell Hi-C Data', 'Carstens, Simeon|Nilges, Michael|Habeck, Michael', 'Unité de Bioinformatique Structurale, Department of Structural Biology and Chemistry, Institut Pasteur, Paris, France.|Unité de Bioinformatique Structurale, Department of Structural Biology and Chemistry, Institut Pasteur, Paris, France.|Statistical Inverse Problems in Biophysics, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany.', 'France|France|Germany', 19, 'Algorithms|Chromosome Mapping|Chromosomes|Computer Simulation|Databases, Genetic|Genome, Human|Humans|Models, Genetic|Models, Statistical|Pattern Recognition, Automated|Single-Cell Analysis', 'Journal Article', '10.1371/journal.pcbi.1005292', '28027298', 'Chromosome conformation capture (3C) techniques have revealed many fascinating insights into the spatial organization of genomes. 3C methods typically provide information about chromosomal contacts in a large population of cells, which makes it difficult to draw conclusions about the three-dimensional organization of genomes in individual cells. Recently it became possible to study single cells with Hi-C, a genome-wide 3C variant, demonstrating a high cell-to-cell variability of genome organization. In principle, restraint-based modeling should allow us to infer the 3D structure of chromosomes from single-cell contact data, but suffers from the sparsity and low resolution of chromosomal contacts. To address these challenges, we adapt the Bayesian Inferential Structure Determination (ISD) framework, originally developed for NMR structure determination of proteins, to infer statistical ensembles of chromosome structures from single-cell data. Using ISD, we are able to compute structural error bars and estimate model parameters, thereby eliminating potential bias imposed by ad hoc parameter choices. We apply and compare different models for representing the chromatin fiber and for incorporating singe-cell contact information. Finally, we extend our approach to the analysis of diploid chromosome data.'),
(88178, 2016, 'plos', 'Feedback, Mass Conservation and Reaction Kinetics Impact the Robustness of Cellular Oscillations', 'Baum, Katharina|Politi, Antonio Z|Kofahl, Bente|Steuer, Ralf|Wolf, Jana', 'Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany.|Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany.|Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany.|Institute for Theoretical Biology, Humboldt University of Berlin, Berlin, Germany.|Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany.', 'Germany|Germany|Germany|Germany|Germany', 4, 'Animals|Biological Clocks|Calcium Signaling|Cell Physiological Phenomena|Circadian Rhythm|Computer Simulation|Humans|Kinetics|Models, Biological|Oscillometry', 'Journal Article', '10.1371/journal.pcbi.1005298', '28027301', 'Oscillations occur in a wide variety of cellular processes, for example in calcium and p53 signaling responses, in metabolic pathways or within gene-regulatory networks, e.g. the circadian system. Since it is of central importance to understand the influence of perturbations on the dynamics of these systems a number of experimental and theoretical studies have examined their robustness. The period of circadian oscillations has been found to be very robust and to provide reliable timing. For intracellular calcium oscillations the period has been shown to be very sensitive and to allow for frequency-encoded signaling. We here apply a comprehensive computational approach to study the robustness of period and amplitude of oscillatory systems. We employ different prototype oscillator models and a large number of parameter sets obtained by random sampling. This framework is used to examine the effect of three design principles on the sensitivities towards perturbations of the kinetic parameters. We find that a prototype oscillator with negative feedback has lower period sensitivities than a prototype oscillator relying on positive feedback, but on average higher amplitude sensitivities. For both oscillator types, the use of Michaelis-Menten instead of mass action kinetics in all degradation and conversion reactions leads to an increase in period as well as amplitude sensitivities. We observe moderate changes in sensitivities if replacing mass conversion reactions by purely regulatory reactions. These insights are validated for a set of established models of various cellular rhythms. Overall, our work highlights the importance of reaction kinetics and feedback type for the variability of period and amplitude and therefore for the establishment of predictive models.'),
(88179, 2016, 'plos', 'Stereotypical Escape Behavior in Caenorhabditis elegans Allows Quantification of Effective Heat Stimulus Level', 'Leung, Kawai|Mohammadi, Aylia|Ryu, William S|Nemenman, Ilya', 'Department of Physics, Emory University, Atlanta, Georgia, United States of America.|Department of Physics, University of Toronto, Toronto, Ontario, Canada.|Department of Physics, University of Toronto, Toronto, Ontario, Canada.|Department of Physics, Emory University, Atlanta, Georgia, United States of America.', 'Georgia|Canada|Canada|Georgia', 2, 'Animals|Caenorhabditis elegans|Computer Simulation|Escape Reaction|Heat-Shock Response|Hot Temperature|Models, Biological|Pain Measurement|Pain Perception|Physical Stimulation|Stereotyped Behavior', 'Journal Article', '10.1371/journal.pcbi.1005262', '28027302', 'A goal of many sensorimotor studies is to quantify the stimulus-behavioral response relation for specific organisms and specific sensory stimuli. This is especially important to do in the context of painful stimuli since most animals in these studies cannot easily communicate to us their perceived levels of such noxious stimuli. Thus progress on studies of nociception and pain-like responses in animal models depends crucially on our ability to quantitatively and objectively infer the sensed levels of these stimuli from animal behaviors. Here we develop a quantitative model to infer the perceived level of heat stimulus from the stereotyped escape response of individual nematodes Caenorhabditis elegans stimulated by an IR laser. The model provides a method for quantification of analgesic-like effects of chemical stimuli or genetic mutations in C. elegans. We test ibuprofen-treated worms and a TRPV (transient receptor potential) mutant, and we show that the perception of heat stimuli for the ibuprofen treated worms is lower than the wild-type. At the same time, our model shows that the mutant changes the worms behavior beyond affecting the thermal sensory system. Finally, we determine the stimulus level that best distinguishes the analgesic-like effects and the minimum number of worms that allow for a statistically significant identification of these effects.'),
(88180, 2016, 'plos', 'Decoding Single Molecule Time Traces with Dynamic Disorder', 'Hwang, Wonseok|Lee, Il-Buem|Hong, Seok-Cheol|Hyeon, Changbong', 'Korea Institute for Advanced Study, Seoul, Republic of Korea.|Department of Physics, Korea University, Seoul, Republic of Korea.|Korea Institute for Advanced Study, Seoul, Republic of Korea.|Korea Institute for Advanced Study, Seoul, Republic of Korea.', '-|-|-|-', 2, 'Algorithms|Computer Simulation|DNA|Fluorescence Resonance Energy Transfer|Kinetics|Models, Chemical|Models, Molecular', 'Journal Article', '10.1371/journal.pcbi.1005286', '28027304', 'Single molecule time trajectories of biomolecules provide glimpses into complex folding landscapes that are difficult to visualize using conventional ensemble measurements. Recent experiments and theoretical analyses have highlighted dynamic disorder in certain classes of biomolecules, whose dynamic pattern of conformational transitions is affected by slower transition dynamics of internal state hidden in a low dimensional projection. A systematic means to analyze such data is, however, currently not well developed. Here we report a new algorithm-Variational Bayes-double chain Markov model (VB-DCMM)-to analyze single molecule time trajectories that display dynamic disorder. The proposed analysis employing VB-DCMM allows us to detect the presence of dynamic disorder, if any, in each trajectory, identify the number of internal states, and estimate transition rates between the internal states as well as the rates of conformational transition within each internal state. Applying VB-DCMM algorithm to single molecule FRET data of H-DNA in 100 mM-Na+ solution, followed by data clustering, we show that at least 6 kinetic paths linking 4 distinct internal states are required to correctly interpret the duplex-triplex transitions of H-DNA.'),
(88181, 2016, 'plos', 'Population Heterogeneity in the Epithelial to Mesenchymal Transition Is Controlled by NFAT and Phosphorylated Sp1', 'Gould, Russell|Bassen, David M|Chakrabarti, Anirikh|Varner, Jeffrey D|Butcher, Jonathan', 'Nancy E. and Peter C. Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, United States of America.|Nancy E. and Peter C. Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, United States of America.|Robert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, New York, United States of America.|Robert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, New York, United States of America.|Nancy E. and Peter C. Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, United States of America.', 'United States|United States|United States|United States|United States', 0, 'Cells, Cultured|Computer Simulation|Epithelial-Mesenchymal Transition|Gene Expression Regulation, Developmental|Humans|Models, Biological|Morphogenesis|NFATC Transcription Factors|Phosphorylation|Sp1 Transcription Factor|Transcription Factors|Transcriptional Activation', 'Journal Article', '10.1371/journal.pcbi.1005251', '28027307', 'Epithelial to mesenchymal transition (EMT) is an essential differentiation program during tissue morphogenesis and remodeling. EMT is induced by soluble transforming growth factor xcexb2 (TGF-xcexb2) family members, and restricted by vascular endothelial growth factor family members. While many downstream molecular regulators of EMT have been identified, these have been largely evaluated individually without considering potential crosstalk. In this study, we created an ensemble of dynamic mathematical models describing TGF-xcexb2 induced EMT to better understand the operational hierarchy of this complex molecular program. We used ordinary differential equations (ODEs) to describe the transcriptional and post-translational regulatory events driving EMT. Model parameters were estimated from multiple data sets using multiobjective optimization, in combination with cross-validation. TGF-xcexb2 exposure drove the model population toward a mesenchymal phenotype, while an epithelial phenotype was enhanced following vascular endothelial growth factor A (VEGF-A) exposure. Simulations predicted that the transcription factors phosphorylated SP1 and NFAT were master regulators promoting or inhibiting EMT, respectively. Surprisingly, simulations also predicted that a cellular population could exhibit phenotypic heterogeneity (characterized by a significant fraction of the population with both high epithelial and mesenchymal marker expression) if treated simultaneously with TGF-xcexb2 and VEGF-A. We tested this prediction experimentally in both MCF10A and DLD1 cells and found that upwards of 45% of the cellular population acquired this hybrid state in the presence of both TGF-xcexb2 and VEGF-A. We experimentally validated the predicted NFAT/Sp1 signaling axis for each phenotype response. Lastly, we found that cells in the hybrid state had significantly different functional behavior when compared to VEGF-A or TGF-xcexb2 treatment alone. Together, these results establish a predictive mechanistic model of EMT susceptibility, and potentially reveal a novel signaling axis which regulates carcinoma progression through an EMT versus tubulogenesis response.'),
(88182, 2016, 'plos', 'Novel Models of Visual Topographic Map Alignment in the Superior Colliculus', 'Tikidji-Hamburyan, Ruben A|El-Ghazawi, Tarek A|Triplett, Jason W', 'School of Engineering and Applied Science, George Washington University, Washington, DC, United States of America.|School of Engineering and Applied Science, George Washington University, Washington, DC, United States of America.|Center for Neuroscience Research, Childrens National Health System, Washington, DC, United States of America.', 'United States|United States|United States', 0, 'Animals|Computer Simulation|Mice|Models, Anatomic|Models, Neurological|Motion Perception|Retinal Ganglion Cells|Superior Colliculi|Visual Cortex|Visual Pathways', 'Journal Article', '10.1371/journal.pcbi.1005315', '28027309', 'The establishment of precise neuronal connectivity during development is critical for sensing the external environment and informing appropriate behavioral responses. In the visual system, many connections are organized topographically, which preserves the spatial order of the visual scene. The superior colliculus (SC) is a midbrain nucleus that integrates visual inputs from the retina and primary visual cortex (V1) to regulate goal-directed eye movements. In the SC, topographically organized inputs from the retina and V1 must be aligned to facilitate integration. Previously, we showed that retinal input instructs the alignment of V1 inputs in the SC in a manner dependent on spontaneous neuronal activity, however, the mechanism of activity-dependent instruction remains unclear. To begin to address this gap, we developed two novel computational models of visual map alignment in the SC that incorporate distinct activity-dependent components. First, a Correlational Model assumes that V1 inputs achieve alignment with established retinal inputs through simple correlative firing mechanisms. A second Integrational Model assumes that V1 inputs contribute to the firing of SC neurons during alignment. Both models accurately replicate in vivo findings in wild type, transgenic and combination mutant mouse models, suggesting either activity-dependent mechanism is plausible. In silico experiments reveal distinct behaviors in response to weakening retinal drive, providing insight into the nature of the system governing map alignment depending on the activity-dependent strategy utilized. Overall, we describe novel computational frameworks of visual map alignment that accurately model many aspects of the in vivo process and propose experiments to test them.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(88183, 2016, 'plos', 'm6A-Driver: Identifying Context-Specific mRNA m6A Methylation-Driven Gene Interaction Networks', 'Zhang, Song-Yao|Zhang, Shao-Wu|Liu, Lian|Meng, Jia|Huang, Yufei', 'School of Automation, Northwestern Polytechnical University, Xian, Shaanxi, China.|School of Automation, Northwestern Polytechnical University, Xian, Shaanxi, China.|School of Automation, Northwestern Polytechnical University, Xian, Shaanxi, China.|Department of Biological Sciences, HRINU, SUERI, Xian Jiaotong-Liverpool University, Suzhou, Jiangsu, China.|Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, Texas, United States of America.', 'China|China|China|China|United States', 15, 'Adenosine|Algorithms|Computer Simulation|DNA Methylation|Gene Regulatory Networks|Models, Genetic|RNA|Sequence Analysis, RNA', 'Journal Article', '10.1371/journal.pcbi.1005287', '28027310', 'As the most prevalent mammalian mRNA epigenetic modification, N6-methyladenosine (m6A) has been shown to possess important post-transcriptional regulatory functions. However, the regulatory mechanisms and functional circuits of m6A are still largely elusive. To help unveil the regulatory circuitry mediated by mRNA m6A methylation, we develop here m6A-Driver, an algorithm for predicting m6A-driven genes and associated networks, whose functional interactions are likely to be actively modulated by m6A methylation under a specific condition. Specifically, m6A-Driver integrates the PPI network and the predicted differential m6A methylation sites from methylated RNA immunoprecipitation sequencing (MeRIP-Seq) data using a Random Walk with Restart (RWR) algorithm and then builds a consensus m6A-driven network of m6A-driven genes. To evaluate the performance, we applied m6A-Driver to build the context-specific m6A-driven networks for 4 known m6A (de)methylases, i.e., FTO, METTL3, METTL14 and WTAP. Our results suggest that m6A-Driver can robustly and efficiently identify m6A-driven genes that are functionally more enriched and associated with higher degree of differential expression than differential m6A methylated genes. Pathway analysis of the constructed context-specific m6A-driven gene networks further revealed the regulatory circuitry underlying the dynamic interplays between the methyltransferases and demethylase at the epitranscriptomic layer of gene regulation.'),
(88184, 2016, 'plos', 'Tissue-Specificity of Gene Expression Diverges Slowly between Orthologs, and Rapidly between Paralogs', 'Kryuchkova-Mostacci, Nadezda|Robinson-Rechavi, Marc', 'Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland.|Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland.', 'Switzerland|Switzerland', 18, 'Animals|Anura|Computational Biology|Evolution, Molecular|Gene Expression|Humans|Mice|Models, Genetic|Organ Specificity', 'Journal Article', '10.1371/journal.pcbi.1005274', '28030541', 'The ortholog conjecture implies that functional similarity between orthologous genes is higher than between paralogs. It has been supported using levels of expression and Gene Ontology term analysis, although the evidence was rather weak and there were also conflicting reports. In this study on 12 species we provide strong evidence of high conservation in tissue-specificity between orthologs, in contrast to low conservation between within-species paralogs. This allows us to shed a new light on the evolution of gene expression patterns. While there have been several studies of the correlation of expression between species, little is known about the evolution of tissue-specificity itself. Ortholog tissue-specificity is strongly conserved between all tetrapod species, with the lowest Pearson correlation between mouse and frog at r = 0.66. Tissue-specificity correlation decreases strongly with divergence time. Paralogs in human show much lower conservation, even for recent Primate-specific paralogs. When both paralogs from ancient whole genome duplication tissue-specific paralogs are tissue-specific, it is often to different tissues, while other tissue-specific paralogs are mostly specific to the same tissue. The same patterns are observed using human or mouse as focal species, and are robust to choices of datasets and of thresholds. Our results support the following model of evolution: in the absence of duplication, tissue-specificity evolves slowly, and tissue-specific genes do not change their main tissue of expression, after small-scale duplication the less expressed paralog loses the ancestral specificity, leading to an immediate difference between paralogs, over time, both paralogs become more broadly expressed, but remain poorly correlated. Finally, there is a small number of paralog pairs which stay tissue-specific with the same main tissue of expression, for at least 300 million years.'),
(88185, 2016, 'plos', 'Human Inferences about Sequences: A Minimal Transition Probability Model', 'Meyniel, Florent|Maheu, Maxime|Dehaene, Stanislas', 'Cognitive Neuroimaging Unit, CEA DRF/I2BM, INSERM, Université Paris-Sud, Université Paris-Saclay, NeuroSpin center, Gif-sur-Yvette, France.|Cognitive Neuroimaging Unit, CEA DRF/I2BM, INSERM, Université Paris-Sud, Université Paris-Saclay, NeuroSpin center, Gif-sur-Yvette, France.|Cognitive Neuroimaging Unit, CEA DRF/I2BM, INSERM, Université Paris-Sud, Université Paris-Saclay, NeuroSpin center, Gif-sur-Yvette, France.', 'France|France|France', 22, 'Bayes Theorem|Brain|Computational Biology|Computer Simulation|Electroencephalography|Humans|Models, Neurological|Perception', 'Journal Article', '10.1371/journal.pcbi.1005260', '28030543', 'The brain constantly infers the causes of the inputs it receives and uses these inferences to generate statistical expectations about future observations. Experimental evidence for these expectations and their violations include explicit reports, sequential effects on reaction times, and mismatch or surprise signals recorded in electrophysiology and functional MRI. Here, we explore the hypothesis that the brain acts as a near-optimal inference device that constantly attempts to infer the time-varying matrix of transition probabilities between the stimuli it receives, even when those stimuli are in fact fully unpredictable. This parsimonious Bayesian model, with a single free parameter, accounts for a broad range of findings on surprise signals, sequential effects and the perception of randomness. Notably, it explains the pervasive asymmetry between repetitions and alternations encountered in those studies. Our analysis suggests that a neural machinery for inferring transition probabilities lies at the core of human sequence knowledge.'),
(88186, 2016, 'plos', 'Improving Phylogeny Reconstruction at the Strain Level Using Peptidome Datasets', 'Blanco-Míguez, Aitor|Meier-Kolthoff, Jan P|Gutiérrez-Jácome, Alberto|Göker, Markus|Fdez-Riverola, Florentino|Sánchez, Borja|Lourenço, Anália', 'ESEI-Department of Computer Science, University of Vigo, Edificio Politécnico, Campus Universitario As Lagoas s/n, Ourense, Spain.|Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures GmbH, Inhoffenstraße 7B, Braunschweig, Germany.|ESEI-Department of Computer Science, University of Vigo, Edificio Politécnico, Campus Universitario As Lagoas s/n, Ourense, Spain.|Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures GmbH, Inhoffenstraße 7B, Braunschweig, Germany.|ESEI-Department of Computer Science, University of Vigo, Edificio Politécnico, Campus Universitario As Lagoas s/n, Ourense, Spain.|Department of Microbiology and Biochemistry of Dairy Products, Instituto de Productos Lácteos de Asturias (IPLA), Consejo Superior de Investigaciones Científicas (CSIC), Villaviciosa, Asturias, Spain.|ESEI-Department of Computer Science, University of Vigo, Edificio Politécnico, Campus Universitario As Lagoas s/n, Ourense, Spain.', 'Spain|Germany|Spain|Germany|Spain|Spain|Spain', 3, 'Bacillus|Bacterial Proteins|DNA, Bacterial|Databases, Protein|Models, Genetic|Peptides|Phylogeny|Proteome|Proteomics|Species Specificity', 'Journal Article', '10.1371/journal.pcbi.1005271', '28033346', 'Typical bacterial strain differentiation methods are often challenged by high genetic similarity between strains. To address this problem, we introduce a novel in silico peptide fingerprinting method based on conventional wet-lab protocols that enables the identification of potential strain-specific peptides. These can be further investigated using in vitro approaches, laying a foundation for the development of biomarker detection and application-specific methods. This novel method aims at reducing large amounts of comparative peptide data to binary matrices while maintaining a high phylogenetic resolution. The underlying case study concerns the Bacillus cereus group, namely the differentiation of Bacillus thuringiensis, Bacillus anthracis and Bacillus cereus strains. Results show that trees based on cytoplasmic and extracellular peptidomes are only marginally in conflict with those based on whole proteomes, as inferred by the established Genome-BLAST Distance Phylogeny (GBDP) method. Hence, these results indicate that the two approaches can most likely be used complementarily even in other organismal groups. The obtained results confirm previous reports about the misclassification of many strains within the B. cereus group. Moreover, our method was able to separate the B. anthracis strains with high resolution, similarly to the GBDP results as benchmarked via Bayesian inference and both Maximum Likelihood and Maximum Parsimony. In addition to the presented phylogenomic applications, whole-peptide fingerprinting might also become a valuable complementary technique to digital DNA-DNA hybridization, notably for bacterial classification at the species and subspecies level in the future.'),
(88187, 2016, 'plos', 'The 5th ISCB Wikipedia Competition: Coming to a Classroom Near You?', 'Kilpatrick, Alastair M', 'Department of Pediatrics, University of California, San Diego, La Jolla, California, United States of America.', 'United States', 2, 'Computational Biology|Humans|Internet|Societies, Scientific', 'News', '10.1371/journal.pcbi.1005235', '28033360', '-'),
(88188, 2016, 'plos', 'Stability of Cross-Feeding Polymorphisms in Microbial Communities', 'Gudelj, Ivana|Kinnersley, Margie|Rashkov, Peter|Schmidt, Karen|Rosenzweig, Frank', 'Biosciences, University of Exeter, Exeter, United Kingdom.|Division of Biological Sciences, University of Montana, Missoula, Montana, United States of America.|Biosciences, University of Exeter, Exeter, United Kingdom.|Division of Biological Sciences, University of Montana, Missoula, Montana, United States of America.|Division of Biological Sciences, University of Montana, Missoula, Montana, United States of America.', 'United Kingdom|United States|United Kingdom|United States|United States', 18, 'Computational Biology|Environment|Escherichia coli|Glucose|Metabolism|Microbial Consortia|Models, Biological', 'Journal Article', '10.1371/journal.pcbi.1005269', '28036324', 'Cross-feeding, a relationship wherein one organism consumes metabolites excreted by another, is a ubiquitous feature of natural and clinically-relevant microbial communities and could be a key factor promoting diversity in extreme and/or nutrient-poor environments. However, it remains unclear how readily cross-feeding interactions form, and therefore our ability to predict their emergence is limited. In this paper we developed a mathematical model parameterized using data from the biochemistry and ecology of an E. coli cross-feeding laboratory system. The model accurately captures short-term dynamics of the two competitors that have been observed empirically and we use it to systematically explore the stability of cross-feeding interactions for a range of environmental conditions. We find that our simple system can display complex dynamics including multi-stable behavior separated by a critical point. Therefore whether cross-feeding interactions form depends on the complex interplay between density and frequency of the competitors as well as on the concentration of resources in the environment. Moreover, we find that subtly different environmental conditions can lead to dramatically different results regarding the establishment of cross-feeding, which could explain the apparently unpredictable between-population differences in experimental outcomes. We argue that mathematical models are essential tools for disentangling the complexities of cross-feeding interactions.'),
(88189, 2016, 'plos', 'Game-Changing Innovations: How Culture Can Change the Parameters of Its Own Evolution and Induce Abrupt Cultural Shifts', 'Kolodny, Oren|Creanza, Nicole|Feldman, Marcus W', 'Department of Biology, Stanford University, Stanford, California, United States of America.|Department of Biological Sciences, Vanderbilt University, Nashville, Tennessee, United States of America.|Department of Biology, Stanford University, Stanford, California, United States of America.', 'United States|United States|United States', 20, 'Animals|Cultural Evolution|Hominidae|Humans|Models, Biological|Population Dynamics|Tool Use Behavior', 'Journal Article', '10.1371/journal.pcbi.1005302', '28036346', 'One of the most puzzling features of the prehistoric record of hominid stone tools is its apparent punctuation: it consists of abrupt bursts of dramatic change that separate long periods of largely unchanging technology. Within each such period, small punctuated cultural modifications take place. Punctuation on multiple timescales and magnitudes is also found in cultural trajectories from historical times. To explain these sharp cultural bursts, researchers invoke such external factors as sudden environmental change, rapid cognitive or morphological change in the hominids that created the tools, or replacement of one species or population by another. Here we propose a dynamic model of cultural evolution that accommodates empirical observations: without invoking external factors, it gives rise to a pattern of rare, dramatic cultural bursts, interspersed by more frequent, smaller, punctuated cultural modifications. Our model includes interdependent innovation processes that occur at different rates. It also incorporates a realistic aspect of cultural evolution: cultural innovations, such as those that increase food availability or that affect cultural transmission, can change the parameters that affect cultural evolution, thereby altering the populations cultural dynamics and steady state. This steady state can be regarded as a cultural carrying capacity. These parameter-changing cultural innovations occur very rarely, but whenever one occurs, it triggers a dramatic shift towards a new cultural steady state. The smaller and more frequent punctuated cultural changes, on the other hand, are brought about by innovations that spur the invention of further, related, technology, and which occur regardless of whether the population is near its cultural steady state. Our model suggests that common interpretations of cultural shifts as evidence of biological change, for example the appearance of behaviorally modern humans, may be unwarranted.'),
(88190, 2017, 'plos', 'Correction: Large-Scale Off-Target Identification Using Fast and Accurate Dual Regularized One-Class Collaborative Filtering and Its Application to Drug Repurposing', 'PLOS Computational Biology Staff', 'Department of Biology, Stanford University, Stanford, California, United States of America.', 'United States', 0, '-', 'Published Erratum', '10.1371/journal.pcbi.1005312', '28045897', '[This corrects the article DOI: 10.1371/journal.pcbi.1005135.].'),
(88191, 2017, 'plos', 'Transcriptional and Post-Transcriptional Regulation of Thrombospondin-1 Expression: A Computational Model', 'Zhao, Chen|Isenberg, Jeffrey S|Popel, Aleksander S', 'Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America.|Vascular Medicine Institute, Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.|Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America.', 'United States|United States|United States', 9, 'Cell Hypoxia|Computational Biology|Computer Simulation|Gene Expression Regulation|Humans|Models, Biological|Oxygen|Smad Proteins|Thrombospondin 1|Transcription Factors', 'Journal Article', '10.1371/journal.pcbi.1005272', '28045898', 'Hypoxia is an important physiological stress signal that drives angiogenesis, the formation of new blood vessels. Besides an increase in the production of pro-angiogenic signals such as vascular endothelial growth factor (VEGF), hypoxia also stimulates the production of anti-angiogenic signals. Thrombospondin-1 (TSP-1) is one of the anti-angiogenic factors whose synthesis is driven by hypoxia. Cellular synthesis of TSP-1 is tightly regulated by different intermediate biomolecules including proteins that interact with hypoxia-inducible factors (HIFs), transcription factors that are activated by receptor and intracellular signaling, and microRNAs which are small non-coding RNA molecules that function in post-transcriptional modification of gene expression. Here we present a computational model that describes the mechanistic interactions between intracellular biomolecules and cooperation between signaling pathways that together make up the complex network of TSP-1 regulation both at the transcriptional and post-transcriptional level. Assisted by the model, we conduct in silico experiments to compare the efficacy of different therapeutic strategies designed to modulate TSP-1 synthesis in conditions that simulate tumor and peripheral arterial disease microenvironment. We conclude that TSP-1 production in endothelial cells depends on not only the availability of certain growth factors but also the fine-tuned signaling cascades that are initiated by hypoxia.'),
(88192, 2017, 'plos', 'Suboptimal Criterion Learning in Static and Dynamic Environments', 'Norton, Elyse H|Fleming, Stephen M|Daw, Nathaniel D|Landy, Michael S', 'Department of Psychology, New York University, New York, New York, United States of America.|Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom.|Department of Psychology, Princeton University, Princeton, New Jersey, United States of America.|Department of Psychology, New York University, New York, New York, United States of America.', 'United States|United Kingdom|Jersey|United States', 9, 'Adult|Computational Biology|Computer Simulation|Decision Making|Female|Humans|Learning|Male|Memory|Orientation|Task Performance and Analysis|Uncertainty|Young Adult', 'Journal Article', '10.1371/journal.pcbi.1005304', '28046006', 'Humans often make decisions based on uncertain sensory information. Signal detection theory (SDT) describes detection and discrimination decisions as a comparison of stimulus strength to a fixed decision criterion. However, recent research suggests that current responses depend on the recent history of stimuli and previous responses, suggesting that the decision criterion is updated trial-by-trial. The mechanisms underpinning criterion setting remain unknown. Here, we examine how observers learn to set a decision criterion in an orientation-discrimination task under both static and dynamic conditions. To investigate mechanisms underlying trial-by-trial criterion placement, we introduce a novel task in which participants explicitly set the criterion, and compare it to a more traditional discrimination task, allowing us to model this explicit indication of criterion dynamics. In each task, stimuli were ellipses with principal orientations drawn from two categories: Gaussian distributions with different means and equal variance. In the covert-criterion task, observers categorized a displayed ellipse. In the overt-criterion task, observers adjusted the orientation of a line that served as the discrimination criterion for a subsequently presented ellipse. We compared performance to the ideal Bayesian learner and several suboptimal models that varied in both computational and memory demands. Under static and dynamic conditions, we found that, in both tasks, observers used suboptimal learning rules. In most conditions, a model in which the recent history of past samples determines a belief about category means fit the data best for most observers and on average. Our results reveal dynamic adjustment of discrimination criterion, even after prolonged training, and indicate how decision criteria are updated over time.'),
(88193, 2017, 'plos', 'Structure-Function Network Mapping and Its Assessment via Persistent Homology', 'Liang, Hualou|Wang, Hongbin', 'School of Biomedical Engineering, Science & Health Systems, Drexel University, Philadelphia, PA, United States of America.|Center for Biomedical Informatics, Texas A&M University Health Science Center, Houston, TX, United States of America.', 'United States|United States', 14, 'Algorithms|Brain|Brain Mapping|Computational Biology|Databases, Factual|Humans', 'Journal Article', '10.1371/journal.pcbi.1005325', '28046127', 'Understanding the relationship between brain structure and function is a fundamental problem in network neuroscience. This work deals with the general method of structure-function mapping at the whole-brain level. We formulate the problem as a topological mapping of structure-function connectivity via matrix function, and find a stable solution by exploiting a regularization procedure to cope with large matrices. We introduce a novel measure of network similarity based on persistent homology for assessing the quality of the network mapping, which enables a detailed comparison of network topological changes across all possible thresholds, rather than just at a single, arbitrary threshold that may not be optimal. We demonstrate that our approach can uncover the direct and indirect structural paths for predicting functional connectivity, and our network similarity measure outperforms other currently available methods. We systematically validate our approach with (1) a comparison of regularized vs. non-regularized procedures, (2) a null model of the degree-preserving random rewired structural matrix, (3) different network types (binary vs. weighted matrices), and (4) different brain parcellation schemes (low vs. high resolutions). Finally, we evaluate the scalability of our method with relatively large matrices (2514x2514) of structural and functional connectivity obtained from 12 healthy human subjects measured non-invasively while at rest. Our results reveal a nonlinear structure-function relationship, suggesting that the resting-state functional connectivity depends on direct structural connections, as well as relatively parsimonious indirect connections via polysynaptic pathways.'),
(88194, 2017, 'plos', 'Ten Simple Rules for Developing Usable Software in Computational Biology', 'List, Markus|Ebert, Peter|Albrecht, Felipe', 'Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken, Germany.|Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken, Germany.|Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken, Germany.', 'Germany|Germany|Germany', 25, 'Computational Biology|Guidelines as Topic|Software', 'Editorial', '10.1371/journal.pcbi.1005265', '28056032', '-'),
(88195, 2017, 'plos', 'FIJI Macro 3D ART VeSElecT: 3D Automated Reconstruction Tool for Vesicle Structures of Electron Tomograms', 'Kaltdorf, Kristin Verena|Schulze, Katja|Helmprobst, Frederik|Kollmannsberger, Philip|Dandekar, Thomas|Stigloher, Christian', 'Division of Electron Microscopy, Biocenter, University of Wuerzburg, Wuerzburg, Germany.|Molecular Biotechnology and Functional Genomics, Technical University of Applied Sciences Wildau, Wildau, Germany.|Division of Electron Microscopy, Biocenter, University of Wuerzburg, Wuerzburg, Germany.|Center for Computational and Theoretical Biology, University of Wuerzburg, Wuerzburg, Germany.|Department of Bioinformatics, Biocenter, University of Wuerzburg, Wuerzburg, Germany.|Division of Electron Microscopy, Biocenter, University of Wuerzburg, Wuerzburg, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany', 3, 'Algorithms|Animals|Caenorhabditis elegans|Electron Microscope Tomography|Embryo, Nonmammalian|Imaging, Three-Dimensional|Synaptic Vesicles|Zebrafish', 'Journal Article', '10.1371/journal.pcbi.1005317', '28056033', 'Automatic image reconstruction is critical to cope with steadily increasing data from advanced microscopy. We describe here the Fiji macro 3D ART VeSElecT which we developed to study synaptic vesicles in electron tomograms. We apply this tool to quantify vesicle properties (i) in embryonic Danio rerio 4 and 8 days past fertilization (dpf) and (ii) to compare Caenorhabditis elegans N2 neuromuscular junctions (NMJ) wild-type and its septin mutant (unc-59(e261)). We demonstrate development-specific and mutant-specific changes in synaptic vesicle pools in both models. We confirm the functionality of our macro by applying our 3D ART VeSElecT on zebrafish NMJ showing smaller vesicles in 8 dpf embryos then 4 dpf, which was validated by manual reconstruction of the vesicle pool. Furthermore, we analyze the impact of C. elegans septin mutant unc-59(e261) on vesicle pool formation and vesicle size. Automated vesicle registration and characterization was implemented in Fiji as two macros (registration and measurement). This flexible arrangement allows in particular reducing false positives by an optional manual revision step. Preprocessing and contrast enhancement work on image-stacks of 1nm/pixel in x and y direction. Semi-automated cell selection was integrated. 3D ART VeSElecT removes interfering components, detects vesicles by 3D segmentation and calculates vesicle volume and diameter (spherical approximation, inner/outer diameter). Results are collected in color using the RoiManager plugin including the possibility of manual removal of non-matching confounder vesicles. Detailed evaluation considered performance (detected vesicles) and specificity (true vesicles) as well as precision and recall. We furthermore show gain in segmentation and morphological filtering compared to learning based methods and a large time gain compared to manual segmentation. 3D ART VeSElecT shows small error rates and its speed gain can be up to 68 times faster in comparison to manual annotation. Both automatic and semi-automatic modes are explained including a tutorial.'),
(88196, 2017, 'plos', 'Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model', 'Wang, Sheng|Sun, Siqi|Li, Zhen|Zhang, Renyu|Xu, Jinbo', 'Toyota Technological Institute at Chicago, Chicago, Illinois, United States of America.|Toyota Technological Institute at Chicago, Chicago, Illinois, United States of America.|Toyota Technological Institute at Chicago, Chicago, Illinois, United States of America.|Toyota Technological Institute at Chicago, Chicago, Illinois, United States of America.|Toyota Technological Institute at Chicago, Chicago, Illinois, United States of America.', 'United States|United States|United States|United States|United States', 237, 'Algorithms|Computational Biology|Databases, Protein|Machine Learning|Neural Networks (Computer)|Protein Conformation|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1371/journal.pcbi.1005324', '28056090', 'Protein contacts contain key information for the understanding of protein structure and function and thus, contact prediction from sequence is an important problem. Recently exciting progress has been made on this problem, but the predicted contacts for proteins without many sequence homologs is still of low quality and not very useful for de novo structure prediction.'),
(88197, 2017, 'plos', 'Efficient Switches in Biology and Computer Science', 'Cardelli, Luca|Hernansaiz-Ballesteros, Rosa D|Dalchau, Neil|Csikász-Nagy, Attila', 'Microsoft Research, Cambridge, United Kingdom.|Randall Division of Cell and Molecular Biophysics, Kings College London, London, United Kingdom.|Microsoft Research, Cambridge, United Kingdom.|Randall Division of Cell and Molecular Biophysics, Kings College London, London, United Kingdom.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Cell Cycle|Computational Biology|Computers', 'Journal Article', '10.1371/journal.pcbi.1005100', '28056093', '-'),
(88198, 2017, 'plos', 'Functionality and Robustness of Injured Connectomic Dynamics in C elegans: Linking Behavioral Deficits to Neural Circuit Damage', 'Kunert, James M|Maia, Pedro D|Kutz, J Nathan', 'Department of Physics, University of Washington, Seattle, Washington, United States of America.|Department of Applied Mathematics, University of Washington, Seattle, Washington, United States of America.|Department of Applied Mathematics, University of Washington, Seattle, Washington, United States of America.', 'United States|United States|United States', 4, 'Animals|Caenorhabditis elegans|Computational Biology|Connectome|Models, Neurological|Nerve Net', 'Journal Article', '10.1371/journal.pcbi.1005261', '28056097', 'Using a model for the dynamics of the full somatic nervous system of the nematode C. elegans, we address how biological network architectures and their functionality are degraded in the presence of focal axonal swellings (FAS) arising from neurodegenerative disease and/or traumatic brain injury. Using biophysically measured FAS distributions and swelling sizes, we are able to simulate the effects of injuries on the neural dynamics of C. elegans, showing how damaging the network degrades its low-dimensional dynamical responses. We visualize these injured neural dynamics by mapping them onto the worms low-dimensional postures, i.e. eigenworm modes. We show that a diversity of functional deficits arise from the same level of injury on a connectomic network. Functional deficits are quantified using a statistical shape analysis, a procrustes analysis, for deformations of the limit cycles that characterize key behaviors such as forward crawling. This procrustes metric carries information on the functional outcome of injuries in the model. Furthermore, we apply classification trees to relate injury structure to the behavioral outcome. This makes testable predictions for the structure of an injury given a defined functional deficit. More critically, this study demonstrates the potential role of computational simulation studies in understanding how neuronal networks process biological signals, and how this processing is impacted by network injury.'),
(88199, 2017, 'plos', 'Using Chemical Reaction Kinetics to Predict Optimal Antibiotic Treatment Strategies', 'Abel Zur Wiesch, Pia|Clarelli, Fabrizio|Cohen, Ted', 'Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America.|Department of Pharmacy, Faculty of Health Sciences, University of Tromsø, Tromsø, Norway.|Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America.', 'United States|Norway|United States', 9, 'Anti-Bacterial Agents|Bacterial Infections|Bacterial Physiological Phenomena|Computational Biology|Drug Resistance, Bacterial|Humans|Kinetics|Microbial Sensitivity Tests|Models, Biological', 'Journal Article', '10.1371/journal.pcbi.1005321', '28060813', 'Identifying optimal dosing of antibiotics has proven challenging-some antibiotics are most effective when they are administered periodically at high doses, while others work best when minimizing concentration fluctuations. Mechanistic explanations for why antibiotics differ in their optimal dosing are lacking, limiting our ability to predict optimal therapy and leading to long and costly experiments. We use mathematical models that describe both bacterial growth and intracellular antibiotic-target binding to investigate the effects of fluctuating antibiotic concentrations on individual bacterial cells and bacterial populations. We show that physicochemical parameters, e.g. the rate of drug transmembrane diffusion and the antibiotic-target complex half-life are sufficient to explain which treatment strategy is most effective. If the drug-target complex dissociates rapidly, the antibiotic must be kept constantly at a concentration that prevents bacterial replication. If antibiotics cross bacterial cell envelopes slowly to reach their target, there is a delay in the onset of action that may be reduced by increasing initial antibiotic concentration. Finally, slow drug-target dissociation and slow diffusion out of cells act to prolong antibiotic effects, thereby allowing for less frequent dosing. Our model can be used as a tool in the rational design of treatment for bacterial infections. It is easily adaptable to other biological systems, e.g. HIV, malaria and cancer, where the effects of physiological fluctuations of drug concentration are also poorly understood.'),
(88200, 2017, 'plos', 'Customized Regulation of Diverse Stress Response Genes by the Multiple Antibiotic Resistance Activator MarA', 'Rossi, Nicholas A|Dunlop, Mary J', 'Molecular Biology, Cell Biology & Biochemistry Program, Boston University, Boston, MA United States of America.|Department of Biomedical Engineering, Boston University, Boston, MA United States of America.', 'United States|United States', 0, 'Computational Biology|DNA-Binding Proteins|Drug Resistance, Bacterial|Escherichia coli|Escherichia coli Proteins|Gene Expression Regulation, Bacterial|Promoter Regions, Genetic|Single-Cell Analysis|Stress, Physiological', 'Journal Article', '10.1371/journal.pcbi.1005310', '28060821', 'Stress response networks frequently have a single upstream regulator that controls many downstream genes. However, the downstream targets are often diverse, therefore it remains unclear how their expression is specialized when under the command of a common regulator. To address this, we focused on a stress response network where the multiple antibiotic resistance activator MarA from Escherichia coli regulates diverse targets ranging from small RNAs to efflux pumps. Using single-cell experiments and computational modeling, we showed that each downstream gene studied has distinct activation, noise, and information transmission properties. Critically, our results demonstrate that understanding biological context is essential, we found examples where strong activation only occurs outside physiologically relevant ranges of MarA and others where noise is high at wild type MarA levels and decreases as MarA reaches its physiological limit. These results demonstrate how a single regulatory protein can maintain specificity while orchestrating the response of many downstream genes.'),
(88201, 2017, 'plos', 'Modelling Systemic Iron Regulation during Dietary Iron Overload and Acute Inflammation: Role of Hepcidin-Independent Mechanisms', 'Enculescu, Mihaela|Metzendorf, Christoph|Sparla, Richard|Hahnel, Maximilian|Bode, Johannes|Muckenthaler, Martina U|Legewie, Stefan', 'Institute of Molecular Biology, Mainz, Germany.|Pediatric Oncology, Hematology & Immunology, University Hospital Heidelberg, Heidelberg, Germany.|Molecular Medicine Partnership Unit, Heidelberg University, Heidelberg, Germany.|Department of Gastroenterology, Hepatology and Infectious Diseases, Medical Faculty, University Hospital, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany.|Department of Gastroenterology, Hepatology and Infectious Diseases, Medical Faculty, University Hospital, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany.|Pediatric Oncology, Hematology & Immunology, University Hospital Heidelberg, Heidelberg, Germany.|Institute of Molecular Biology, Mainz, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Hepcidins|Inflammation|Iron|Iron Overload|Metabolic Networks and Pathways|Models, Biological', 'Journal Article', '10.1371/journal.pcbi.1005322', '28068331', 'Systemic iron levels must be maintained in physiological concentrations to prevent diseases associated with iron deficiency or iron overload. A key role in this process plays ferroportin, the only known mammalian transmembrane iron exporter, which releases iron from duodenal enterocytes, hepatocytes, or iron-recycling macrophages into the blood stream. Ferroportin expression is tightly controlled by transcriptional and post-transcriptional mechanisms in response to hypoxia, iron deficiency, heme iron and inflammatory cues by cell-autonomous and systemic mechanisms. At the systemic level, the iron-regulatory hormone hepcidin is released from the liver in response to these cues, binds to ferroportin and triggers its degradation. The relative importance of individual ferroportin control mechanisms and their interplay at the systemic level is incompletely understood. Here, we built a mathematical model of systemic iron regulation. It incorporates the dynamics of organ iron pools as well as regulation by the hepcidin/ferroportin system. We calibrated and validated the model with time-resolved measurements of iron responses in mice challenged with dietary iron overload and/or inflammation. The model demonstrates that inflammation mainly reduces the amount of iron in the blood stream by reducing intracellular ferroportin transcription, and not by hepcidin-dependent ferroportin protein destabilization. In contrast, ferroportin regulation by hepcidin is the predominant mechanism of iron homeostasis in response to changing iron diets for a big range of dietary iron contents. The model further reveals that additional homeostasis mechanisms must be taken into account at very high dietary iron levels, including the saturation of intestinal uptake of nutritional iron and the uptake of circulating, non-transferrin-bound iron, into liver. Taken together, our model quantitatively describes systemic iron metabolism and generated experimentally testable predictions for additional ferroportin-independent homeostasis mechanisms.'),
(88202, 2017, 'plos', 'Spikelets in Pyramidal Neurons: Action Potentials Initiated in the Axon Initial Segment That Do Not Activate the Soma', 'Michalikova, Martina|Remme, Michiel W H|Kempter, Richard', 'Institute for Theoretical Biology, Department of Biology, Humboldt-Universität zu Berlin, Berlin, Germany.|Institute for Theoretical Biology, Department of Biology, Humboldt-Universität zu Berlin, Berlin, Germany.|Institute for Theoretical Biology, Department of Biology, Humboldt-Universität zu Berlin, Berlin, Germany.', 'Germany|Germany|Germany', 10, 'Action Potentials|Axons|Computational Biology|Computer Simulation|Models, Neurological|Pyramidal Cells', 'Journal Article', '10.1371/journal.pcbi.1005237', '28068338', 'Spikelets are small spike-like depolarizations that can be measured in somatic intracellular recordings. Their origin in pyramidal neurons remains controversial. To explain spikelet generation, we propose a novel single-cell mechanism: somato-dendritic input generates action potentials at the axon initial segment that may fail to activate the soma and manifest as somatic spikelets. Using mathematical analysis and numerical simulations of compartmental neuron models, we identified four key factors controlling spikelet generation: (1) difference in firing threshold, (2) impedance mismatch, and (3) electrotonic separation between the soma and the axon initial segment, as well as (4) input amplitude. Because spikelets involve forward propagation of action potentials along the axon while they avoid full depolarization of the somato-dendritic compartments, we conjecture that this mode of operation saves energy and regulates dendritic plasticity while still allowing for a read-out of results of neuronal computations.'),
(88203, 2017, 'plos', 'A Network Model of Local Field Potential Activity in Essential Tremor and the Impact of Deep Brain Stimulation', 'Yousif, Nada|Mace, Michael|Pavese, Nicola|Borisyuk, Roman|Nandi, Dipankar|Bain, Peter', 'Division of Brain Sciences, Imperial College London, London, United Kingdom.|Department of Bioengineering, Imperial College London, London, United Kingdom.|Division of Brain Sciences, Imperial College London, London, United Kingdom.|School of Computing and Mathematics, University of Plymouth, Plymouth, United Kingdom.|Division of Brain Sciences, Imperial College London, London, United Kingdom.|Division of Brain Sciences, Imperial College London, London, United Kingdom.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 8, 'Aged|Brain|Computational Biology|Computer Simulation|Deep Brain Stimulation|Electromyography|Essential Tremor|Female|Humans|Male|Middle Aged|Thalamus', 'Journal Article', '10.1371/journal.pcbi.1005326', '28068428', 'Essential tremor (ET), a movement disorder characterised by an uncontrollable shaking of the affected body part, is often professed to be the most common movement disorder, affecting up to one percent of adults over 40 years of age. The precise cause of ET is unknown, however pathological oscillations of a network of a number of brain regions are implicated in leading to the disorder. Deep brain stimulation (DBS) is a clinical therapy used to alleviate the symptoms of a number of movement disorders. DBS involves the surgical implantation of electrodes into specific nuclei in the brain. For ET the targeted region is the ventralis intermedius (Vim) nucleus of the thalamus. Though DBS is effective for treating ET, the mechanism through which the therapeutic effect is obtained is not understood. To elucidate the mechanism underlying the pathological network activity and the effect of DBS on such activity, we take a computational modelling approach combined with electrophysiological data. The pathological brain activity was recorded intra-operatively via implanted DBS electrodes, whilst simultaneously recording muscle activity of the affected limbs. We modelled the network hypothesised to underlie ET using the Wilson-Cowan approach. The modelled network exhibited oscillatory behaviour within the tremor frequency range, as did our electrophysiological data. By applying a DBS-like input we suppressed these oscillations. This study shows that the dynamics of the ET network support oscillations at the tremor frequency and the application of a DBS-like input disrupts this activity, which could be one mechanism underlying the therapeutic benefit.'),
(88204, 2017, 'plos', 'Systematic Analysis of Transcriptional and Post-transcriptional Regulation of Metabolism in Yeast', 'Gonçalves, Emanuel|Raguz Nakic, Zrinka|Zampieri, Mattia|Wagih, Omar|Ochoa, David|Sauer, Uwe|Beltrao, Pedro|Saez-Rodriguez, Julio', 'European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom.|Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.|Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom.|Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom.', 'United Kingdom|Switzerland|Switzerland|United Kingdom|United Kingdom|Switzerland|United Kingdom|Unit', 16, 'Databases, Genetic|Gene Expression Regulation, Fungal|Metabolomics|Pheromones|Proteins|Saccharomyces cerevisiae|Sodium Chloride|Systems Biology|Transcription Factors', 'Journal Article', '10.1371/journal.pcbi.1005297', '28072816', 'Cells react to extracellular perturbations with complex and intertwined responses. Systematic identification of the regulatory mechanisms that control these responses is still a challenge and requires tailored analyses integrating different types of molecular data. Here we acquired time-resolved metabolomics measurements in yeast under salt and pheromone stimulation and developed a machine learning approach to explore regulatory associations between metabolism and signal transduction. Existing phosphoproteomics measurements under the same conditions and kinase-substrate regulatory interactions were used to in silico estimate the enzymatic activity of signalling kinases. Our approach identified informative associations between kinases and metabolic enzymes capable of predicting metabolic changes. We extended our analysis to two studies containing transcriptomics, phosphoproteomics and metabolomics measurements across a comprehensive panel of kinases/phosphatases knockouts and time-resolved perturbations to the nitrogen metabolism. Changes in activity of transcription factors, kinases and phosphatases were estimated in silico and these were capable of building predictive models to infer the metabolic adaptations of previously unseen conditions across different dynamic experiments. Time-resolved experiments were significantly more informative than genetic perturbations to infer metabolic adaptation. This difference may be due to the indirect nature of the associations and of general cellular states that can hinder the identification of causal relationships. This work provides a novel genome-scale integrative analysis to propose putative transcriptional and post-translational regulatory mechanisms of metabolic processes.'),
(88205, 2017, 'plos', 'Spatiotemporal Feedback and Network Structure Drive and Encode Caenorhabditis elegans Locomotion', 'Kunert, James M|Proctor, Joshua L|Brunton, Steven L|Kutz, J Nathan', 'Department of Physics, University of Washington, Seattle, Washington, United States of America.|Institute for Disease Modeling, Bellevue, Washington, United States of America.|Department of Mechanical Engineering, University of Washington, Seattle, Washington, United States of America.|Department of Applied Mathematics, University of Washington, Seattle, Washington, United States of America.', 'United States|United States|United States|United States', 10, 'Animals|Caenorhabditis elegans|Computational Biology|Connectome|Feedback, Physiological|Locomotion|Models, Neurological|Neurons|Proprioception', 'Journal Article', '10.1371/journal.pcbi.1005303', '28076347', 'Using a computational model of the Caenorhabditis elegans connectome dynamics, we show that proprioceptive feedback is necessary for sustained dynamic responses to external input. This is consistent with the lack of biophysical evidence for a central pattern generator, and recent experimental evidence that proprioception drives locomotion. The low-dimensional functional response of the Caenorhabditis elegans network of neurons to proprioception-like feedback is optimized by input of specific spatial wavelengths which correspond to the spatial scale of real body shape dynamics. Furthermore, we find that the motor subcircuit of the network is responsible for regulating this response, in agreement with experimental expectations. To explore how the connectomic dynamics produces the observed two-mode, oscillatory limit cycle behavior from a static fixed point, we probe the fixed points low-dimensional structure using Dynamic Mode Decomposition. This reveals that the nonlinear network dynamics encode six clusters of dynamic modes, with timescales spanning three orders of magnitude. Two of these six dynamic mode clusters correspond to previously-discovered behavioral modes related to locomotion. These dynamic modes and their timescales are encoded by the networks degree distribution and specific connectivity. This suggests that behavioral dynamics are partially encoded within the connectome itself, the connectivity of which facilitates proprioceptive control.');
