INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(86668, 2014, 'plos', 'LipidWrapper: an algorithm for generating large-scale membrane models of arbitrary geometry', 'Durrant, Jacob D|Amaro, Rommie E', 'Department of Chemistry & Biochemistry, University of California San Diego, La Jolla, California, United States of America.|Department of Chemistry & Biochemistry, University of California San Diego, La Jolla, California, United States of America.', 'United States|United States', 0, 'Algorithms|Cell Membrane|Computational Biology|Lipid Bilayers|Models, Molecular|Orthomyxoviridae|Software|Viral Proteins', 'Journal Article', '10.1371/journal.pcbi.1003720', '25032790', 'As ever larger and more complex biological systems are modeled in silico, approximating physiological lipid bilayers with simple planar models becomes increasingly unrealistic. In order to build accurate large-scale models of subcellular environments, models of lipid membranes with carefully considered, biologically relevant curvature will be essential. In the current work, we present a multi-scale utility called LipidWrapper capable of creating curved membrane models with geometries derived from various sources, both experimental and theoretical. To demonstrate its utility, we use LipidWrapper to examine an important mechanism of influenza virulence. A copy of the program can be downloaded free of charge under the terms of the open-source FreeBSD License from http://nbcr.ucsd.edu/lipidwrapper. LipidWrapper has been tested on all major computer operating systems. '),
(86669, 2014, 'plos', 'Stability curve prediction of homologous proteins using temperature-dependent statistical potentials', 'Pucci, Fabrizio|Rooman, Marianne', 'Department of BioModeling, BioInformatics & BioProcesses, Universitxc3xa9 Libre de Bruxelles, Brussels, Belgium.|Department of BioModeling, BioInformatics & BioProcesses, Universitxc3xa9 Libre de Bruxelles, Brussels, Belgium.', 'Belgium|Belgium', 14, 'Computational Biology|Protein Folding|Protein Stability|Proteins|Thermodynamics', 'Journal Article', '10.1371/journal.pcbi.1003689', '25032839', 'The unraveling and control of protein stability at different temperatures is a fundamental problem in biophysics that is substantially far from being quantitatively and accurately solved, as it requires a precise knowledge of the temperature dependence of amino acid interactions. In this paper we attempt to gain insight into the thermal stability of proteins by designing a tool to predict the full stability curve as a function of the temperature for a set of 45 proteins belonging to 11 homologous families, given their sequence and structure, as well as the melting temperature (Tm) and the change in heat capacity (xcex94CP) of proteins belonging to the same family. Stability curves constitute a fundamental instrument to analyze in detail the thermal stability and its relation to the thermodynamic stability, and to estimate the enthalpic and entropic contributions to the folding free energy. In summary, our approach for predicting the protein stability curves relies on temperature-dependent statistical potentials derived from three datasets of protein structures with targeted thermal stability properties. Using these potentials, the folding free energies (xcex94G) at three different temperatures were computed for each protein. The Gibbs-Helmholtz equation was then used to predict the proteins stability curve as the curve that best fits these three points. The results are quite encouraging: the standard deviations between the experimental and predicted Tms, xcex94CPs and folding free energies at room temperature (xcex94G25) are equal to 13xc2xb0 C, 1.3 kcal/(molxc2xb0 C) and 4.1 kcal/mol, respectively, in cross-validation. The main sources of error and some further improvements and perspectives are briefly discussed.'),
(86670, 2014, 'plos', 'The long and viscous road: uncovering nuclear diffusion barriers in closed mitosis', 'Zavala, Eder|Marquez-Lago, Tatiana T', 'Integrative Systems Biology Unit, Okinawa Institute of Science and Technology, Okinawa, Japan.|Integrative Systems Biology Unit, Okinawa Institute of Science and Technology, Okinawa, Japan.', 'Japan|Japan', 3, 'Anaphase|Cell Membrane|Cell Membrane Permeability|Cell Nucleus|Computational Biology|Computer Simulation|Diffusion|Models, Biological|Saccharomyces cerevisiae|Sphingolipids|Stochastic Processes', 'Journal Article', '10.1371/journal.pcbi.1003725', '25032937', 'Diffusion barriers are effective means for constraining protein lateral exchange in cellular membranes. In Saccharomyces cerevisiae, they have been shown to sustain parental identity through asymmetric segregation of ageing factors during closed mitosis. Even though barriers have been extensively studied in the plasma membrane, their identity and organization within the nucleus remains poorly understood. Based on different lines of experimental evidence, we present a model of the composition and structural organization of a nuclear diffusion barrier during anaphase. By means of spatial stochastic simulations, we propose how specialised lipid domains, protein rings, and morphological changes of the nucleus may coordinate to restrict protein exchange between mother and daughter nuclear lobes. We explore distinct, plausible configurations of these diffusion barriers and offer testable predictions regarding their protein exclusion properties and the diffusion regimes they generate. Our model predicts that, while a specialised lipid domain and an immobile protein ring at the bud neck can compartmentalize the nucleus during early anaphase, a specialised lipid domain spanning the elongated bridge between lobes would be entirely sufficient during late anaphase. Our work shows how complex nuclear diffusion barriers in closed mitosis may arise from simple nanoscale biophysical interactions. '),
(86671, 2014, 'plos', 'Modeling bi-modality improves characterization of cell cycle on gene expression in single cells', 'McDavid, Andrew|Dennis, Lucas|Danaher, Patrick|Finak, Greg|Krouse, Michael|Wang, Alice|Webster, Philippa|Beechem, Joseph|Gottardo, Raphael', 'Department of Statistics, University of Washington, Seattle, Washington, United States of America, Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.|NanoString Technologies, Seattle, Washington, United States of America.|NanoString Technologies, Seattle, Washington, United States of America.|Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.|NanoString Technologies, Seattle, Washington, United States of America.|BD Biosciences, San Jose, California, United States of America.|NanoString Technologies, Seattle, Washington, United States of America.|NanoString Technologies, Seattle, Washington, United States of America.|Department of Statistics, University of Washington, Seattle, Washington, United States of America, Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.', 'United States|United States|United States|United States|United States|United States|United States|Un', 37, 'Cell Cycle|Cell Line|Computational Biology|Gene Expression|Gene Expression Profiling|Gene Regulatory Networks|Genes, cdc|Humans|Models, Genetic', 'Journal Article', '10.1371/journal.pcbi.1003696', '25032992', 'Advances in high-throughput, single cell gene expression are allowing interrogation of cell heterogeneity. However, there is concern that the cell cycle phase of a cell might bias characterizations of gene expression at the single-cell level. We assess the effect of cell cycle phase on gene expression in single cells by measuring 333 genes in 930 cells across three phases and three cell lines. We determine each cells phase non-invasively without chemical arrest and use it as a covariate in tests of differential expression. We observe bi-modal gene expression, a previously-described phenomenon, wherein the expression of otherwise abundant genes is either strongly positive, or undetectable within individual cells. This bi-modality is likely both biologically and technically driven. Irrespective of its source, we show that it should be modeled to draw accurate inferences from single cell expression experiments. To this end, we propose a semi-continuous modeling framework based on the generalized linear model, and use it to characterize genes with consistent cell cycle effects across three cell lines. Our new computational framework improves the detection of previously characterized cell-cycle genes compared to approaches that do not account for the bi-modality of single-cell data. We use our semi-continuous modelling framework to estimate single cell gene co-expression networks. These networks suggest that in addition to having phase-dependent shifts in expression (when averaged over many cells), some, but not all, canonical cell cycle genes tend to be co-expressed in groups in single cells. We estimate the amount of single cell expression variability attributable to the cell cycle. We find that the cell cycle explains only 5%-17% of expression variability, suggesting that the cell cycle will not tend to be a large nuisance factor in analysis of the single cell transcriptome. '),
(86672, 2014, 'plos', 'A novel nutritional predictor links microbial fastidiousness with lowered ubiquity, growth rate, and cooperativeness', 'Zarecki, Raphy|Oberhardt, Matthew A|Reshef, Leah|Gophna, Uri|Ruppin, Eytan', 'School of Computer Sciences & Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.|School of Computer Sciences & Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel, Department of Molecular Microbiology and Biotechnology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel.|Department of Molecular Microbiology and Biotechnology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel.|Department of Molecular Microbiology and Biotechnology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel.|School of Computer Sciences & Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.', 'Israel|Israel|Israel|Israel|Israel', 11, 'Bacteria|Databases, Genetic|Ecosystem|Genome, Bacterial|Genomics|Metabolic Networks and Pathways|Microbiota', 'Journal Article', '10.1371/journal.pcbi.1003726', '25033033', 'Understanding microbial nutritional requirements is a key challenge in microbiology. Here we leverage the recent availability of thousands of automatically generated genome-scale metabolic models to develop a predictor of microbial minimal medium requirements, which we apply to thousands of species to study the relationship between their nutritional requirements and their ecological and genomic traits. We first show that nutritional requirements are more similar among species that co-habit many ecological niches. We then reveal three fundamental characteristics of microbial fastidiousness (i.e., complex and specific nutritional requirements): (1) more fastidious microorganisms tend to be more ecologically limited, (2) fastidiousness is positively associated with smaller genomes and smaller metabolic networks, and (3) more fastidious species grow more slowly and have less ability to cooperate with other species than more metabolically versatile organisms. These associations reflect the adaptation of fastidious microorganisms to unique niches with few cohabitating species. They also explain how non-fastidious species inhabit many ecological niches with high abundance rates. Taken together, these results advance our understanding microbial nutrition on a large scale, by presenting new nutrition-related associations that govern the distribution of microorganisms in nature. '),
(86673, 2014, 'plos', 'CeleST: computer vision software for quantitative analysis of C elegans swim behavior reveals novel features of locomotion', 'Restif, Christophe|Ibáñez-Ventoso, Carolina|Vora, Mehul M|Guo, Suzhen|Metaxas, Dimitris|Driscoll, Monica', 'Center for Computational Biomedicine Imaging and Modeling, Rutgers, The State University of New Jersey, Piscataway, New Jersey, United States of America.|Department of Molecular Biology and Biochemistry, Nelson Biological Laboratories, Rutgers, The State University of New Jersey, Piscataway, New Jersey, United States of America.|Department of Molecular Biology and Biochemistry, Nelson Biological Laboratories, Rutgers, The State University of New Jersey, Piscataway, New Jersey, United States of America.|Department of Molecular Biology and Biochemistry, Nelson Biological Laboratories, Rutgers, The State University of New Jersey, Piscataway, New Jersey, United States of America.|Center for Computational Biomedicine Imaging and Modeling, Rutgers, The State University of New Jersey, Piscataway, New Jersey, United States of America.|Department of Molecular Biology and Biochemistry, Nelson Biological Laboratories, Rutgers, The State University of New Jersey, Piscataway, New Jersey, United States of America.', 'Jersey|Jersey|Jersey|Jersey|Jersey|Jersey', 38, 'Animals|Caenorhabditis elegans|Computational Biology|Databases, Factual|Models, Biological|Phenotype|Software|Swimming', 'Journal Article', '10.1371/journal.pcbi.1003702', '25033081', 'In the effort to define genes and specific neuronal circuits that control behavior and plasticity, the capacity for high-precision automated analysis of behavior is essential. We report on comprehensive computer vision software for analysis of swimming locomotion of C. elegans, a simple animal model initially developed to facilitate elaboration of genetic influences on behavior. C. elegans swim test software CeleST tracks swimming of multiple animals, measures 10 novel parameters of swim behavior that can fully report dynamic changes in posture and speed, and generates data in several analysis formats, complete with statistics. Our measures of swim locomotion utilize a deformable model approach and a novel mathematical analysis of curvature maps that enable even irregular patterns and dynamic changes to be scored without need for thresholding or dropping outlier swimmers from study. Operation of CeleST is mostly automated and only requires minimal investigator interventions, such as the selection of videotaped swim trials and choice of data output format. Data can be analyzed from the level of the single animal to populations of thousands. We document how the CeleST program reveals unexpected preferences for specific swim gaits in wild-type C. elegans, uncovers previously unknown mutant phenotypes, efficiently tracks changes in aging populations, and distinguishes graceful from poor aging. The sensitivity, dynamic range, and comprehensive nature of CeleST measures elevate swim locomotion analysis to a new level of ease, economy, and detail that enables behavioral plasticity resulting from genetic, cellular, or experience manipulation to be analyzed in ways not previously possible. '),
(86674, 2014, 'plos', 'P-loop conformation governed crizotinib resistance in G2032R-mutated ROS1 tyrosine kinase: clues from free energy landscape', 'Sun, Huiyong|Li, Youyong|Tian, Sheng|Wang, Junmei|Hou, Tingjun', 'Institute of Functional Nano & Soft Materials (FUNSOM) and Collaborative Innovation Center of Suzhou Nano Science and Technology, Soochow University, Suzhou, Jiangsu, China, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China.|Institute of Functional Nano & Soft Materials (FUNSOM) and Collaborative Innovation Center of Suzhou Nano Science and Technology, Soochow University, Suzhou, Jiangsu, China.|Institute of Functional Nano & Soft Materials (FUNSOM) and Collaborative Innovation Center of Suzhou Nano Science and Technology, Soochow University, Suzhou, Jiangsu, China.|Department of Biochemistry, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America.|Institute of Functional Nano & Soft Materials (FUNSOM) and Collaborative Innovation Center of Suzhou Nano Science and Technology, Soochow University, Suzhou, Jiangsu, China, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China.', 'China|China|China|United States|China', 70, 'Antineoplastic Agents|Binding Sites|Crizotinib|Drug Resistance, Neoplasm|Humans|Molecular Dynamics Simulation|Mutation|Protein Kinase Inhibitors|Protein-Tyrosine Kinases|Proto-Oncogene Proteins|Pyrazoles|Pyridines|Thermodynamics', 'Journal Article', '10.1371/journal.pcbi.1003729', '25033171', 'Tyrosine kinases are regarded as excellent targets for chemical drug therapy of carcinomas. However, under strong purifying selection, drug resistance usually occurs in the cancer cells within a short term. Many cases of drug resistance have been found to be associated with secondary mutations in drug target, which lead to the attenuated drug-target interactions. For example, recently, an acquired secondary mutation, G2032R, has been detected in the drug target, ROS1 tyrosine kinase, from a crizotinib-resistant patient, who responded poorly to crizotinib within a very short therapeutic term. It was supposed that the mutation was located at the solvent front and might hinder the drug binding. However, a different fact could be uncovered by the simulations reported in this study. Here, free energy surfaces were characterized by the drug-target distance and the phosphate-binding loop (P-loop) conformational change of the crizotinib-ROS1 complex through advanced molecular dynamics techniques, and it was revealed that the more rigid P-loop region in the G2032R-mutated ROS1 was primarily responsible for the crizotinib resistance, which on one hand, impaired the binding of crizotinib directly, and on the other hand, shortened the residence time induced by the flattened free energy surface. Therefore, both of the binding affinity and the drug residence time should be emphasized in rational drug design to overcome the kinase resistance. '),
(86675, 2014, 'plos', 'Rethinking transcriptional activation in the Arabidopsis circadian clock', 'Fogelmark, Karl|Troein, Carl', 'Computational Biology and Biological Physics, Department of Astronomy and Theoretical Physics, Lund University, Lund, Sweden.|Computational Biology and Biological Physics, Department of Astronomy and Theoretical Physics, Lund University, Lund, Sweden.', 'Sweden|Sweden', 67, 'Arabidopsis|Arabidopsis Proteins|Circadian Clocks|Computational Biology|Models, Genetic|Transcription Factors|Transcriptional Activation', 'Journal Article', '10.1371/journal.pcbi.1003705', '25033214', 'Circadian clocks are biological timekeepers that allow living cells to time their activity in anticipation of predictable daily changes in light and other environmental factors. The complexity of the circadian clock in higher plants makes it difficult to understand the role of individual genes or molecular interactions, and mathematical modelling has been useful in guiding clock research in model organisms such as Arabidopsis thaliana. We present a model of the circadian clock in Arabidopsis, based on a large corpus of published time course data. It appears from experimental evidence in the literature that most interactions in the clock are repressive. Hence, we remove all transcriptional activation found in previous models of this system, and instead extend the system by including two new components, the morning-expressed activator RVE8 and the nightly repressor/activator NOX. Our modelling results demonstrate that the clock does not need a large number of activators in order to reproduce the observed gene expression patterns. For example, the sequential expression of the PRR genes does not require the genes to be connected as a series of activators. In the presented model, transcriptional activation is exclusively the task of RVE8. Predictions of how strongly RVE8 affects its targets are found to agree with earlier interpretations of the experimental data, but generally we find that the many negative feedbacks in the system should discourage intuitive interpretations of mutant phenotypes. The dynamics of the clock are difficult to predict without mathematical modelling, and the clock is better viewed as a tangled web than as a series of loops. '),
(86676, 2014, 'plos', 'Analysis of the protein domain and domain architecture content in fungi and its application in the search of new antifungal targets', 'Barrera, Alejandro|Alastruey-Izquierdo, Ana|Martín, María J|Cuesta, Isabel|Vizcaíno, Juan Antonio', 'European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom, National Centre for Microbiology, Instituto de Salud Carlos III, Majadahonda, Madrid, Spain.|National Centre for Microbiology, Instituto de Salud Carlos III, Majadahonda, Madrid, Spain.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom.|National Centre for Microbiology, Instituto de Salud Carlos III, Majadahonda, Madrid, Spain.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom.', 'Spain|Spain|United Kingdom|Spain|United Kingdom', 10, 'Antifungal Agents|Aspergillus|Databases, Protein|Drug Discovery|Fungal Proteins|Fungi|Protein Structure, Tertiary|Proteome|Proteomics', 'Journal Article', '10.1371/journal.pcbi.1003733', '25033262', 'Over the past several years fungal infections have shown an increasing incidence in the susceptible population, and caused high mortality rates. In parallel, multi-resistant fungi are emerging in human infections. Therefore, the identification of new potential antifungal targets is a priority. The first task of this study was to analyse the protein domain and domain architecture content of the 137 fungal proteomes (corresponding to 111 species) available in UniProtKB (UniProt KnowledgeBase) by January 2013. The resulting list of core and exclusive domain and domain architectures is provided in this paper. It delineates the different levels of fungal taxonomic classification: phylum, subphylum, order, genus and species. The analysis highlighted Aspergillus as the most diverse genus in terms of exclusive domain content. In addition, we also investigated which domains could be considered promiscuous in the different organisms. As an application of this analysis, we explored three different ways to detect potential targets for antifungal drugs. First, we compared the domain and domain architecture content of the human and fungal proteomes, and identified those domains and domain architectures only present in fungi. Secondly, we looked for information regarding fungal pathways in public repositories, where proteins containing promiscuous domains could be involved. Three pathways were identified as a result: lovastatin biosynthesis, xylan degradation and biosynthesis of siroheme. Finally, we classified a subset of the studied fungi in five groups depending on their occurrence in clinical samples. We then looked for exclusive domains in the groups that were more relevant clinically and determined which of them had the potential to bind small molecules. Overall, this study provides a comprehensive analysis of the available fungal proteomes and shows three approaches that can be used as a first step in the detection of new antifungal targets. '),
(86677, 2014, 'plos', 'Signaling domain of Sonic Hedgehog as cannibalistic calcium-regulated zinc-peptidase', 'Rebollido-Rios, Rocio|Bandari, Shyam|Wilms, Christoph|Jakuschev, Stanislav|Vortkamp, Andrea|Grobe, Kay|Hoffmann, Daniel', 'Research Group Bioinformatics, Faculty of Biology, Center of Medical Biotechnology, University of Duisburg-Essen, Essen, Germany.|Institute of Physiological Chemistry and Pathobiochemistry, Faculty of Medicine, University of Mxc3xbcnster, Mxc3xbcnster, Germany.|Research Group Bioinformatics, Faculty of Biology, Center of Medical Biotechnology, University of Duisburg-Essen, Essen, Germany.|Research Group Bioinformatics, Faculty of Biology, Center of Medical Biotechnology, University of Duisburg-Essen, Essen, Germany.|Department of Developmental Biology, Faculty of Biology, Center of Medical Biotechnology, University of Duisburg-Essen, Essen, Germany.|Institute of Physiological Chemistry and Pathobiochemistry, Faculty of Medicine, University of Mxc3xbcnster, Mxc3xbcnster, Germany.|Research Group Bioinformatics, Faculty of Biology, Center of Medical Biotechnology, University of Duisburg-Essen, Essen, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany', 6, 'Animals|Binding Sites|Calcium|Hedgehog Proteins|Mice|Molecular Dynamics Simulation|Peptide Hydrolases|Zinc', 'Journal Article', '10.1371/journal.pcbi.1003707', '25033298', 'Sonic Hedgehog (Shh) is a representative of the evolutionary closely related class of Hedgehog proteins that have essential signaling functions in animal development. The N-terminal domain (ShhN) is also assigned to the group of LAS proteins (LASxe2x80x8a=xe2x80x8aLysostaphin type enzymes, D-Ala-D-Ala metalloproteases, Sonic Hedgehog), of which all members harbor a structurally well-defined Zn2+ center, however, it is remarkable that ShhN so far is the only LAS member without proven peptidase activity. Another unique feature of ShhN in the LAS group is a double-Ca2+ center close to the zinc. We have studied the effect of these calcium ions on ShhN structure, dynamics, and interactions. We find that the presence of calcium has a marked impact on ShhN properties, with the two calcium ions having different effects. The more strongly bound calcium ion significantly stabilizes the overall structure. Surprisingly, the binding of the second calcium ion switches the putative catalytic center from a state similar to LAS enzymes to a state that probably is catalytically inactive. We describe in detail the mechanics of the switch, including the effect on substrate co-ordinating residues and on the putative catalytic water molecule. The properties of the putative substrate binding site suggest that ShhN could degrade other ShhN molecules, e.g. by cleavage at highly conserved glycines in ShhN. To test experimentally the stability of ShhN against autodegradation, we compare two ShhN mutants in vitro: (1) a ShhN mutant unable to bind calcium but with putative catalytic center intact, and thus, according to our hypothesis, a constitutively active peptidase, and (2) a mutant carrying additionally mutation E177A, i.e., with the putative catalytically active residue knocked out. The in vitro results are consistent with ShhN being a cannibalistic zinc-peptidase. These experiments also reveal that the peptidase activity depends on pH.'),
(86678, 2014, 'plos', 'Enhanced regulatory sequence prediction using gapped k-mer features', 'Ghandi, Mahmoud|Lee, Dongwon|Mohammad-Noori, Morteza|Beer, Michael A', 'Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America.|Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America.|School of Mathematics, Statistics and Computer Science, University of Tehran, Tehran, Iran, School of Computer Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.|Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America.', 'United States|United States|-|United States', 154, 'Base Sequence|Bayes Theorem|Chromatin Immunoprecipitation|Computational Biology|Models, Genetic|Oligonucleotides|Organ Specificity|Regulatory Sequences, Nucleic Acid|Sequence Analysis, DNA|Support Vector Machine', 'Journal Article', '10.1371/journal.pcbi.1003711', '25033408', 'Oligomers of length k, or k-mers, are convenient and widely used features for modeling the properties and functions of DNA and protein sequences. However, k-mers suffer from the inherent limitation that if the parameter k is increased to resolve longer features, the probability of observing any specific k-mer becomes very small, and k-mer counts approach a binary variable, with most k-mers absent and a few present once. Thus, any statistical learning approach using k-mers as features becomes susceptible to noisy training set k-mer frequencies once k becomes large. To address this problem, we introduce alternative feature sets using gapped k-mers, a new classifier, gkm-SVM, and a general method for robust estimation of k-mer frequencies. To make the method applicable to large-scale genome wide applications, we develop an efficient tree data structure for computing the kernel matrix. We show that compared to our original kmer-SVM and alternative approaches, our gkm-SVM predicts functional genomic regulatory elements and tissue specific enhancers with significantly improved accuracy, increasing the precision by up to a factor of two. We then show that gkm-SVM consistently outperforms kmer-SVM on human ENCODE ChIP-seq datasets, and further demonstrate the general utility of our method using a Naïve-Bayes classifier. Although developed for regulatory sequence analysis, these methods can be applied to any sequence classification problem. '),
(86679, 2014, 'plos', 'A multiscale approach to modelling drug metabolism by membrane-bound cytochrome P450 enzymes', 'Lonsdale, Richard|Rouse, Sarah L|Sansom, Mark S P|Mulholland, Adrian J', 'Centre for Computational Chemistry, School of Chemistry, University of Bristol, Bristol, United Kingdom.|Department of Biochemistry, University of Oxford, Oxford, United Kingdom.|Department of Biochemistry, University of Oxford, Oxford, United Kingdom.|Centre for Computational Chemistry, School of Chemistry, University of Bristol, Bristol, United Kingdom.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 31, 'Catalytic Domain|Cell Membrane|Computational Biology|Cytochrome P-450 CYP3A|Heme|Humans|Molecular Dynamics Simulation|Pliability|Protein Binding|Protein Conformation|Warfarin', 'Journal Article', '10.1371/journal.pcbi.1003714', '25033460', 'Cytochrome P450 enzymes are found in all life forms. P450s play an important role in drug metabolism, and have potential uses as biocatalysts. Human P450s are membrane-bound proteins. However, the interactions between P450s and their membrane environment are not well-understood. To date, all P450 crystal structures have been obtained from engineered proteins, from which the transmembrane helix was absent. A significant number of computational studies have been performed on P450s, but the majority of these have been performed on the solubilised forms of P450s. Here we present a multiscale approach for modelling P450s, spanning from coarse-grained and atomistic molecular dynamics simulations to reaction modelling using hybrid quantum mechanics/molecular mechanics (QM/MM) methods. To our knowledge, this is the first application of such an integrated multiscale approach to modelling of a membrane-bound enzyme. We have applied this protocol to a key human P450 involved in drug metabolism: CYP3A4. A biologically realistic model of CYP3A4, complete with its transmembrane helix and a membrane, has been constructed and characterised. The dynamics of this complex have been studied, and the oxidation of the anticoagulant R-warfarin has been modelled in the active site. Calculations have also been performed on the soluble form of the enzyme in aqueous solution. Important differences are observed between the membrane and solution systems, most notably for the gating residues and channels that control access to the active site. The protocol that we describe here is applicable to other membrane-bound enzymes. '),
(86680, 2014, 'plos', 'From spontaneous motor activity to coordinated behaviour: a developmental model', 'Marques, Hugo Gravato|Bharadwaj, Arjun|Iida, Fumiya', 'Dept. of Mechanical and Process Engineering, ETH, Zurich, Switzerland.|Dept. of Mechanical and Process Engineering, ETH, Zurich, Switzerland.|Dept. of Mechanical and Process Engineering, ETH, Zurich, Switzerland.', 'Switzerland|Switzerland|Switzerland', 14, 'Adult|Behavior|Humans|Leg|Models, Neurological|Motor Activity|Muscle, Skeletal|Psychomotor Performance', 'Journal Article', '10.1371/journal.pcbi.1003653', '25057775', 'In mammals, the developmental path that links the primary behaviours observed during foetal stages to the full fledged behaviours observed in adults is still beyond our understanding. Often theories of motor control try to deal with the process of incremental learning in an abstract and modular way without establishing any correspondence with the mammalian developmental stages. In this paper, we propose a computational model that links three distinct behaviours which appear at three different stages of development. In order of appearance, these behaviours are: spontaneous motor activity (SMA), reflexes, and coordinated behaviours, such as locomotion. The goal of our model is to address in silico four hypotheses that are currently hard to verify in vivo: First, the hypothesis that spinal reflex circuits can be self-organized from the sensor and motor activity induced by SMA. Second, the hypothesis that supraspinal systems can modulate reflex circuits to achieve coordinated behaviour. Third, the hypothesis that, since SMA is observed in an organism throughout its entire lifetime, it provides a mechanism suitable to maintain the reflex circuits aligned with the musculoskeletal system, and thus adapt to changes in body morphology. And fourth, the hypothesis that by changing the modulation of the reflex circuits over time, one can switch between different coordinated behaviours. Our model is tested in a simulated musculoskeletal leg actuated by six muscles arranged in a number of different ways. Hopping is used as a case study of coordinated behaviour. Our results show that reflex circuits can be self-organized from SMA, and that, once these circuits are in place, they can be modulated to achieve coordinated behaviour. In addition, our results show that our model can naturally adapt to different morphological changes and perform behavioural transitions. '),
(86681, 2014, 'plos', 'Collective behaviour without collective order in wild swarms of midges', 'Attanasi, Alessandro|Cavagna, Andrea|Del Castello, Lorenzo|Giardina, Irene|Melillo, Stefania|Parisi, Leonardo|Pohl, Oliver|Rossaro, Bruno|Shen, Edward|Silvestri, Edmondo|Viale, Massimiliano', 'Istituto Sistemi Complessi, Consiglio Nazionale delle Ricerche, UOS Sapienza, Rome, Italy, Dipartimento di Fisica, Università Sapienza, Rome, Italy.|Istituto Sistemi Complessi, Consiglio Nazionale delle Ricerche, UOS Sapienza, Rome, Italy, Dipartimento di Fisica, Università Sapienza, Rome, Italy, Initiative for the Theoretical Sciences, The Graduate Center, City University of New York, New York, New York, United States of America.|Istituto Sistemi Complessi, Consiglio Nazionale delle Ricerche, UOS Sapienza, Rome, Italy, Dipartimento di Fisica, Università Sapienza, Rome, Italy.|Istituto Sistemi Complessi, Consiglio Nazionale delle Ricerche, UOS Sapienza, Rome, Italy, Dipartimento di Fisica, Università Sapienza, Rome, Italy, Initiative for the Theoretical Sciences, The Graduate Center, City University of New York, New York, New York, United States of America.|Istituto Sistemi Complessi, Consiglio Nazionale delle Ricerche, UOS Sapienza, Rome, Italy, Dipartimento di Fisica, Università Sapienza, Rome, Italy.|Istituto Sistemi Complessi, Consiglio Nazionale delle Ricerche, UOS Sapienza, Rome, Italy, Dipartimento di Informatica, Università Sapienza, Rome, Italy.|Istituto Sistemi Complessi, Consiglio Nazionale delle Ricerche, UOS Sapienza, Rome, Italy, Dipartimento di Fisica, Università Sapienza, Rome, Italy.|Dipartimento di Fisica, Università di Roma 3, Rome, Italy.|Istituto Sistemi Complessi, Consiglio Nazionale delle Ricerche, UOS Sapienza, Rome, Italy, Dipartimento di Fisica, Università Sapienza, Rome, Italy.|Istituto Sistemi Complessi, Consiglio Nazionale delle Ricerche, UOS Sapienza, Rome, Italy, Dipartimento di Scienze per gli Alimenti la Nutrizione e lAmbiente, Università degli Studi di Milano, Milano, Italy.|Istituto Sistemi Complessi, Consiglio Nazionale delle Ricerche, UOS Sapienza, Rome, Italy, Dipartimento di Fisica, Università Sapienza, Rome, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 85, 'Animals|Behavior, Animal|Computational Biology|Diptera|Male|Models, Biological|Spatial Behavior', 'Journal Article', '10.1371/journal.pcbi.1003697', '25057853', 'Collective behaviour is a widespread phenomenon in biology, cutting through a huge span of scales, from cell colonies up to bird flocks and fish schools. The most prominent trait of collective behaviour is the emergence of global order: individuals synchronize their states, giving the stunning impression that the group behaves as one. In many biological systems, though, it is unclear whether global order is present. A paradigmatic case is that of insect swarms, whose erratic movements seem to suggest that group formation is a mere epiphenomenon of the independent interaction of each individual with an external landmark. In these cases, whether or not the group behaves truly collectively is debated. Here, we experimentally study swarms of midges in the field and measure how much the change of direction of one midge affects that of other individuals. We discover that, despite the lack of collective order, swarms display very strong correlations, totally incompatible with models of non-interacting particles. We find that correlation increases sharply with the swarms density, indicating that the interaction between midges is based on a metric perception mechanism. By means of numerical simulations we demonstrate that such growing correlation is typical of a system close to an ordering transition. Our findings suggest that correlation, rather than order, is the true hallmark of collective behaviour in biological systems. '),
(86682, 2014, 'plos', 'Network analysis of breast cancer progression and reversal using a tree-evolving network algorithm', 'Parikh, Ankur P|Curtis, Ross E|Kuhn, Irene|Becker-Weimann, Sabine|Bissell, Mina|Xing, Eric P|Wu, Wei', 'Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.|Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.|Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America.|Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America.|Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America.|Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America, Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America, Joint Carnegie Mellon University-University of Pittsburgh PhD Program in Computational Biology, Pittsburgh, Pennsylvania, United States of America.|Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.', 'United States|United States|United States|United States|United States|United States|United States', 9, 'Algorithms|Breast Neoplasms|Cell Line, Tumor|Computational Biology|Computer Simulation|Databases, Factual|Disease Progression|Female|Gene Regulatory Networks|Humans|Kaplan-Meier Estimate|Markov Chains|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1371/journal.pcbi.1003713', '25057922', 'The HMT3522 progression series of human breast cells have been used to discover how tissue architecture, microenvironment and signaling molecules affect breast cell growth and behaviors. However, much remains to be elucidated about malignant and phenotypic reversion behaviors of the HMT3522-T4-2 cells of this series. We employed a pan-cell-state strategy, and analyzed jointly microarray profiles obtained from different state-specific cell populations from this progression and reversion model of the breast cells using a tree-lineage multi-network inference algorithm, Treegl. We found that different breast cell states contain distinct gene networks. The network specific to non-malignant HMT3522-S1 cells is dominated by genes involved in normal processes, whereas the T4-2-specific network is enriched with cancer-related genes. The networks specific to various conditions of the reverted T4-2 cells are enriched with pathways suggestive of compensatory effects, consistent with clinical data showing patient resistance to anticancer drugs. We validated the findings using an external dataset, and showed that aberrant expression values of certain hubs in the identified networks are associated with poor clinical outcomes. Thus, analysis of various reversion conditions (including non-reverted) of HMT3522 cells using Treegl can be a good model system to study drug effects on breast cancer. '),
(86683, 2014, 'plos', 'Web-based computational chemistry education with CHARMMing I: Lessons and tutorial', 'Miller, Benjamin T|Singh, Rishi P|Schalk, Vinushka|Pevzner, Yuri|Sun, Jingjun|Miller, Carrie S|Boresch, Stefan|Ichiye, Toshiko|Brooks, Bernard R|Woodcock, H Lee', 'Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, Bethesda, Maryland, United States of America.|Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, Bethesda, Maryland, United States of America.|Department of Natural Sciences, New College of Florida, Sarasota, Florida, United States of America.|Department of Chemistry, University of South Florida, Tampa, Florida, United States of America.|Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, Bethesda, Maryland, United States of America.|Department of Chemistry, Georgetown University, Washington, D.C., United States of America.|Department of Computational Biological Chemistry, Faculty of Chemistry, University of Vienna, Vienna, Austria.|Department of Chemistry, Georgetown University, Washington, D.C., United States of America.|Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, Bethesda, Maryland, United States of America.|Department of Chemistry, University of South Florida, Tampa, Florida, United States of America.', 'United States|United States|United States|United States|United States|United States|Austria|United S', 7, 'Computational Biology|Computer Simulation|Computer-Assisted Instruction|Databases, Protein|Internet|Models, Molecular|Software', 'Journal Article', '10.1371/journal.pcbi.1003719', '25057988', 'This article describes the development, implementation, and use of web-based lessons to introduce students and other newcomers to computer simulations of biological macromolecules. These lessons, i.e., interactive step-by-step instructions for performing common molecular simulation tasks, are integrated into the collaboratively developed CHARMM INterface and Graphics (CHARMMing) web user interface (http://www.charmming.org). Several lessons have already been developed with new ones easily added via a provided Python script. In addition to CHARMMings new lessons functionality, web-based graphical capabilities have been overhauled and are fully compatible with modern mobile web browsers (e.g., phones and tablets), allowing easy integration of these advanced simulation techniques into coursework. Finally, one of the primary objections to web-based systems like CHARMMing has been that point and click simulation set-up does little to teach the user about the underlying physics, biology, and computational methods being applied. In response to this criticism, we have developed a freely available tutorial to bridge the gap between graphical simulation setup and the technical knowledge necessary to perform simulations without user interface assistance. '),
(86684, 2014, 'plos', 'Phase-coherence transitions and communication in the gamma range between delay-coupled neuronal populations', 'Barardi, Alessandro|Sancristóbal, Belen|Garcia-Ojalvo, Jordi', 'Departament of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona Biomedical Research Park, Barcelona, Spain, Departament de Física i Enginyeria Nuclear, Universitat Politècnica de Catalunya, Terrassa, Spain.|Center for Genomic Regulation, Barcelona Biomedical Research Park, Barcelona, Spain.|Departament of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona Biomedical Research Park, Barcelona, Spain.', 'Spain|Spain|Spain', 0, 'Action Potentials|Computational Biology|Computer Simulation|Models, Neurological|Neurons', 'Journal Article', '10.1371/journal.pcbi.1003723', '25058021', 'Synchronization between neuronal populations plays an important role in information transmission between brain areas. In particular, collective oscillations emerging from the synchronized activity of thousands of neurons can increase the functional connectivity between neural assemblies by coherently coordinating their phases. This synchrony of neuronal activity can take place within a cortical patch or between different cortical regions. While short-range interactions between neurons involve just a few milliseconds, communication through long-range projections between different regions could take up to tens of milliseconds. How these heterogeneous transmission delays affect communication between neuronal populations is not well known. To address this question, we have studied the dynamics of two bidirectionally delayed-coupled neuronal populations using conductance-based spiking models, examining how different synaptic delays give rise to in-phase/anti-phase transitions at particular frequencies within the gamma range, and how this behavior is related to the phase coherence between the two populations at different frequencies. We have used spectral analysis and information theory to quantify the information exchanged between the two networks. For different transmission delays between the two coupled populations, we analyze how the local field potential and multi-unit activity calculated from one population convey information in response to a set of external inputs applied to the other population. The results confirm that zero-lag synchronization maximizes information transmission, although out-of-phase synchronization allows for efficient communication provided the coupling delay, the phase lag between the populations, and the frequency of the oscillations are properly matched. ');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(86685, 2014, 'plos', 'iRegulon: from a gene list to a gene regulatory network using large motif and track collections', 'Janky, Rekins|Verfaillie, Annelien|Imrichová, Hana|Van de Sande, Bram|Standaert, Laura|Christiaens, Valerie|Hulselmans, Gert|Herten, Koen|Naval Sanchez, Marina|Potier, Delphine|Svetlichnyy, Dmitry|Kalender Atak, Zeynep|Fiers, Mark|Marine, Jean-Christophe|Aerts, Stein', 'Laboratory of Computational Biology, KU Leuven Center for Human Genetics, Leuven, Belgium.|Laboratory of Computational Biology, KU Leuven Center for Human Genetics, Leuven, Belgium.|Laboratory of Computational Biology, KU Leuven Center for Human Genetics, Leuven, Belgium.|Laboratory of Computational Biology, KU Leuven Center for Human Genetics, Leuven, Belgium.|Laboratory for Molecular Cancer Biology, KU Leuven Center for Human Genetics, Leuven, Belgium, VIB Center for the Biology of Disease, Laboratory for Molecular Cancer Biology, Leuven, Belgium.|Laboratory of Computational Biology, KU Leuven Center for Human Genetics, Leuven, Belgium.|Laboratory of Computational Biology, KU Leuven Center for Human Genetics, Leuven, Belgium.|Laboratory of Computational Biology, KU Leuven Center for Human Genetics, Leuven, Belgium.|Laboratory of Computational Biology, KU Leuven Center for Human Genetics, Leuven, Belgium.|Laboratory of Computational Biology, KU Leuven Center for Human Genetics, Leuven, Belgium.|Laboratory of Computational Biology, KU Leuven Center for Human Genetics, Leuven, Belgium.|Laboratory of Computational Biology, KU Leuven Center for Human Genetics, Leuven, Belgium.|VIB Center for the Biology of Disease, Laboratory for Molecular Cancer Biology, Leuven, Belgium.|Laboratory for Molecular Cancer Biology, KU Leuven Center for Human Genetics, Leuven, Belgium, VIB Center for the Biology of Disease, Laboratory for Molecular Cancer Biology, Leuven, Belgium.|Laboratory of Computational Biology, KU Leuven Center for Human Genetics, Leuven, Belgium.', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belg', 325, 'Breast Neoplasms|Cell Line, Tumor|Chromatin Immunoprecipitation|Computational Biology|Databases, Genetic|Gene Expression Regulation|Gene Regulatory Networks|Genes, p53|Humans|Models, Genetic|Sequence Analysis, RNA|Transcription Factors', 'Journal Article', '10.1371/journal.pcbi.1003731', '25058159', 'Identifying master regulators of biological processes and mapping their downstream gene networks are key challenges in systems biology. We developed a computational method, called iRegulon, to reverse-engineer the transcriptional regulatory network underlying a co-expressed gene set using cis-regulatory sequence analysis. iRegulon implements a genome-wide ranking-and-recovery approach to detect enriched transcription factor motifs and their optimal sets of direct targets. We increase the accuracy of network inference by using very large motif collections of up to ten thousand position weight matrices collected from various species, and linking these to candidate human TFs via a motif2TF procedure. We validate iRegulon on gene sets derived from ENCODE ChIP-seq data with increasing levels of noise, and we compare iRegulon with existing motif discovery methods. Next, we use iRegulon on more challenging types of gene lists, including microRNA target sets, protein-protein interaction networks, and genetic perturbation data. In particular, we over-activate p53 in breast cancer cells, followed by RNA-seq and ChIP-seq, and could identify an extensive up-regulated network controlled directly by p53. Similarly we map a repressive network with no indication of direct p53 regulation but rather an indirect effect via E2F and NFY. Finally, we generalize our computational framework to include regulatory tracks such as ChIP-seq data and show how motif and track discovery can be combined to map functional regulatory interactions among co-expressed genes. iRegulon is available as a Cytoscape plugin from http://iregulon.aertslab.org. '),
(86686, 2014, 'plos', 'Web-based computational chemistry education with CHARMMing II: Coarse-grained protein folding', 'Pickard, Frank C|Miller, Benjamin T|Schalk, Vinushka|Lerner, Michael G|Woodcock, H Lee|Brooks, Bernard R', 'Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, United States of America.|Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, United States of America.|Department of Natural Sciences, New College of Florida, Sarasota, Florida, United States of America.|Department of Physics and Astronomy, Earlham College, Richmond, Indiana, United States of America.|Department of Chemistry, University of South Florida, Tampa, Florida, United States of America.|Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, United States of America.', 'United States|United States|United States|India|United States|United States', 0, 'Computational Biology|Internet|Molecular Dynamics Simulation|Protein Folding|Proteins|Software|User-Computer Interface', 'Journal Article', '10.1371/journal.pcbi.1003738', '25058338', 'A lesson utilizing a coarse-grained (CG) Gxc5x8d-like model has been implemented into the CHARMM INterface and Graphics (CHARMMing) web portal (www.charmming.org) to the Chemistry at HARvard Macromolecular Mechanics (CHARMM) molecular simulation package. While widely used to model various biophysical processes, such as protein folding and aggregation, CG models can also serve as an educational tool because they can provide qualitative descriptions of complex biophysical phenomena for a relatively cheap computational cost. As a proof of concept, this lesson demonstrates the construction of a CG model of a small globular protein, its simulation via Langevin dynamics, and the analysis of the resulting data. This lesson makes connections between modern molecular simulation techniques and topics commonly presented in an advanced undergraduate lecture on physical chemistry. It culminates in a straightforward analysis of a short dynamics trajectory of a small fast folding globular protein, we briefly describe the thermodynamic properties that can be calculated from this analysis. The assumptions inherent in the model and the data analysis are laid out in a clear, concise manner, and the techniques used are consistent with those employed by specialists in the field of CG modeling. One of the major tasks in building the Gxc5x8d-like model is determining the relative strength of the nonbonded interactions between coarse-grained sites. New functionality has been added to CHARMMing to facilitate this process. The implementation of these features into CHARMMing helps automate many of the tedious aspects of constructing a CG Gxc5x8d model. The CG model builder and its accompanying lesson should be a valuable tool to chemistry students, teachers, and modelers in the field.'),
(86687, 2014, 'plos', 'Web-based computational chemistry education with CHARMMing III: Reduction potentials of electron transfer proteins', 'Perrin, B Scott|Miller, Benjamin T|Schalk, Vinushka|Woodcock, H Lee|Brooks, Bernard R|Ichiye, Toshiko', 'Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, United States of America.|Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, United States of America.|Department of Natural Sciences, New College of Florida, Sarasota, Florida, United States of America.|Department of Chemistry, University of South Florida, Tampa, Florida, United States of America.|Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, United States of America.|Department of Chemistry, Georgetown University, Washington, D.C., United States of America.', 'United States|United States|United States|United States|United States|United States', 0, 'Computational Biology|Electron Transport|Internet|Oxidation-Reduction|Proteins|Thermodynamics', 'Journal Article', '10.1371/journal.pcbi.1003739', '25058418', 'A module for fast determination of reduction potentials, E°, of redox-active proteins has been implemented in the CHARMM INterface and Graphics (CHARMMing) web portal (www.charmming.org). The free energy of reduction, which is proportional to E°, is composed of an intrinsic contribution due to the redox site and an environmental contribution due to the protein and solvent. Here, the intrinsic contribution is selected from a library of pre-calculated density functional theory values for each type of redox site and redox couple, while the environmental contribution is calculated from a crystal structure of the protein using Poisson-Boltzmann continuum electrostatics. An accompanying lesson demonstrates a calculation of E°. In this lesson, an ionizable residue in a [4Fe-4S]-protein that causes a pH-dependent E° is identified, and the E° of a mutant that would test the identification is predicted. This demonstration is valuable to both computational chemistry students and researchers interested in predicting sequence determinants of E° for mutagenesis. '),
(86688, 2014, 'plos', 'Membrane interaction of bound ligands contributes to the negative binding cooperativity of the EGF receptor', 'Arkhipov, Anton|Shan, Yibing|Kim, Eric T|Shaw, David E', 'D. E. Shaw Research, New York, New York, United States of America.|D. E. Shaw Research, New York, New York, United States of America.|D. E. Shaw Research, New York, New York, United States of America.|D. E. Shaw Research, New York, New York, United States of America, Department of Biochemistry and Molecular Biophysics, Columbia University, New York, New York, United States of America.', 'United States|United States|United States|United States', 27, 'Animals|Binding Sites|Cell Membrane|Chickens|Computational Biology|ErbB Receptors|Ligands|Mammals|Molecular Dynamics Simulation|Protein Binding|Protein Conformation|Protein Structure, Tertiary|Zebrafish', 'Journal Article', '10.1371/journal.pcbi.1003742', '25058506', 'The epidermal growth factor receptor (EGFR) plays a key role in regulating cell proliferation, migration, and differentiation, and aberrant EGFR signaling is implicated in a variety of cancers. EGFR signaling is triggered by extracellular ligand binding, which promotes EGFR dimerization and activation. Ligand-binding measurements are consistent with a negatively cooperative model in which the ligand-binding affinity at either binding site in an EGFR dimer is weaker when the other site is occupied by a ligand. This cooperativity is widely believed to be central to the effects of ligand concentration on EGFR-mediated intracellular signaling. Although the extracellular portion of the human EGFR dimer has been resolved crystallographically, the crystal structures do not reveal the structural origin of this negative cooperativity, which has remained unclear. Here we report the results of molecular dynamics simulations suggesting that asymmetrical interactions of the two binding sites with the membrane may be responsible (perhaps along with other factors) for this negative cooperativity. In particular, in our simulations the extracellular domains of an EGFR dimer spontaneously lay down on the membrane in an orientation in which favorable membrane contacts were made with one of the bound ligands, but could not be made with the other. Similar interactions were observed when EGFR was glycosylated, as it is in vivo. '),
(86689, 2014, 'plos', 'Propagating waves of directionality and coordination orchestrate collective cell migration', 'Zaritsky, Assaf|Kaplan, Doron|Hecht, Inbal|Natan, Sari|Wolf, Lior|Gov, Nir S|Ben-Jacob, Eshel|Tsarfaty, Ilan', 'Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel.|Israel Institute for Biological Research, Ness Ziona, Israel.|School of Physics and Astronomy, The Raymond and Beverly Sackler Faculty of Exact Sciences, Tel Aviv University, Tel Aviv, Israel.|Department of Clinical Microbiology and Immunology, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.|Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel.|Department of Chemical Physics, Weizmann Institute of Science, Rehovot, Israel.|School of Physics and Astronomy, The Raymond and Beverly Sackler Faculty of Exact Sciences, Tel Aviv University, Tel Aviv, Israel, Center for Theoretical Biological Physics, Rice University, Houston, Texas, United States of America, Research & Development Unit, Assaf Harofeh Medical Center, Zerifin, Israel.|Department of Clinical Microbiology and Immunology, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.', 'Israel|Israel|Israel|Israel|Israel|Israel|Israel|Israel', 0, 'Animals|Biomechanical Phenomena|Cell Communication|Cell Line, Tumor|Cell Movement|Computational Biology|Dogs|Madin Darby Canine Kidney Cells|Mice|Signal Transduction|Wound Healing', 'Journal Article', '10.1371/journal.pcbi.1003747', '25058592', 'The ability of cells to coordinately migrate in groups is crucial to enable them to travel long distances during embryonic development, wound healing and tumorigenesis, but the fundamental mechanisms underlying intercellular coordination during collective cell migration remain elusive despite considerable research efforts. A novel analytical framework is introduced here to explicitly detect and quantify cell clusters that move coordinately in a monolayer. The analysis combines and associates vast amount of spatiotemporal data across multiple experiments into transparent quantitative measures to report the emergence of new modes of organized behavior during collective migration of tumor and epithelial cells in wound healing assays. First, we discovered the emergence of a wave of coordinated migration propagating backward from the wound front, which reflects formation of clusters of coordinately migrating cells that are generated further away from the wound edge and disintegrate close to the advancing front. This wave emerges in both normal and tumor cells, and is amplified by Met activation with hepatocyte growth factor/scatter factor. Second, Met activation was found to induce coinciding waves of cellular acceleration and stretching, which in turn trigger the emergence of a backward propagating wave of directional migration with about an hour phase lag. Assessments of the relations between the waves revealed that amplified coordinated migration is associated with the emergence of directional migration. Taken together, our data and simplified modeling-based assessments suggest that increased velocity leads to enhanced coordination: higher motility arises due to acceleration and stretching that seems to increase directionality by temporarily diminishing the velocity components orthogonal to the direction defined by the monolayer geometry. Spatial and temporal accumulation of directionality thus defines coordination. The findings offer new insight and suggest a basic cellular mechanism for long-term cell guidance and intercellular communication during collective cell migration. '),
(86690, 2014, 'plos', 'Analysis of stop-gain and frameshift variants in human innate immunity genes', 'Rausell, Antonio|Mohammadi, Pejman|McLaren, Paul J|Bartha, Istvan|Xenarios, Ioannis|Fellay, Jacques|Telenti, Amalio', 'SIB Swiss Institute of Bioinformatics, Lausanne and Basel, Lausanne, Switzerland, Department of Laboratories, University Hospital of Lausanne, Lausanne, Switzerland, University of Lausanne, Lausanne, Switzerland, Vital-IT group, SIB Swiss Institute of Bioinformatics Lausanne, Lausanne, Switzerland.|SIB Swiss Institute of Bioinformatics, Lausanne and Basel, Lausanne, Switzerland, Computational Biology Group, ETH Zurich, Zurich, Switzerland.|SIB Swiss Institute of Bioinformatics, Lausanne and Basel, Lausanne, Switzerland, Department of Laboratories, University Hospital of Lausanne, Lausanne, Switzerland, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.|SIB Swiss Institute of Bioinformatics, Lausanne and Basel, Lausanne, Switzerland, Department of Laboratories, University Hospital of Lausanne, Lausanne, Switzerland, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.|SIB Swiss Institute of Bioinformatics, Lausanne and Basel, Lausanne, Switzerland, Vital-IT group, SIB Swiss Institute of Bioinformatics Lausanne, Lausanne, Switzerland, Swiss-Prot group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.|SIB Swiss Institute of Bioinformatics, Lausanne and Basel, Lausanne, Switzerland, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.|Department of Laboratories, University Hospital of Lausanne, Lausanne, Switzerland, University of Lausanne, Lausanne, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 19, 'Bayes Theorem|Computational Biology|Databases, Genetic|Humans|Immunity, Innate|Interferons|Mutation|Virus Diseases', 'Journal Article', '10.1371/journal.pcbi.1003757', '25058640', 'Loss-of-function variants in innate immunity genes are associated with Mendelian disorders in the form of primary immunodeficiencies. Recent resequencing projects report that stop-gains and frameshifts are collectively prevalent in humans and could be responsible for some of the inter-individual variability in innate immune response. Current computational approaches evaluating loss-of-function in genes carrying these variants rely on gene-level characteristics such as evolutionary conservation and functional redundancy across the genome. However, innate immunity genes represent a particular case because they are more likely to be under positive selection and duplicated. To create a ranking of severity that would be applicable to innate immunity genes we evaluated 17,764 stop-gain and 13,915 frameshift variants from the NHLBI Exome Sequencing Project and 1,000 Genomes Project. Sequence-based features such as loss of functional domains, isoform-specific truncation and nonsense-mediated decay were found to correlate with variant allele frequency and validated with gene expression data. We integrated these features in a Bayesian classification scheme and benchmarked its use in predicting pathogenic variants against Online Mendelian Inheritance in Man (OMIM) disease stop-gains and frameshifts. The classification scheme was applied in the assessment of 335 stop-gains and 236 frameshifts affecting 227 interferon-stimulated genes. The sequence-based score ranks variants in innate immunity genes according to their potential to cause disease, and complements existing gene-based pathogenicity scores. Specifically, the sequence-based score improves measurement of functional gene impairment, discriminates across different variants in a given gene and appears particularly useful for analysis of less conserved genes. '),
(86691, 2014, 'plos', 'Quantifying the length and variance of the eukaryotic cell cycle phases by a stochastic model and dual nucleoside pulse labelling', 'Weber, Tom Serge|Jaehnert, Irene|Schichor, Christian|Or-Guil, Michal|Carneiro, Jorge', 'Instituto Gulbenkian de Ciência, Oeiras, Portugal, Department of Biology, Humboldt-Universität zu Berlin, Berlin Germany and Research Center ImmunoSciences, Charité - Universitätsmedizin Berlin, Berlin, Germany.|Tumorbiological Laboratory, Neurosurgical Department, Ludwig-Maximilians-University Munich, Klinikum Grosshadern, Munich, Germany.|Tumorbiological Laboratory, Neurosurgical Department, Ludwig-Maximilians-University Munich, Klinikum Grosshadern, Munich, Germany.|Department of Biology, Humboldt-Universität zu Berlin, Berlin Germany and Research Center ImmunoSciences, Charité - Universitätsmedizin Berlin, Berlin, Germany.|Instituto Gulbenkian de Ciência, Oeiras, Portugal.', 'Germany|Germany|Germany|Germany|Portugal', 17, 'Cell Cycle|Cell Death|Cell Proliferation|Cytological Techniques|Eukaryotic Cells|Kinetics|Models, Biological|Nucleosides|Stochastic Processes', 'Journal Article', '10.1371/journal.pcbi.1003616', '25058870', 'A fundamental property of cell populations is their growth rate as well as the time needed for cell division and its variance. The eukaryotic cell cycle progresses in an ordered sequence through the phases G1, S, G2, and M, and is regulated by environmental cues and by intracellular checkpoints. Reflecting this regulatory complexity, the length of each phase varies considerably in different kinds of cells but also among genetically and morphologically indistinguishable cells. This article addresses the question of how to describe and quantify the mean and variance of the cell cycle phase lengths. A phase-resolved cell cycle model is introduced assuming that phase completion times are distributed as delayed exponential functions, capturing the observations that each realization of a cycle phase is variable in length and requires a minimal time. In this model, the total cell cycle length is distributed as a delayed hypoexponential function that closely reproduces empirical distributions. Analytic solutions are derived for the proportions of cells in each cycle phase in a population growing under balanced growth and under specific non-stationary conditions. These solutions are then adapted to describe conventional cell cycle kinetic assays based on pulse labelling with nucleoside analogs. The model fits well to data obtained with two distinct proliferating cell lines labelled with a single bromodeoxiuridine pulse. However, whereas mean lengths are precisely estimated for all phases, the respective variances remain uncertain. To overcome this limitation, a redesigned experimental protocol is derived and validated in silico. The novelty is the timing of two consecutive pulses with distinct nucleosides that enables accurate and precise estimation of both the mean and the variance of the length of all phases. The proposed methodology to quantify the phase length distributions gives results potentially equivalent to those obtained with modern phase-specific biosensor-based fluorescent imaging.'),
(86692, 2014, 'plos', 'Comments on Human dominant disease genes are enriched in paralogs originating from whole genome duplication', 'Chen, Wei-Hua|Zhao, Xing-Ming|van Noort, Vera|Bork, Peer', 'European Molecular Biology Laboratory (EMBL) Heidelberg, Heidelberg, Germany.|Department of Computer Science, School of Electronics and Information Engineering, Tongji University, Shanghai, China.|European Molecular Biology Laboratory (EMBL) Heidelberg, Heidelberg, Germany.|European Molecular Biology Laboratory (EMBL) Heidelberg, Heidelberg, Germany, Max-Delbrück-Centrum für Molekulare Medizin (MDC), Berlin, Germany.', 'Germany|China|Germany|Germany', 0, 'Disease|Genes, Duplicate|Genome, Human|Humans', 'Journal Article', '10.1371/journal.pcbi.1003758', '25077479', '-'),
(86693, 2014, 'plos', 'Apoptotic lymphocytes of H sapiens lose nucleosomes in GC-rich promoters', 'Hosid, Sergey|Ioshikhes, Ilya', 'Department of Biochemistry, Microbiology and Immunology, University of Ottawa, Ottawa, Ontario, Canada, Ottawa Institute of Systems Biology, University of Ottawa, Ottawa, Ontario, Canada.|Department of Biochemistry, Microbiology and Immunology, University of Ottawa, Ottawa, Ontario, Canada, Ottawa Institute of Systems Biology, University of Ottawa, Ottawa, Ontario, Canada.', 'Canada|Canada', 2, 'Apoptosis|Base Composition|DNA|High-Throughput Nucleotide Sequencing|Humans|Lymphocytes|Nucleosomes|Promoter Regions, Genetic', 'Journal Article', '10.1371/journal.pcbi.1003760', '25077608', 'We analyzed two sets of human CD4+ nucleosomal DNA directly sequenced by Illumina (Solexa) high throughput sequencing method. The first set has xe2x88xbc40 M sequences and was produced from the normal CD4+ T lymphocytes by micrococcal nuclease. The second set has xe2x88xbc44 M sequences and was obtained from peripheral blood lymphocytes by apoptotic nucleases. The different nucleosome sets showed similar dinucleotide positioning AA/TT, GG/CC, and RR/YY (R is purine, Y--pyrimidine) patterns with periods of 10-10.4 bp. Peaks of GG/CC and AA/TT patterns were shifted by 5 bp from each other. Two types of promoters in H. sapiens: AT and GC-rich were identified. AT-rich promoters in apoptotic cell had +1 nucleosome shifts 50-60 bp downstream from those in normal lymphocytes. GC-rich promoters in apoptotic cells lost 80% of nucleosomes around transcription start sites as well as in total DNA. Nucleosome positioning was predicted by combination of {AA, TT}, {GG, CC}, {WW, SS} and {RR, YY} patterns. In our study we found that the combinations of {AA, TT} and {GG, CC} provide the best results and successfully mapped 33% of nucleosomes 147 bp long with precision xc2xb115 bp (only 31/147 or 21% is expected).'),
(86694, 2014, 'plos', 'Robustness and evolvability of the human signaling network', 'Kim, Junil|Vandamme, Drieke|Kim, Jeong-Rae|Munoz, Amaya Garcia|Kolch, Walter|Cho, Kwang-Hyun', 'Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Yuseong-gu, Daejeon, Republic of Korea.|Systems Biology Ireland, University College Dublin, Dublin, Ireland.|Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Yuseong-gu, Daejeon, Republic of Korea, Department of Mathematics, University of Seoul, Seoul, Republic of Korea.|Systems Biology Ireland, University College Dublin, Dublin, Ireland.|Systems Biology Ireland, University College Dublin, Dublin, Ireland, Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland, School of Medicine and Medical Science, University College Dublin, Dublin, Ireland.|Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Yuseong-gu, Daejeon, Republic of Korea.', '-|Ireland|-|Ireland|Ireland|-', 22, 'Algorithms|Drug Discovery|Evolution, Molecular|Humans|Models, Biological|Signal Transduction', 'Journal Article', '10.1371/journal.pcbi.1003763', '25077791', 'Biological systems are known to be both robust and evolvable to internal and external perturbations, but what causes these apparently contradictory properties? We used Boolean network modeling and attractor landscape analysis to investigate the evolvability and robustness of the human signaling network. Our results show that the human signaling network can be divided into an evolvable core where perturbations change the attractor landscape in state space, and a robust neighbor where perturbations have no effect on the attractor landscape. Using chemical inhibition and overexpression of nodes, we validated that perturbations affect the evolvable core more strongly than the robust neighbor. We also found that the evolvable core has a distinct network structure, which is enriched in feedback loops, and features a higher degree of scale-freeness and longer path lengths connecting the nodes. In addition, the genes with high evolvability scores are associated with evolvability-related properties such as rapid evolvability, low species broadness, and immunity whereas the genes with high robustness scores are associated with robustness-related properties such as slow evolvability, high species broadness, and oncogenes. Intriguingly, US Food and Drug Administration-approved drug targets have high evolvability scores whereas experimental drug targets have high robustness scores. '),
(86695, 2014, 'plos', 'Dendritic nonlinearities reduce network size requirements and mediate ON and OFF states of persistent activity in a PFC microcircuit model', 'Papoutsi, Athanasia|Sidiropoulou, Kyriaki|Poirazi, Panayiota', 'Institute of Molecular Biology and Biotechnology (IMBB) - Foundation for Research and Technology-Hellas (FORTH), Heraklion, Crete, Greece, Department of Biology, University of Crete, Heraklion, Crete, Greece.|Institute of Molecular Biology and Biotechnology (IMBB) - Foundation for Research and Technology-Hellas (FORTH), Heraklion, Crete, Greece, Department of Biology, University of Crete, Heraklion, Crete, Greece.|Institute of Molecular Biology and Biotechnology (IMBB) - Foundation for Research and Technology-Hellas (FORTH), Heraklion, Crete, Greece.', 'Greece|Greece|Greece', 0, 'Action Potentials|Biophysical Phenomena|Computational Biology|Computer Simulation|Models, Neurological|N-Methylaspartate|Neurons|gamma-Aminobutyric Acid', 'Journal Article', '10.1371/journal.pcbi.1003764', '25077940', 'Technological advances have unraveled the existence of small clusters of co-active neurons in the neocortex. The functional implications of these microcircuits are in large part unexplored. Using a heavily constrained biophysical model of a L5 PFC microcircuit, we recently showed that these structures act as tunable modules of persistent activity, the cellular correlate of working memory. Here, we investigate the mechanisms that underlie persistent activity emergence (ON) and termination (OFF) and search for the minimum network size required for expressing these states within physiological regimes. We show that (a) NMDA-mediated dendritic spikes gate the induction of persistent firing in the microcircuit. (b) The minimum network size required for persistent activity induction is inversely proportional to the synaptic drive of each excitatory neuron. (c) Relaxation of connectivity and synaptic delay constraints eliminates the gating effect of NMDA spikes, albeit at a cost of much larger networks. (d) Persistent activity termination by increased inhibition depends on the strength of the synaptic input and is negatively modulated by dADP. (e) Slow synaptic mechanisms and network activity contain predictive information regarding the ability of a given stimulus to turn ON and/or OFF persistent firing in the microcircuit model. Overall, this study zooms out from dendrites to cell assemblies and suggests a tight interaction between dendritic non-linearities and network properties (size/connectivity) that may facilitate the short-memory function of the PFC. '),
(86696, 2014, 'plos', 'CCAST: a model-based gating strategy to isolate homogeneous subpopulations in a heterogeneous population of single cells', 'Anchang, Benedict|Do, Mary T|Zhao, Xi|Plevritis, Sylvia K', 'Department of Radiology, Center for Cancer Systems Biology, Stanford University, Stanford, California, United States of America.|Department of Radiology, Center for Cancer Systems Biology, Stanford University, Stanford, California, United States of America.|Department of Radiology, Center for Cancer Systems Biology, Stanford University, Stanford, California, United States of America.|Department of Radiology, Center for Cancer Systems Biology, Stanford University, Stanford, California, United States of America.', 'United States|United States|United States|United States', 10, 'Algorithms|Biomarkers|Cell Line, Tumor|Cell Separation|Cluster Analysis|Computational Biology|Computer Simulation|Humans', 'Journal Article', '10.1371/journal.pcbi.1003664', '25078380', 'A model-based gating strategy is developed for sorting cells and analyzing populations of single cells. The strategy, named CCAST, for Clustering, Classification and Sorting Tree, identifies a gating strategy for isolating homogeneous subpopulations from a heterogeneous population of single cells using a data-derived decision tree representation that can be applied to cell sorting. Because CCAST does not rely on expert knowledge, it removes human bias and variability when determining the gating strategy. It combines any clustering algorithm with silhouette measures to identify underlying homogeneous subpopulations, then applies recursive partitioning techniques to generate a decision tree that defines the gating strategy. CCAST produces an optimal strategy for cell sorting by automating the selection of gating markers, the corresponding gating thresholds and gating sequence, all of these parameters are typically manually defined. Even though CCAST is optimized for cell sorting, it can be applied for the identification and analysis of homogeneous subpopulations among heterogeneous single cell data. We apply CCAST on single cell data from both breast cancer cell lines and normal human bone marrow. On the SUM159 breast cancer cell line data, CCAST indicates at least five distinct cell states based on two surface markers (CD24 and EPCAM) and provides a gating sorting strategy that produces more homogeneous subpopulations than previously reported. When applied to normal bone marrow data, CCAST reveals an efficient strategy for gating T-cells without prior knowledge of the major T-cell subtypes and the markers that best define them. On the normal bone marrow data, CCAST also reveals two major mature B-cell subtypes, namely CD123+ and CD123- cells, which were not revealed by manual gating but show distinct intracellular signaling responses. More generally, the CCAST framework could be used on other biological and non-biological high dimensional data types that are mixtures of unknown homogeneous subpopulations. '),
(86697, 2014, 'plos', 'Correlated inter-domain motions in adenylate kinase', 'Esteban-Martín, Santiago|Fenwick, Robert Bryn|Ådén, Jörgen|Cossins, Benjamin|Bertoncini, Carlos W|Guallar, Victor|Wolf-Watz, Magnus|Salvatella, Xavier', 'Joint BSC-CRG-IRB Research Programme in Computational Biology, Barcelona Supercomputing Center - BSC, Barcelona, Spain.|Joint BSC-CRG-IRB Research Programme in Computational Biology, Institute for Research in Biomedicine - IRB Barcelona, Barcelona, Spain.|Department of Chemistry, Chemical Biological Centre, Umeå University, Umeå, Sweden.|Joint BSC-CRG-IRB Research Programme in Computational Biology, Barcelona Supercomputing Center - BSC, Barcelona, Spain.|Joint BSC-CRG-IRB Research Programme in Computational Biology, Institute for Research in Biomedicine - IRB Barcelona, Barcelona, Spain.|Joint BSC-CRG-IRB Research Programme in Computational Biology, Barcelona Supercomputing Center - BSC, Barcelona, Spain, Institució Catalana de Recerca i Estudis Avançats - ICREA, Barcelona, Spain.|Department of Chemistry, Chemical Biological Centre, Umeå University, Umeå, Sweden.|Joint BSC-CRG-IRB Research Programme in Computational Biology, Institute for Research in Biomedicine - IRB Barcelona, Barcelona, Spain, Institució Catalana de Recerca i Estudis Avançats - ICREA, Barcelona, Spain.', 'Spain|Spain|Sweden|Spain|Spain|Spain|Sweden|Spain', 13, 'Adenylate Kinase|Algorithms|Allosteric Regulation|Computational Biology|Models, Molecular|Nuclear Magnetic Resonance, Biomolecular|Protein Structure, Tertiary', 'Journal Article', '10.1371/journal.pcbi.1003721', '25078441', 'Correlated inter-domain motions in proteins can mediate fundamental biochemical processes such as signal transduction and allostery. Here we characterize at structural level the inter-domain coupling in a multidomain enzyme, Adenylate Kinase (AK), using computational methods that exploit the shape information encoded in residual dipolar couplings (RDCs) measured under steric alignment by nuclear magnetic resonance (NMR). We find experimental evidence for a multi-state equilibrium distribution along the opening/closing pathway of Adenylate Kinase, previously proposed from computational work, in which inter-domain interactions disfavour states where only the AMP binding domain is closed. In summary, we provide a robust experimental technique for study of allosteric regulation in AK and other enzymes. '),
(86698, 2014, 'plos', 'Antibody responses during hepatitis B viral infection', 'Ciupe, Stanca M|Ribeiro, Ruy M|Perelson, Alan S', 'Department of Mathematics, Virginia Tech, Blacksburg, Virginia, United States of America.|Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.|Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.', 'United States|Mexico|Mexico', 34, 'Computational Biology|Hepatitis B|Hepatitis B Antibodies|Hepatitis B virus|Host-Pathogen Interactions|Humans|Models, Immunological|Viral Load', 'Journal Article', '10.1371/journal.pcbi.1003730', '25078553', 'Hepatitis B is a DNA virus that infects liver cells and can cause both acute and chronic disease. It is believed that both viral and host factors are responsible for determining whether the infection is cleared or becomes chronic. Here we investigate the mechanism of protection by developing a mathematical model of the antibody response following hepatitis B virus (HBV) infection. We fitted the model to data from seven infected adults identified during acute infection and determined the ability of the virus to escape neutralization through overproduction of non-infectious subviral particles, which have HBs proteins on their surface, but do not contain nucleocapsid protein and viral nucleic acids. We showed that viral clearance can be achieved for high anti-HBV antibody levels, as in vaccinated individuals, when: (1) the rate of synthesis of hepatitis B subviral particles is slow, (2) the rate of synthesis of hepatitis B subviral particles is high but either anti-HBV antibody production is fast, the antibody affinity is high, or the levels of pre-existent HBV-specific antibody at the time of infection are high, as could be attained by vaccination. We further showed that viral clearance can be achieved for low equilibrium anti-HBV antibody levels, as in unvaccinated individuals, when a strong cellular immune response controls early infection. '),
(86699, 2014, 'plos', 'Structure-function discrepancy: inhomogeneity and delays in synchronized neural networks', 'Ton, Robert|Deco, Gustavo|Daffertshofer, Andreas', 'MOVE Research Institute Amsterdam, VU University, Amsterdam, The Netherlands, Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain.|Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain, Institució Catalana de la Recerca i Estudis Avançats (ICREA), Universitat Pompeu Fabra, Barcelona, Spain.|MOVE Research Institute Amsterdam, VU University, Amsterdam, The Netherlands.', 'Spain|Spain|Netherlands', 21, 'Computational Biology|Computer Simulation|Models, Neurological|Nerve Net|Neural Networks (Computer)', 'Journal Article', '10.1371/journal.pcbi.1003736', '25078715', 'The discrepancy between structural and functional connectivity in neural systems forms the challenge in understanding general brain functioning. To pinpoint a mapping between structure and function, we investigated the effects of (in)homogeneity in coupling structure and delays on synchronization behavior in networks of oscillatory neural masses by deriving the phase dynamics of these generic networks. For homogeneous delays, the structural coupling matrix is largely preserved in the coupling between phases, resulting in clustered stationary phase distributions. Accordingly, we found only a small number of synchronized groups in the network. Distributed delays, by contrast, introduce inhomogeneity in the phase coupling so that clustered stationary phase distributions no longer exist. The effect of distributed delays mimicked that of structural inhomogeneity. Hence, we argue that phase (de-)synchronization patterns caused by inhomogeneous coupling cannot be distinguished from those caused by distributed delays, at least not by the naked eye. The here-derived analytical expression for the effective coupling between phases as a function of structural coupling constitutes a direct relationship between structural and functional connectivity. Structural connectivity constrains synchronizability that may be modified by the delay distribution. This explains why structural and functional connectivity bear much resemblance albeit not a one-to-one correspondence. We illustrate this in the context of resting-state activity, using the anatomical connectivity structure reported by Hagmann and others. '),
(86700, 2014, 'plos', 'Algorithms to model single gene, single chromosome, and whole genome copy number changes jointly in tumor phylogenetics', 'Chowdhury, Salim Akhter|Shackney, Stanley E|Heselmeyer-Haddad, Kerstin|Ried, Thomas|Schäffer, Alejandro A|Schwartz, Russell', 'Joint Carnegie Mellon/University of Pittsburgh Ph.D. Program in Computational Biology, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America, Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.|Intelligent Oncotherapeutics, Pittsburgh, Pennsylvania, United States of America.|Genetics Branch, Center for Cancer Research, NCI, NIH, Bethesda, Maryland, United States of America.|Genetics Branch, Center for Cancer Research, NCI, NIH, Bethesda, Maryland, United States of America.|Computational Biology Branch, NCBI, NIH, Bethesda, Maryland, United States of America.|Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America, Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.', 'United States|United States|United States|United States|United States|United States', 32, 'Algorithms|Breast Neoplasms|Computational Biology|Computer Simulation|DNA Copy Number Variations|Databases, Genetic|Disease Progression|Female|Humans|Models, Genetic|Neoplasms|Uterine Cervical Neoplasms', 'Journal Article', '10.1371/journal.pcbi.1003740', '25078894', 'We present methods to construct phylogenetic models of tumor progression at the cellular level that include copy number changes at the scale of single genes, entire chromosomes, and the whole genome. The methods are designed for data collected by fluorescence in situ hybridization (FISH), an experimental technique especially well suited to characterizing intratumor heterogeneity using counts of probes to genetic regions frequently gained or lost in tumor development. Here, we develop new provably optimal methods for computing an edit distance between the copy number states of two cells given evolution by copy number changes of single probes, all probes on a chromosome, or all probes in the genome. We then apply this theory to develop a practical heuristic algorithm, implemented in publicly available software, for inferring tumor phylogenies on data from potentially hundreds of single cells by this evolutionary model. We demonstrate and validate the methods on simulated data and published FISH data from cervical cancers and breast cancers. Our computational experiments show that the new model and algorithm lead to more parsimonious trees than prior methods for single-tumor phylogenetics and to improved performance on various classification tasks, such as distinguishing primary tumors from metastases obtained from the same patient population. '),
(86701, 2014, 'plos', 'Structure-based druggability assessment of the mammalian structural proteome with inclusion of light protein flexibility', 'Loving, Kathryn A|Lin, Andy|Cheng, Alan C', 'Schrödinger LLC, New York, New York, United States of America.|Amgen Inc., South San Francisco, California, United States of America.|Amgen Inc., South San Francisco, California, United States of America.', 'United States|United States|United States', 14, 'Animals|Binding Sites|Drug Design|Mammals|Models, Molecular|Models, Statistical|Naphthalenes|Pharmaceutical Preparations|Pliability|Protein Binding|Protein Conformation|Proteins|Proteome|Proteomics|Reproducibility of Results', 'Journal Article', '10.1371/journal.pcbi.1003741', '25079060', 'Advances reported over the last few years and the increasing availability of protein crystal structure data have greatly improved structure-based druggability approaches. However, in practice, nearly all druggability estimation methods are applied to protein crystal structures as rigid proteins, with protein flexibility often not directly addressed. The inclusion of protein flexibility is important in correctly identifying the druggability of pockets that would be missed by methods based solely on the rigid crystal structure. These include cryptic pockets and flexible pockets often found at protein-protein interaction interfaces. Here, we apply an approach that uses protein modeling in concert with druggability estimation to account for light protein backbone movement and protein side-chain flexibility in protein binding sites. We assess the advantages and limitations of this approach on widely-used protein druggability sets. Applying the approach to all mammalian protein crystal structures in the PDB results in identification of 69 proteins with potential druggable cryptic pockets. '),
(86702, 2014, 'plos', 'No, there is no 150 ms lead of visual speech on auditory speech, but a range of audiovisual asynchronies varying from small audio lead to large audio lag', 'Schwartz, Jean-Luc|Savariaux, Christophe', 'GIPSA-Lab, Speech and Cognition Department, UMR 5216 CNRS Grenoble-Alps University, Grenoble, France.|GIPSA-Lab, Speech and Cognition Department, UMR 5216 CNRS Grenoble-Alps University, Grenoble, France.', 'France|France', 41, 'Auditory Perception|Computational Biology|Humans|Male|Models, Biological|Speech|Visual Perception', 'Journal Article', '10.1371/journal.pcbi.1003743', '25079216', 'An increasing number of neuroscience papers capitalize on the assumption published in this journal that visual speech would be typically 150 ms ahead of auditory speech. It happens that the estimation of audiovisual asynchrony in the reference paper is valid only in very specific cases, for isolated consonant-vowel syllables or at the beginning of a speech utterance, in what we call preparatory gestures. However, when syllables are chained in sequences, as they are typically in most parts of a natural speech utterance, asynchrony should be defined in a different way. This is what we call comodulatory gestures providing auditory and visual events more or less in synchrony. We provide audiovisual data on sequences of plosive-vowel syllables (pa, ta, ka, ba, da, ga, ma, na) showing that audiovisual synchrony is actually rather precise, varying between 20 ms audio lead and 70 ms audio lag. We show how more complex speech material should result in a range typically varying between 40 ms audio lead and 200 ms audio lag, and we discuss how this natural coordination is reflected in the so-called temporal integration window for audiovisual speech perception. Finally we present a toy model of auditory and audiovisual predictive coding, showing that visual lead is actually not necessary for visual prediction. '),
(86703, 2014, 'plos', 'Hydrophobin film structure for HFBI and HFBII and mechanism for accelerated film formation', 'Magarkar, Aniket|Mele, Nawel|Abdel-Rahman, Noha|Butcher, Sarah|Torkkeli, Mika|Serimaa, Ritva|Paananen, Arja|Linder, Markus|Bunker, Alex', 'Centre for Drug Research, Faculty of Pharmacy, University of Helsinki, Helsinki, Finland.|Centre for Drug Research, Faculty of Pharmacy, University of Helsinki, Helsinki, Finland.|Institute of Biotechnology, University of Helsinki, Helsinki, Finland.|Institute of Biotechnology, University of Helsinki, Helsinki, Finland.|Department of Physics, University of Helsinki, Helsinki, Finland.|Department of Physics, University of Helsinki, Helsinki, Finland.|VTT Technical Research Centre of Finland, Espoo, Finland.|VTT Technical Research Centre of Finland, Espoo, Finland, School of Chemical Technology, Aalto University, Espoo, Finland.|Centre for Drug Research, Faculty of Pharmacy, University of Helsinki, Helsinki, Finland.', 'Finland|Finland|Finland|Finland|Finland|Finland|Finland|Finland|Finland', 19, 'Fungal Proteins|Microscopy, Electron|Molecular Dynamics Simulation|Protein Binding|Protein Conformation|Static Electricity|Surface Properties|Trichoderma', 'Journal Article', '10.1371/journal.pcbi.1003745', '25079355', 'Hydrophobins represent an important group of proteins from both a biological and nanotechnological standpoint. They are the means through which filamentous fungi affect their environment to promote growth, and their properties at interfaces have resulted in numerous applications. In our study we have combined protein docking, molecular dynamics simulation, and electron cryo-microscopy to gain atomistic level insight into the surface structure of films composed of two class II hydrophobins: HFBI and HFBII produced by Trichoderma reesei. Together our results suggest a unit cell composed of six proteins, however, our computational results suggest P6 symmetry, while our experimental results show P3 symmetry with a unit cell size of 56 Å. Our computational results indicate the possibility of an alternate ordering with a three protein unit cell with P3 symmetry and a smaller unit cell size, and we have used a Monte Carlo simulation of a spin model representing the hydrophobin film to show how this alternate metastable structure may play a role in increasing the rate of surface coverage by hydrophobin films, possibly indicating a mechanism of more general significance to both biology and nanotechnology. ');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(86704, 2014, 'plos', 'Different inward and outward conduction mechanisms in NaVMs suggested by molecular dynamics simulations', 'Ke, Song|Timin, E N|Stary-Weinzinger, Anna', 'Department of Pharmacology and Toxicology, University of Vienna, Vienna, Austria.|Department of Pharmacology and Toxicology, University of Vienna, Vienna, Austria.|Department of Pharmacology and Toxicology, University of Vienna, Vienna, Austria.', 'Austria|Austria|Austria', 18, 'Computational Biology|Computer Simulation|Electric Conductivity|Glutamic Acid|Ion Transport|Membrane Potentials|Molecular Dynamics Simulation|Protein Binding|Protein Conformation|Sodium|Voltage-Gated Sodium Channels', 'Journal Article', '10.1371/journal.pcbi.1003746', '25079564', 'Rapid and selective ion transport is essential for the generation and regulation of electrical signaling pathways in living organisms. Here, we use molecular dynamics (MD) simulations with an applied membrane potential to investigate the ion flux of bacterial sodium channel NaVMs. 5.9 xc2xb5s simulations with 500 mM NaCl suggest different mechanisms for inward and outward flux. The predicted inward conductance rate of xe2x88xbc27xc2xb13 pS, agrees with experiment. The estimated outward conductance rate is 15xc2xb13 pS, which is considerably lower. Comparing inward and outward flux, the mean ion dwell time in the selectivity filter (SF) is prolonged from 13.5xc2xb10.6 ns to 20.1xc2xb11.1 ns. Analysis of the Na+ distribution revealed distinct patterns for influx and efflux events. In 32.0xc2xb15.9% of the simulation time, the E53 side chains adopted a flipped conformation during outward conduction, whereas this conformational change was rarely observed (2.7xc2xb10.5%) during influx. Further, simulations with dihedral restraints revealed that influx is less affected by the E53 conformational flexibility. In contrast, during outward conduction, our simulations indicate that the flipped E53 conformation provides direct coordination for Na+. The free energy profile (potential of mean force calculations) indicates that this conformational change lowers the putative barriers between sites SCEN and SHFS during outward conduction. We hypothesize that during an action potential, the increased Na+ outward transition propensities at depolarizing potentials might increase the probability of E53 conformational changes in the SF. Subsequently, this might be a first step towards initiating slow inactivation. '),
(86705, 2014, 'plos', 'Hotspot mutations in KIT receptor differentially modulate its allosterically coupled conformational dynamics: impact on activation and drug sensitivity', 'Chauvot de Beauchêne, Isaure|Allain, Ariane|Panel, Nicolas|Laine, Elodie|Trouvé, Alain|Dubreuil, Patrice|Tchertanov, Luba', 'Bioinformatics, Molecular Dynamics & Modeling (BiMoDyM), Laboratoire de Biologie et Pharmacologie Appliqués (LBPA-CNRS), Ecole Normale Supérieure de Cachan, Cachan, France.|Bioinformatics, Molecular Dynamics & Modeling (BiMoDyM), Laboratoire de Biologie et Pharmacologie Appliqués (LBPA-CNRS), Ecole Normale Supérieure de Cachan, Cachan, France.|Bioinformatics, Molecular Dynamics & Modeling (BiMoDyM), Laboratoire de Biologie et Pharmacologie Appliqués (LBPA-CNRS), Ecole Normale Supérieure de Cachan, Cachan, France.|Bioinformatics, Molecular Dynamics & Modeling (BiMoDyM), Laboratoire de Biologie et Pharmacologie Appliqués (LBPA-CNRS), Ecole Normale Supérieure de Cachan, Cachan, France.|Centre de Mathématiques et de Leurs Applications (CMLA-CNRS), Ecole Normale Supérieure de Cachan, Cachan, France.|Inserm, U1068, Signaling, Hematopoiesis and Mechanism of Oncogenesis (CRCM), Institut Paoli-Calmettes, Aix-Marseille University, CNRS, UMR7258, Marseille, France.|Bioinformatics, Molecular Dynamics & Modeling (BiMoDyM), Laboratoire de Biologie et Pharmacologie Appliqués (LBPA-CNRS), Ecole Normale Supérieure de Cachan, Cachan, France, Centre de Mathématiques et de Leurs Applications (CMLA-CNRS), Ecole Normale Supérieure de Cachan, Cachan, France.', 'France|France|France|France|France|France|France', 19, 'Allosteric Regulation|Antineoplastic Agents|Cytoplasm|Humans|Hydrogen Bonding|Molecular Dynamics Simulation|Mutation|Protein Structure, Tertiary|Proto-Oncogene Proteins c-kit|Signal Transduction', 'Journal Article', '10.1371/journal.pcbi.1003749', '25079768', 'Receptor tyrosine kinase KIT controls many signal transduction pathways and represents a typical allosterically regulated protein. The mutation-induced deregulation of KIT activity impairs cellular physiological functions and causes serious human diseases. The impact of hotspots mutations (D816H/Y/N/V and V560G/D) localized in crucial regulatory segments, the juxtamembrane region (JMR) and the activation (A-) loop, on KIT internal dynamics was systematically studied by molecular dynamics simulations. The mutational outcomes predicted in silico were correlated with in vitro and in vivo activation rates and drug sensitivities of KIT mutants. The allosteric regulation of KIT in the native and mutated forms is described in terms of communication between the two remote segments, JMR and A-loop. A strong correlation between the communication profile and the structural and dynamical features of KIT in the native and mutated forms was established. Our results provide new insight on the determinants of receptor KIT constitutive activation by mutations and resistance of KIT mutants to inhibitors. Depiction of an intra-molecular component of the communication network constitutes a first step towards an integrated description of vast communication pathways established by KIT in physiopathological contexts. '),
(86706, 2014, 'plos', 'A real-time all-atom structural search engine for proteins', 'Gonzalez, Gabriel|Hannigan, Brett|DeGrado, William F', 'Cardiovascular Research Institute, University of California, San Francisco, San Francisco, California, United States of America.|Cardiovascular Research Institute, University of California, San Francisco, San Francisco, California, United States of America.|Cardiovascular Research Institute, University of California, San Francisco, San Francisco, California, United States of America.', 'United States|United States|United States', 7, 'Algorithms|Amino Acid Motifs|Databases, Protein|Internet|Protein Engineering|Proteins|Proteomics|Search Engine|Software', 'Journal Article', '10.1371/journal.pcbi.1003750', '25079944', 'Protein designers use a wide variety of software tools for de novo design, yet their repertoire still lacks a fast and interactive all-atom search engine. To solve this, we have built the Suns program: a real-time, atomic search engine integrated into the PyMOL molecular visualization system. Users build atomic-level structural search queries within PyMOL and receive a stream of search results aligned to their query within a few seconds. This instant feedback cycle enables a new designability-inspired approach to protein design where the designer searches for and interactively incorporates native-like fragments from proven protein structures. We demonstrate the use of Suns to interactively build protein motifs, tertiary interactions, and to identify scaffolds compatible with hot-spot residues. The official web site and installer are located at http://www.degradolab.org/suns/ and the source code is hosted at https://github.com/godotgildor/Suns (PyMOL plugin, BSD license), https://github.com/Gabriel439/suns-cmd (command line client, BSD license), and https://github.com/Gabriel439/suns-search (search engine server, GPLv2 license). '),
(86707, 2014, 'plos', 'Human dominant disease genes are enriched in paralogs originating from whole genome duplication', 'Singh, Param Priya|Affeldt, Séverine|Malaguti, Giulia|Isambert, Hervé', 'CNRS-UMR168, UPMC, Institut Curie, Research Center, Paris, France.|CNRS-UMR168, UPMC, Institut Curie, Research Center, Paris, France.|CNRS-UMR168, UPMC, Institut Curie, Research Center, Paris, France.|CNRS-UMR168, UPMC, Institut Curie, Research Center, Paris, France.', 'France|France|France|France', 12, 'Disease|Genes, Duplicate|Genome, Human|Humans', 'Journal Article', '10.1371/journal.pcbi.1003754', '25080083', '-'),
(86708, 2014, 'plos', 'Identifying selection in the within-host evolution of influenza using viral sequence data', 'Illingworth, Christopher J R|Fischer, Andrej|Mustonen, Ville', 'Department of Genetics, University of Cambridge, Cambridge, United Kingdom.|Wellcome Trust Sanger Institute, Hinxton, Cambridge, United Kingdom.|Wellcome Trust Sanger Institute, Hinxton, Cambridge, United Kingdom.', 'United Kingdom|United Kingdom|United Kingdom', 19, 'Animals|Computational Biology|Evolution, Molecular|Models, Genetic|Orthomyxoviridae|Orthomyxoviridae Infections|Selection, Genetic|Swine', 'Journal Article', '10.1371/journal.pcbi.1003755', '25080215', 'The within-host evolution of influenza is a vital component of its epidemiology. A question of particular interest is the role that selection plays in shaping the viral population over the course of a single infection. We here describe a method to measure selection acting upon the influenza virus within an individual host, based upon time-resolved genome sequence data from an infection. Analysing sequence data from a transmission study conducted in pigs, describing part of the haemagglutinin gene (HA1) of an influenza virus, we find signatures of non-neutrality in six of a total of sixteen infections. We find evidence for both positive and negative selection acting upon specific alleles, while in three cases, the data suggest the presence of time-dependent selection. In one infection we observe what is potentially a specific immune response against the virus, a non-synonymous mutation in an epitope region of the virus is found to be under initially positive, then strongly negative selection. Crucially, given the lack of homologous recombination in influenza, our method accounts for linkage disequilibrium between nucleotides at different positions in the haemagglutinin gene, allowing for the analysis of populations in which multiple mutations are present at any given time. Our approach offers a new insight into the dynamics of influenza infection, providing a detailed characterisation of the forces that underlie viral evolution. '),
(86709, 2014, 'plos', 'Unsupervised feature learning improves prediction of human brain activity in response to natural images', 'Güçlü, Umut|van Gerven, Marcel A J', 'Radboud University Nijmegen, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, Netherlands.|Radboud University Nijmegen, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, Netherlands.', 'Netherlands|Netherlands', 22, 'Artificial Intelligence|Functional Neuroimaging|Humans|Magnetic Resonance Imaging|Models, Neurological|Visual Cortex', 'Journal Article', '10.1371/journal.pcbi.1003724', '25101625', 'Encoding and decoding in functional magnetic resonance imaging has recently emerged as an area of research to noninvasively characterize the relationship between stimulus features and human brain activity. To overcome the challenge of formalizing what stimulus features should modulate single voxel responses, we introduce a general approach for making directly testable predictions of single voxel responses to statistically adapted representations of ecologically valid stimuli. These representations are learned from unlabeled data without supervision. Our approach is validated using a parsimonious computational model of (i) how early visual cortical representations are adapted to statistical regularities in natural images and (ii) how populations of these representations are pooled by single voxels. This computational model is used to predict single voxel responses to natural images and identify natural images from stimulus-evoked multiple voxel responses. We show that statistically adapted low-level sparse and invariant representations of natural images better span the space of early visual cortical representations and can be more effectively exploited in stimulus identification than hand-designed Gabor wavelets. Our results demonstrate the potential of our approach to better probe unknown cortical representations. '),
(86710, 2014, 'plos', 'Collective learning and optimal consensus decisions in social animal groups', 'Kao, Albert B|Miller, Noam|Torney, Colin|Hartnett, Andrew|Couzin, Iain D', '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.|Centre for Mathematics and the Environment, University of Exeter, Cornwall, United Kingdom.|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.', 'Jersey|Jersey|United Kingdom|Jersey|Jersey', 40, 'Animals|Behavior, Animal|Cooperative Behavior|Decision Making|Learning|Models, Biological', 'Journal Article', '10.1371/journal.pcbi.1003762', '25101642', 'Learning has been studied extensively in the context of isolated individuals. However, many organisms are social and consequently make decisions both individually and as part of a collective. Reaching consensus necessarily means that a single option is chosen by the group, even when there are dissenting opinions. This decision-making process decouples the otherwise direct relationship between animals preferences and their experiences (the outcomes of decisions). Instead, because an individuals learned preferences influence what others experience, and therefore learn about, collective decisions couple the learning processes between social organisms. This introduces a new, and previously unexplored, dynamical relationship between preference, action, experience and learning. Here we model collective learning within animal groups that make consensus decisions. We reveal how learning as part of a collective results in behavior that is fundamentally different from that learned in isolation, allowing grouping organisms to spontaneously (and indirectly) detect correlations between group members observations of environmental cues, adjust strategy as a function of changing group size (even if that group size is not known to the individual), and achieve a decision accuracy that is very close to that which is provably optimal, regardless of environmental contingencies. Because these properties make minimal cognitive demands on individuals, collective learning, and the capabilities it affords, may be widespread among group-living organisms. Our work emphasizes the importance and need for theoretical and experimental work that considers the mechanism and consequences of learning in a social context. '),
(86711, 2014, 'plos', 'Memory capacity of networks with stochastic binary synapses', 'Dubreuil, Alexis M|Amit, Yali|Brunel, Nicolas', 'UMR 8118, CNRS, Université Paris Descartes, Paris, France, Departments of Statistics and Neurobiology, University of Chicago, Chicago, Illinois, United States of America.|Departments of Statistics and Computer Science, University of Chicago, Chicago, Illinois, United States of America.|UMR 8118, CNRS, Université Paris Descartes, Paris, France, Departments of Statistics and Neurobiology, University of Chicago, Chicago, Illinois, United States of America.', 'France|United States|France', 5, 'Animals|Cerebral Cortex|Computational Biology|Hippocampus|Memory|Models, Neurological|Nerve Net|Neurons|Rats|Synapses', 'Journal Article', '10.1371/journal.pcbi.1003727', '25101662', 'In standard attractor neural network models, specific patterns of activity are stored in the synaptic matrix, so that they become fixed point attractors of the network dynamics. The storage capacity of such networks has been quantified in two ways: the maximal number of patterns that can be stored, and the stored information measured in bits per synapse. In this paper, we compute both quantities in fully connected networks of N binary neurons with binary synapses, storing patterns with coding level [Formula: see text], in the large [Formula: see text] and sparse coding limits ([Formula: see text]). We also derive finite-size corrections that accurately reproduce the results of simulations in networks of tens of thousands of neurons. These methods are applied to three different scenarios: (1) the classic Willshaw model, (2) networks with stochastic learning in which patterns are shown only once (one shot learning), (3) networks with stochastic learning in which patterns are shown multiple times. The storage capacities are optimized over network parameters, which allows us to compare the performance of the different models. We show that finite-size effects strongly reduce the capacity, even for networks of realistic sizes. We discuss the implications of these results for memory storage in the hippocampus and cerebral cortex. '),
(86712, 2014, 'plos', 'Quantitatively characterizing the ligand binding mechanisms of choline binding protein using Markov state model analysis', 'Gu, Shuo|Silva, Daniel-Adriano|Meng, Luming|Yue, Alexander|Huang, Xuhui', 'Department of Chemistry, Institute for Advance Study and School of Science, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong.|Department of Chemistry, Institute for Advance Study and School of Science, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, Department of Biochemistry, University of Washington, Seattle, Washington, United States of America.|Department of Chemistry, Institute for Advance Study and School of Science, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong.|Department of Chemistry, Institute for Advance Study and School of Science, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong.|Department of Chemistry, Institute for Advance Study and School of Science, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, Division of Biomedical Engineering, Institute for Advance Study and School of Science, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, Center of Systems Biology and Human Health, Institute for Advance Study and School of Science, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong.', 'Hong Kong|Hong Kong|Hong Kong|Hong Kong|Hong Kong', 41, 'Choline|Markov Chains|Membrane Transport Proteins|Molecular Dynamics Simulation|Protein Binding|Protein Conformation|Thermodynamics', 'Journal Article', '10.1371/journal.pcbi.1003767', '25101697', 'Protein-ligand recognition plays key roles in many biological processes. One of the most fascinating questions about protein-ligand recognition is to understand its underlying mechanism, which often results from a combination of induced fit and conformational selection. In this study, we have developed a three-pronged approach of Markov State Models, Molecular Dynamics simulations, and flux analysis to determine the contribution of each model. Using this approach, we have quantified the recognition mechanism of the choline binding protein (ChoX) to be xe2x88xbc90% conformational selection dominant under experimental conditions. This is achieved by recovering all the necessary parameters for the flux analysis in combination with available experimental data. Our results also suggest that ChoX has several metastable conformational states, of which an apo-closed state is dominant, consistent with previous experimental findings. Our methodology holds great potential to be widely applied to understand recognition mechanisms underlining many fundamental biological processes. '),
(86713, 2014, 'plos', 'A micellar on-pathway intermediate step explains the kinetics of prion amyloid formation', 'Hingant, Erwan|Fontes, Pascaline|Alvarez-Martinez, Maria Teresa|Arnaud, Jacques-Damien|Liautard, Jean-Pierre|Pujo-Menjouet, Laurent', 'CI2MA, Universidad de Concepción, Concepción, Chile.|INSERM U710, Université Montpellier 2, Place E. Bataillon, 3eme étage, Montpellier, France.|Etablissement Confiné dExpérimentation, Plateforme RAM, UMS 3426-BioCampus, Place E. Bataillon, UM2, Montpellier, France.|Etablissement Confiné dExpérimentation, Plateforme RAM, UMS 3426-BioCampus, Place E. Bataillon, UM2, Montpellier, France.|Centre de Recherche sur les Pathogènes et Biologie pour la Santé, CPBS UMR5236, Université Montpellier 2, Place E. Bataillon, Montpellier, France.|Université de Lyon, CNRS, Université Lyon 1, Institut Camille Jordan UMR5208, Villeurbanne, France, INRIA Team Dracula, Inria Center Grenoble Rhône-Alpes, France.', 'Chile|France|France|France|France|France', 0, 'Amyloid|Kinetics|Micelles|Models, Molecular|Prions|Protein Conformation', 'Journal Article', '10.1371/journal.pcbi.1003735', '25101755', 'In a previous work by Alvarez-Martinez et al. (2011), the authors pointed out some fallacies in the mainstream interpretation of the prion amyloid formation. It appeared necessary to propose an original hypothesis able to reconcile the in vitro data with the predictions of a mathematical model describing the problem. Here, a model is developed accordingly with the hypothesis that an intermediate on-pathway leads to the conformation of the prion protein into an amyloid competent isoform thanks to a structure, called micelles, formed from hydrodynamic interaction. The authors also compare data to the prediction of their model and propose a new hypothesis for the formation of infectious prion amyloids. '),
(86714, 2014, 'plos', 'The upside of failure: how regional student groups learn from their mistakes', 'Mishra, Tarun|Parra, R Gonzalo|Abeel, Thomas', 'Bioinformatics Division, School of Bio Sciences and Technology, VIT University, Tamil Nadu, India.|Protein Physiology Laboratory, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina.|Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America, VIB Department of Plant Systems Biology, Ghent University, Ghent, Belgium.', 'India|Argentina|Belgium', 4, 'Argentina|Communication|Computational Biology|Congresses as Topic|Humans|India|Leadership|Learning|Students', 'Journal Article', '10.1371/journal.pcbi.1003768', '25101799', 'Success is the result of planning, hard work, determination, foresight, and a little bit of luck. Unfortunately, nobody has thought to pave the road to success. Although failure can be discouraging and time-consuming, it presents incredible learning opportunities-the biggest difference between those who succeed and those who abandon their projects lies in their response to adversity. This article reviews events undertaken by the Regional Student Groups (RSGs) in India and Argentina, the problems they encountered, and what can be learned from them. RSG-India attempted to organize an online scientific meeting (also known as a virtual conference) with geographically dispersed stakeholders, a totally new concept for them. RSG-Argentina tackled the challenge of organizing a two-day symposium, their first event ever. Some of the complications they faced were easy to fix, others led to the cancellation of activities, and all of them resulted in valuable lessons. The main goal of this article is to highlight, through their experiences, the universal importance of a healthy panel of contingency plans. '),
(86715, 2014, 'plos', 'Large scale characterization of the LC13 TCR and HLA-B8 structural landscape in reaction to 172 altered peptide ligands: a molecular dynamics simulation study', 'Knapp, Bernhard|Dunbar, James|Deane, Charlotte M', 'Department of Statistics, Protein Informatics Group, University of Oxford, Oxford, United Kingdom.|Department of Statistics, Protein Informatics Group, University of Oxford, Oxford, United Kingdom.|Department of Statistics, Protein Informatics Group, University of Oxford, Oxford, United Kingdom.', 'United Kingdom|United Kingdom|United Kingdom', 25, 'Computational Biology|HLA-B8 Antigen|Humans|Molecular Dynamics Simulation|Peptides|Protein Binding|Receptors, Antigen, T-Cell', 'Journal Article', '10.1371/journal.pcbi.1003748', '25101830', 'The interplay between T cell receptors (TCRs) and peptides bound by major histocompatibility complexes (MHCs) is one of the most important interactions in the adaptive immune system. Several previous studies have computationally investigated their structural dynamics. On the basis of these simulations several structural and dynamical properties have been proposed as effectors of the immunogenicity. Here we present the results of a large scale Molecular Dynamics simulation study consisting of 100 ns simulations of 172 different complexes. These complexes consisted of all possible point mutations of the Epstein Barr Virus peptide FLRGRAYGL bound by HLA-B*08:01 and presented to the LC13 TCR. We compare the results of these 172 structural simulations with experimental immunogenicity data. We found that simulations with more immunogenic peptides and those with less immunogenic peptides are in fact highly similar and on average only minor differences in the hydrogen binding footprints, interface distances, and the relative orientation between the TCR chains are present. Thus our large scale data analysis shows that many previously suggested dynamical and structural properties of the TCR/peptide/MHC interface are unlikely to be conserved causal factors for peptide immunogenicity. '),
(86716, 2014, 'plos', 'A hepatitis C virus infection model with time-varying drug effectiveness: solution and analysis', 'Conway, Jessica M|Perelson, Alan S', 'Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.|Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.', 'Mexico|Mexico', 0, 'Antiviral Agents|Computational Biology|Hepacivirus|Hepatitis C|Humans|Models, Biological|Viral Load', 'Journal Article', '10.1371/journal.pcbi.1003769', '25101902', 'Simple models of therapy for viral diseases such as hepatitis C virus (HCV) or human immunodeficiency virus assume that, once therapy is started, the drug has a constant effectiveness. More realistic models have assumed either that the drug effectiveness depends on the drug concentration or that the effectiveness varies over time. Here a previously introduced varying-effectiveness (VE) model is studied mathematically in the context of HCV infection. We show that while the model is linear, it has no closed-form solution due to the time-varying nature of the effectiveness. We then show that the model can be transformed into a Bessel equation and derive an analytic solution in terms of modified Bessel functions, which are defined as infinite series, with time-varying arguments. Fitting the solution to data from HCV infected patients under therapy has yielded values for the parameters in the model. We show that for biologically realistic parameters, the predicted viral decay on therapy is generally biphasic and resembles that predicted by constant-effectiveness (CE) models. We introduce a general method for determining the time at which the transition between decay phases occurs based on calculating the point of maximum curvature of the viral decay curve. For the parameter regimes of interest, we also find approximate solutions for the VE model and establish the asymptotic behavior of the system. We show that the rate of second phase decay is determined by the death rate of infected cells multiplied by the maximum effectiveness of therapy, whereas the rate of first phase decline depends on multiple parameters including the rate of increase of drug effectiveness with time. '),
(86717, 2014, 'plos', 'Phenotypic signatures arising from unbalanced bacterial growth', 'Tan, Cheemeng|Smith, Robert Phillip|Tsai, Ming-Chi|Schwartz, Russell|You, Lingchong', 'Lane Center of Computational Biology, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America, Department of Biomedical Engineering, University of California Davis, Davis, California, United States of America.|Division of Mathematics, Science and Technology, Nova Southeastern University, Fort Lauderdale, Florida, United States of America, Department of Biomedical Engineering, Duke University, Durham, North Carolina, United States of America.|Lane Center of Computational Biology, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.|Lane Center of Computational Biology, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America, Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.|Department of Biomedical Engineering, Duke University, Durham, North Carolina, United States of America, Institute for Genome Sciences and Policy, Duke University, Durham, North Carolina, United States of America.', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Bacteria|Bacterial Physiological Phenomena|Bioengineering|Phenotype|Signal Transduction|Systems Biology', 'Journal Article', '10.1371/journal.pcbi.1003751', '25101949', 'Fluctuations in the growth rate of a bacterial culture during unbalanced growth are generally considered undesirable in quantitative studies of bacterial physiology. Under well-controlled experimental conditions, however, these fluctuations are not random but instead reflect the interplay between intra-cellular networks underlying bacterial growth and the growth environment. Therefore, these fluctuations could be considered quantitative phenotypes of the bacteria under a specific growth condition. Here, we present a method to identify phenotypic signatures by time-frequency analysis of unbalanced growth curves measured with high temporal resolution. The signatures are then applied to differentiate amongst different bacterial strains or the same strain under different growth conditions, and to identify the essential architecture of the gene network underlying the observed growth dynamics. Our method has implications for both basic understanding of bacterial physiology and for the classification of bacterial strains. '),
(86718, 2014, 'plos', 'Modeling viral evolutionary dynamics after telaprevir-based treatment', 'Haseltine, Eric L|De Meyer, Sandra|Dierynck, Inge|Bartels, Doug J|Ghys, Anne|Davis, Andrew|Zhang, Eileen Z|Tigges, Ann M|Spanks, Joan|Picchio, Gaston|Kieffer, Tara L|Sullivan, James C', 'Vertex Pharmaceuticals Incorporated, Boston, Massachussets, United States of America.|Janssen Infectious Diseases BVBA, Beerse, Belgium.|Janssen Infectious Diseases BVBA, Beerse, Belgium.|Vertex Pharmaceuticals Incorporated, Boston, Massachussets, United States of America.|Janssen Infectious Diseases BVBA, Beerse, Belgium.|Vertex Pharmaceuticals Incorporated, Boston, Massachussets, United States of America.|Vertex Pharmaceuticals Incorporated, Boston, Massachussets, United States of America.|Vertex Pharmaceuticals Incorporated, Boston, Massachussets, United States of America.|Vertex Pharmaceuticals Incorporated, Boston, Massachussets, United States of America.|Janssen Research & Development, Titusville, New Jersey, United States of America.|Vertex Pharmaceuticals Incorporated, Boston, Massachussets, United States of America.|Vertex Pharmaceuticals Incorporated, Boston, Massachussets, United States of America.', 'United States|Belgium|Belgium|United States|Belgium|United States|United States|United States|United', 7, 'Antiviral Agents|Computational Biology|Drug Resistance, Viral|Drug Therapy, Combination|Hepacivirus|Hepatitis C|Humans|Models, Biological|Oligopeptides|Viral Load', 'Journal Article', '10.1371/journal.pcbi.1003772', '25101970', 'For patients infected with hepatitis C virus (HCV), the combination of the direct-acting antiviral agent telaprevir, pegylated-interferon alfa (Peg-IFN), and ribavirin (RBV) significantly increases the chances of sustained virologic response (SVR) over treatment with Peg-IFN and RBV alone. If patients do not achieve SVR with telaprevir-based treatment, their viral population is often significantly enriched with telaprevir-resistant variants at the end of treatment. We sought to quantify the evolutionary dynamics of these post-treatment resistant variant populations. Previous estimates of these dynamics were limited by analyzing only population sequence data (20% sensitivity, qualitative resistance information) from 388 patients enrolled in Phase 3 clinical studies. Here we add clonal sequence analysis (5% sensitivity, quantitative) for a subset of these patients. We developed a computational model which integrates both the qualitative and quantitative sequence data, and which forms a framework for future analyses of drug resistance. The model was qualified by showing that deep-sequence data (1% sensitivity) from a subset of these patients are consistent with model predictions. When determining the median time for viral populations to revert to 20% resistance in these patients, the model predicts 8.3 (95% CI: 7.6, 8.4) months versus 10.7 (9.9, 12.8) months estimated using solely population sequence data for genotype 1a, and 1.0 (0.0, 1.4) months versus 0.9 (0.0, 2.7) months for genotype 1b. For each individual patient, the time to revert to 20% resistance predicted by the model was typically comparable to or faster than that estimated using solely population sequence data. Furthermore, the model predicts a median of 11.0 and 2.1 months after treatment failure for viral populations to revert to 99% wild-type in patients with HCV genotypes 1a or 1b, respectively. Our modeling approach provides a framework for projecting accurate, quantitative assessment of HCV resistance dynamics from a data set consisting of largely qualitative information. '),
(86719, 2014, 'plos', 'Random migration and signal integration promote rapid and robust T cell recruitment', 'Textor, Johannes|Henrickson, Sarah E|Mandl, Judith N|von Andrian, Ulrich H|Westermann, Jürgen|de Boer, Rob J|Beltman, Joost B', 'Theoretical Biology & Bioinformatics, Utrecht University, Utrecht, The Netherlands.|Department of Pathology, Harvard Medical School, Boston, Massachusetts, United States of America.|Lymphocyte Biology Section, National Insitutes of Health, Bethesda, Maryland, United States of America.|Department of Pathology, Harvard Medical School, Boston, Massachusetts, United States of America.|Institute for Anatomy, University of Lübeck, Lübeck, Germany.|Theoretical Biology & Bioinformatics, Utrecht University, Utrecht, The Netherlands.|Theoretical Biology & Bioinformatics, Utrecht University, Utrecht, The Netherlands, Division of Immunology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.', 'Netherlands|United States|United States|United States|Germany|Netherlands|Netherlands', 0, 'Animals|Cell Communication|Cell Movement|Computational Biology|Humans|Mice|Models, Immunological|Signal Transduction|T-Lymphocytes', 'Journal Article', '10.1371/journal.pcbi.1003752', '25102014', 'To fight infections, rare T cells must quickly home to appropriate lymph nodes (LNs), and reliably localize the antigen (Ag) within them. The first challenge calls for rapid trafficking between LNs, whereas the second may require extensive search within each LN. Here we combine simulations and experimental data to investigate which features of random T cell migration within and between LNs allow meeting these two conflicting demands. Our model indicates that integrating signals from multiple random encounters with Ag-presenting cells permits reliable detection of even low-dose Ag, and predicts a kinetic feature of cognate T cell arrest in LNs that we confirm using intravital two-photon data. Furthermore, we obtain the most reliable retention if T cells transit through LNs stochastically, which may explain the long and widely distributed LN dwell times observed in vivo. Finally, we demonstrate that random migration, both between and within LNs, allows recruiting the majority of cognate precursors within a few days for various realistic infection scenarios. Thus, the combination of two-scale stochastic migration and signal integration is an efficient and robust strategy for T cell immune surveillance. '),
(86720, 2014, 'plos', 'The fitness landscape of HIV-1 gag: advanced modeling approaches and validation of model predictions by in vitro testing', 'Mann, Jaclyn K|Barton, John P|Ferguson, Andrew L|Omarjee, Saleha|Walker, Bruce D|Chakraborty, Arup|Ndungu, Thumbi', 'HIV Pathogenesis Programme, Doris Duke Medical Research Institute, Nelson R. Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa, KwaZulu-Natal Research Institute for Tuberculosis and HIV (K-RITH), Nelson R Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa.|Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America, Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology and Harvard University, Boston, Massachusetts, United States of America.|Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America.|HIV Pathogenesis Programme, Doris Duke Medical Research Institute, Nelson R. Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa, KwaZulu-Natal Research Institute for Tuberculosis and HIV (K-RITH), Nelson R Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa.|HIV Pathogenesis Programme, Doris Duke Medical Research Institute, Nelson R. Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa, Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology and Harvard University, Boston, Massachusetts, United States of America, Howard Hughes Medical Institute, Chevy Chase, Maryland, United States of America.|Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America, Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology and Harvard University, Boston, Massachusetts, United States of America, Departments of Chemistry and Physics, Massachusetts Institute of Technology, Boston, Massachusetts, United States of America.|HIV Pathogenesis Programme, Doris Duke Medical Research Institute, Nelson R. Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa, KwaZulu-Natal Research Institute for Tuberculosis and HIV (K-RITH), Nelson R Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa, Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology and Harvard University, Boston, Massachusetts, United States of America, Max Planck Institute for Infection Biology, Berlin, Germany.', 'South Africa|United States|United States|South Africa|United States|United States|Germany', 61, 'Computer Simulation|Genes, gag|Genetic Fitness|HIV Infections|HIV-1|Humans|Immune Evasion|Models, Biological|Mutation|RNA, Viral', 'Journal Article', '10.1371/journal.pcbi.1003776', '25102049', 'Viral immune evasion by sequence variation is a major hindrance to HIV-1 vaccine design. To address this challenge, our group has developed a computational model, rooted in physics, that aims to predict the fitness landscape of HIV-1 proteins in order to design vaccine immunogens that lead to impaired viral fitness, thus blocking viable escape routes. Here, we advance the computational models to address previous limitations, and directly test model predictions against in vitro fitness measurements of HIV-1 strains containing multiple Gag mutations. We incorporated regularization into the model fitting procedure to address finite sampling. Further, we developed a model that accounts for the specific identity of mutant amino acids (Potts model), generalizing our previous approach (Ising model) that is unable to distinguish between different mutant amino acids. Gag mutation combinations (17 pairs, 1 triple and 25 single mutations within these) predicted to be either harmful to HIV-1 viability or fitness-neutral were introduced into HIV-1 NL4-3 by site-directed mutagenesis and replication capacities of these mutants were assayed in vitro. The predicted and measured fitness of the corresponding mutants for the original Ising model (rxe2x80x8a=xe2x80x8a-0.74, pxe2x80x8a=xe2x80x8a3.6xc3x9710-6) are strongly correlated, and this was further strengthened in the regularized Ising model (rxe2x80x8a=xe2x80x8a-0.83, pxe2x80x8a=xe2x80x8a3.7xc3x9710-12). Performance of the Potts model (rxe2x80x8a=xe2x80x8a-0.73, pxe2x80x8a=xe2x80x8a9.7xc3x9710-9) was similar to that of the Ising model, indicating that the binary approximation is sufficient for capturing fitness effects of common mutants at sites of low amino acid diversity. However, we show that the Potts model is expected to improve predictive power for more variable proteins. Overall, our results support the ability of the computational models to robustly predict the relative fitness of mutant viral strains, and indicate the potential value of this approach for understanding viral immune evasion, and harnessing this knowledge for immunogen design. '),
(86721, 2014, 'plos', 'Cost-effective control of plant disease when epidemiological knowledge is incomplete: modelling Bahia bark scaling of citrus', 'Cunniffe, Nik J|Laranjeira, Francisco F|Neri, Franco M|DeSimone, R Erik|Gilligan, Christopher A', 'Department of Plant Sciences, University of Cambridge, Cambridge, United Kingdom.|Embrapa Cassava and Fruits, Cruz das Almas, Brazil.|Department of Plant Sciences, University of Cambridge, Cambridge, United Kingdom.|Department of Plant Sciences, University of Cambridge, Cambridge, United Kingdom.|Department of Plant Sciences, University of Cambridge, Cambridge, United Kingdom.', 'United Kingdom|Brazil|United Kingdom|United Kingdom|United Kingdom', 0, 'Citrus|Computational Biology|Markov Chains|Models, Biological|Models, Statistical|Monte Carlo Method|Plant Diseases', 'Journal Article', '10.1371/journal.pcbi.1003753', '25102099', 'A spatially-explicit, stochastic model is developed for Bahia bark scaling, a threat to citrus production in north-eastern Brazil, and is used to assess epidemiological principles underlying the cost-effectiveness of disease control strategies. The model is fitted via Markov chain Monte Carlo with data augmentation to snapshots of disease spread derived from a previously-reported multi-year experiment. Goodness-of-fit tests strongly supported the fit of the model, even though the detailed etiology of the disease is unknown and was not explicitly included in the model. Key epidemiological parameters including the infection rate, incubation period and scale of dispersal are estimated from the spread data. This allows us to scale-up the experimental results to predict the effect of the level of initial inoculum on disease progression in a typically-sized citrus grove. The efficacies of two cultural control measures are assessed: altering the spacing of host plants, and roguing symptomatic trees. Reducing planting density can slow disease spread significantly if the distance between hosts is sufficiently large. However, low density groves have fewer plants per hectare. The optimum density of productive plants is therefore recovered at an intermediate host spacing. Roguing, even when detection of symptomatic plants is imperfect, can lead to very effective control. However, scouting for disease symptoms incurs a cost. We use the model to balance the cost of scouting against the number of plants lost to disease, and show how to determine a roguing schedule that optimises profit. The trade-offs underlying the two optima we identify-the optimal host spacing and the optimal roguing schedule-are applicable to many pathosystems. Our work demonstrates how a carefully parameterised mathematical model can be used to find these optima. It also illustrates how mathematical models can be used in even this most challenging of situations in which the underlying epidemiology is ill-understood. '),
(86722, 2014, 'plos', 'Blind predictions of DNA and RNA tweezers experiments with force and torque', 'Chou, Fang-Chieh|Lipfert, Jan|Das, Rhiju', 'Department of Biochemistry, Stanford University, Stanford, California, United States of America.|Department of Bionanoscience, Kavli Institute of Nanoscience, Delft University of Technology, Delft, The Netherlands, Department of Physics and Center for Nanoscience (CeNS), University of Munich, Munich, Germany.|Department of Biochemistry, Stanford University, Stanford, California, United States of America, Biophysics Program, Stanford University, Stanford, California, United States of America, Department of Physics, Stanford University, Stanford, California, United States of America.', 'United States|Germany|United States', 0, 'Algorithms|Computer Simulation|DNA|Elastic Modulus|Models, Molecular|Nanotechnology|Nucleic Acid Conformation|RNA|Torque', 'Journal Article', '10.1371/journal.pcbi.1003756', '25102226', 'Single-molecule tweezers measurements of double-stranded nucleic acids (dsDNA and dsRNA) provide unprecedented opportunities to dissect how these fundamental molecules respond to forces and torques analogous to those applied by topoisomerases, viral capsids, and other biological partners. However, tweezers data are still most commonly interpreted post facto in the framework of simple analytical models. Testing falsifiable predictions of state-of-the-art nucleic acid models would be more illuminating but has not been performed. Here we describe a blind challenge in which numerical predictions of nucleic acid mechanical properties were compared to experimental data obtained recently for dsRNA under applied force and torque. The predictions were enabled by the HelixMC package, first presented in this paper. HelixMC advances crystallography-derived base-pair level models (BPLMs) to simulate kilobase-length dsDNAs and dsRNAs under external forces and torques, including their global linking numbers. These calculations recovered the experimental bending persistence length of dsRNA within the error of the simulations and accurately predicted that dsRNAs spring-like conformation would give a two-fold decrease of stretch modulus relative to dsDNA. Further blind predictions of helix torsional properties, however, exposed inaccuracies in current BPLM theory, including three-fold discrepancies in torsional persistence length at the high force limit and the incorrect sign of dsRNA link-extension (twist-stretch) coupling. Beyond these experiments, HelixMC predicted that nucleosome-excluding poly(A)/poly(T) is at least two-fold stiffer than random-sequence dsDNA in bending, stretching, and torsional behaviors, Z-DNA to be at least three-fold stiffer than random-sequence dsDNA, with a near-zero link-extension coupling, and non-negligible effects from base pair step correlations. We propose that experimentally testing these predictions should be powerful next steps for understanding the flexibility of dsDNA and dsRNA in sequence contexts and under mechanical stresses relevant to their biology. ');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(86723, 2014, 'plos', 'SciClone: inferring clonal architecture and tracking the spatial and temporal patterns of tumor evolution', 'Miller, Christopher A|White, Brian S|Dees, Nathan D|Griffith, Malachi|Welch, John S|Griffith, Obi L|Vij, Ravi|Tomasson, Michael H|Graubert, Timothy A|Walter, Matthew J|Ellis, Matthew J|Schierding, William|DiPersio, John F|Ley, Timothy J|Mardis, Elaine R|Wilson, Richard K|Ding, Li', 'The Genome Institute, Washington University, St. Louis, Missouri, United States of America.|The Genome Institute, Washington University, St. Louis, Missouri, United States of America, Department of Internal Medicine, Division of Oncology, Washington University School of Medicine, St. Louis, Missouri, United States of America.|The Genome Institute, Washington University, St. Louis, Missouri, United States of America.|The Genome Institute, Washington University, St. Louis, Missouri, United States of America, Department of Genetics, Washington University, St. Louis, Missouri, United States of America.|Department of Internal Medicine, Division of Oncology, Washington University School of Medicine, St. Louis, Missouri, United States of America, Siteman Cancer Center, Barnes-Jewish Hospital, Washington University School of Medicine, St. Louis, Missouri, United States of America.|The Genome Institute, Washington University, St. Louis, Missouri, United States of America, Department of Internal Medicine, Division of Oncology, Washington University School of Medicine, St. Louis, Missouri, United States of America.|Department of Internal Medicine, Division of Oncology, Washington University School of Medicine, St. Louis, Missouri, United States of America, Siteman Cancer Center, Barnes-Jewish Hospital, Washington University School of Medicine, St. Louis, Missouri, United States of America.|Department of Internal Medicine, Division of Oncology, Washington University School of Medicine, St. Louis, Missouri, United States of America, Siteman Cancer Center, Barnes-Jewish Hospital, Washington University School of Medicine, St. Louis, Missouri, United States of America.|Department of Internal Medicine, Division of Oncology, Washington University School of Medicine, St. Louis, Missouri, United States of America, Siteman Cancer Center, Barnes-Jewish Hospital, Washington University School of Medicine, St. Louis, Missouri, United States of America, Massachusetts General Hospital, Boston, Massachusetts, United States of America.|Department of Internal Medicine, Division of Oncology, Washington University School of Medicine, St. Louis, Missouri, United States of America, Department of Genetics, Washington University, St. Louis, Missouri, United States of America, Siteman Cancer Center, Barnes-Jewish Hospital, Washington University School of Medicine, St. Louis, Missouri, United States of America.|Department of Internal Medicine, Division of Oncology, Washington University School of Medicine, St. Louis, Missouri, United States of America, Siteman Cancer Center, Barnes-Jewish Hospital, Washington University School of Medicine, St. Louis, Missouri, United States of America.|Liggins Institute, Auckland, New Zealand.|Department of Internal Medicine, Division of Oncology, Washington University School of Medicine, St. Louis, Missouri, United States of America, Siteman Cancer Center, Barnes-Jewish Hospital, Washington University School of Medicine, St. Louis, Missouri, United States of America.|The Genome Institute, Washington University, St. Louis, Missouri, United States of America, Department of Internal Medicine, Division of Oncology, Washington University School of Medicine, St. Louis, Missouri, United States of America, Department of Genetics, Washington University, St. Louis, Missouri, United States of America, Siteman Cancer Center, Barnes-Jewish Hospital, Washington University School of Medicine, St. Louis, Missouri, United States of America.|The Genome Institute, Washington University, St. Louis, Missouri, United States of America, Department of Internal Medicine, Division of Oncology, Washington University School of Medicine, St. Louis, Missouri, United States of America, Department of Genetics, Washington University, St. Louis, Missouri, United States of America, Siteman Cancer Center, Barnes-Jewish Hospital, Washington University School of Medicine, St. Louis, Missouri, United States of America.|The Genome Institute, Washington University, St. Louis, Missouri, United States of America, Department of Internal Medicine, Division of Oncology, Washington University School of Medicine, St. Louis, Missouri, United States of America, Department of Genetics, Washington University, St. Louis, Missouri, United States of America, Siteman Cancer Center, Barnes-Jewish Hospital, Washington University School of Medicine, St. Louis, Missouri, United States of America.|The Genome Institute, Washington University, St. Louis, Missouri, United States of America, Department of Internal Medicine, Division of Oncology, Washington University School of Medicine, St. Louis, Missouri, United States of America, Department of Genetics, Washington University, St. Louis, Missouri, United States of America, Siteman Cancer Center, Barnes-Jewish Hospital, Washington University School of Medicine, St. Louis, Missouri, United States of America.', 'United States|United States|United States|United States|United States|United States|United States|Un', 203, 'Breast Neoplasms|Clonal Evolution|Computational Biology|Female|Gene Frequency|Humans|Leukemia, Myeloid, Acute|Models, Statistical|Mutation|Neoplasms|Neoplastic Processes', 'Journal Article', '10.1371/journal.pcbi.1003665', '25102416', 'The sensitivity of massively-parallel sequencing has confirmed that most cancers are oligoclonal, with subpopulations of neoplastic cells harboring distinct mutations. A fine resolution view of this clonal architecture provides insight into tumor heterogeneity, evolution, and treatment response, all of which may have clinical implications. Single tumor analysis already contributes to understanding these phenomena. However, cryptic subclones are frequently revealed by additional patient samples (e.g., collected at relapse or following treatment), indicating that accurately characterizing a tumor requires analyzing multiple samples from the same patient. To address this need, we present SciClone, a computational method that identifies the number and genetic composition of subclones by analyzing the variant allele frequencies of somatic mutations. We use it to detect subclones in acute myeloid leukemia and breast cancer samples that, though present at disease onset, are not evident from a single primary tumor sample. By doing so, we can track tumor evolution and identify the spatial origins of cells resisting therapy. '),
(86724, 2014, 'plos', 'The protective role of symmetric stem cell division on the accumulation of heritable damage', 'McHale, Peter T|Lander, Arthur D', 'Center for Complex Biological Systems & Department of Cell and Developmental Biology, University of California Irvine, Irvine, California, United States of America.|Center for Complex Biological Systems & Department of Cell and Developmental Biology, University of California Irvine, Irvine, California, United States of America.', 'United States|United States', 0, 'Animals|Cell Division|Genes, APC|Genomic Instability|Intestine, Small|Mice|Models, Genetic|Mutation|Stem Cells', 'Journal Article', '10.1371/journal.pcbi.1003802', '25121484', 'Stem cell divisions are either asymmetric-in which one daughter cell remains a stem cell and one does not-or symmetric, in which both daughter cells adopt the same fate, either stem or non-stem. Recent studies show that in many tissues operating under homeostatic conditions stem cell division patterns are strongly biased toward the symmetric outcome, raising the question of whether symmetry confers some benefit. Here, we show that symmetry, via extinction of damaged stem-cell clones, reduces the lifetime risk of accumulating phenotypically silent heritable damage (mutations or aberrant epigenetic changes) in individual stem cells. This effect is greatest in rapidly cycling tissues subject to accelerating rates of damage accumulation over time, a scenario that describes the progression of many cancers. A decrease in the rate of cellular damage accumulation may be an important factor favoring symmetric patterns of stem cell division. '),
(86725, 2014, 'plos', 'Phenotypic robustness and the assortativity signature of human transcription factor networks', 'Pechenick, Dov A|Payne, Joshua L|Moore, Jason H', 'Computational Genetics Laboratory, Dartmouth College, Hanover, New Hampshire, United States of America.|Institute of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland.|Computational Genetics Laboratory, Dartmouth College, Hanover, New Hampshire, United States of America.', 'United States|Switzerland|United States', 11, 'Computational Biology|Gene Expression Regulation|Gene Regulatory Networks|Humans|Models, Genetic|Organ Specificity|Phenotype|Transcription Factors', 'Journal Article', '10.1371/journal.pcbi.1003780', '25121490', 'Many developmental, physiological, and behavioral processes depend on the precise expression of genes in space and time. Such spatiotemporal gene expression phenotypes arise from the binding of sequence-specific transcription factors (TFs) to DNA, and from the regulation of nearby genes that such binding causes. These nearby genes may themselves encode TFs, giving rise to a transcription factor network (TFN), wherein nodes represent TFs and directed edges denote regulatory interactions between TFs. Computational studies have linked several topological properties of TFNs - such as their degree distribution - with the robustness of a TFNs gene expression phenotype to genetic and environmental perturbation. Another important topological property is assortativity, which measures the tendency of nodes with similar numbers of edges to connect. In directed networks, assortativity comprises four distinct components that collectively form an assortativity signature. We know very little about how a TFNs assortativity signature affects the robustness of its gene expression phenotype to perturbation. While recent theoretical results suggest that increasing one specific component of a TFNs assortativity signature leads to increased phenotypic robustness, the biological context of this finding is currently limited because the assortativity signatures of real-world TFNs have not been characterized. It is therefore unclear whether these earlier theoretical findings are biologically relevant. Moreover, it is not known how the other three components of the assortativity signature contribute to the phenotypic robustness of TFNs. Here, we use publicly available DNaseI-seq data to measure the assortativity signatures of genome-wide TFNs in 41 distinct human cell and tissue types. We find that all TFNs share a common assortativity signature and that this signature confers phenotypic robustness to model TFNs. Lastly, we determine the extent to which each of the four components of the assortativity signature contributes to this robustness. '),
(86726, 2014, 'plos', 'A simple model of optimal population coding for sensory systems', 'Doi, Eizaburo|Lewicki, Michael S', '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', 0, 'Animals|Image Processing, Computer-Assisted|Models, Neurological|Primates|Retina|Retinal Ganglion Cells|Signal-To-Noise Ratio', 'Journal Article', '10.1371/journal.pcbi.1003761', '25121492', 'A fundamental task of a sensory system is to infer information about the environment. It has long been suggested that an important goal of the first stage of this process is to encode the raw sensory signal efficiently by reducing its redundancy in the neural representation. Some redundancy, however, would be expected because it can provide robustness to noise inherent in the system. Encoding the raw sensory signal itself is also problematic, because it contains distortion and noise. The optimal solution would be constrained further by limited biological resources. Here, we analyze a simple theoretical model that incorporates these key aspects of sensory coding, and apply it to conditions in the retina. The model specifies the optimal way to incorporate redundancy in a population of noisy neurons, while also optimally compensating for sensory distortion and noise. Importantly, it allows an arbitrary input-to-output cell ratio between sensory units (photoreceptors) and encoding units (retinal ganglion cells), providing predictions of retinal codes at different eccentricities. Compared to earlier models based on redundancy reduction, the proposed model conveys more information about the original signal. Interestingly, redundancy reduction can be near-optimal when the number of encoding units is limited, such as in the peripheral retina. We show that there exist multiple, equally-optimal solutions whose receptive field structure and organization vary significantly. Among these, the one which maximizes the spatial locality of the computation, but not the sparsity of either synaptic weights or neural responses, is consistent with known basic properties of retinal receptive fields. The model further predicts that receptive field structure changes less with light adaptation at higher input-to-output cell ratios, such as in the periphery. '),
(86727, 2014, 'plos', 'Logarithmic and power law input-output relations in sensory systems with fold-change detection', 'Adler, Miri|Mayo, Avi|Alon, Uri', 'Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.|Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.|Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.', 'Israel|Israel|Israel', 0, 'Animals|Chemotaxis|Computational Biology|Computer Simulation|Eukaryota|Feedback, Physiological|Humans|Models, Biological|Signal Transduction', 'Journal Article', '10.1371/journal.pcbi.1003781', '25121598', 'Two central biophysical laws describe sensory responses to input signals. One is a logarithmic relationship between input and output, and the other is a power law relationship. These laws are sometimes called the Weber-Fechner law and the Stevens power law, respectively. The two laws are found in a wide variety of human sensory systems including hearing, vision, taste, and weight perception, they also occur in the responses of cells to stimuli. However the mechanistic origin of these laws is not fully understood. To address this, we consider a class of biological circuits exhibiting a property called fold-change detection (FCD). In these circuits the response dynamics depend only on the relative change in input signal and not its absolute level, a property which applies to many physiological and cellular sensory systems. We show analytically that by changing a single parameter in the FCD circuits, both logarithmic and power-law relationships emerge, these laws are modified versions of the Weber-Fechner and Stevens laws. The parameter that determines which law is found is the steepness (effective Hill coefficient) of the effect of the internal variable on the output. This finding applies to major circuit architectures found in biological systems, including the incoherent feed-forward loop and nonlinear integral feedback loops. Therefore, if one measures the response to different fold changes in input signal and observes a logarithmic or power law, the present theory can be used to rule out certain FCD mechanisms, and to predict their cooperativity parameter. We demonstrate this approach using data from eukaryotic chemotaxis signaling. '),
(86728, 2014, 'plos', 'Top-down inputs enhance orientation selectivity in neurons of the primary visual cortex during perceptual learning', 'Moldakarimov, Samat|Bazhenov, Maxim|Sejnowski, Terrence J', 'Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, California, United States of America, The Institute for Neural Computation, University of California, San Diego, La Jolla, California, United States of America.|Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, California, United States of America, Department of Cell Biology and Neuroscience, University of California, Riverside, Riverside, California, United States of America.|Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, California, United States of America, The Institute for Neural Computation, University of California, San Diego, La Jolla, California, United States of America, Division of Biological Sciences, University of California, San Diego, La Jolla, California, United States of America.', 'United States|United States|United States', 11, 'Action Potentials|Animals|Computational Biology|Feedback, Sensory|Haplorhini|Learning|Models, Neurological|Neurons|Visual Cortex', 'Journal Article', '10.1371/journal.pcbi.1003770', '25121603', 'Perceptual learning has been used to probe the mechanisms of cortical plasticity in the adult brain. Feedback projections are ubiquitous in the cortex, but little is known about their role in cortical plasticity. Here we explore the hypothesis that learning visual orientation discrimination involves learning-dependent plasticity of top-down feedback inputs from higher cortical areas, serving a different function from plasticity due to changes in recurrent connections within a cortical area. In a Hodgkin-Huxley-based spiking neural network model of visual cortex, we show that modulation of feedback inputs to V1 from higher cortical areas results in shunting inhibition in V1 neurons, which changes the response properties of V1 neurons. The orientation selectivity of V1 neurons is enhanced without changing orientation preference, preserving the topographic organizations in V1. These results provide new insights to the mechanisms of plasticity in the adult brain, reconciling apparently inconsistent experiments and providing a new hypothesis for a functional role of the feedback connections. '),
(86729, 2014, 'plos', 'Unveiling time in dose-response models to infer host susceptibility to pathogens', 'Pessoa, Delphine|Souto-Maior, Caetano|Gjini, Erida|Lopes, Joao S|Ceña, Bruno|Codeço, Cláudia T|Gomes, M Gabriela M', 'Instituto Gulbenkian de Ciência, Oeiras, Portugal.|Instituto Gulbenkian de Ciência, Oeiras, Portugal.|Instituto Gulbenkian de Ciência, Oeiras, Portugal.|Instituto Gulbenkian de Ciência, Oeiras, Portugal.|Instituto Gulbenkian de Ciência, Oeiras, Portugal.|Programa de Computação Científica, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil.|Instituto Gulbenkian de Ciência, Oeiras, Portugal.', 'Portugal|Portugal|Portugal|Portugal|Portugal|Brazil|Portugal', 0, 'Animals|Dicistroviridae|Disease Susceptibility|Drosophila melanogaster|Host-Pathogen Interactions|Male|Models, Biological|Survival Analysis|Symbiosis|Wolbachia', 'Journal Article', '10.1371/journal.pcbi.1003773', '25121762', 'The biological effects of interventions to control infectious diseases typically depend on the intensity of pathogen challenge. As much as the levels of natural pathogen circulation vary over time and geographical location, the development of invariant efficacy measures is of major importance, even if only indirectly inferrable. Here a method is introduced to assess host susceptibility to pathogens, and applied to a detailed dataset generated by challenging groups of insect hosts (Drosophila melanogaster) with a range of pathogen (Drosophila C Virus) doses and recording survival over time. The experiment was replicated for flies carrying the Wolbachia symbiont, which is known to reduce host susceptibility to viral infections. The entire dataset is fitted by a novel quantitative framework that significantly extends classical methods for microbial risk assessment and provides accurate distributions of symbiont-induced protection. More generally, our data-driven modeling procedure provides novel insights for study design and analyses to assess interventions. '),
(86730, 2014, 'plos', 'A deterministic model predicts the properties of stochastic calcium oscillations in airway smooth muscle cells', 'Cao, Pengxing|Tan, Xiahui|Donovan, Graham|Sanderson, Michael J|Sneyd, James', 'Department of Mathematics, University of Auckland, Auckland, New Zealand.|Department of Microbiology and Physiological Systems, University of Massachusetts Medical School, Worcester, Massachusetts, United States of America.|Department of Mathematics, University of Auckland, Auckland, New Zealand.|Department of Microbiology and Physiological Systems, University of Massachusetts Medical School, Worcester, Massachusetts, United States of America.|Department of Mathematics, University of Auckland, Auckland, New Zealand.', 'New Zealand|United States|New Zealand|United States|New Zealand', 0, 'Animals|Calcium|Calcium Signaling|Inositol 1,4,5-Trisphosphate Receptors|Linear Models|Lung|Mice|Mice, Inbred BALB C|Models, Biological|Myocytes, Smooth Muscle|Stochastic Processes', 'Journal Article', '10.1371/journal.pcbi.1003783', '25121766', 'The inositol trisphosphate receptor ([Formula: see text]) is one of the most important cellular components responsible for oscillations in the cytoplasmic calcium concentration. Over the past decade, two major questions about the [Formula: see text] have arisen. Firstly, how best should the [Formula: see text] be modeled? In other words, what fundamental properties of the [Formula: see text] allow it to perform its function, and what are their quantitative properties? Secondly, although calcium oscillations are caused by the stochastic opening and closing of small numbers of [Formula: see text], is it possible for a deterministic model to be a reliable predictor of calcium behavior? Here, we answer these two questions, using airway smooth muscle cells (ASMC) as a specific example. Firstly, we show that periodic calcium waves in ASMC, as well as the statistics of calcium puffs in other cell types, can be quantitatively reproduced by a two-state model of the [Formula: see text], and thus the behavior of the [Formula: see text] is essentially determined by its modal structure. The structure within each mode is irrelevant for function. Secondly, we show that, although calcium waves in ASMC are generated by a stochastic mechanism, [Formula: see text] stochasticity is not essential for a qualitative prediction of how oscillation frequency depends on model parameters, and thus deterministic [Formula: see text] models demonstrate the same level of predictive capability as do stochastic models. We conclude that, firstly, calcium dynamics can be accurately modeled using simplified [Formula: see text] models, and, secondly, to obtain qualitative predictions of how oscillation frequency depends on parameters it is sufficient to use a deterministic model. '),
(86731, 2014, 'plos', 'Kinetic memory based on the enzyme-limited competition', 'Hatakeyama, Tetsuhiro S|Kaneko, Kunihiko', 'Department of Basic Science, Graduate School of Arts and Sciences, The University of Tokyo, Komaba, Meguro-ku, Tokyo, Japan.|Department of Basic Science, Graduate School of Arts and Sciences, The University of Tokyo, Komaba, Meguro-ku, Tokyo, Japan.', 'Japan|Japan', 0, 'Biochemical Phenomena|Cell Physiological Phenomena|Enzymes|Kinetics|Models, Biological', 'Journal Article', '10.1371/journal.pcbi.1003784', '25121967', 'Cellular memory, which allows cells to retain information from their environment, is important for a variety of cellular functions, such as adaptation to external stimuli, cell differentiation, and synaptic plasticity. Although posttranslational modifications have received much attention as a source of cellular memory, the mechanisms directing such alterations have not been fully uncovered. It may be possible to embed memory in multiple stable states in dynamical systems governing modifications. However, several experiments on modifications of proteins suggest long-term relaxation depending on experienced external conditions, without explicit switches over multi-stable states. As an alternative to a multistability memory scheme, we propose kinetic memory for epigenetic cellular memory, in which memory is stored as a slow-relaxation process far from a stable fixed state. Information from previous environmental exposure is retained as the long-term maintenance of a cellular state, rather than switches over fixed states. To demonstrate this kinetic memory, we study several models in which multimeric proteins undergo catalytic modifications (e.g., phosphorylation and methylation), and find that a slow relaxation process of the modification state, logarithmic in time, appears when the concentration of a catalyst (enzyme) involved in the modification reactions is lower than that of the substrates. Sharp transitions from a normal fast-relaxation phase into this slow-relaxation phase are revealed, and explained by enzyme-limited competition among modification reactions. The slow-relaxation process is confirmed by simulations of several models of catalytic reactions of protein modifications, and it enables the memorization of external stimuli, as its time course depends crucially on the history of the stimuli. This kinetic memory provides novel insight into a broad class of cellular memory and functions. In particular, applications for long-term potentiation are discussed, including dynamic modifications of calcium-calmodulin kinase II and cAMP-response element-binding protein essential for synaptic plasticity. '),
(86732, 2014, 'plos', 'Mechanical cell-matrix feedback explains pairwise and collective endothelial cell behavior in vitro', 'van Oers, René F M|Rens, Elisabeth G|LaValley, Danielle J|Reinhart-King, Cynthia A|Merks, Roeland M H', 'Life Sciences group, Centrum Wiskunde & Informatica, Amsterdam, The Netherlands, Netherlands Consortium for System Biology - Netherlands Institute for Systems Biology, Amsterdam, The Netherlands.|Life Sciences group, Centrum Wiskunde & Informatica, Amsterdam, The Netherlands, Netherlands Consortium for System Biology - Netherlands Institute for Systems Biology, Amsterdam, The Netherlands.|Department of Biomedical Engineering, Cornell University, Ithaca, New York, United States of America.|Department of Biomedical Engineering, Cornell University, Ithaca, New York, United States of America.|Life Sciences group, Centrum Wiskunde & Informatica, Amsterdam, The Netherlands, Netherlands Consortium for System Biology - Netherlands Institute for Systems Biology, Amsterdam, The Netherlands, Mathematical Institute, Leiden University, Leiden, The Netherlands.', 'Netherlands|Netherlands|United States|United States|Netherlands', 85, 'Animals|Cattle|Cell Communication|Cell Movement|Cell Shape|Cells, Cultured|Computer Simulation|Endothelial Cells|Extracellular Matrix|Humans|Models, Biological|Spheroids, Cellular', 'Journal Article', '10.1371/journal.pcbi.1003774', '25121971', 'In vitro cultures of endothelial cells are a widely used model system of the collective behavior of endothelial cells during vasculogenesis and angiogenesis. When seeded in an extracellular matrix, endothelial cells can form blood vessel-like structures, including vascular networks and sprouts. Endothelial morphogenesis depends on a large number of chemical and mechanical factors, including the compliancy of the extracellular matrix, the available growth factors, the adhesion of cells to the extracellular matrix, cell-cell signaling, etc. Although various computational models have been proposed to explain the role of each of these biochemical and biomechanical effects, the understanding of the mechanisms underlying in vitro angiogenesis is still incomplete. Most explanations focus on predicting the whole vascular network or sprout from the underlying cell behavior, and do not check if the same model also correctly captures the intermediate scale: the pairwise cell-cell interactions or single cell responses to ECM mechanics. Here we show, using a hybrid cellular Potts and finite element computational model, that a single set of biologically plausible rules describing (a) the contractile forces that endothelial cells exert on the ECM, (b) the resulting strains in the extracellular matrix, and (c) the cellular response to the strains, suffices for reproducing the behavior of individual endothelial cells and the interactions of endothelial cell pairs in compliant matrices. With the same set of rules, the model also reproduces network formation from scattered cells, and sprouting from endothelial spheroids. Combining the present mechanical model with aspects of previously proposed mechanical and chemical models may lead to a more complete understanding of in vitro angiogenesis. '),
(86733, 2014, 'plos', 'Epigenetic landscapes explain partially reprogrammed cells and identify key reprogramming genes', 'Lang, Alex H|Li, Hu|Collins, James J|Mehta, Pankaj', 'Department of Physics, Boston University, Boston, Massachusetts, United States of America, Center for Regenerative Medicine, Boston University, Boston, Massachusetts, United States of America.|Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States of America, Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, Massachusetts, United States of America.|Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States of America, Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, Massachusetts, United States of America, Howard Hughes Medical Institute, Boston, Massachusetts, United States of America, Center for BioDynamics, Boston University, Boston, Massachusetts, United States of America.|Department of Physics, Boston University, Boston, Massachusetts, United States of America, Center for Regenerative Medicine, Boston University, Boston, Massachusetts, United States of America.', 'United States|United States|United States|United States', 53, 'Animals|Cells, Cultured|Cellular Reprogramming|Epigenesis, Genetic|Gene Expression Regulation, Developmental|Genomics|Humans|Mice|Models, Genetic|Stem Cells', 'Journal Article', '10.1371/journal.pcbi.1003734', '25122086', 'A common metaphor for describing development is a rugged epigenetic landscape where cell fates are represented as attracting valleys resulting from a complex regulatory network. Here, we introduce a framework for explicitly constructing epigenetic landscapes that combines genomic data with techniques from spin-glass physics. Each cell fate is a dynamic attractor, yet cells can change fate in response to external signals. Our model suggests that partially reprogrammed cells are a natural consequence of high-dimensional landscapes, and predicts that partially reprogrammed cells should be hybrids that co-express genes from multiple cell fates. We verify this prediction by reanalyzing existing datasets. Our model reproduces known reprogramming protocols and identifies candidate transcription factors for reprogramming to novel cell fates, suggesting epigenetic landscapes are a powerful paradigm for understanding cellular identity. '),
(86734, 2014, 'plos', 'Construction and validation of a regulatory network for pluripotency and self-renewal of mouse embryonic stem cells', 'Xu, Huilei|Ang, Yen-Sin|Sevilla, Ana|Lemischka, Ihor R|Maayan, Avi', 'Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America, Department of Developmental and Regenerative Biology, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America, Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America.|Department of Developmental and Regenerative Biology, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America, Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America.|Department of Developmental and Regenerative Biology, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America, Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America.|Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America, Department of Developmental and Regenerative Biology, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America, Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America.|Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America, Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America.', 'United States|United States|United States|United States|United States', 0, 'Animals|Cell Line|Computer Simulation|Embryonic Stem Cells|Gene Expression Profiling|Gene Knockdown Techniques|Gene Regulatory Networks|Mice|Pluripotent Stem Cells|Reproducibility of Results|Systems Biology', 'Journal Article', '10.1371/journal.pcbi.1003777', '25122140', 'A 30-node signed and directed network responsible for self-renewal and pluripotency of mouse embryonic stem cells (mESCs) was extracted from several ChIP-Seq and knockdown followed by expression prior studies. The underlying regulatory logic among network components was then learned using the initial network topology and single cell gene expression measurements from mESCs cultured in serum/LIF or serum-free 2i/LIF conditions. Comparing the learned network regulatory logic derived from cells cultured in serum/LIF vs. 2i/LIF revealed differential roles for Nanog, Oct4/Pou5f1, Sox2, Esrrb and Tcf3. Overall, gene expression in the serum/LIF condition was more variable than in the 2i/LIF but mostly consistent across the two conditions. Expression levels for most genes in single cells were bimodal across the entire population and this motivated a Boolean modeling approach. In silico predictions derived from removal of nodes from the Boolean dynamical model were validated with experimental single and combinatorial RNA interference (RNAi) knockdowns of selected network components. Quantitative post-RNAi expression level measurements of remaining network components showed good agreement with the in silico predictions. Computational removal of nodes from the Boolean network model was also used to predict lineage specification outcomes. In summary, data integration, modeling, and targeted experiments were used to improve our understanding of the regulatory topology that controls mESC fate decisions as well as to develop robust directed lineage specification protocols. '),
(86735, 2014, 'plos', 'Making biomolecular simulations accessible in the post-Nobel Prize era', 'Cui, Qiang|Nussinov, Ruth', 'Department of Chemistry and Theoretical Chemistry Institute, University of Wisconsin-Madison, Madison, Wisconsin, United States of America.|Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, Maryland, United States of America, Sackler Institute of Molecular Medicine, Department of Human Genetics and Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.', 'United States|Israel', 0, 'Biochemistry|Biophysics|Computational Biology|Computer Simulation|Internet', 'Editorial', '10.1371/journal.pcbi.1003786', '25122142', '-'),
(86736, 2014, 'plos', 'A scalable and accurate targeted gene assembly tool (SAT-Assembler) for next-generation sequencing data', 'Zhang, Yuan|Sun, Yanni|Cole, James R', 'Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, United States of America.|Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, United States of America.|Center for Microbial Ecology, Michigan State University, East Lansing, Michigan, United States of America.', 'United States|United States|United States', 17, 'Chromosome Mapping|Databases, Genetic|High-Throughput Nucleotide Sequencing|Humans|Metagenomics|Microbiota|Sequence Analysis, DNA|Software', 'Journal Article', '10.1371/journal.pcbi.1003737', '25122209', 'Gene assembly, which recovers gene segments from short reads, is an important step in functional analysis of next-generation sequencing data. Lacking quality reference genomes, de novo assembly is commonly used for RNA-Seq data of non-model organisms and metagenomic data. However, heterogeneous sequence coverage caused by heterogeneous expression or species abundance, similarity between isoforms or homologous genes, and large data size all pose challenges to de novo assembly. As a result, existing assembly tools tend to output fragmented contigs or chimeric contigs, or have high memory footprint. In this work, we introduce a targeted gene assembly program SAT-Assembler, which aims to recover gene families of particular interest to biologists. It addresses the above challenges by conducting family-specific homology search, homology-guided overlap graph construction, and careful graph traversal. It can be applied to both RNA-Seq and metagenomic data. Our experimental results on an Arabidopsis RNA-Seq data set and two metagenomic data sets show that SAT-Assembler has smaller memory usage, comparable or better gene coverage, and lower chimera rate for assembling a set of genes from one or multiple pathways compared with other assembly tools. Moreover, the family-specific design and rapid homology search allow SAT-Assembler to be naturally compatible with parallel computing platforms. The source code of SAT-Assembler is available at https://sourceforge.net/projects/sat-assembler/. The data sets and experimental settings can be found in supplementary material. '),
(86737, 2014, 'plos', 'Quantifying the role of population subdivision in evolution on rugged fitness landscapes', 'Bitbol, Anne-Florence|Schwab, David J', 'Lewis-Sigler Institute for Integrative Genomics and Department of Physics, Princeton University, Princeton, New Jersey, United States of America.|Lewis-Sigler Institute for Integrative Genomics and Department of Physics, Princeton University, Princeton, New Jersey, United States of America.', 'Jersey|Jersey', 0, 'Animal Migration|Biological Evolution|Computational Biology|Computer Simulation|Genetic Fitness|Models, Biological|Population Dynamics|Selection, Genetic', 'Journal Article', '10.1371/journal.pcbi.1003778', '25122220', 'Natural selection drives populations towards higher fitness, but crossing fitness valleys or plateaus may facilitate progress up a rugged fitness landscape involving epistasis. We investigate quantitatively the effect of subdividing an asexual population on the time it takes to cross a fitness valley or plateau. We focus on a generic and minimal model that includes only population subdivision into equivalent demes connected by global migration, and does not require significant size changes of the demes, environmental heterogeneity or specific geographic structure. We determine the optimal speedup of valley or plateau crossing that can be gained by subdivision, if the process is driven by the deme that crosses fastest. We show that isolated demes have to be in the sequential fixation regime for subdivision to significantly accelerate crossing. Using Markov chain theory, we obtain analytical expressions for the conditions under which optimal speedup is achieved: valley or plateau crossing by the subdivided population is then as fast as that of its fastest deme. We verify our analytical predictions through stochastic simulations. We demonstrate that subdivision can substantially accelerate the crossing of fitness valleys and plateaus in a wide range of parameters extending beyond the optimal window. We study the effect of varying the degree of subdivision of a population, and investigate the trade-off between the magnitude of the optimal speedup and the width of the parameter range over which it occurs. Our results, obtained for fitness valleys and plateaus, also hold for weakly beneficial intermediate mutations. Finally, we extend our work to the case of a population connected by migration to one or several smaller islands. Our results demonstrate that subdivision with migration alone can significantly accelerate the crossing of fitness valleys and plateaus, and shed light onto the quantitative conditions necessary for this to occur. '),
(86738, 2014, 'plos', 'Dynamic mechanisms of neocortical focal seizure onset', 'Wang, Yujiang|Goodfellow, Marc|Taylor, Peter Neal|Baier, Gerold', 'Manchester Interdisciplinary Biocentre, The University of Manchester, Manchester, United Kingdom, School of Computing Science, Newcastle University, Newcastle upon Tyne, United Kingdom.|College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom.|School of Computing Science, Newcastle University, Newcastle upon Tyne, United Kingdom.|Cell and Developmental Biology, University College London, London, United Kingdom.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Cerebral Cortex|Computational Biology|Computer Simulation|Epilepsy|Humans|Models, Neurological|Seizures', 'Journal Article', '10.1371/journal.pcbi.1003787', '25122455', 'Recent experimental and clinical studies have provided diverse insight into the mechanisms of human focal seizure initiation and propagation. Often these findings exist at different scales of observation, and are not reconciled into a common understanding. Here we develop a new, multiscale mathematical model of cortical electric activity with realistic mesoscopic connectivity. Relating the model dynamics to experimental and clinical findings leads us to propose three classes of dynamical mechanisms for the onset of focal seizures in a unified framework. These three classes are: (i) globally induced focal seizures, (ii) globally supported focal seizures, (iii) locally induced focal seizures. Using model simulations we illustrate these onset mechanisms and show how the three classes can be distinguished. Specifically, we find that although all focal seizures typically appear to arise from localised tissue, the mechanisms of onset could be due to either localised processes or processes on a larger spatial scale. We conclude that although focal seizures might have different patient-specific aetiologies and electrographic signatures, our model suggests that dynamically they can still be classified in a clinically useful way. Additionally, this novel classification according to the dynamical mechanisms is able to resolve some of the previously conflicting experimental and clinical findings. '),
(86739, 2014, 'plos', 'Optimal behavioral hierarchy', 'Solway, Alec|Diuk, Carlos|Córdova, Natalia|Yee, Debbie|Barto, Andrew G|Niv, Yael|Botvinick, Matthew M', 'Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States of America.|Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States of America.|Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States of America.|Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States of America.|School of Computer Science, University of Massachusetts Amherst, Amherst, Massachusetts, United States of America.|Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States of America, Department of Psychology, Princeton University, Princeton, New Jersey, United States of America.|Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States of America, Department of Psychology, Princeton University, Princeton, New Jersey, United States of America.', 'Jersey|Jersey|Jersey|Jersey|United States|Jersey|Jersey', 0, 'Adolescent|Adult|Behavior|Computational Biology|Female|Goals|Humans|Learning|Male|Middle Aged|Models, Neurological|Young Adult', 'Journal Article', '10.1371/journal.pcbi.1003779', '25122479', 'Human behavior has long been recognized to display hierarchical structure: actions fit together into subtasks, which cohere into extended goal-directed activities. Arranging actions hierarchically has well established benefits, allowing behaviors to be represented efficiently by the brain, and allowing solutions to new tasks to be discovered easily. However, these payoffs depend on the particular way in which actions are organized into a hierarchy, the specific way in which tasks are carved up into subtasks. We provide a mathematical account for what makes some hierarchies better than others, an account that allows an optimal hierarchy to be identified for any set of tasks. We then present results from four behavioral experiments, suggesting that human learners spontaneously discover optimal action hierarchies. '),
(86740, 2014, 'plos', 'MODMatcher: multi-omics data matcher for integrative genomic analysis', 'Yoo, Seungyeul|Huang, Tao|Campbell, Joshua D|Lee, Eunjee|Tu, Zhidong|Geraci, Mark W|Powell, Charles A|Schadt, Eric E|Spira, Avrum|Zhu, Jun', 'Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America.|Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America.|Division of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts, United States of America.|Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America.|Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America.|Division of Pulmonary Sciences and Critical Care Medicine, University of Colorado Denver, Aurora, Colorado, United States of America.|Division of Pulmonary, Critical Care and Sleep Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America.|Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America.|Division of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts, United States of America.|Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America.', 'United States|United States|United States|United States|United States|United States|United States|Un', 15, 'DNA Methylation|Databases, Genetic|Female|Gene Expression Profiling|Genomics|Humans|Male|Molecular Sequence Annotation|Neoplasms|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Sequence Analysis, RNA', 'Journal Article', '10.1371/journal.pcbi.1003790', '25122495', 'Errors in sample annotation or labeling often occur in large-scale genetic or genomic studies and are difficult to avoid completely during data generation and management. For integrative genomic studies, it is critical to identify and correct these errors. Different types of genetic and genomic data are inter-connected by cis-regulations. On that basis, we developed a computational approach, Multi-Omics Data Matcher (MODMatcher), to identify and correct sample labeling errors in multiple types of molecular data, which can be used in further integrative analysis. Our results indicate that inspection of sample annotation and labeling error is an indispensable data quality assurance step. Applied to a large lung genomic study, MODMatcher increased statistically significant genetic associations and genomic correlations by more than two-fold. In a simulation study, MODMatcher provided more robust results by using three types of omics data than two types of omics data. We further demonstrate that MODMatcher can be broadly applied to large genomic data sets containing multiple types of omics data, such as The Cancer Genome Atlas (TCGA) data sets. ');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(86741, 2014, 'plos', 'Bursts and heavy tails in temporal and sequential dynamics of foraging decisions', 'Jung, Kanghoon|Jang, Hyeran|Kralik, Jerald D|Jeong, Jaeseung', 'Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea, Department of Psychological and Brain Sciences, Dartmouth College, Hanover, New Hampshire, United States of America.|Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea.|Department of Psychological and Brain Sciences, Dartmouth College, Hanover, New Hampshire, United States of America.|Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea.', 'United States|-|United States|-', 7, 'Animals|Appetitive Behavior|Choice Behavior|Circadian Rhythm|Male|Models, Biological|Rats|Rats, Sprague-Dawley|Time Factors', 'Journal Article', '10.1371/journal.pcbi.1003759', '25122498', 'A fundamental understanding of behavior requires predicting when and what an individual will choose. However, the actual temporal and sequential dynamics of successive choices made among multiple alternatives remain unclear. In the current study, we tested the hypothesis that there is a general bursting property in both the timing and sequential patterns of foraging decisions. We conducted a foraging experiment in which rats chose among four different foods over a continuous two-week time period. Regarding when choices were made, we found bursts of rapidly occurring actions, separated by time-varying inactive periods, partially based on a circadian rhythm. Regarding what was chosen, we found sequential dynamics in affective choices characterized by two key features: (a) a highly biased choice distribution, and (b) preferential attachment, in which the animals were more likely to choose what they had previously chosen. To capture the temporal dynamics, we propose a dual-state model consisting of active and inactive states. We also introduce a satiation-attainment process for bursty activity, and a non-homogeneous Poisson process for longer inactivity between bursts. For the sequential dynamics, we propose a dual-control model consisting of goal-directed and habit systems, based on outcome valuation and choice history, respectively. This study provides insights into how the bursty nature of behavior emerges from the interaction of different underlying systems, leading to heavy tails in the distribution of behavior over time and choices. '),
(86742, 2014, 'plos', 'Tracing the evolution of lineage-specific transcription factor binding sites in a birth-death framework', 'Yokoyama, Ken Daigoro|Zhang, Yang|Ma, Jian', 'Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America.|Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America.|Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America, Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America.', 'United States|United States|United States', 0, 'Animals|Base Sequence|Binding Sites|Computational Biology|Evolution, Molecular|Humans|Mice|Models, Genetic|Molecular Sequence Data|Polymorphism, Single Nucleotide|Primates|Sequence Alignment|Species Specificity|Transcription Factors', 'Journal Article', '10.1371/journal.pcbi.1003771', '25144359', 'Changes in cis-regulatory element composition that result in novel patterns of gene expression are thought to be a major contributor to the evolution of lineage-specific traits. Although transcription factor binding events show substantial variation across species, most computational approaches to study regulatory elements focus primarily upon highly conserved sites, and rely heavily upon multiple sequence alignments. However, sequence conservation based approaches have limited ability to detect lineage-specific elements that could contribute to species-specific traits. In this paper, we describe a novel framework that utilizes a birth-death model to trace the evolution of lineage-specific binding sites without relying on detailed base-by-base cross-species alignments. Our model was applied to analyze the evolution of binding sites based on the ChIP-seq data for six transcription factors (GATA1, SOX2, CTCF, MYC, MAX, ETS1) along the lineage toward human after human-mouse common ancestor. We estimate that a substantial fraction of binding sites (xe2x88xbc58-79% for each factor) in humans have origins since the divergence with mouse. Over 15% of all binding sites are unique to hominids. Such elements are often enriched near genes associated with specific pathways, and harbor more common SNPs than older binding sites in the human genome. These results support the ability of our method to identify lineage-specific regulatory elements and help understand their roles in shaping variation in gene regulation across species. '),
(86743, 2014, 'plos', 'Spatially distributed dendritic resonance selectively filters synaptic input', 'Laudanski, Jonathan|Torben-Nielsen, Benjamin|Segev, Idan|Shamma, Shihab', 'Scientific and Clinical Research Department, Neurelec, Vallauris, France, Equipe Audition, Département détudes cognitives, Ecole Normale Supérieure, Paris, France.|Computational Neuroscience Unit, Okinawa Institute of Science and Technology, Okinawa, Japan, Department of Neurobiology and the Edmond and Lily Safra Center for Brain Science, Hebrew University of Jerusalem, Jerusalem, Israel.|Department of Neurobiology and the Edmond and Lily Safra Center for Brain Science, Hebrew University of Jerusalem, Jerusalem, Israel.|Equipe Audition, Département détudes cognitives, Ecole Normale Supérieure, Paris, France, Institute for Systems Research and Department of Electrical & Computer Engineering, University of Maryland, College Park, Maryland, United States of America.', 'France|Israel|Israel|France', 0, 'Computational Biology|Dendrites|Ion Channels|Models, Neurological|Neuronal Plasticity|Neurons|Synapses', 'Journal Article', '10.1371/journal.pcbi.1003775', '25144440', 'An important task performed by a neuron is the selection of relevant inputs from among thousands of synapses impinging on the dendritic tree. Synaptic plasticity enables this by strenghtening a subset of synapses that are, presumably, functionally relevant to the neuron. A different selection mechanism exploits the resonance of the dendritic membranes to preferentially filter synaptic inputs based on their temporal rates. A widely held view is that a neuron has one resonant frequency and thus can pass through one rate. Here we demonstrate through mathematical analyses and numerical simulations that dendritic resonance is inevitably a spatially distributed property, and therefore the resonance frequency varies along the dendrites, and thus endows neurons with a powerful spatiotemporal selection mechanism that is sensitive both to the dendritic location and the temporal structure of the incoming synaptic inputs. '),
(86744, 2014, 'plos', 'Specificity and affinity quantification of flexible recognition from underlying energy landscape topography', 'Chu, Xiakun|Wang, Jin', 'College of Physics, Jilin University, Changchun, Jilin, P. R. China, State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin, P. R. China.|College of Physics, Jilin University, Changchun, Jilin, P. R. China, State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin, P. R. China, Department of Chemistry and Physics, State University of New York at Stony Brook, Stony Brook, New York, United States of America.', 'China|China', 16, 'Computational Biology|Computer Simulation|Kinetics|Models, Chemical|Pliability|Protein Binding|Protein Folding|Proteins|Thermodynamics', 'Journal Article', '10.1371/journal.pcbi.1003782', '25144525', 'Flexibility in biomolecular recognition is essential and critical for many cellular activities. Flexible recognition often leads to moderate affinity but high specificity, in contradiction with the conventional wisdom that high affinity and high specificity are coupled. Furthermore, quantitative understanding of the role of flexibility in biomolecular recognition is still challenging. Here, we meet the challenge by quantifying the intrinsic biomolecular recognition energy landscapes with and without flexibility through the underlying density of states. We quantified the thermodynamic intrinsic specificity by the topography of the intrinsic binding energy landscape and the kinetic specificity by association rate. We found that the thermodynamic and kinetic specificity are strongly correlated. Furthermore, we found that flexibility decreases binding affinity on one hand, but increases binding specificity on the other hand, and the decreasing or increasing proportion of affinity and specificity are strongly correlated with the degree of flexibility. This shows more (less) flexibility leads to weaker (stronger) coupling between affinity and specificity. Our work provides a theoretical foundation and quantitative explanation of the previous qualitative studies on the relationship among flexibility, affinity and specificity. In addition, we found that the folding energy landscapes are more funneled with binding, indicating that binding helps folding during the recognition. Finally, we demonstrated that the whole binding-folding energy landscapes can be integrated by the rigid binding and isolated folding energy landscapes under weak flexibility. Our results provide a novel way to quantify the affinity and specificity in flexible biomolecular recognition. '),
(86745, 2014, 'plos', 'Defining the estimated core genome of bacterial populations using a Bayesian decision model', 'van Tonder, Andries J|Mistry, Shilan|Bray, James E|Hill, Dorothea M C|Cody, Alison J|Farmer, Chris L|Klugman, Keith P|von Gottberg, Anne|Bentley, Stephen D|Parkhill, Julian|Jolley, Keith A|Maiden, Martin C J|Brueggemann, Angela B', 'Nuffield Department of Medicine, The Peter Medawar Building for Pathogen Research, University of Oxford, Oxford, United Kingdom.|Mathematical Institute, University of Oxford, Oxford, United Kingdom.|Department of Zoology, University of Oxford, Oxford, United Kingdom.|Department of Zoology, University of Oxford, Oxford, United Kingdom.|Department of Zoology, University of Oxford, Oxford, United Kingdom.|Mathematical Institute, University of Oxford, Oxford, United Kingdom.|Hubert Department of Global Health Epidemiology, Rollins School of Public Health, Atlanta, Georgia, United States of America.|Centre for Respiratory Diseases and Meningitis, National Institute for Communicable Diseases, Johannesburg, South Africa.|Pathogen Genomics Team, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, United Kingdom, Department of Medicine, University of Cambridge, Addenbrookes Hospital, Cambridge, United Kingdom.|Pathogen Genomics Team, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, United Kingdom.|Department of Zoology, University of Oxford, Oxford, United Kingdom.|Department of Zoology, University of Oxford, Oxford, United Kingdom.|Nuffield Department of Medicine, The Peter Medawar Building for Pathogen Research, University of Oxford, Oxford, United Kingdom.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|Georgia|So', 35, 'Bacterial Proteins|Bayes Theorem|Campylobacter jejuni|Databases, Genetic|Genome, Bacterial|Genomics|Models, Genetic|Neisseria meningitidis', 'Journal Article', '10.1371/journal.pcbi.1003788', '25144616', 'The bacterial core genome is of intense interest and the volume of whole genome sequence data in the public domain available to investigate it has increased dramatically. The aim of our study was to develop a model to estimate the bacterial core genome from next-generation whole genome sequencing data and use this model to identify novel genes associated with important biological functions. Five bacterial datasets were analysed, comprising 2096 genomes in total. We developed a Bayesian decision model to estimate the number of core genes, calculated pairwise evolutionary distances (p-distances) based on nucleotide sequence diversity, and plotted the median p-distance for each core gene relative to its genome location. We designed visually-informative genome diagrams to depict areas of interest in genomes. Case studies demonstrated how the model could identify areas for further study, e.g. 25% of the core genes with higher sequence diversity in the Campylobacter jejuni and Neisseria meningitidis genomes encoded hypothetical proteins. The core gene with the highest p-distance value in C. jejuni was annotated in the reference genome as a putative hydrolase, but further work revealed that it shared sequence homology with beta-lactamase/metallo-beta-lactamases (enzymes that provide resistance to a range of broad-spectrum antibiotics) and thioredoxin reductase genes (which reduce oxidative stress and are essential for DNA replication) in other C. jejuni genomes. Our Bayesian model of estimating the core genome is principled, easy to use and can be applied to large genome datasets. This study also highlighted the lack of knowledge currently available for many core genes in bacterial genomes of significant global public health importance. '),
(86746, 2014, 'plos', 'Ten simple rules of live tweeting at scientific conferences', 'Ekins, Sean|Perlstein, Ethan O', 'Collaborations in Chemistry, Fuquay-Varina, North Carolina, United States of America, Collaborative Drug Discovery, Burlingame, California, United States of America.|Perlstein Lab, San Francisco, California, United States of America.', 'United States|United States', 0, 'Communication|Congresses as Topic|Humans|Internet|Science|Social Media', 'Editorial', '10.1371/journal.pcbi.1003789', '25144683', '-'),
(86747, 2014, 'plos', 'A new model for pore formation by cholesterol-dependent cytolysins', 'Reboul, Cyril F|Whisstock, James C|Dunstone, Michelle A', 'Department of Biochemistry and Molecular Biology, Monash University, Clayton Campus, Melbourne, Victoria, Australia, ARC Centre of Excellence in Advanced Molecular Imaging, Monash University, Clayton Campus, Melbourne, Victoria, Australia.|Department of Biochemistry and Molecular Biology, Monash University, Clayton Campus, Melbourne, Victoria, Australia, ARC Centre of Excellence in Advanced Molecular Imaging, Monash University, Clayton Campus, Melbourne, Victoria, Australia.|Department of Biochemistry and Molecular Biology, Monash University, Clayton Campus, Melbourne, Victoria, Australia, ARC Centre of Excellence in Advanced Molecular Imaging, Monash University, Clayton Campus, Melbourne, Victoria, Australia, Department of Microbiology, Monash University, Clayton Campus, Melbourne, Victoria, Australia.', 'Australia|Australia|Australia', 19, 'Bacterial Proteins|Bacterial Toxins|Cell Membrane|Cholesterol|Molecular Dynamics Simulation|Pore Forming Cytotoxic Proteins|Protein Conformation', 'Journal Article', '10.1371/journal.pcbi.1003791', '25144725', 'Cholesterol Dependent Cytolysins (CDCs) are important bacterial virulence factors that form large (200-300 xc3x85) membrane embedded pores in target cells. Currently, insights from X-ray crystallography, biophysical and single particle cryo-Electron Microscopy (cryo-EM) experiments suggest that soluble monomers first interact with the membrane surface via a C-terminal Immunoglobulin-like domain (Ig, Domain 4). Membrane bound oligomers then assemble into a prepore oligomeric form, following which the prepore assembly collapses towards the membrane surface, with concomitant release and insertion of the membrane spanning subunits. During this rearrangement it is proposed that Domain 2, a region comprising three xcexb2-strands that links the pore forming region (Domains 1 and 3) and the Ig domain, must undergo a significant yet currently undetermined, conformational change. Here we address this problem through a systematic molecular modeling and structural bioinformatics approach. Our work shows that simple rigid body rotations may account for the observed collapse of the prepore towards the membrane surface. Support for this idea comes from analysis of published cryo-EM maps of the pneumolysin pore, available crystal structures and molecular dynamics simulations. The latter data in particular reveal that Domains 1, 2 and 4 are able to undergo significant rotational movements with respect to each other. Together, our data provide new and testable insights into the mechanism of pore formation by CDCs. '),
(86748, 2014, 'plos', 'Sparsity and compressed coding in sensory systems', 'Barranca, Victor J|Kovaxc4x8dixc4x8d, Gregor|Zhou, Douglas|Cai, David', 'Courant Institute of Mathematical Sciences and Center for Neural Science, New York University, New York, New York, United States of America, NYUAD Institute, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates.|Mathematical Sciences Department, Rensselaer Polytechnic Institute, Troy, New York, United States of America.|Department of Mathematics, MOE-LSC, and Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, China.|Courant Institute of Mathematical Sciences and Center for Neural Science, New York University, New York, New York, United States of America, NYUAD Institute, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates, Department of Mathematics, MOE-LSC, and Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, China.', 'United Arab Emirates|United States|China|United Arab Emirates', 12, 'Algorithms|Animals|Cats|Computer Simulation|Models, Neurological|Retina|Retinal Ganglion Cells|Signal Transduction', 'Journal Article', '10.1371/journal.pcbi.1003793', '25144745', 'Considering that many natural stimuli are sparse, can a sensory system evolve to take advantage of this sparsity? We explore this question and show that significant downstream reductions in the numbers of neurons transmitting stimuli observed in early sensory pathways might be a consequence of this sparsity. First, we model an early sensory pathway using an idealized neuronal network comprised of receptors and downstream sensory neurons. Then, by revealing a linear structure intrinsic to neuronal network dynamics, our work points to a potential mechanism for transmitting sparse stimuli, related to compressed-sensing (CS) type data acquisition. Through simulation, we examine the characteristics of networks that are optimal in sparsity encoding, and the impact of localized receptive fields beyond conventional CS theory. The results of this work suggest a new network framework of signal sparsity, freeing the notion from any dependence on specific component-space representations. We expect our CS network mechanism to provide guidance for studying sparse stimulus transmission along realistic sensory pathways as well as engineering network designs that utilize sparsity encoding. '),
(86749, 2014, 'plos', 'Modeling of the dorsal gradient across species reveals interaction between embryo morphology and Toll signaling pathway during evolution', 'Ambrosi, Priscilla|Chahda, Juan Sebastian|Koslen, Hannah R|Chiel, Hillel J|Mizutani, Claudia Mieko', 'Department of Biology, Case Western Reserve University, Cleveland, Ohio, United States of America.|Department of Biology, Case Western Reserve University, Cleveland, Ohio, United States of America.|Department of Biology, Case Western Reserve University, Cleveland, Ohio, United States of America.|Department of Biology, Case Western Reserve University, Cleveland, Ohio, United States of America.|Department of Biology, Case Western Reserve University, Cleveland, Ohio, United States of America, Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, Ohio, United States of America.', 'United States|United States|United States|United States|United States', 0, 'Animals|Computational Biology|Computer Simulation|Drosophila Proteins|Drosophila melanogaster|Embryo, Nonmammalian|Models, Biological|Signal Transduction|Toll-Like Receptors', 'Journal Article', '10.1371/journal.pcbi.1003807', '25165818', 'Morphogenetic gradients are essential to allocate cell fates in embryos of varying sizes within and across closely related species. We previously showed that the maternal NF-xcexbaB/Dorsal (Dl) gradient has acquired different shapes in Drosophila species, which result in unequally scaled germ layers along the dorso-ventral axis and the repositioning of the neuroectodermal borders. Here we combined experimentation and mathematical modeling to investigate which factors might have contributed to the fast evolutionary changes of this gradient. To this end, we modified a previously developed model that employs differential equations of the main biochemical interactions of the Toll (Tl) signaling pathway, which regulates Dl nuclear transport. The original model simulations fit well the D. melanogaster wild type, but not mutant conditions. To broaden the applicability of this model and probe evolutionary changes in gradient distributions, we adjusted a set of 19 independent parameters to reproduce three quantified experimental conditions (i.e. Dl levels lowered, nuclear size and density increased or decreased). We next searched for the most relevant parameters that reproduce the species-specific Dl gradients. We show that adjusting parameters relative to morphological traits (i.e. embryo diameter, nuclear size and density) alone is not sufficient to reproduce the species Dl gradients. Since components of the Tl pathway simulated by the model are fast-evolving, we next asked which parameters related to Tl would most effectively reproduce these gradients and identified a particular subset. A sensitivity analysis reveals the existence of nonlinear interactions between the two fast-evolving traits tested above, namely the embryonic morphological changes and Tl pathway components. Our modeling further suggests that distinct Dl gradient shapes observed in closely related melanogaster sub-group lineages may be caused by similar sequence modifications in Tl pathway components, which are in agreement with their phylogenetic relationships. '),
(86750, 2014, 'plos', 'Communication through resonance in spiking neuronal networks', 'Hahn, Gerald|Bujan, Alejandro F|Frégnac, Yves|Aertsen, Ad|Kumar, Arvind', 'Unité de Neuroscience, Information et Complexité (UNIC), CNRS, Gif-sur-Yvette, France.|Faculty of Biology, and Bernstein Center Freiburg, University of Freiburg, Freiburg, Germany.|Unité de Neuroscience, Information et Complexité (UNIC), CNRS, Gif-sur-Yvette, France.|Faculty of Biology, and Bernstein Center Freiburg, University of Freiburg, Freiburg, Germany.|Faculty of Biology, and Bernstein Center Freiburg, University of Freiburg, Freiburg, Germany.', 'France|Germany|France|Germany|Germany', 0, 'Action Potentials|Computational Biology|Feedback|Models, Neurological|Nerve Net|Neurons|Synaptic Transmission', 'Journal Article', '10.1371/journal.pcbi.1003811', '25165853', 'The cortex processes stimuli through a distributed network of specialized brain areas. This processing requires mechanisms that can route neuronal activity across weakly connected cortical regions. Routing models proposed thus far are either limited to propagation of spiking activity across strongly connected networks or require distinct mechanisms that create local oscillations and establish their coherence between distant cortical areas. Here, we propose a novel mechanism which explains how synchronous spiking activity propagates across weakly connected brain areas supported by oscillations. In our model, oscillatory activity unleashes network resonance that amplifies feeble synchronous signals and promotes their propagation along weak connections (communication through resonance). The emergence of coherent oscillations is a natural consequence of synchronous activity propagation and therefore the assumption of different mechanisms that create oscillations and provide coherence is not necessary. Moreover, the phase-locking of oscillations is a side effect of communication rather than its requirement. Finally, we show how the state of ongoing activity could affect the communication through resonance and propose that modulations of the ongoing activity state could influence information processing in distributed cortical networks. '),
(86751, 2014, 'plos', 'Protein surface softness is the origin of enzyme cold-adaptation of trypsin', 'Isaksen, Geir Villy|Åqvist, Johan|Brandsdal, Bjørn Olav', 'The Norwegian Structural Biology Center and the Center for Theoretical Computational Chemistry, Department of Chemistry, Faculty of Science and Technology, University of Tromsø, Tromsø, Norway.|Department of Cell and Molecular Biology, Biomedical Center, Uppsala University, Uppsala, Sweden.|The Norwegian Structural Biology Center and the Center for Theoretical Computational Chemistry, Department of Chemistry, Faculty of Science and Technology, University of Tromsø, Tromsø, Norway.', 'Norway|Sweden|Norway', 0, 'Cold Temperature|Computational Biology|Molecular Dynamics Simulation|Mutation|Protein Stability|Surface Properties|Thermodynamics|Trypsin', 'Journal Article', '10.1371/journal.pcbi.1003813', '25165981', 'Life has effectively colonized most of our planet and extremophilic organisms require specialized enzymes to survive under harsh conditions. Cold-loving organisms (psychrophiles) express heat-labile enzymes that possess a high specific activity and catalytic efficiency at low temperatures. A remarkable universal characteristic of cold-active enzymes is that they show a reduction both in activation enthalpy and entropy, compared to mesophilic orthologs, which makes their reaction rates less sensitive to falling temperature. Despite significant efforts since the early 1970s, the important question of the origin of this effect still largely remains unanswered. Here we use cold- and warm-active trypsins as model systems to investigate the temperature dependence of the reaction rates with extensive molecular dynamics free energy simulations. The calculations quantitatively reproduce the catalytic rates of the two enzymes and further yield high-precision Arrhenius plots, which show the characteristic trends in activation enthalpy and entropy. Detailed structural analysis indicates that the relationship between these parameters and the 3D structure is reflected by significantly different internal protein energy changes during the reaction. The origin of this effect is not localized to the active site, but is found in the outer regions of the protein, where the cold-active enzyme has a higher degree of softness. Several structural mechanisms for softening the protein surface are identified, together with key mutations responsible for this effect. Our simulations further show that single point-mutations can significantly affect the thermodynamic activation parameters, indicating how these can be optimized by evolution. '),
(86752, 2014, 'plos', 'Unbiased functional clustering of gene variants with a phenotypic-linkage network', 'Honti, Frantisek|Meader, Stephen|Webber, Caleb', 'MRC Functional Genomics Unit, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom.|MRC Functional Genomics Unit, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom.|MRC Functional Genomics Unit, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom.', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Animals|Cluster Analysis|Databases, Genetic|Epilepsy|Gene Expression Profiling|Genetic Variation|Genomics|Humans|Mental Disorders|Mice|Phenotype', 'Journal Article', '10.1371/journal.pcbi.1003815', '25166029', 'Groupwise functional analysis of gene variants is becoming standard in next-generation sequencing studies. As the function of many genes is unknown and their classification to pathways is scant, functional associations between genes are often inferred from large-scale omics data. Such data types--including protein-protein interactions and gene co-expression networks--are used to examine the interrelations of the implicated genes. Statistical significance is assessed by comparing the interconnectedness of the mutated genes with that of random gene sets. However, interconnectedness can be affected by confounding bias, potentially resulting in false positive findings. We show that genes implicated through de novo sequence variants are biased in their coding-sequence length and longer genes tend to cluster together, which leads to exaggerated p-values in functional studies, we present here an integrative method that addresses these bias. To discern molecular pathways relevant to complex disease, we have inferred functional associations between human genes from diverse data types and assessed them with a novel phenotype-based method. Examining the functional association between de novo gene variants, we control for the heretofore unexplored confounding bias in coding-sequence length. We test different data types and networks and find that the disease-associated genes cluster more significantly in an integrated phenotypic-linkage network than in other gene networks. We present a tool of superior power to identify functional associations among genes mutated in the same disease even after accounting for significant sequencing study bias and demonstrate the suitability of this method to functionally cluster variant genes underlying polygenic disorders.'),
(86753, 2014, 'plos', 'Dynamic modelling of pathways to cellular senescence reveals strategies for targeted interventions', 'Dalle Pezze, Piero|Nelson, Glyn|Otten, Elsje G|Korolchuk, Viktor I|Kirkwood, Thomas B L|von Zglinicki, Thomas|Shanley, Daryl P', 'Institute for Ageing and Health, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, United Kingdom, Centre for Integrated Systems Biology of Ageing and Nutrition, Institute for Ageing and Health, Newcastle University, Newcastle upon Tyne, United Kingdom.|Institute for Ageing and Health, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, United Kingdom, Centre for Integrated Systems Biology of Ageing and Nutrition, Institute for Ageing and Health, Newcastle University, Newcastle upon Tyne, United Kingdom.|Institute for Ageing and Health, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, United Kingdom, Centre for Integrated Systems Biology of Ageing and Nutrition, Institute for Ageing and Health, Newcastle University, Newcastle upon Tyne, United Kingdom.|Institute for Ageing and Health, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, United Kingdom, Centre for Integrated Systems Biology of Ageing and Nutrition, Institute for Ageing and Health, Newcastle University, Newcastle upon Tyne, United Kingdom.|Institute for Ageing and Health, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, United Kingdom, Centre for Integrated Systems Biology of Ageing and Nutrition, Institute for Ageing and Health, Newcastle University, Newcastle upon Tyne, United Kingdom.|Institute for Ageing and Health, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, United Kingdom, Centre for Integrated Systems Biology of Ageing and Nutrition, Institute for Ageing and Health, Newcastle University, Newcastle upon Tyne, United Kingdom.|Institute for Ageing and Health, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, United Kingdom, Centre for Integrated Systems Biology of Ageing and Nutrition, Institute for Ageing and Health, Newcastle University, Newcastle upon Tyne, United Kingdom.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kin', 49, 'Cell Line|Cellular Senescence|Computer Simulation|DNA Damage|Humans|Mitochondria|Models, Biological|Reactive Oxygen Species|Signal Transduction|Systems Biology|TOR Serine-Threonine Kinases|Transcription Factors', 'Journal Article', '10.1371/journal.pcbi.1003728', '25166345', 'Cellular senescence, a state of irreversible cell cycle arrest, is thought to help protect an organism from cancer, yet also contributes to ageing. The changes which occur in senescence are controlled by networks of multiple signalling and feedback pathways at the cellular level, and the interplay between these is difficult to predict and understand. To unravel the intrinsic challenges of understanding such a highly networked system, we have taken a systems biology approach to cellular senescence. We report a detailed analysis of senescence signalling via DNA damage, insulin-TOR, FoxO3a transcription factors, oxidative stress response, mitochondrial regulation and mitophagy. We show in silico and in vitro that inhibition of reactive oxygen species can prevent loss of mitochondrial membrane potential, whilst inhibition of mTOR shows a partial rescue of mitochondrial mass changes during establishment of senescence. Dual inhibition of ROS and mTOR in vitro confirmed computational model predictions that it was possible to further reduce senescence-induced mitochondrial dysfunction and DNA double-strand breaks. However, these interventions were unable to abrogate the senescence-induced mitochondrial dysfunction completely, and we identified decreased mitochondrial fission as the potential driving force for increased mitochondrial mass via prevention of mitophagy. Dynamic sensitivity analysis of the model showed the network stabilised at a new late state of cellular senescence. This was characterised by poor network sensitivity, high signalling noise, low cellular energy, high inflammation and permanent cell cycle arrest suggesting an unsatisfactory outcome for treatments aiming to delay or reverse cellular senescence at late time points. Combinatorial targeted interventions are therefore possible for intervening in the cellular pathway to senescence, but in the cases identified here, are only capable of delaying senescence onset. '),
(86754, 2014, 'plos', 'Identification of allelic imbalance with a statistical model for subtle genomic mosaicism', 'Xia, Rui|Vattathil, Selina|Scheet, Paul', 'Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America, Division of Biostatistics, The University of Texas School of Public Health, Houston, Texas, United States of America.|Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America, Human & Molecular Genetics Program, The University of Texas Graduate School of Biomedical Sciences, Houston, Texas, United States of America.|Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America, Division of Biostatistics, The University of Texas School of Public Health, Houston, Texas, United States of America, Human & Molecular Genetics Program, The University of Texas Graduate School of Biomedical Sciences, Houston, Texas, United States of America.', 'United States|United States|United States', 0, 'Allelic Imbalance|Cell Line, Tumor|Gene Expression Profiling|Genome|Genomics|Humans|Models, Genetic|Models, Statistical|Mosaicism', 'Journal Article', '10.1371/journal.pcbi.1003765', '25166618', 'Genetic heterogeneity in a mixed sample of tumor and normal DNA can confound characterization of the tumor genome. Numerous computational methods have been proposed to detect aberrations in DNA samples from tumor and normal tissue mixtures. Most of these require tumor purities to be at least 10-15%. Here, we present a statistical model to capture information, contained in the individuals germline haplotypes, about expected patterns in the B allele frequencies from SNP microarrays while fully modeling their magnitude, the first such model for SNP microarray data. Our model consists of a pair of hidden Markov models--one for the germline and one for the tumor genome--which, conditional on the observed array data and patterns of population haplotype variation, have a dependence structure induced by the relative imbalance of an individuals inherited haplotypes. Together, these hidden Markov models offer a powerful approach for dealing with mixtures of DNA where the main component represents the germline, thus suggesting natural applications for the characterization of primary clones when stromal contamination is extremely high, and for identifying lesions in rare subclones of a tumor when tumor purity is sufficient to characterize the primary lesions. Our joint model for germline haplotypes and acquired DNA aberration is flexible, allowing a large number of chromosomal alterations, including balanced and imbalanced losses and gains, copy-neutral loss-of-heterozygosity (LOH) and tetraploidy. We found our model (which we term J-LOH) to be superior for localizing rare aberrations in a simulated 3% mixture sample. More generally, our model provides a framework for full integration of the germline and tumor genomes to deal more effectively with missing or uncertain features, and thus extract maximal information from difficult scenarios where existing methods fail.'),
(86755, 2014, 'plos', 'VASP-E: specificity annotation with a volumetric analysis of electrostatic isopotentials', 'Chen, Brian Y', 'Department of Computer Science and Engineering, P.C. Rossin College of Engineering and Applied Sciences, Lehigh University, Bethlehem, Pennsylvania, United States of America.', 'United States', 15, 'Algorithms|Amino Acids|Animals|Cattle|Cluster Analysis|Computational Biology|Humans|Molecular Sequence Annotation|Protein Binding|Proteins|Sequence Analysis, Protein|Software|Static Electricity|Surface Properties', 'Journal Article', '10.1371/journal.pcbi.1003792', '25166865', 'Algorithms for comparing protein structure are frequently used for function annotation. By searching for subtle similarities among very different proteins, these algorithms can identify remote homologs with similar biological functions. In contrast, few comparison algorithms focus on specificity annotation, where the identification of subtle differences among very similar proteins can assist in finding small structural variations that create differences in binding specificity. Few specificity annotation methods consider electrostatic fields, which play a critical role in molecular recognition. To fill this gap, this paper describes VASP-E (Volumetric Analysis of Surface Properties with Electrostatics), a novel volumetric comparison tool based on the electrostatic comparison of protein-ligand and protein-protein binding sites. VASP-E exploits the central observation that three dimensional solids can be used to fully represent and compare both electrostatic isopotentials and molecular surfaces. With this integrated representation, VASP-E is able to dissect the electrostatic environments of protein-ligand and protein-protein binding interfaces, identifying individual amino acids that have an electrostatic influence on binding specificity. VASP-E was used to examine a nonredundant subset of the serine and cysteine proteases as well as the barnase-barstar and Rap1a-raf complexes. Based on amino acids established by various experimental studies to have an electrostatic influence on binding specificity, VASP-E identified electrostatically influential amino acids with 100% precision and 83.3% recall. We also show that VASP-E can accurately classify closely related ligand binding cavities into groups with different binding preferences. These results suggest that VASP-E should prove a useful tool for the characterization of specific binding and the engineering of binding preferences in proteins. '),
(86756, 2014, 'plos', 'Possible role of interleukin-1xcexb2 in type 2 diabetes onset and implications for anti-inflammatory therapy strategies', 'Zhao, Gang|Dharmadhikari, Gitanjali|Maedler, Kathrin|Meyer-Hermann, Michael', 'Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology, Helmholtz Centre for Infection Research, Braunschweig, Germany.|Centre for Biomolecular Interactions Bremen, University of Bremen, Bremen, Germany.|Centre for Biomolecular Interactions Bremen, University of Bremen, Bremen, Germany.|Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology, Helmholtz Centre for Infection Research, Braunschweig, Germany, Institute of Biochemistry, Biotechnology and Bioinformatics, Technische Universitxc3xa4t Braunschweig, Braunschweig, Germany.', 'Germany|Germany|Germany|Germany', 0, 'Anti-Inflammatory Agents|Blood Glucose|Diabetes Mellitus, Type 2|Humans|Hypoglycemic Agents|Inflammation|Interleukin 1 Receptor Antagonist Protein|Interleukin-1beta|Models, Immunological', 'Journal Article', '10.1371/journal.pcbi.1003798', '25167060', 'Increasing evidence of a role of chronic inflammation in type 2 diabetes progression has led to the development of therapies targeting the immune system. We develop a model of interleukin-1xcexb2 dynamics in order to explain principles of disease onset. The parameters in the model are derived from in vitro experiments and patient data. In the framework of this model, an IL-1xcexb2 switch is sufficient and necessary to account for type 2 diabetes onset. The model suggests that treatments targeting glucose bear the potential of stopping progression from pre-diabetes to overt type 2 diabetes. However, once in overt type 2 diabetes, these treatments have to be complemented by adjuvant anti-inflammatory therapies in order to stop or decelerate disease progression. Moreover, the model suggests that while glucose-lowering therapy needs to be continued all the way, dose and duration of the anti-inflammatory therapy needs to be specifically controlled. The model proposes a framework for the discussion of clinical trial outcomes. '),
(86757, 2014, 'plos', 'Classical mathematical models for description and prediction of experimental tumor growth', 'Benzekry, Sxc3xa9bastien|Lamont, Clare|Beheshti, Afshin|Tracz, Amanda|Ebos, John M L|Hlatky, Lynn|Hahnfeldt, Philip', 'Inria Bordeaux Sud-Ouest, Institut de Mathxc3xa9matiques de Bordeaux, Bordeaux, France, Center of Cancer Systems Biology, GRI, Tufts University School of Medicine, Boston, Massachusetts, United States of America.|Center of Cancer Systems Biology, GRI, Tufts University School of Medicine, Boston, Massachusetts, United States of America.|Center of Cancer Systems Biology, GRI, Tufts University School of Medicine, Boston, Massachusetts, United States of America.|Department of Medicine, Roswell Park Cancer Institute, Buffalo, New York, United States of America.|Department of Medicine, Roswell Park Cancer Institute, Buffalo, New York, United States of America.|Center of Cancer Systems Biology, GRI, Tufts University School of Medicine, Boston, Massachusetts, United States of America.|Center of Cancer Systems Biology, GRI, Tufts University School of Medicine, Boston, Massachusetts, United States of America.', 'France|United States|United States|United States|United States|United States|United States', 0, 'Animals|Breast Neoplasms|Cell Line, Tumor|Computational Biology|Female|Humans|Lung Neoplasms|Male|Mice|Mice, Inbred C57BL|Models, Biological|Models, Statistical|Neoplasms|Neoplasms, Experimental', 'Journal Article', '10.1371/journal.pcbi.1003800', '25167199', 'Despite internal complexity, tumor growth kinetics follow relatively simple laws that can be expressed as mathematical models. To explore this further, quantitative analysis of the most classical of these were performed. The models were assessed against data from two in vivo experimental systems: an ectopic syngeneic tumor (Lewis lung carcinoma) and an orthotopically xenografted human breast carcinoma. The goals were threefold: 1) to determine a statistical model for description of the measurement error, 2) to establish the descriptive power of each model, using several goodness-of-fit metrics and a study of parametric identifiability, and 3) to assess the models ability to forecast future tumor growth. The models included in the study comprised the exponential, exponential-linear, power law, Gompertz, logistic, generalized logistic, von Bertalanffy and a model with dynamic carrying capacity. For the breast data, the dynamics were best captured by the Gompertz and exponential-linear models. The latter also exhibited the highest predictive power, with excellent prediction scores (xe2x89xa580%) extending out as far as 12 days in the future. For the lung data, the Gompertz and power law models provided the most parsimonious and parametrically identifiable description. However, not one of the models was able to achieve a substantial prediction rate (xe2x89xa570%) beyond the next day data point. In this context, adjunction of a priori information on the parameter distribution led to considerable improvement. For instance, forecast success rates went from 14.9% to 62.7% when using the power law model to predict the full future tumor growth curves, using just three data points. These results not only have important implications for biological theories of tumor growth and the use of mathematical modeling in preclinical anti-cancer drug investigations, but also may assist in defining how mathematical models could serve as potential prognostic tools in the clinic. ');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(86758, 2014, 'plos', 'OpenCyto: an open source infrastructure for scalable, robust, reproducible, and automated, end-to-end flow cytometry data analysis', 'Finak, Greg|Frelinger, Jacob|Jiang, Wenxin|Newell, Evan W|Ramey, John|Davis, Mark M|Kalams, Spyros A|De Rosa, Stephen C|Gottardo, Raphael', 'Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.|Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.|Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.|Agency for Science Technology and Research, Singapore Immunology Network, Singapore.|Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.|Department of Microbiology and Immunology, Stanford University, Stanford, California, United States of America, Institute for Immunity, Transplantation and Infection, Stanford University, Stanford, California, United States of America, The Howard Hughes Medical Institute, Stanford University, Stanford, California, United States of America.|Infectious Diseases Division, Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America, Department of Pathology, Microbiology, and Immunology, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America.|Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America, Department of Laboratory Medicine, University of Washington, Seattle, Washington, United States of America.|Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America, Department of Statistics, University of Washington, Seattle, Washington, United States of America.', 'United States|United States|United States|Singapore|United States|United States|United States|United', 0, 'CD8-Positive T-Lymphocytes|Computational Biology|Databases, Factual|Flow Cytometry|Humans|Reproducibility of Results|Software', 'Journal Article', '10.1371/journal.pcbi.1003806', '25167361', 'Flow cytometry is used increasingly in clinical research for cancer, immunology and vaccines. Technological advances in cytometry instrumentation are increasing the size and dimensionality of data sets, posing a challenge for traditional data management and analysis. Automated analysis methods, despite a general consensus of their importance to the future of the field, have been slow to gain widespread adoption. Here we present OpenCyto, a new BioConductor infrastructure and data analysis framework designed to lower the barrier of entry to automated flow data analysis algorithms by addressing key areas that we believe have held back wider adoption of automated approaches. OpenCyto supports end-to-end data analysis that is robust and reproducible while generating results that are easy to interpret. We have improved the existing, widely used core BioConductor flow cytometry infrastructure by allowing analysis to scale in a memory efficient manner to the large flow data sets that arise in clinical trials, and integrating domain-specific knowledge as part of the pipeline through the hierarchical relationships among cell populations. Pipelines are defined through a text-based csv file, limiting the need to write data-specific code, and are data agnostic to simplify repetitive analysis for core facilities. We demonstrate how to analyze two large cytometry data sets: an intracellular cytokine staining (ICS) data set from a published HIV vaccine trial focused on detecting rare, antigen-specific T-cell populations, where we identify a new subset of CD8 T-cells with a vaccine-regimen specific response that could not be identified through manual analysis, and a CyTOF T-cell phenotyping data set where a large staining panel and many cell populations are a challenge for traditional analysis. The substantial improvements to the core BioConductor flow cytometry packages give OpenCyto the potential for wide adoption. It can rapidly leverage new developments in computational cytometry and facilitate reproducible analysis in a unified environment. '),
(86759, 2014, 'plos', 'Inferring on the intentions of others by hierarchical Bayesian learning', 'Diaconescu, Andreea O|Mathys, Christoph|Weber, Lilian A E|Daunizeau, Jean|Kasper, Lars|Lomakina, Ekaterina I|Fehr, Ernst|Stephan, Klaas E', 'Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland, Laboratory for Social and Neural Systems Research, Department of Economics, University of Zurich, Zurich, Switzerland.|Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland, Laboratory for Social and Neural Systems Research, Department of Economics, University of Zurich, Zurich, Switzerland, Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom.|Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland, Laboratory for Social and Neural Systems Research, Department of Economics, University of Zurich, Zurich, Switzerland.|Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom, Institut du Cerveau et de la Moelle épinière (ICM), Hôpital Pitié Salpêtrière, Paris, France.|Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland, Laboratory for Social and Neural Systems Research, Department of Economics, University of Zurich, Zurich, Switzerland.|Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland, Laboratory for Social and Neural Systems Research, Department of Economics, University of Zurich, Zurich, Switzerland, Department of Computer Science, ETH Zurich, Zurich, Switzerland.|Laboratory for Social and Neural Systems Research, Department of Economics, University of Zurich, Zurich, Switzerland.|Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland, Laboratory for Social and Neural Systems Research, Department of Economics, University of Zurich, Zurich, Switzerland, Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom.', 'Switzerland|Switzerland|Switzerland|France|Switzerland|Switzerland|Switzerland|Switzerland', 0, 'Adult|Bayes Theorem|Decision Making|Games, Experimental|Humans|Intention|Learning|Male|Models, Psychological|Motivation|Social Behavior|Young Adult', 'Journal Article', '10.1371/journal.pcbi.1003810', '25187943', 'Inferring on others (potentially time-varying) intentions is a fundamental problem during many social transactions. To investigate the underlying mechanisms, we applied computational modeling to behavioral data from an economic game in which 16 pairs of volunteers (randomly assigned to player or adviser roles) interacted. The player performed a probabilistic reinforcement learning task, receiving information about a binary lottery from a visual pie chart. The adviser, who received more predictive information, issued an additional recommendation. Critically, the game was structured such that the advisers incentives to provide helpful or misleading information varied in time. Using a meta-Bayesian modeling framework, we found that the players behavior was best explained by the deployment of hierarchical learning: they inferred upon the volatility of the advisers intentions in order to optimize their predictions about the validity of their advice. Beyond learning, volatility estimates also affected the trial-by-trial variability of decisions: participants were more likely to rely on their estimates of advice accuracy for making choices when they believed that the advisers intentions were presently stable. Finally, our model of the players inference predicted the players interpersonal reactivity index (IRI) scores, explicit ratings of the advisers helpfulness and the advisers self-reports on their chosen strategy. Overall, our results suggest that humans (i) employ hierarchical generative models to infer on the changing intentions of others, (ii) use volatility estimates to inform decision-making in social interactions, and (iii) integrate estimates of advice accuracy with non-social sources of information. The Bayesian framework presented here can quantify individual differences in these mechanisms from simple behavioral readouts and may prove useful in future clinical studies of maladaptive social cognition. '),
(86760, 2014, 'plos', 'Baseline CD4+ T cell counts correlates with HIV-1 synonymous rate in HLA-B*5701 subjects with different risk of disease progression', 'Norström, Melissa M|Veras, Nazle M|Huang, Wei|Proper, Mattia C F|Cook, Jennifer|Hartogensis, Wendy|Hecht, Frederick M|Karlsson, Annika C|Karlsoon, Annika C|Salemi, Marco', 'Division of Clinical Microbiology & Center for HIV Research, Department of Laboratory Medicine, Karolinska Institutet, Stockholm, Sweden.|Department of Pathology, Immunology and Laboratory Medicine & Emerging Pathogens Institute, University of Florida, Gainesville, Florida, United States of America.|Monogram Biosciences Inc., South San Francisco, California, United States of America.|Centre for Health Informatics, Institute of Population Health, University of Manchester, Manchester, United Kingdom.|Monogram Biosciences Inc., South San Francisco, California, United States of America.|UCSF Positive Health Program, San Francisco General Hospital, University of California, San Francisco, San Francisco, California, United States of America.|UCSF Positive Health Program, San Francisco General Hospital, University of California, San Francisco, San Francisco, California, United States of America.|UCSF Positive Health Program, San Francisco General Hospital, University of California, San Francisco, San Francisco, California, United States of America.|Division of Clinical Microbiology & Center for HIV Research, Department of Laboratory Medicine, Karolinska Institutet, Stockholm, Sweden.|Department of Pathology, Immunology and Laboratory Medicine & Emerging Pathogens Institute, University of Florida, Gainesville, Florida, United States of America.', 'Sweden|United States|United States|United Kingdom|United States|United States|United States|United S', 2, 'Bayes Theorem|CD4-Positive T-Lymphocytes|Computational Biology|Disease Progression|HIV Core Protein p24|HIV Infections|HIV-1|HLA-B Antigens|Humans|Molecular Sequence Data|Mutation|Virus Replication', 'Journal Article', '10.1371/journal.pcbi.1003830', '25187947', 'HLA-B*5701 is the host factor most strongly associated with slow HIV-1 disease progression, although risk of progression may vary among patients carrying this allele. The interplay between HIV-1 evolutionary rate variation and risk of progression to AIDS in HLA-B*5701 subjects was studied using longitudinal viral sequences from high-risk progressors (HRPs) and low-risk progressors (LRPs). Posterior distributions of HIV-1 genealogies assuming a Bayesian relaxed molecular clock were used to estimate the absolute rates of nonsynonymous and synonymous substitutions for different set of branches. Rates of viral evolution, as well as in vitro viral replication capacity assessed using a novel phenotypic assay, were correlated with various clinical parameters. HIV-1 synonymous substitution rates were significantly lower in LRPs than HRPs, especially for sets of internal branches. The viral population infecting LRPs was also characterized by a slower increase in synonymous divergence over time. This pattern did not correlate to differences in viral fitness, as measured by in vitro replication capacity, nor could be explained by differences among subjects in T cell activation or selection pressure. Interestingly, a significant inverse correlation was found between baseline CD4+ T cell counts and mean HIV-1 synonymous rate (which is proportional to the viral replication rate) along branches representing viral lineages successfully propagating through time up to the last sampled time point. The observed lower replication rate in HLA-B*5701 subjects with higher baseline CD4+ T cell counts provides a potential model to explain differences in risk of disease progression among individuals carrying this allele. '),
(86761, 2014, 'plos', 'Communication routes in ARID domains between distal residues in helix 5 and the DNA-binding loops', 'Invernizzi, Gaetano|Tiberti, Matteo|Lambrughi, Matteo|Lindorff-Larsen, Kresten|Papaleo, Elena', 'Structural Biology and NMR Laboratory, Department of Biology, University of Copenhagen, Copenhagen, Denmark.|Structural Biology and NMR Laboratory, Department of Biology, University of Copenhagen, Copenhagen, Denmark, Department of Biotechnology and Biosciences, University of Milano-Bicocca, Milan, Italy.|Structural Biology and NMR Laboratory, Department of Biology, University of Copenhagen, Copenhagen, Denmark, Department of Biotechnology and Biosciences, University of Milano-Bicocca, Milan, Italy.|Structural Biology and NMR Laboratory, Department of Biology, University of Copenhagen, Copenhagen, Denmark.|Structural Biology and NMR Laboratory, Department of Biology, University of Copenhagen, Copenhagen, Denmark.', 'Denmark|Denmark|Denmark|Denmark|Denmark', 0, 'DNA|DNA-Binding Proteins|Humans|Molecular Dynamics Simulation|Protein Binding|Protein Structure, Tertiary|Transcription Factors', 'Journal Article', '10.1371/journal.pcbi.1003744', '25187961', 'ARID is a DNA-binding domain involved in several transcriptional regulatory processes, including cell-cycle regulation and embryonic development. ARID domains are also targets of the Human Cancer Protein Interaction Network. Little is known about the molecular mechanisms related to conformational changes in the family of ARID domains. Thus, we have examined their structural dynamics to enrich the knowledge on this important family of regulatory proteins. In particular, we used an approach that integrates atomistic simulations and methods inspired by graph theory. To relate these properties to protein function we studied both the free and DNA-bound forms. The interaction with DNA not only stabilizes the conformations of the DNA-binding loops, but also strengthens pre-existing paths in the native ARID ensemble for long-range communication to those loops. Residues in helix 5 are identified as critical mediators for intramolecular communication to the DNA-binding regions. In particular, we identified a distal tyrosine that plays a key role in long-range communication to the DNA-binding loops and that is experimentally known to impair DNA-binding. Mutations at this tyrosine and in other residues of helix 5 are also demonstrated, by our approach, to affect the paths of communication to the DNA-binding loops and alter their native dynamics. Overall, our results are in agreement with a scenario in which ARID domains exist as an ensemble of substates, which are shifted by external perturbation, such as the interaction with DNA. Conformational changes at the DNA-binding loops are transmitted long-range by intramolecular paths, which have their heart in helix 5. '),
(86762, 2014, 'plos', 'Seasonally dependent relationships between indicators of malaria transmission and disease provided by mathematical model simulations', 'Stuckey, Erin M|Smith, Thomas|Chitnis, Nakul', 'Swiss Tropical and Public Health Institute, Basel, Switzerland, University of Basel, Basel, Switzerland.|Swiss Tropical and Public Health Institute, Basel, Switzerland, University of Basel, Basel, Switzerland.|Swiss Tropical and Public Health Institute, Basel, Switzerland, University of Basel, Basel, Switzerland.', 'Switzerland|Switzerland|Switzerland', 0, 'Animals|Child|Child, Preschool|Computational Biology|Computer Simulation|Humans|Infant|Infant, Newborn|Malaria|Models, Biological|Prevalence|Public Health Surveillance|Seasons', 'Journal Article', '10.1371/journal.pcbi.1003812', '25187979', 'Evaluating the effectiveness of malaria control interventions on the basis of their impact on transmission as well as impact on morbidity and mortality is becoming increasingly important as countries consider pre-elimination and elimination as well as disease control. Data on prevalence and transmission are traditionally obtained through resource-intensive epidemiological and entomological surveys that become difficult as transmission decreases. This work employs mathematical modeling to examine the relationships between malaria indicators allowing more easily measured data, such as routine health systems data on case incidence, to be translated into measures of transmission and other malaria indicators. Simulations of scenarios with different levels of malaria transmission, patterns of seasonality and access to treatment were run with an ensemble of models of malaria epidemiology and within-host dynamics, as part of the OpenMalaria modeling platform. For a given seasonality profile, regression analysis mapped simulation results of malaria indicators, such as annual average entomological inoculation rate, prevalence, incidence of uncomplicated and severe episodes, and mortality, to an expected range of values of any of the other indicators. Results were validated by comparing simulated relationships between indicators with previously published data on these same indicators as observed in malaria endemic areas. These results allow for direct comparisons of malaria transmission intensity estimates made using data collected with different methods on different indicators. They also address key concerns with traditional methods of quantifying transmission in areas of differing transmission intensity and sparse data. Although seasonality of transmission is often ignored in data compilations, the models suggest it can be critically important in determining the relationship between transmission and disease. Application of these models could help public health officials detect changes of disease dynamics in a population and plan and assess the impact of malaria control interventions. '),
(86763, 2014, 'plos', 'Torsion-mediated interaction between adjacent genes', 'Meyer, Sam|Beslon, Guillaume', 'Université de Lyon, INSA Lyon, INRIA, LIRIS, CNRS UMR5205, Lyon, France.|Université de Lyon, INSA Lyon, INRIA, LIRIS, CNRS UMR5205, Lyon, France.', 'France|France', 27, 'Animals|Computational Biology|DNA|Drosophila melanogaster|Models, Genetic|Torsion, Mechanical|Transcription, Genetic|Yeasts', 'Journal Article', '10.1371/journal.pcbi.1003785', '25188032', 'DNA torsional stress is generated by virtually all biomolecular processes involving the double helix, in particular transcription where a significant level of stress propagates over several kilobases. If another promoter is located in this range, this stress may strongly modify its opening properties, and hence facilitate or hinder its transcription. This mechanism implies that transcribed genes distant of a few kilobases are not independent, but coupled by torsional stress, an effect for which we propose the first quantitative and systematic model. In contrast to previously proposed mechanisms of transcriptional interference, the suggested coupling is not mediated by the transcription machineries, but results from the universal mechanical features of the double-helix. The model shows that the effect likely affects prokaryotes as well as eukaryotes, but with different consequences owing to their different basal levels of torsion. It also depends crucially on the relative orientation of the genes, enhancing the expression of eukaryotic divergent pairs while reducing that of prokaryotic convergent ones. To test the in vivo influence of the torsional coupling, we analyze the expression of isolated gene pairs in the Drosophila melanogaster genome. Their orientation and distance dependence is fully consistent with the model, suggesting that torsional gene coupling may constitute a widespread mechanism of (co)regulation in eukaryotes. '),
(86764, 2014, 'plos', 'Phase transitions in the multi-cellular regulatory behavior of pancreatic islet excitability', 'Hraha, Thomas H|Westacott, Matthew J|Pozzoli, Marina|Notary, Aleena M|McClatchey, P Mason|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.|Department of Bioengineering, University of Colorado, Anschutz Medical Campus, Aurora, Colorado, United States of America, Barbara Davis Center for Childhood Diabetes, University of Colorado, Anschutz Medical Campus, Aurora, Colorado, United States of America.', 'United States|United States|United States|United States|United States|United States', 24, 'Animals|Blood Glucose|Insulin|Islets of Langerhans|Mice|Mice, Transgenic|Models, Biological|Systems Biology', 'Journal Article', '10.1371/journal.pcbi.1003819', '25188228', 'The pancreatic islets of Langerhans are multicellular micro-organs integral to maintaining glucose homeostasis through secretion of the hormone insulin. xcexb2-cells within the islet exist as a highly coupled electrical network which coordinates electrical activity and insulin release at high glucose, but leads to global suppression at basal glucose. Despite its importance, how network dynamics generate this emergent binary on/off behavior remains to be elucidated. Previous work has suggested that a small threshold of quiescent cells is able to suppress the entire network. By modeling the islet as a Boolean network, we predicted a phase-transition between globally active and inactive states would emerge near this threshold number of cells, indicative of critical behavior. This was tested using islets with an inducible-expression mutation which renders defined numbers of cells electrically inactive, together with pharmacological modulation of electrical activity. This was combined with real-time imaging of intracellular free-calcium activity [Ca2+]i and measurement of physiological parameters in mice. As the number of inexcitable cells was increased beyond xe2x88xbc15%, a phase-transition in islet activity occurred, switching from globally active wild-type behavior to global quiescence. This phase-transition was also seen in insulin secretion and blood glucose, indicating physiological impact. This behavior was reproduced in a multicellular dynamical model suggesting critical behavior in the islet may obey general properties of coupled heterogeneous networks. This study represents the first detailed explanation for how the islet facilitates inhibitory activity in spite of a heterogeneous cell population, as well as the role this plays in diabetes and its reversal. We further explain how islets utilize this critical behavior to leverage cellular heterogeneity and coordinate a robust insulin response with high dynamic range. These findings also give new insight into emergent multicellular dynamics in general which are applicable to many coupled physiological systems, specifically where inhibitory dynamics result from coupled networks. '),
(86765, 2014, 'plos', 'Stochastic dynamics of interacting haematopoietic stem cell niche lineages', 'Székely, Tamás|Burrage, Kevin|Mangel, Marc|Bonsall, Michael B', 'Department of Computer Science, University of Oxford, Oxford, United Kingdom.|Department of Computer Science, University of Oxford, Oxford, United Kingdom, Department of Mathematics, Queensland University of Technology, Brisbane, Queensland, Australia.|Department of Applied Mathematics and Statistics, University of California Santa Cruz, Santa Cruz, California, United States of America, Department of Biology, University of Bergen, Bergen, Norway.|Mathematical Ecology Research Group, Department of Zoology, University of Oxford, Oxford, United Kingdom.', 'United Kingdom|Australia|Norway|United Kingdom', 0, 'Computational Biology|Computer Simulation|Hematopoietic Stem Cells|Humans|Models, Biological|Stem Cell Niche|Stochastic Processes', 'Journal Article', '10.1371/journal.pcbi.1003794', '25188267', 'Since we still know very little about stem cells in their natural environment, it is useful to explore their dynamics through modelling and simulation, as well as experimentally. Most models of stem cell systems are based on deterministic differential equations that ignore the natural heterogeneity of stem cell populations. This is not appropriate at the level of individual cells and niches, when randomness is more likely to affect dynamics. In this paper, we introduce a fast stochastic method for simulating a metapopulation of stem cell niche lineages, that is, many sub-populations that together form a heterogeneous metapopulation, over time. By selecting the common limiting timestep, our method ensures that the entire metapopulation is simulated synchronously. This is important, as it allows us to introduce interactions between separate niche lineages, which would otherwise be impossible. We expand our method to enable the coupling of many lineages into niche groups, where differentiated cells are pooled within each niche group. Using this method, we explore the dynamics of the haematopoietic system from a demand control system perspective. We find that coupling together niche lineages allows the organism to regulate blood cell numbers as closely as possible to the homeostatic optimum. Furthermore, coupled lineages respond better than uncoupled ones to random perturbations, here the loss of some myeloid cells. This could imply that it is advantageous for an organism to connect together its niche lineages into groups. Our results suggest that a potential fruitful empirical direction will be to understand how stem cell descendants communicate with the niche and how cancer may arise as a result of a failure of such communication. '),
(86766, 2014, 'plos', 'Evolution of the F0F1 ATP synthase complex in light of the patchy distribution of different bioenergetic pathways across prokaryotes', 'Koumandou, Vassiliki Lila|Kossida, Sophia', 'Bioinformatics & Medical Informatics Team, Biomedical Research Foundation, Academy of Athens, Athens, Greece.|Bioinformatics & Medical Informatics Team, Biomedical Research Foundation, Academy of Athens, Athens, Greece.', 'Greece|Greece', 16, 'Archaea|Bacteria|DNA, Archaeal|DNA, Bacterial|Energy Metabolism|Phylogeny|Proton-Translocating ATPases|RNA, Ribosomal, 16S|Sequence Analysis, DNA', 'Journal Article', '10.1371/journal.pcbi.1003821', '25188293', 'Bacteria and archaea are characterized by an amazing metabolic diversity, which allows them to persist in diverse and often extreme habitats. Apart from oxygenic photosynthesis and oxidative phosphorylation, well-studied processes from chloroplasts and mitochondria of plants and animals, prokaryotes utilize various chemo- or lithotrophic modes, such as anoxygenic photosynthesis, iron oxidation and reduction, sulfate reduction, and methanogenesis. Most bioenergetic pathways have a similar general structure, with an electron transport chain composed of protein complexes acting as electron donors and acceptors, as well as a central cytochrome complex, mobile electron carriers, and an ATP synthase. While each pathway has been studied in considerable detail in isolation, not much is known about their relative evolutionary relationships. Wanting to address how this metabolic diversity evolved, we mapped the distribution of nine bioenergetic modes on a phylogenetic tree based on 16S rRNA sequences from 272 species representing the full diversity of prokaryotic lineages. This highlights the patchy distribution of many pathways across different lineages, and suggests either up to 26 independent origins or 17 horizontal gene transfer events. Next, we used comparative genomics and phylogenetic analysis of all subunits of the F0F1 ATP synthase, common to most bacterial lineages regardless of their bioenergetic mode. Our results indicate an ancient origin of this protein complex, and no clustering based on bioenergetic mode, which suggests that no special modifications are needed for the ATP synthase to work with different electron transport chains. Moreover, examination of the ATP synthase genetic locus indicates various gene rearrangements in the different bacterial lineages, ancient duplications of atpI and of the beta subunit of the F0 subcomplex, as well as more recent stochastic lineage-specific and species-specific duplications of all subunits. We discuss the implications of the overall pattern of conservation and flexibility of the F0F1 ATP synthase genetic locus. '),
(86767, 2014, 'plos', 'Data-derived modeling characterizes plasticity of MAPK signaling in melanoma', 'Bernardo-Faura, Marti|Massen, Stefan|Falk, Christine S|Brady, Nathan R|Eils, Roland', 'Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany, Institute of Pharmacy and Molecular Biotechnology (IPMB), Bioquant, Heidelberg University, Heidelberg, Germany.|Lysosomal Systems Biology, German Cancer Research Center (DKFZ), Bioquant, Heidelberg, Germany, Institute of Transplant Immunology, IFB-Tx, Hannover Medical School, Hannover, Germany.|Institute of Transplant Immunology, IFB-Tx, Hannover Medical School, Hannover, Germany, German Center for Infectious Diseases (DZIF) TTU-IICH, Hannover, Germany.|Systems Biology of Cell Death Mechanisms, German Cancer Research Center (DKFZ), Bioquant, Heidelberg, Germany, Department of Surgery, Heidelberg University Hospital, Heidelberg, Germany.|Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany, Institute of Pharmacy and Molecular Biotechnology (IPMB), Bioquant, Heidelberg University, Heidelberg, Germany.', 'Germany|Germany|Germany|Germany|Germany', 0, 'Computational Biology|Fuzzy Logic|Humans|MAP Kinase Signaling System|Melanoma|Models, Biological|Phosphorylation', 'Journal Article', '10.1371/journal.pcbi.1003795', '25188314', 'The majority of melanomas have been shown to harbor somatic mutations in the RAS-RAF-MEK-MAPK and PI3K-AKT pathways, which play a major role in regulation of proliferation and survival. The prevalence of these mutations makes these kinase signal transduction pathways an attractive target for cancer therapy. However, tumors have generally shown adaptive resistance to treatment. This adaptation is achieved in melanoma through its ability to undergo neovascularization, migration and rearrangement of signaling pathways. To understand the dynamic, nonlinear behavior of signaling pathways in cancer, several computational modeling approaches have been suggested. Most of those models require that the pathway topology remains constant over the entire observation period. However, changes in topology might underlie adaptive behavior to drug treatment. To study signaling rearrangements, here we present a new approach based on Fuzzy Logic (FL) that predicts changes in network architecture over time. This adaptive modeling approach was used to investigate pathway dynamics in a newly acquired experimental dataset describing total and phosphorylated protein signaling over four days in A375 melanoma cell line exposed to different kinase inhibitors. First, a generalized strategy was established to implement a parameter-reduced FL model encoding non-linear activity of a signaling network in response to perturbation. Next, a literature-based topology was generated and parameters of the FL model were derived from the full experimental dataset. Subsequently, the temporal evolution of model performance was evaluated by leaving time-defined data points out of training. Emerging discrepancies between model predictions and experimental data at specific time points allowed the characterization of potential network rearrangement. We demonstrate that this adaptive FL modeling approach helps to enhance our mechanistic understanding of the molecular plasticity of melanoma. '),
(86768, 2014, 'plos', 'Pep2Path: automated mass spectrometry-guided genome mining of peptidic natural products', 'Medema, Marnix H|Paalvast, Yared|Nguyen, Don D|Melnik, Alexey|Dorrestein, Pieter C|Takano, Eriko|Breitling, Rainer', 'Department of Microbial Physiology, University of Groningen, Groningen, The Netherlands, Groningen Bioinformatics Centre, University of Groningen, Groningen, The Netherlands.|Department of Microbial Physiology, University of Groningen, Groningen, The Netherlands, Groningen Bioinformatics Centre, University of Groningen, Groningen, The Netherlands.|Skaggs School of Pharmacy and Pharmaceutical Sciences and Departments of Pharmacology, Chemistry and Biochemistry, University of California, San Diego, La Jolla, California, United States of America.|Skaggs School of Pharmacy and Pharmaceutical Sciences and Departments of Pharmacology, Chemistry and Biochemistry, University of California, San Diego, La Jolla, California, United States of America.|Skaggs School of Pharmacy and Pharmaceutical Sciences and Departments of Pharmacology, Chemistry and Biochemistry, University of California, San Diego, La Jolla, California, United States of America.|Department of Microbial Physiology, University of Groningen, Groningen, The Netherlands, Manchester Institute of Biotechnology, Faculty of Life Sciences, University of Manchester, Manchester, United Kingdom.|Groningen Bioinformatics Centre, University of Groningen, Groningen, The Netherlands, Manchester Institute of Biotechnology, Faculty of Life Sciences, University of Manchester, Manchester, United Kingdom.', 'Netherlands|Netherlands|United States|United States|United States|United Kingdom|United Kingdom', 0, 'Algorithms|Amino Acid Sequence|Bacteria|Base Sequence|Bayes Theorem|Biological Products|Data Mining|Databases, Genetic|Genomics|Molecular Sequence Data|Peptides|Software|Tandem Mass Spectrometry', 'Journal Article', '10.1371/journal.pcbi.1003822', '25188327', 'Nonribosomally and ribosomally synthesized bioactive peptides constitute a source of molecules of great biomedical importance, including antibiotics such as penicillin, immunosuppressants such as cyclosporine, and cytostatics such as bleomycin. Recently, an innovative mass-spectrometry-based strategy, peptidogenomics, has been pioneered to effectively mine microbial strains for novel peptidic metabolites. Even though mass-spectrometric peptide detection can be performed quite fast, true high-throughput natural product discovery approaches have still been limited by the inability to rapidly match the identified tandem mass spectra to the gene clusters responsible for the biosynthesis of the corresponding compounds. With Pep2Path, we introduce a software package to fully automate the peptidogenomics approach through the rapid Bayesian probabilistic matching of mass spectra to their corresponding biosynthetic gene clusters. Detailed benchmarking of the method shows that the approach is powerful enough to correctly identify gene clusters even in data sets that consist of hundreds of genomes, which also makes it possible to match compounds from unsequenced organisms to closely related biosynthetic gene clusters in other genomes. Applying Pep2Path to a data set of compounds without known biosynthesis routes, we were able to identify candidate gene clusters for the biosynthesis of five important compounds. Notably, one of these clusters was detected in a genome from a different subphylum of Proteobacteria than that in which the molecule had first been identified. All in all, our approach paves the way towards high-throughput discovery of novel peptidic natural products. Pep2Path is freely available from http://pep2path.sourceforge.net/, implemented in Python, licensed under the GNU General Public License v3 and supported on MS Windows, Linux and Mac OS X. '),
(86769, 2014, 'plos', 'Emergence of assortative mixing between clusters of cultured neurons', 'Teller, Sara|Granell, Clara|De Domenico, Manlio|Soriano, Jordi|Gómez, Sergio|Arenas, Alex', 'Departament dEstructura i Constituents de la Matèria, Universitat de Barcelona, Barcelona, Spain.|Departament dEnginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, Tarragona, Spain.|Departament dEnginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, Tarragona, Spain.|Departament dEstructura i Constituents de la Matèria, Universitat de Barcelona, Barcelona, Spain.|Departament dEnginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, Tarragona, Spain.|Departament dEnginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, Tarragona, Spain.', 'Spain|Spain|Spain|Spain|Spain|Spain', 36, 'Animals|Cells, Cultured|Cerebral Cortex|Embryo, Mammalian|Models, Neurological|Nerve Net|Neurons|Rats|Rats, Sprague-Dawley', 'Journal Article', '10.1371/journal.pcbi.1003796', '25188377', 'The analysis of the activity of neuronal cultures is considered to be a good proxy of the functional connectivity of in vivo neuronal tissues. Thus, the functional complex network inferred from activity patterns is a promising way to unravel the interplay between structure and functionality of neuronal systems. Here, we monitor the spontaneous self-sustained dynamics in neuronal cultures formed by interconnected aggregates of neurons (clusters). Dynamics is characterized by the fast activation of groups of clusters in sequences termed bursts. The analysis of the time delays between clusters activations within the bursts allows the reconstruction of the directed functional connectivity of the network. We propose a method to statistically infer this connectivity and analyze the resulting properties of the associated complex networks. Surprisingly enough, in contrast to what has been reported for many biological networks, the clustered neuronal cultures present assortative mixing connectivity values, meaning that there is a preference for clusters to link to other clusters that share similar functional connectivity, as well as a rich-club core, which shapes a connectivity backbone in the network. These results point out that the grouping of neurons and the assortative connectivity between clusters are intrinsic survival mechanisms of the culture. '),
(86770, 2014, 'plos', 'A probabilistic model to predict clinical phenotypic traits from genome sequencing', 'Chen, Yun-Ching|Douville, Christopher|Wang, Cheng|Niknafs, Noushin|Yeo, Grace|Beleva-Guthrie, Violeta|Carter, Hannah|Stenson, Peter D|Cooper, David N|Li, Biao|Mooney, Sean|Karchin, Rachel', 'Department of Biomedical Engineering and Institute for Computational Medicine, The Johns Hopkins University, Baltimore, Maryland, United States of America.|Department of Biomedical Engineering and Institute for Computational Medicine, The Johns Hopkins University, Baltimore, Maryland, United States of America.|Department of Biomedical Engineering and Institute for Computational Medicine, The Johns Hopkins University, Baltimore, Maryland, United States of America.|Department of Biomedical Engineering and Institute for Computational Medicine, The Johns Hopkins University, Baltimore, Maryland, United States of America.|Department of Biomedical Engineering and Institute for Computational Medicine, The Johns Hopkins University, Baltimore, Maryland, United States of America.|Department of Biomedical Engineering and Institute for Computational Medicine, The Johns Hopkins University, Baltimore, Maryland, United States of America.|Department of Biomedical Engineering and Institute for Computational Medicine, The Johns Hopkins University, Baltimore, Maryland, United States of America.|Institute of Medical Genetics, School of Medicine, Cardiff University, Heath Park, Cardiff, United Kingdom.|Institute of Medical Genetics, School of Medicine, Cardiff University, Heath Park, Cardiff, United Kingdom.|Buck Institute for Research on Aging, Novato, California, United States of America.|Buck Institute for Research on Aging, Novato, California, United States of America.|Department of Biomedical Engineering and Institute for Computational Medicine, The Johns Hopkins University, Baltimore, Maryland, United States of America.', 'United States|United States|United States|United States|United States|United States|United States|Un', 6, 'Bayes Theorem|Genetic Predisposition to Disease|Genome|Genome-Wide Association Study|Genomics|Human Genome Project|Humans|Models, Statistical|Phenotype|Sequence Analysis, DNA', 'Journal Article', '10.1371/journal.pcbi.1003825', '25188385', 'Genetic screening is becoming possible on an unprecedented scale. However, its utility remains controversial. Although most variant genotypes cannot be easily interpreted, many individuals nevertheless attempt to interpret their genetic information. Initiatives such as the Personal Genome Project (PGP) and Illuminas Understand Your Genome are sequencing thousands of adults, collecting phenotypic information and developing computational pipelines to identify the most important variant genotypes harbored by each individual. These pipelines consider database and allele frequency annotations and bioinformatics classifications. We propose that the next step will be to integrate these different sources of information to estimate the probability that a given individual has specific phenotypes of clinical interest. To this end, we have designed a Bayesian probabilistic model to predict the probability of dichotomous phenotypes. When applied to a cohort from PGP, predictions of Gilbert syndrome, Graves disease, non-Hodgkin lymphoma, and various blood groups were accurate, as individuals manifesting the phenotype in question exhibited the highest, or among the highest, predicted probabilities. Thirty-eight PGP phenotypes (26%) were predicted with area-under-the-ROC curve (AUC)>0.7, and 23 (15.8%) of these were statistically significant, based on permutation tests. Moreover, in a Critical Assessment of Genome Interpretation (CAGI) blinded prediction experiment, the models were used to match 77 PGP genomes to phenotypic profiles, generating the most accurate prediction of 16 submissions, according to an independent assessor. Although the models are currently insufficiently accurate for diagnostic utility, we expect their performance to improve with growth of publicly available genomics data and model refinement by domain experts. '),
(86771, 2014, 'plos', 'Revealing molecular mechanisms by integrating high-dimensional functional screens with protein interaction data', 'Simeone, Angela|Marsico, Giovanni|Collinet, Claudio|Galvez, Thierry|Kalaidzidis, Yannis|Zerial, Marino|Beyer, Andreas', 'Biotechnology Center, TU Dresden, Dresden, Germany.|Max Planck Institute for Molecular Cell Biology and Genetics, Dresden, Germany.|Max Planck Institute for Molecular Cell Biology and Genetics, Dresden, Germany.|Max Planck Institute for Molecular Cell Biology and Genetics, Dresden, Germany.|Max Planck Institute for Molecular Cell Biology and Genetics, Dresden, Germany, Belozersky Institute of Physico-Chemical Biology & Faculty of Bioengineering and Bioinformatics, Moscow State University, Moscow, Russia.|Max Planck Institute for Molecular Cell Biology and Genetics, Dresden, Germany.|Biotechnology Center, TU Dresden, Dresden, Germany, Center for Regenerative Therapy, Dresden, Germany, University of Cologne, Cologne, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Computational Biology|Endocytosis|HEK293 Cells|Humans|Models, Biological|Models, Molecular|Neoplasms|Protein Interaction Mapping|Proteins|Signal Transduction', 'Journal Article', '10.1371/journal.pcbi.1003801', '25188415', 'Functional genomics screens using multi-parametric assays are powerful approaches for identifying genes involved in particular cellular processes. However, they suffer from problems like noise, and often provide little insight into molecular mechanisms. A bottleneck for addressing these issues is the lack of computational methods for the systematic integration of multi-parametric phenotypic datasets with molecular interactions. Here, we present Integrative Multi Profile Analysis of Cellular Traits (IMPACT). The main goal of IMPACT is to identify the most consistent phenotypic profile among interacting genes. This approach utilizes two types of external information: sets of related genes (IMPACT-sets) and network information (IMPACT-modules). Based on the notion that interacting genes are more likely to be involved in similar functions than non-interacting genes, this data is used as a prior to inform the filtering of phenotypic profiles that are similar among interacting genes. IMPACT-sets selects the most frequent profile among a set of related genes. IMPACT-modules identifies sub-networks containing genes with similar phenotype profiles. The statistical significance of these selections is subsequently quantified via permutations of the data. IMPACT (1) handles multiple profiles per gene, (2) rescues genes with weak phenotypes and (3) accounts for multiple biases e.g. caused by the network topology. Application to a genome-wide RNAi screen on endocytosis showed that IMPACT improved the recovery of known endocytosis-related genes, decreased off-target effects, and detected consistent phenotypes. Those findings were confirmed by rescreening 468 genes. Additionally we validated an unexpected influence of the IGF-receptor on EGF-endocytosis. IMPACT facilitates the selection of high-quality phenotypic profiles using different types of independent information, thereby supporting the molecular interpretation of functional screens. ');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(86772, 2014, 'plos', 'A peptide-based method for 13C Metabolic Flux Analysis in microbial communities', 'Ghosh, Amit|Nilmeier, Jerome|Weaver, Daniel|Adams, Paul D|Keasling, Jay D|Mukhopadhyay, Aindrila|Petzold, Christopher J|Martín, Héctor García', 'Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America, Joint BioEnergy Institute, Emeryville, California, United States of America.|Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America, Joint BioEnergy Institute, Emeryville, California, United States of America.|Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America, Joint BioEnergy Institute, Emeryville, California, United States of America.|Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America, Joint BioEnergy Institute, Emeryville, California, United States of America, Department of Bioengineering, University of California, Berkeley, Berkeley, California, United States of America.|Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America, Joint BioEnergy Institute, Emeryville, California, United States of America, Department of Bioengineering, University of California, Berkeley, Berkeley, California, United States of America, Department of Chemical Engineering, University of California, Berkeley, Berkeley, United States of America.|Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America, Joint BioEnergy Institute, Emeryville, California, United States of America.|Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America, Joint BioEnergy Institute, Emeryville, California, United States of America.|Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America, Joint BioEnergy Institute, Emeryville, California, United States of America.', 'United States|United States|United States|United States|United States|United States|United States|Un', 30, 'Amino Acids|Carbon Isotopes|Computational Biology|Desulfovibrio vulgaris|Metabolic Flux Analysis|Methanococcus|Microbial Consortia|Models, Biological|Peptides', 'Journal Article', '10.1371/journal.pcbi.1003827', '25188426', 'The study of intracellular metabolic fluxes and inter-species metabolite exchange for microbial communities is of crucial importance to understand and predict their behaviour. The most authoritative method of measuring intracellular fluxes, 13C Metabolic Flux Analysis (13C MFA), uses the labeling pattern obtained from metabolites (typically amino acids) during 13C labeling experiments to derive intracellular fluxes. However, these metabolite labeling patterns cannot easily be obtained for each of the members of the community. Here we propose a new type of 13C MFA that infers fluxes based on peptide labeling, instead of amino acid labeling. The advantage of this method resides in the fact that the peptide sequence can be used to identify the microbial species it originates from and, simultaneously, the peptide labeling can be used to infer intracellular metabolic fluxes. Peptide identity and labeling patterns can be obtained in a high-throughput manner from modern proteomics techniques. We show that, using this method, it is theoretically possible to recover intracellular metabolic fluxes in the same way as through the standard amino acid based 13C MFA, and quantify the amount of information lost as a consequence of using peptides instead of amino acids. We show that by using a relatively small number of peptides we can counter this information loss. We computationally tested this method with a well-characterized simple microbial community consisting of two species. '),
(86773, 2014, 'plos', 'Dynamic conformational change regulates the protein-DNA recognition: an investigation on binding of a Y-family polymerase to its target DNA', 'Chu, Xiakun|Liu, Fei|Maxwell, Brian A|Wang, Yong|Suo, Zucai|Wang, Haijun|Han, Wei|Wang, Jin', 'State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin, P.R. China, College of Physics, Jilin University, Changchun, Jilin, P.R. China.|State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin, P.R. China, College of Physics, Jilin University, Changchun, Jilin, P.R. China.|Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio, United States of America.|State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin, P.R. China.|Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio, United States of America.|College of Physics, Jilin University, Changchun, Jilin, P.R. China.|College of Physics, Jilin University, Changchun, Jilin, P.R. China.|State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin, P.R. China, College of Physics, Jilin University, Changchun, Jilin, P.R. China, Department of Chemistry and Physics, State University of New York at Stony Brook, Stony Brook, New York, United States of America.', 'China|China|United States|China|United States|China|China|China', 0, 'DNA|DNA-Directed DNA Polymerase|Molecular Dynamics Simulation|Protein Binding|Protein Conformation|Temperature', 'Journal Article', '10.1371/journal.pcbi.1003804', '25188490', 'Protein-DNA recognition is a central biological process that governs the life of cells. A protein will often undergo a conformational transition to form the functional complex with its target DNA. The protein conformational dynamics are expected to contribute to the stability and specificity of DNA recognition and therefore may control the functional activity of the protein-DNA complex. Understanding how the conformational dynamics influences the protein-DNA recognition is still challenging. Here, we developed a two-basin structure-based model to explore functional dynamics in Sulfolobus solfataricus DNA Y-family polymerase IV (DPO4) during its binding to DNA. With explicit consideration of non-specific and specific interactions between DPO4 and DNA, we found that DPO4-DNA recognition is comprised of first 3D diffusion, then a short-range adjustment sliding on DNA and finally specific binding. Interestingly, we found that DPO4 is under a conformational equilibrium between multiple states during the binding process and the distributions of the conformations vary at different binding stages. By modulating the strength of the electrostatic interactions, the flexibility of the linker, and the conformational dynamics in DPO4, we drew a clear picture on how DPO4 dynamically regulates the DNA recognition. We argue that the unique features of flexibility and conformational dynamics in DPO4-DNA recognition have direct implications for low-fidelity translesion DNA synthesis, most of which is found to be accomplished by the Y-family DNA polymerases. Our results help complete the description of the DNA synthesis process for the Y-family polymerases. Furthermore, the methods developed here can be widely applied for future investigations on how various proteins recognize and bind specific DNA substrates. '),
(86774, 2014, 'plos', 'Ten simple rules for better figures', 'Rougier, Nicolas P|Droettboom, Michael|Bourne, Philip E', 'INRIA Bordeaux Sud-Ouest, Talence, France, LaBRI, UMR 5800 CNRS, Talence, France, Institute of Neurodegenerative Diseases, UMR 5293 CNRS, Bordeaux, France.|Space Telescope Science Institute, Baltimore, Maryland, United States of America.|Office of the Director, The National Institutes of Health, Bethesda, Maryland, United States of America.', 'France|United States|United States', 0, 'Biomedical Research|Communication|Computational Biology|Computer Graphics|Humans|Information Dissemination', 'Editorial', '10.1371/journal.pcbi.1003833', '25210732', '-'),
(86775, 2014, 'plos', 'Molecular simulation-based structural prediction of protein complexes in mass spectrometry: the human insulin dimer', 'Li, Jinyu|Rossetti, Giulia|Dreyer, Jens|Raugei, Simone|Ippoliti, Emiliano|Lüscher, Bernhard|Carloni, Paolo', 'Computational Biophysics, German Research School for Simulation Sciences (joint venture of RWTH Aachen University and Forschungszentrum Jülich, Germany), Jülich, Germany and Computational Biomedicine, Institute for Advanced Simulation IAS-5 and Institute of Neuroscience and Medicine INM-9, Forschungszentrum Jülich, Jülich, Germany, Institute of Biochemistry and Molecular Biology, RWTH Aachen University, Aachen, Germany.|Computational Biophysics, German Research School for Simulation Sciences (joint venture of RWTH Aachen University and Forschungszentrum Jülich, Germany), Jülich, Germany and Computational Biomedicine, Institute for Advanced Simulation IAS-5 and Institute of Neuroscience and Medicine INM-9, Forschungszentrum Jülich, Jülich, Germany, Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich, Jülich, Germany.|Computational Biophysics, German Research School for Simulation Sciences (joint venture of RWTH Aachen University and Forschungszentrum Jülich, Germany), Jülich, Germany and Computational Biomedicine, Institute for Advanced Simulation IAS-5 and Institute of Neuroscience and Medicine INM-9, Forschungszentrum Jülich, Jülich, Germany.|Fundamental and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington, United States of America.|Computational Biophysics, German Research School for Simulation Sciences (joint venture of RWTH Aachen University and Forschungszentrum Jülich, Germany), Jülich, Germany and Computational Biomedicine, Institute for Advanced Simulation IAS-5 and Institute of Neuroscience and Medicine INM-9, Forschungszentrum Jülich, Jülich, Germany.|Institute of Biochemistry and Molecular Biology, RWTH Aachen University, Aachen, Germany.|Computational Biophysics, German Research School for Simulation Sciences (joint venture of RWTH Aachen University and Forschungszentrum Jülich, Germany), Jülich, Germany and Computational Biomedicine, Institute for Advanced Simulation IAS-5 and Institute of Neuroscience and Medicine INM-9, Forschungszentrum Jülich, Jülich, Germany.', 'Germany|Germany|Germany|United States|Germany|Germany|Germany', 0, 'Humans|Insulin|Mass Spectrometry|Molecular Dynamics Simulation|Protein Conformation|Proteomics|Thermodynamics', 'Journal Article', '10.1371/journal.pcbi.1003838', '25210764', 'Protein electrospray ionization (ESI) mass spectrometry (MS)-based techniques are widely used to provide insight into structural proteomics under the assumption that non-covalent protein complexes being transferred into the gas phase preserve basically the same intermolecular interactions as in solution. Here we investigate the applicability of this assumption by extending our previous structural prediction protocol for single proteins in ESI-MS to protein complexes. We apply our protocol to the human insulin dimer (hIns2) as a test case. Our calculations reproduce the main charge and the collision cross section (CCS) measured in ESI-MS experiments. Molecular dynamics simulations for 0.075 ms show that the complex maximizes intermolecular non-bonded interactions relative to the structure in water, without affecting the cross section. The overall gas-phase structure of hIns2 does exhibit differences with the one in aqueous solution, not inferable from a comparison with calculated CCS. Hence, care should be exerted when interpreting ESI-MS proteomics data based solely on NMR and/or X-ray structural information. '),
(86776, 2014, 'plos', 'Mathematical modeling of bacterial kinetics to predict the impact of antibiotic colonic exposure and treatment duration on the amount of resistant enterobacteria excreted', 'Nguyen, Thu Thuy|Guedj, Jeremie|Chachaty, Elisabeth|de Gunzburg, Jean|Andremont, Antoine|Mentré, France', 'IAME, UMR 1137, INSERM, Paris, France, IAME, UMR 1137, Univ Paris Diderot, Sorbonne Paris Cité, Paris, France.|IAME, UMR 1137, INSERM, Paris, France, IAME, UMR 1137, Univ Paris Diderot, Sorbonne Paris Cité, Paris, France.|Institut Gustave-Roussy, Villejuif, France.|Da Volterra, Paris, France.|IAME, UMR 1137, INSERM, Paris, France, IAME, UMR 1137, Univ Paris Diderot, Sorbonne Paris Cité, Paris, France, AP-HP, Hôpital Bichat, Paris, France.|IAME, UMR 1137, INSERM, Paris, France, IAME, UMR 1137, Univ Paris Diderot, Sorbonne Paris Cité, Paris, France, AP-HP, Hôpital Bichat, Paris, France.', 'France|France|France|France|France|France', 19, 'Animals|Anti-Bacterial Agents|Ciprofloxacin|Colon|Drug Resistance, Bacterial|Enterobacteriaceae|Feces|Fluoroquinolones|Kinetics|Models, Biological|Swine', 'Journal Article', '10.1371/journal.pcbi.1003840', '25210849', 'Fecal excretion of antibiotics and resistant bacteria in the environment are major public health threats associated with extensive farming and modern medical care. Innovative strategies that can reduce the intestinal antibiotic concentrations during treatments are in development. However, the effect of lower exposure on the amount of resistant enterobacteria excreted has not been quantified, making it difficult to anticipate the impact of these strategies. Here, we introduce a bacterial kinetic model to capture the complex relationships between drug exposure, loss of susceptible enterobacteria and growth of resistant strains in the feces of piglets receiving placebo, 1.5 or 15 mg/kg/day ciprofloxacin, a fluoroquinolone, for 5 days. The model could well describe the kinetics of drug susceptible and resistant enterobacteria observed during treatment, and up to 22 days after treatment cessation. Next, the model was used to predict the expected amount of resistant enterobacteria excreted over an average piglets lifetime (150 days) when varying drug exposure and treatment duration. For the clinically relevant dose of 15 mg/kg/day for 5 days, the total amount of resistant enterobacteria excreted was predicted to be reduced by 75% and 98% when reducing treatment duration to 3 and 1 day treatment, respectively. Alternatively, for a fixed 5-days treatment, the level of resistance excreted could be reduced by 18%, 33%, 57.5% and 97% if 3, 5, 10 and 30 times lower levels of colonic drug concentrations were achieved, respectively. This characterization on in vivo data of the dynamics of resistance to antibiotics in the colonic flora could provide new insights into the mechanism of dissemination of resistance and can be used to design strategies aiming to reduce it. '),
(86777, 2014, 'plos', 'SANTA: quantifying the functional content of molecular networks', 'Cornish, Alex J|Markowetz, Florian', 'Department of Life Sciences, Imperial College London, London, United Kingdom.|Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, Cambridgeshire, United Kingdom.', 'United Kingdom|United Kingdom', 0, 'Cell Line, Tumor|Gene Regulatory Networks|Humans|Models, Biological|Models, Molecular|RNA Interference|Saccharomyces cerevisiae|Signal Transduction|Systems Biology', 'Journal Article', '10.1371/journal.pcbi.1003808', '25210953', 'Linking networks of molecular interactions to cellular functions and phenotypes is a key goal in systems biology. Here, we adapt concepts of spatial statistics to assess the functional content of molecular networks. Based on the guilt-by-association principle, our approach (called SANTA) quantifies the strength of association between a gene set and a network, and functionally annotates molecular networks like other enrichment methods annotate lists of genes. As a general association measure, SANTA can (i) functionally annotate experimentally derived networks using a collection of curated gene sets and (ii) annotate experimentally derived gene sets using a collection of curated networks, as well as (iii) prioritize genes for follow-up analyses. We exemplify the efficacy of SANTA in several case studies using the S. cerevisiae genetic interaction network and genome-wide RNAi screens in cancer cell lines. Our theory, simulations, and applications show that SANTA provides a principled statistical way to quantify the association between molecular networks and cellular functions and phenotypes. SANTA is available from http://bioconductor.org/packages/release/bioc/html/SANTA.html. '),
(86778, 2014, 'plos', 'Strategies for controlling non-transmissible infection outbreaks using a large human movement data set', 'Hancock, Penelope A|Rehman, Yasmin|Hall, Ian M|Edeghere, Obaghe|Danon, Leon|House, Thomas A|Keeling, Matthew J', 'Department of Zoology, University of Oxford, Oxford, United Kingdom, School of Life Sciences, University of Warwick, Coventry, United Kingdom.|Field Epidemiology Service (Birmingham), Public Health England, West Midlands, United Kingdom.|Emergency Response Unit, Public Health England, Porton Down, United Kingdom.|Field Epidemiology Service (Birmingham), Public Health England, West Midlands, United Kingdom.|School of Mathematics, Queen Mary University of London, London, United Kingdom, Warwick Mathematics Institute, University of Warwick, Coventry, United Kingdom.|Warwick Mathematics Institute, University of Warwick, Coventry, United Kingdom.|School of Life Sciences, University of Warwick, Coventry, United Kingdom, Warwick Mathematics Institute, University of Warwick, Coventry, United Kingdom.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kin', 0, 'Computational Biology|Databases, Factual|Disease Outbreaks|Female|Humans|Legionnaires Disease|Male|Models, Theoretical|Population Surveillance|United Kingdom', 'Journal Article', '10.1371/journal.pcbi.1003809', '25211122', 'Prediction and control of the spread of infectious disease in human populations benefits greatly from our growing capacity to quantify human movement behavior. Here we develop a mathematical model for non-transmissible infections contracted from a localized environmental source, informed by a detailed description of movement patterns of the population of Great Britain. The model is applied to outbreaks of Legionnaires disease, a potentially life-threatening form of pneumonia caused by the bacteria Legionella pneumophilia. We use case-report data from three recent outbreaks that have occurred in Great Britain where the source has already been identified by public health agencies. We first demonstrate that the amount of individual-level heterogeneity incorporated in the movement data greatly influences our ability to predict the source location. The most accurate predictions were obtained using reported travel histories to describe movements of infected individuals, but using detailed simulation models to estimate movement patterns offers an effective fast alternative. Secondly, once the source is identified, we show that our model can be used to accurately determine the population likely to have been exposed to the pathogen, and hence predict the residential locations of infected individuals. The results give rise to an effective control strategy that can be implemented rapidly in response to an outbreak. '),
(86779, 2014, 'plos', 'PTEN hopping on the cell membrane is regulated via a positively-charged C2 domain', 'Yasui, Masato|Matsuoka, Satomi|Ueda, Masahiro', 'Laboratories for Nanobiology, Graduate School of Frontier Biosciences, Osaka University, Suita, Osaka, Japan, Laboratory for Cell Signaling Dynamics, QBiC (Quantitative Biology Center), RIKEN, Suita, Osaka, Japan, Laboratory of Single Molecule Biology, Department of Biological Sciences, Graduate School of Science, Osaka University, Toyonaka, Osaka, Japan.|Laboratory for Cell Signaling Dynamics, QBiC (Quantitative Biology Center), RIKEN, Suita, Osaka, Japan.|Laboratories for Nanobiology, Graduate School of Frontier Biosciences, Osaka University, Suita, Osaka, Japan, Laboratory for Cell Signaling Dynamics, QBiC (Quantitative Biology Center), RIKEN, Suita, Osaka, Japan, Laboratory of Single Molecule Biology, Department of Biological Sciences, Graduate School of Science, Osaka University, Toyonaka, Osaka, Japan.', 'Japan|Japan|Japan', 19, 'Cell Membrane|Dictyostelium|Humans|PTEN Phosphohydrolase|Protein Structure, Tertiary|Static Electricity', 'Journal Article', '10.1371/journal.pcbi.1003817', '25211206', 'PTEN, a tumor suppressor that is frequently mutated in a wide spectrum of cancers, exerts PI(3,4,5)P3 phosphatase activities that are regulated by its dynamic shuttling between the membrane and cytoplasm. Direct observation of PTEN in the interfacial environment can offer quantitative information about the shuttling dynamics, but remains elusive. Here we show that positively charged residues located in the cxcexb12 helix of the C2 domain are necessary for the membrane localization of PTEN via stable electrostatic interactions in Dictyostelium discoideum. Single-molecule imaging analyses revealed that PTEN molecules moved distances much larger than expected had they been caused by lateral diffusion, a phenomenon we call hopping. Our novel single-particle tracking analysis method found that the cxcexb12 helix aids in regulating the hopping and stable-binding states. The dynamically established membrane localization of PTEN was revealed to be essential for developmental processes and clarified a fundamental regulation mechanism of the protein quantity and activity on the plasma membrane. '),
(86780, 2014, 'plos', 'The time scale of evolutionary innovation', 'Chatterjee, Krishnendu|Pavlogiannis, Andreas|Adlam, Ben|Nowak, Martin A', 'IST Austria, Klosterneuburg, Austria.|IST Austria, Klosterneuburg, Austria.|Program for Evolutionary Dynamics, Department of Organismic and Evolutionary Biology, Department of Mathematics, Harvard University, Cambridge, Massachusetts, United States of America.|Program for Evolutionary Dynamics, Department of Organismic and Evolutionary Biology, Department of Mathematics, Harvard University, Cambridge, Massachusetts, United States of America.', 'Austria|Austria|United States|United States', 0, 'Algorithms|Biological Evolution|Computational Biology|Markov Chains|Models, Biological', 'Journal Article', '10.1371/journal.pcbi.1003818', '25211329', 'A fundamental question in biology is the following: what is the time scale that is needed for evolutionary innovations? There are many results that characterize single steps in terms of the fixation time of new mutants arising in populations of certain size and structure. But here we ask a different question, which is concerned with the much longer time scale of evolutionary trajectories: how long does it take for a population exploring a fitness landscape to find target sequences that encode new biological functions? Our key variable is the length, L, of the genetic sequence that undergoes adaptation. In computer science there is a crucial distinction between problems that require algorithms which take polynomial or exponential time. The latter are considered to be intractable. Here we develop a theoretical approach that allows us to estimate the time of evolution as function of L. We show that adaptation on many fitness landscapes takes time that is exponential in L, even if there are broad selection gradients and many targets uniformly distributed in sequence space. These negative results lead us to search for specific mechanisms that allow evolution to work on polynomial time scales. We study a regeneration process and show that it enables evolution to work in polynomial time.'),
(86781, 2014, 'plos', 'Principal component analysis characterizes shared pathogenetics from genome-wide association studies', 'Chang, Diana|Keinan, Alon', 'Department of Biological Statistics & Computational Biology, Cornell University, Ithaca, New York, United States of America, Program in Computational Biology and Medicine, Cornell University, Ithaca, New York, United States of America.|Department of Biological Statistics & Computational Biology, Cornell University, Ithaca, New York, United States of America, Program in Computational Biology and Medicine, Cornell University, Ithaca, New York, United States of America.', 'United States|United States', 5, 'Autoimmune Diseases|Cluster Analysis|Computational Biology|Computer Simulation|Databases, Genetic|Genome-Wide Association Study|Humans|Principal Component Analysis|Risk Factors', 'Journal Article', '10.1371/journal.pcbi.1003820', '25211452', 'Genome-wide association studies (GWASs) have recently revealed many genetic associations that are shared between different diseases. We propose a method, disPCA, for genome-wide characterization of shared and distinct risk factors between and within disease classes. It flips the conventional GWAS paradigm by analyzing the diseases themselves, across GWAS datasets, to explore their shared pathogenetics. The method applies principal component analysis (PCA) to gene-level significance scores across all genes and across GWASs, thereby revealing shared pathogenetics between diseases in an unsupervised fashion. Importantly, it adjusts for potential sources of heterogeneity present between GWAS which can confound investigation of shared disease etiology. We applied disPCA to 31 GWASs, including autoimmune diseases, cancers, psychiatric disorders, and neurological disorders. The leading principal components separate these disease classes, as well as inflammatory bowel diseases from other autoimmune diseases. Generally, distinct diseases from the same class tend to be less separated, which is in line with their increased shared etiology. Enrichment analysis of genes contributing to leading principal components revealed pathways that are implicated in the immune system, while also pointing to pathways that have yet to be explored before in this context. Our results point to the potential of disPCA in going beyond epidemiological findings of the co-occurrence of distinct diseases, to highlighting novel genes and pathways that unsupervised learning suggest to be key players in the variability across diseases. '),
(86782, 2014, 'plos', 'Determining individual variation in growth and its implication for life-history and population processes using the empirical Bayes method', 'Vincenzi, Simone|Mangel, Marc|Crivelli, Alain J|Munch, Stephan|Skaug, Hans J', 'Center for Stock Assessment Research, Department of Applied Mathematics and Statistics, University of California, Santa Cruz, Santa Cruz, California, United States of America, Dipartimento di Elettronica, Informazione e Bioingegneria Politecnico di Milano, Milan, Italy.|Center for Stock Assessment Research, Department of Applied Mathematics and Statistics, University of California, Santa Cruz, Santa Cruz, California, United States of America, Department of Biology, University of Bergen, Bergen, Norway.|Station Biologique de la Tour du Valat, Le Sambuc, Arles, France.|Fisheries Ecology Division, Southwest Fisheries Science Center, National Marine Fisheries Service, NOAA, Santa Cruz, Santa Cruz, California, United States of America.|Department of Mathematics, University of Bergen, Bergen, Norway.', 'Italy|Norway|France|United States|Norway', 0, 'Animals|Bayes Theorem|Body Size|Computational Biology|Growth and Development|Models, Biological|Models, Statistical|Trout', 'Journal Article', '10.1371/journal.pcbi.1003828', '25211603', 'The differences in demographic and life-history processes between organisms living in the same population have important consequences for ecological and evolutionary dynamics. Modern statistical and computational methods allow the investigation of individual and shared (among homogeneous groups) determinants of the observed variation in growth. We use an Empirical Bayes approach to estimate individual and shared variation in somatic growth using a von Bertalanffy growth model with random effects. To illustrate the power and generality of the method, we consider two populations of marble trout Salmo marmoratus living in Slovenian streams, where individually tagged fish have been sampled for more than 15 years. We use year-of-birth cohort, population density during the first year of life, and individual random effects as potential predictors of the von Bertalanffy growth functions parameters k (rate of growth) and Lxe2x88x9e (asymptotic size). Our results showed that size ranks were largely maintained throughout marble trout lifetime in both populations. According to the Akaike Information Criterion (AIC), the best models showed different growth patterns for year-of-birth cohorts as well as the existence of substantial individual variation in growth trajectories after accounting for the cohort effect. For both populations, models including density during the first year of life showed that growth tended to decrease with increasing population density early in life. Model validation showed that predictions of individual growth trajectories using the random-effects model were more accurate than predictions based on mean size-at-age of fish.'),
(86783, 2014, 'plos', 'The structural basis of ATP as an allosteric modulator', 'Lu, Shaoyong|Huang, Wenkang|Wang, Qi|Shen, Qiancheng|Li, Shuai|Nussinov, Ruth|Zhang, Jian', 'Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai JiaoTong University, School of Medicine, Shanghai, China.|Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai JiaoTong University, School of Medicine, Shanghai, China.|Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai JiaoTong University, School of Medicine, Shanghai, China.|Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai JiaoTong University, School of Medicine, Shanghai, China.|Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai JiaoTong University, School of Medicine, Shanghai, China.|Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory, National Cancer Institute, Frederick, Maryland, United States of America, Department of Human Genetics and Molecular Medicine, Sackler School of Medicine, Sackler Institute of Molecular Medicine, Tel Aviv University, Tel Aviv, Israel.|Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai JiaoTong University, School of Medicine, Shanghai, China, Shanghai Key Laboratory of Tumor Microenvironment and Inflammation, Shanghai Jiao Tong University School of Medicine, Shanghai, China.', 'China|China|China|China|China|Israel|China', 0, 'Adenosine Triphosphate|Allosteric Site|Bacteria|Enzymes|Fungi|Humans|Molecular Dynamics Simulation|Proteins|Signal Transduction|Thermodynamics', 'Journal Article', '10.1371/journal.pcbi.1003831', '25211773', 'Adenosine-5-triphosphate (ATP) is generally regarded as a substrate for energy currency and protein modification. Recent findings uncovered the allosteric function of ATP in cellular signal transduction but little is understood about this critical behavior of ATP. Through extensive analysis of ATP in solution and proteins, we found that the free ATP can exist in the compact and extended conformations in solution, and the two different conformational characteristics may be responsible for ATP to exert distinct biological functions: ATP molecules adopt both compact and extended conformations in the allosteric binding sites but conserve extended conformations in the substrate binding sites. Nudged elastic band simulations unveiled the distinct dynamic processes of ATP binding to the corresponding allosteric and substrate binding sites of uridine monophosphate kinase, and suggested that in solution ATP preferentially binds to the substrate binding sites of proteins. When the ATP molecules occupy the allosteric binding sites, the allosteric trigger from ATP to fuel allosteric communication between allosteric and functional sites is stemmed mainly from the triphosphate part of ATP, with a small number from the adenine part of ATP. Taken together, our results provide overall understanding of ATP allosteric functions responsible for regulation in biological systems. '),
(86784, 2014, 'plos', 'eMatchSite: sequence order-independent structure alignments of ligand binding pockets in protein models', 'Brylinski, Michal', 'Department of Biological Sciences, Louisiana State University, Baton Rouge, Louisiana, United States of America, Center for Computation & Technology, Louisiana State University, Baton Rouge, Louisiana, United States of America.', 'United States', 20, 'Algorithms|Databases, Protein|Protein Binding|Proteins|Proteomics|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1371/journal.pcbi.1003829', '25232727', 'Detecting similarities between ligand binding sites in the absence of global homology between target proteins has been recognized as one of the critical components of modern drug discovery. Local binding site alignments can be constructed using sequence order-independent techniques, however, to achieve a high accuracy, many current algorithms for binding site comparison require high-quality experimental protein structures, preferably in the bound conformational state. This, in turn, complicates proteome scale applications, where only various quality structure models are available for the majority of gene products. To improve the state-of-the-art, we developed eMatchSite, a new method for constructing sequence order-independent alignments of ligand binding sites in protein models. Large-scale benchmarking calculations using adenine-binding pockets in crystal structures demonstrate that eMatchSite generates accurate alignments for almost three times more protein pairs than SOIPPA. More importantly, eMatchSite offers a high tolerance to structural distortions in ligand binding regions in protein models. For example, the percentage of correctly aligned pairs of adenine-binding sites in weakly homologous protein models is only 4-9% lower than those aligned using crystal structures. This represents a significant improvement over other algorithms, e.g. the performance of eMatchSite in recognizing similar binding sites is 6% and 13% higher than that of SiteEngine using high- and moderate-quality protein models, respectively. Constructing biologically correct alignments using predicted ligand binding sites in protein models opens up the possibility to investigate drug-protein interaction networks for complete proteomes with prospective systems-level applications in polypharmacology and rational drug repositioning. eMatchSite is freely available to the academic community as a web-server and a stand-alone software distribution at http://www.brylinski.org/ematchsite. '),
(86785, 2014, 'plos', 'Pupil-linked arousal determines variability in perceptual decision making', 'Murphy, Peter R|Vandekerckhove, Joachim|Nieuwenhuis, Sander', 'Department of Psychology and Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands.|Department of Cognitive Sciences, University of California, Irvine, Irvine, California, United States of America.|Department of Psychology and Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands.', 'Netherlands|United States|Netherlands', 49, 'Adolescent|Adult|Arousal|Choice Behavior|Computational Biology|Computer Simulation|Female|Humans|Male|Pupil|Task Performance and Analysis|Young Adult', 'Journal Article', '10.1371/journal.pcbi.1003854', '25232732', 'Decision making between several alternatives is thought to involve the gradual accumulation of evidence in favor of each available choice. This process is profoundly variable even for nominally identical stimuli, yet the neuro-cognitive substrates that determine the magnitude of this variability are poorly understood. Here, we demonstrate that arousal state is a powerful determinant of variability in perceptual decision making. We measured pupil size, a highly sensitive index of arousal, while human subjects performed a motion-discrimination task, and decomposed task behavior into latent decision making parameters using an established computational model of the decision process. In direct contrast to previous theoretical accounts specifying a role for arousal in several discrete aspects of decision making, we found that pupil diameter was uniquely related to a model parameter representing variability in the rate of decision evidence accumulation: Periods of increased pupil size, reflecting heightened arousal, were characterized by greater variability in accumulation rate. Pupil diameter also correlated trial-by-trial with specific patterns of behavior that collectively are diagnostic of changing accumulation rate variability, and explained substantial individual differences in this computational quantity. These findings provide a uniquely clear account of how arousal state impacts decision making, and may point to a relationship between pupil-linked neuromodulation and behavioral variability. They also pave the way for future studies aimed at augmenting the precision with which people make decisions. '),
(86786, 2014, 'plos', 'Multidimensional epistasis and the transitory advantage of sex', 'Nowak, Stefan|Neidhart, Johannes|Szendro, Ivan G|Krug, Joachim', 'Institut für Theoretische Physik, Universität zu Köln, Cologne, Germany.|Institut für Theoretische Physik, Universität zu Köln, Cologne, Germany.|Institut für Theoretische Physik, Universität zu Köln, Cologne, Germany.|Institut für Theoretische Physik, Universität zu Köln, Cologne, Germany.', 'Germany|Germany|Germany|Germany', 0, 'Computational Biology|Epistasis, Genetic|Evolution, Molecular|Models, Genetic|Recombination, Genetic|Reproductive Physiological Phenomena|Selection, Genetic', 'Journal Article', '10.1371/journal.pcbi.1003836', '25232825', 'Identifying and quantifying the benefits of sex and recombination is a long-standing problem in evolutionary theory. In particular, contradictory claims have been made about the existence of a benefit of recombination on high dimensional fitness landscapes in the presence of sign epistasis. Here we present a comparative numerical study of sexual and asexual evolutionary dynamics of haploids on tunably rugged model landscapes under strong selection, paying special attention to the temporal development of the evolutionary advantage of recombination and the link between population diversity and the rate of adaptation. We show that the adaptive advantage of recombination on static rugged landscapes is strictly transitory. At early times, an advantage of recombination arises through the possibility to combine individually occurring beneficial mutations, but this effect is reversed at longer times by the much more efficient trapping of recombining populations at local fitness peaks. These findings are explained by means of well-established results for a setup with only two loci. In accordance with the Red Queen hypothesis the transitory advantage can be prolonged indefinitely in fluctuating environments, and it is maximal when the environment fluctuates on the same time scale on which trapping at local optima typically occurs. '),
(86787, 2014, 'plos', 'A systems approach to predict oncometabolites via context-specific genome-scale metabolic networks', 'Nam, Hojung|Campodonico, Miguel|Bordbar, Aarash|Hyduke, Daniel R|Kim, Sangwoo|Zielinski, Daniel C|Palsson, Bernhard O', 'Department of Bioengineering, University of California San Diego, La Jolla, California, United States of America, School of Information & Communications, Gwangju Institute of Science and Technology (GIST), Buk-gu, Gwangju, Republic of Korea.|Department of Bioengineering, University of California San Diego, La Jolla, California, United States of America, Centre for Biotechnology and Bioengineering, CeBiB, University of Chile, Santiago, Chile.|Department of Bioengineering, University of California San Diego, La Jolla, California, United States of America.|Department of Bioengineering, University of California San Diego, La Jolla, California, United States of America.|Severance Biomedical Science Institute, Yonsei University College of Medicine, Seodaemun-gu, Seoul, South Korea.|Department of Bioengineering, University of California San Diego, La Jolla, California, United States of America.|Department of Bioengineering, University of California San Diego, La Jolla, California, United States of America, Department of Pediatrics, University of California San Diego, La Jolla, California, United States of America.', 'United States|Chile|United States|United States|-|United States|United States', 0, 'Cell Line, Tumor|Cluster Analysis|Computer Simulation|Gene Expression Profiling|Humans|Metabolic Networks and Pathways|Metabolome|Models, Biological|Mutation|Neoplasms|Systems Biology', 'Journal Article', '10.1371/journal.pcbi.1003837', '25232952', 'Altered metabolism in cancer cells has been viewed as a passive response required for a malignant transformation. However, this view has changed through the recently described metabolic oncogenic factors: mutated isocitrate dehydrogenases (IDH), succinate dehydrogenase (SDH), and fumarate hydratase (FH) that produce oncometabolites that competitively inhibit epigenetic regulation. In this study, we demonstrate in silico predictions of oncometabolites that have the potential to dysregulate epigenetic controls in nine types of cancer by incorporating massive scale genetic mutation information (collected from more than 1,700 cancer genomes), expression profiling data, and deploying Recon 2 to reconstruct context-specific genome-scale metabolic models. Our analysis predicted 15 compounds and 24 substructures of potential oncometabolites that could result from the loss-of-function and gain-of-function mutations of metabolic enzymes, respectively. These results suggest a substantial potential for discovering unidentified oncometabolites in various forms of cancers. '),
(86788, 2014, 'plos', 'Allele-specific network reveals combinatorial interaction that transcends small effects in psoriasis GWAS', 'Climer, Sharlee|Templeton, Alan R|Zhang, Weixiong', 'Department of Computer Science and Engineering, Washington University, St. Louis, Missouri, United States of America.|Department of Biology, Washington University, St. Louis, Missouri, United States of America, Department of Genetics, Washington University, St. Louis, Missouri, United States of America, Institute of Evolution, and Department of Evolutionary and Environmental Biology, University of Haifa, Haifa, Israel.|Department of Computer Science and Engineering, Washington University, St. Louis, Missouri, United States of America, Department of Genetics, Washington University, St. Louis, Missouri, United States of America, Institute for Systems Biology, Jianghan University, Wuhan, Hubei, China.', 'United States|Israel|China', 0, 'Alleles|Computational Biology|Genetic Markers|Genome-Wide Association Study|Humans|Polymorphism, Single Nucleotide|Psoriasis', 'Journal Article', '10.1371/journal.pcbi.1003766', '25233071', 'Hundreds of genetic markers have shown associations with various complex diseases, yet the missing heritability remains alarmingly elusive. Combinatorial interactions may account for a substantial portion of this missing heritability, but their discoveries have been impeded by computational complexity and genetic heterogeneity. We present BlocBuster, a novel systems-level approach that efficiently constructs genome-wide, allele-specific networks that accurately segregate homogenous combinations of genetic factors, tests the associations of these combinations with the given phenotype, and rigorously validates the results using a series of unbiased validation methods. BlocBuster employs a correlation measure that is customized for single nucleotide polymorphisms and returns a multi-faceted collection of values that captures genetic heterogeneity. We applied BlocBuster to analyze psoriasis, discovering a combinatorial pattern with an odds ratio of 3.64 and Bonferroni-corrected p-value of 5.01×10(-16). This pattern was replicated in independent data, reflecting robustness of the method. In addition to improving prediction of disease susceptibility and broadening our understanding of the pathogenesis underlying psoriasis, these results demonstrate BlocBusters potential for discovering combinatorial genetic associations within heterogeneous genome-wide data, thereby transcending the limiting small effects produced by individual markers examined in isolation.'),
(86789, 2014, 'plos', 'NESmapper: accurate prediction of leucine-rich nuclear export signals using activity-based profiles', 'Kosugi, Shunichi|Yanagawa, Hiroshi|Terauchi, Ryohei|Tabata, Satoshi', 'Kazusa DNA Research Institute, Kisarazu, Chiba, Japan.|Department of Biosciences and Informatics, Faculty of Science and Technology, Keio University, Yokohama, Japan.|Iwate Biotechnology Research Center, Kitakami, Iwate, Japan.|Kazusa DNA Research Institute, Kisarazu, Chiba, Japan.', 'Japan|Japan|Japan|Japan', 19, 'Active Transport, Cell Nucleus|Amino Acid Sequence|Animals|Computational Biology|DNA Mutational Analysis|Leucine|Mice|Molecular Sequence Data|NIH 3T3 Cells|Nuclear Export Signals|ROC Curve|Sequence Analysis, Protein|Software', 'Journal Article', '10.1371/journal.pcbi.1003841', '25233087', 'The nuclear export of proteins is regulated largely through the exportin/CRM1 pathway, which involves the specific recognition of leucine-rich nuclear export signals (NESs) in the cargo proteins, and modulates nuclear-cytoplasmic protein shuttling by antagonizing the nuclear import activity mediated by importins and the nuclear import signal (NLS). Although the prediction of NESs can help to define proteins that undergo regulated nuclear export, current methods of predicting NESs, including computational tools and consensus-sequence-based searches, have limited accuracy, especially in terms of their specificity. We found that each residue within an NES largely contributes independently and additively to the entire nuclear export activity. We created activity-based profiles of all classes of NESs with a comprehensive mutational analysis in mammalian cells. The profiles highlight a number of specific activity-affecting residues not only at the conserved hydrophobic positions but also in the linker and flanking regions. We then developed a computational tool, NESmapper, to predict NESs by using profiles that had been further optimized by training and combining the amino acid properties of the NES-flanking regions. This tool successfully reduced the considerable number of false positives, and the overall prediction accuracy was higher than that of other methods, including NESsential and Wregex. This profile-based prediction strategy is a reliable way to identify functional protein motifs. NESmapper is available at http://sourceforge.net/projects/nesmapper. ');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(86790, 2014, 'plos', 'Geometry shapes evolution of early multicellularity', 'Libby, Eric|Ratcliff, William|Travisano, Michael|Kerr, Ben', 'Santa Fe Institute, Santa Fe, New Mexico, United States of America.|School of Biology, Georgia Institute of Technology, Atlanta, Georgia, United States of America.|Department of Ecology, Evolution, and Behavior, University of Minnesota, St. Paul, Minnesota, United States of America.|Department of Biology and BEACON Center, University of Washington, Seattle, Washington, United States of America.', 'Mexico|Georgia|United States|United States', 19, 'Apoptosis|Biological Evolution|Cell Physiological Phenomena|Computational Biology|Models, Biological|Yeasts', 'Journal Article', '10.1371/journal.pcbi.1003803', '25233196', 'Organisms have increased in complexity through a series of major evolutionary transitions, in which formerly autonomous entities become parts of a novel higher-level entity. One intriguing feature of the higher-level entity after some major transitions is a division of reproductive labor among its lower-level units in which reproduction is the sole responsibility of a subset of units. Although it can have clear benefits once established, it is unknown how such reproductive division of labor originates. We consider a recent evolution experiment on the yeast Saccharomyces cerevisiae as a unique platform to address the issue of reproductive differentiation during an evolutionary transition in individuality. In the experiment, independent yeast lineages evolved a multicellular snowflake-like cluster formed in response to gravity selection. Shortly after the evolution of clusters, the yeast evolved higher rates of cell death. While cell death enables clusters to split apart and form new groups, it also reduces their performance in the face of gravity selection. To understand the selective value of increased cell death, we create a mathematical model of the cellular arrangement within snowflake yeast clusters. The model reveals that the mechanism of cell death and the geometry of the snowflake interact in complex, evolutionarily important ways. We find that the organization of snowflake yeast imposes powerful limitations on the available space for new cell growth. By dying more frequently, cells in clusters avoid encountering space limitations, and, paradoxically, reach higher numbers. In addition, selection for particular group sizes can explain the increased rate of apoptosis both in terms of total cell number and total numbers of collectives. Thus, by considering the geometry of a primitive multicellular organism we can gain insight into the initial emergence of reproductive division of labor during an evolutionary transition in individuality. '),
(86791, 2014, 'plos', 'Genome-scale metabolic network validation of Shewanella oneidensis using transposon insertion frequency analysis', 'Yang, Hong|Krumholz, Elias W|Brutinel, Evan D|Palani, Nagendra P|Sadowsky, Michael J|Odlyzko, Andrew M|Gralnick, Jeffrey A|Libourel, Igor G L', 'Department of Plant Biology, University of Minnesota, St. Paul, Minnesota, United States of America, BioTechnology Institute, University of Minnesota, St. Paul, Minnesota, United States of America.|Department of Plant Biology, University of Minnesota, St. Paul, Minnesota, United States of America.|BioTechnology Institute, University of Minnesota, St. Paul, Minnesota, United States of America, Department of Microbiology, University of Minnesota, Minneapolis, Minnesota, United States of America.|Department of Plant Biology, University of Minnesota, St. Paul, Minnesota, United States of America.|BioTechnology Institute, University of Minnesota, St. Paul, Minnesota, United States of America, Department of Soil, Water, and Climate, University of Minnesota, St. Paul, Minnesota, United States of America.|School of Mathematics, University of Minnesota, Minneapolis, Minnesota, United States of America.|BioTechnology Institute, University of Minnesota, St. Paul, Minnesota, United States of America, Department of Microbiology, University of Minnesota, Minneapolis, Minnesota, United States of America.|Department of Plant Biology, University of Minnesota, St. Paul, Minnesota, United States of America, BioTechnology Institute, University of Minnesota, St. Paul, Minnesota, United States of America.', 'United States|United States|United States|United States|United States|United States|United States|Un', 0, 'DNA Transposable Elements|DNA, Bacterial|Gene Regulatory Networks|Genome, Bacterial|Genomics|High-Throughput Nucleotide Sequencing|Metabolic Networks and Pathways|Mutagenesis, Site-Directed|Shewanella', 'Journal Article', '10.1371/journal.pcbi.1003848', '25233219', 'Transposon mutagenesis, in combination with parallel sequencing, is becoming a powerful tool for en-masse mutant analysis. A probability generating function was used to explain observed miniHimar transposon insertion patterns, and gene essentiality calls were made by transposon insertion frequency analysis (TIFA). TIFA incorporated the observed genome and sequence motif bias of the miniHimar transposon. The gene essentiality calls were compared to: 1) previous genome-wide direct gene-essentiality assignments, and, 2) flux balance analysis (FBA) predictions from an existing genome-scale metabolic model of Shewanella oneidensis MR-1. A three-way comparison between FBA, TIFA, and the direct essentiality calls was made to validate the TIFA approach. The refinement in the interpretation of observed transposon insertions demonstrated that genes without insertions are not necessarily essential, and that genes that contain insertions are not always nonessential. The TIFA calls were in reasonable agreement with direct essentiality calls for S. oneidensis, but agreed more closely with E. coli essentiality calls for orthologs. The TIFA gene essentiality calls were in good agreement with the MR-1 FBA essentiality predictions, and the agreement between TIFA and FBA predictions was substantially better than between the FBA and the direct gene essentiality predictions. '),
(86792, 2014, 'plos', 'A novel model to combine clinical and pathway-based transcriptomic information for the prognosis prediction of breast cancer', 'Huang, Sijia|Yee, Cameron|Ching, Travers|Yu, Herbert|Garmire, Lana X', 'Molecular Biosciences and Bioengineering Graduate Program, University of Hawaii at Manoa, Honolulu, Hawaii, United States of America, Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii, United States of America.|Neurobiology Program of Biology Department, University of Washington, Seattle, Washington, United States of America.|Molecular Biosciences and Bioengineering Graduate Program, University of Hawaii at Manoa, Honolulu, Hawaii, United States of America, Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii, United States of America.|Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii, United States of America.|Molecular Biosciences and Bioengineering Graduate Program, University of Hawaii at Manoa, Honolulu, Hawaii, United States of America, Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii, United States of America.', 'United States|United States|United States|United States|United States', 41, 'Algorithms|Breast Neoplasms|Disease Progression|Female|Gene Expression Profiling|Genomics|Humans|Middle Aged|Models, Statistical|Prognosis|Proportional Hazards Models|ROC Curve|Transcriptome', 'Journal Article', '10.1371/journal.pcbi.1003851', '25233347', 'Breast cancer is the most common malignancy in women worldwide. With the increasing awareness of heterogeneity in breast cancers, better prediction of breast cancer prognosis is much needed for more personalized treatment and disease management. Towards this goal, we have developed a novel computational model for breast cancer prognosis by combining the Pathway Deregulation Score (PDS) based pathifier algorithm, Cox regression and L1-LASSO penalization method. We trained the model on a set of 236 patients with gene expression data and clinical information, and validated the performance on three diversified testing data sets of 606 patients. To evaluate the performance of the model, we conducted survival analysis of the dichotomized groups, and compared the areas under the curve based on the binary classification. The resulting prognosis genomic model is composed of fifteen pathways (e.g., P53 pathway) that had previously reported cancer relevance, and it successfully differentiated relapse in the training set (log rank p-valuexe2x80x8a=xe2x80x8a6.25e-12) and three testing data sets (log rank p-value < 0.0005). Moreover, the pathway-based genomic models consistently performed better than gene-based models on all four data sets. We also find strong evidence that combining genomic information with clinical information improved the p-values of prognosis prediction by at least three orders of magnitude in comparison to using either genomic or clinical information alone. In summary, we propose a novel prognosis model that harnesses the pathway-based dysregulation as well as valuable clinical information. The selected pathways in our prognosis model are promising targets for therapeutic intervention.'),
(86793, 2014, 'plos', 'Spatial heterogeneity and peptide availability determine CTL killing efficiency in vivo', 'Hogan, Thea|Kadolsky, Ulrich|Tung, Sim|Seddon, Benedict|Yates, Andrew', 'Immune Cell Biology, MRC National Institute for Medical Research, Mill Hill, London, United Kingom.|Department of Systems and Computational Biology, Albert Einstein College of Medicine, New York, New York, United States of America.|Immune Cell Biology, MRC National Institute for Medical Research, Mill Hill, London, United Kingom.|Institute of Immunity and Transplantation, Division of Infection and Immunity, UCL, Royal Free Hospital, London, United Kingdom.|Department of Systems and Computational Biology, Albert Einstein College of Medicine, New York, New York, United States of America, Department of Microbiology and Immunology, Albert Einstein College of Medicine, New York, New York, United States of America, Institute of Infection, Immunity & Inflammation, College of Medical, Veterinary & Life Sciences, University of Glasgow, Glasgow, United Kingdom.', '-|United States|-|United Kingdom|United Kingdom', 0, 'Animals|B-Lymphocytes|Cell Death|Disease Models, Animal|Humans|Influenza, Human|Mice|Mice, Transgenic|Models, Immunological|Peptides|Spleen|T-Lymphocytes, Cytotoxic', 'Journal Article', '10.1371/journal.pcbi.1003805', '25233372', 'The rate at which a cytotoxic T lymphocyte (CTL) can survey for infected cells is a key ingredient of models of vertebrate immune responses to intracellular pathogens. Estimates have been obtained using in vivo cytotoxicity assays in which peptide-pulsed splenocytes are killed by CTL in the spleens of immunised mice. However the spleen is a heterogeneous environment and splenocytes comprise multiple cell types. Are some cell types intrinsically more susceptible to lysis than others? Quantitatively, what impacts are made by the spatial distribution of targets and effectors, and the level of peptide-MHC on the target cell surface? To address these questions we revisited the splenocyte killing assay, using CTL specific for an epitope of influenza virus. We found that at the cell population level T cell targets were killed more rapidly than B cells. Using modeling, quantitative imaging and in vitro killing assays we conclude that this difference in vivo likely reflects different migratory patterns of targets within the spleen and a heterogeneous distribution of CTL, with no detectable difference in the intrinsic susceptibilities of the two populations to lysis. Modeling of the stages involved in the detection and killing of peptide-pulsed targets in vitro revealed that peptide dose influenced the ability of CTL to form conjugates with targets but had no detectable effect on the probability that conjugation resulted in lysis, and that T cell targets took longer to lyse than B cells. We also infer that incomplete killing in vivo of cells pulsed with low doses of peptide may be due to a combination of heterogeneity in peptide uptake and the dissociation, but not internalisation, of peptide-MHC complexes. Our analyses demonstrate how population-averaged parameters in models of immune responses can be dissected to account for both spatial and cellular heterogeneity. '),
(86794, 2014, 'plos', 'Examining variable domain orientations in antigen receptors gives insight into TCR-like antibody design', 'Dunbar, James|Knapp, Bernhard|Fuchs, Angelika|Shi, Jiye|Deane, Charlotte M', 'Department of Statistics, University of Oxford, Oxford, United Kingdom.|Department of Statistics, University of Oxford, Oxford, United Kingdom.|F. Hoffmann-La Roche Ltd, Pharma Research and Early Development, Informatics, Penzberg, Germany.|Informatics, UCB Pharma, Slough, United Kingdom.|Department of Statistics, University of Oxford, Oxford, United Kingdom.', 'United Kingdom|United Kingdom|Germany|United Kingdom|United Kingdom', 16, 'Animals|Antibodies|Binding Sites|Cluster Analysis|Humans|Immunoglobulin Variable Region|Mice|Models, Molecular|Receptors, Antigen, T-Cell', 'Journal Article', '10.1371/journal.pcbi.1003852', '25233457', 'The variable domains of antibodies and T-Cell receptors (TCRs) share similar structures. Both molecules act as sensors for the immune system but recognise their respective antigens in different ways. Antibodies bind to a diverse set of antigenic shapes whilst TCRs only recognise linear peptides presented by a major histocompatibility complex (MHC). The antigen specificity and affinity of both receptors is determined primarily by the sequence and structure of their complementarity determining regions (CDRs). In antibodies the binding site is also known to be affected by the relative orientation of the variable domains, VH and VL. Here, the corresponding property for TCRs, the Vxcexb2-Vxcexb1 orientation, is investigated and compared with that of antibodies. We find that TCR and antibody orientations are distinct. General antibody orientations are found to be incompatible with binding to the MHC in a canonical TCR-like mode. Finally, factors that cause the orientation of TCRs and antibodies to be different are investigated. Packing of the long Vxcexb1 CDR3 in the domain-domain interface is found to be influential. In antibodies, a similar packing affect can be achieved using a bulky residue at IMGT position 50 on the VH domain. Along with IMGT VH 50, other positions are identified that may help to promote a TCR-like orientation in antibodies. These positions should provide useful considerations in the engineering of therapeutic TCR-like antibodies. '),
(86795, 2014, 'plos', 'Identifying cell types from spatially referenced single-cell expression datasets', 'Pettit, Jean-Baptiste|Tomer, Raju|Achim, Kaia|Richardson, Sylvia|Azizi, Lamiae|Marioni, John', 'European Bioinformatics Institute-European Molecular Biology Laboratory (EMBL-EBI), Cambridge, United Kingdom.|Developmental Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.|Developmental Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.|MRC Biostatistics Unit (MRC BSU), Cambridge Institute of Public Health, Cambridge, United Kingdom.|MRC Biostatistics Unit (MRC BSU), Cambridge Institute of Public Health, Cambridge, United Kingdom.|European Bioinformatics Institute-European Molecular Biology Laboratory (EMBL-EBI), Cambridge, United Kingdom.', 'United Kingdom|Germany|Germany|United Kingdom|United Kingdom|United Kingdom', 0, 'Animals|Cluster Analysis|Computational Biology|Databases, Factual|Gene Expression Profiling|In Situ Hybridization, Fluorescence|Markov Chains|Polychaeta|Single-Cell Analysis', 'Journal Article', '10.1371/journal.pcbi.1003824', '25254363', 'Complex tissues, such as the brain, are composed of multiple different cell types, each of which have distinct and important roles, for example in neural function. Moreover, it has recently been appreciated that the cells that make up these sub-cell types themselves harbour significant cell-to-cell heterogeneity, in particular at the level of gene expression. The ability to study this heterogeneity has been revolutionised by advances in experimental technology, such as Wholemount in Situ Hybridizations (WiSH) and single-cell RNA-sequencing. Consequently, it is now possible to study gene expression levels in thousands of cells from the same tissue type. After generating such data one of the key goals is to cluster the cells into groups that correspond to both known and putatively novel cell types. Whilst many clustering algorithms exist, they are typically unable to incorporate information about the spatial dependence between cells within the tissue under study. When such information exists it provides important insights that should be directly included in the clustering scheme. To this end we have developed a clustering method that uses a Hidden Markov Random Field (HMRF) model to exploit both quantitative measures of expression and spatial information. To accurately reflect the underlying biology, we extend current HMRF approaches by allowing the degree of spatial coherency to differ between clusters. We demonstrate the utility of our method using simulated data before applying it to cluster single cell gene expression data generated by applying WiSH to study expression patterns in the brain of the marine annelid Platynereis dumereilii. Our approach allows known cell types to be identified as well as revealing new, previously unexplored cell types within the brain of this important model system. '),
(86796, 2014, 'plos', 'Environmental statistics and optimal regulation', 'Sivak, David A|Thomson, Matt', 'Center for Systems and Synthetic Biology, University of California, San Francisco, San Francisco, California, United States of America.|Center for Systems and Synthetic Biology, University of California, San Francisco, San Francisco, California, United States of America.', 'United States|United States', 8, 'Algorithms|Bayes Theorem|Environment|Enzymes|Metabolism|Models, Biological|Signal Processing, Computer-Assisted|Systems Biology', 'Journal Article', '10.1371/journal.pcbi.1003826', '25254493', 'Any organism is embedded in an environment that changes over time. The timescale for and statistics of environmental change, the precision with which the organism can detect its environment, and the costs and benefits of particular protein expression levels all will affect the suitability of different strategies--such as constitutive expression or graded response--for regulating protein levels in response to environmental inputs. We propose a general framework-here specifically applied to the enzymatic regulation of metabolism in response to changing concentrations of a basic nutrient-to predict the optimal regulatory strategy given the statistics of fluctuations in the environment and measurement apparatus, respectively, and the costs associated with enzyme production. We use this framework to address three fundamental questions: (i) when a cell should prefer thresholding to a graded response, (ii) when there is a fitness advantage to implementing a Bayesian decision rule, and (iii) when retaining memory of the past provides a selective advantage. We specifically find that: (i) relative convexity of enzyme expression cost and benefit influences the fitness of thresholding or graded responses, (ii) intermediate levels of measurement uncertainty call for a sophisticated Bayesian decision rule, and (iii) in dynamic contexts, intermediate levels of uncertainty call for retaining memory of the past. Statistical properties of the environment, such as variability and correlation times, set optimal biochemical parameters, such as thresholds and decay rates in signaling pathways. Our framework provides a theoretical basis for interpreting molecular signal processing algorithms and a classification scheme that organizes known regulatory strategies and may help conceptualize heretofore unknown ones.'),
(86797, 2014, 'plos', 'IDEPI: rapid prediction of HIV-1 antibody epitopes and other phenotypic features from sequence data using a flexible machine learning platform', 'Hepler, N Lance|Scheffler, Konrad|Weaver, Steven|Murrell, Ben|Richman, Douglas D|Burton, Dennis R|Poignard, Pascal|Smith, Davey M|Kosakovsky Pond, Sergei L', 'Interdisciplinary Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, California, United States of America.|Department of Medicine, University of California San Diego, La Jolla, California, United States of America.|Department of Medicine, University of California San Diego, La Jolla, California, United States of America.|Department of Medicine, University of California San Diego, La Jolla, California, United States of America.|Department of Pathology, University of California San Diego, La Jolla, California, United States of America, San Diego Veterans Affairs Healthcare System, San Diego, California, United States of America.|The Scripps Research Institute, La Jolla, California, United States of America, Ragon Institute of MGH, MIT, and Harvard, Boston, Massachusetts, United States of America.|The Scripps Research Institute, La Jolla, California, United States of America.|Department of Medicine, University of California San Diego, La Jolla, California, United States of America, San Diego Veterans Affairs Healthcare System, San Diego, California, United States of America.|Department of Medicine, University of California San Diego, La Jolla, California, United States of America.', 'United States|United States|United States|United States|United States|United States|United States|Un', 0, 'AIDS Dementia Complex|Algorithms|Antibodies, Neutralizing|Computational Biology|Drug Resistance, Viral|Epitopes|HIV Antibodies|HIV Infections|HIV-1|Human Immunodeficiency Virus Proteins|Humans|Machine Learning|Phenotype|Sequence Analysis, Protein|Software', 'Journal Article', '10.1371/journal.pcbi.1003842', '25254639', 'Since its identification in 1983, HIV-1 has been the focus of a research effort unprecedented in scope and difficulty, whose ultimate goals--a cure and a vaccine--remain elusive. One of the fundamental challenges in accomplishing these goals is the tremendous genetic variability of the virus, with some genes differing at as many as 40% of nucleotide positions among circulating strains. Because of this, the genetic bases of many viral phenotypes, most notably the susceptibility to neutralization by a particular antibody, are difficult to identify computationally. Drawing upon open-source general-purpose machine learning algorithms and libraries, we have developed a software package IDEPI (IDentify EPItopes) for learning genotype-to-phenotype predictive models from sequences with known phenotypes. IDEPI can apply learned models to classify sequences of unknown phenotypes, and also identify specific sequence features which contribute to a particular phenotype. We demonstrate that IDEPI achieves performance similar to or better than that of previously published approaches on four well-studied problems: finding the epitopes of broadly neutralizing antibodies (bNab), determining coreceptor tropism of the virus, identifying compartment-specific genetic signatures of the virus, and deducing drug-resistance associated mutations. The cross-platform Python source code (released under the GPL 3.0 license), documentation, issue tracking, and a pre-configured virtual machine for IDEPI can be found at https://github.com/veg/idepi.'),
(86798, 2014, 'plos', 'Multi-state modeling of biomolecules', 'Stefan, Melanie I|Bartol, Thomas M|Sejnowski, Terrence J|Kennedy, Mary B', 'Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, United States of America.|Salk Institute for Biological Studies, La Jolla, California, United States of America.|Salk Institute for Biological Studies, La Jolla, California, United States of America.|Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, United States of America.', 'United States|United States|United States|United States', 22, 'Algorithms|Bacteria|Computer Simulation|DNA|Humans|Models, Biological|Models, Molecular|Proteins|Systems Biology', 'Journal Article', '10.1371/journal.pcbi.1003844', '25254957', 'Multi-state modeling of biomolecules refers to a series of techniques used to represent and compute the behavior of biological molecules or complexes that can adopt a large number of possible functional states. Biological signaling systems often rely on complexes of biological macromolecules that can undergo several functionally significant modifications that are mutually compatible. Thus, they can exist in a very large number of functionally different states. Modeling such multi-state systems poses two problems: the problem of how to describe and specify a multi-state system (the specification problem) and the problem of how to use a computer to simulate the progress of the system over time (the computation problem). To address the specification problem, modelers have in recent years moved away from explicit specification of all possible states and towards rule-based formalisms that allow for implicit model specification, including the xcexba-calculus, BioNetGen, the Allosteric Network Compiler, and others. To tackle the computation problem, they have turned to particle-based methods that have in many cases proved more computationally efficient than population-based methods based on ordinary differential equations, partial differential equations, or the Gillespie stochastic simulation algorithm. Given current computing technology, particle-based methods are sometimes the only possible option. Particle-based simulators fall into two further categories: nonspatial simulators, such as StochSim, DYNSTOC, RuleMonkey, and the Network-Free Stochastic Simulator (NFSim), and spatial simulators, including Meredys, SRSim, and MCell. Modelers can thus choose from a variety of tools, the best choice depending on the particular problem. Development of faster and more powerful methods is ongoing, promising the ability to simulate ever more complex signaling processes in the future.'),
(86799, 2014, 'plos', 'A Bayesian framework to identify methylcytosines from high-throughput bisulfite sequencing data', 'Xie, Qing|Liu, Qi|Mao, Fengbiao|Cai, Wanshi|Wu, Honghu|You, Mingcong|Wang, Zhen|Chen, Bingyu|Sun, Zhong Sheng|Wu, Jinyu', 'Institute of Genomic Medicine, Wenzhou Medical University, Wenzhou, China.|Institute of Genomic Medicine, Wenzhou Medical University, Wenzhou, China.|Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, China.|Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, China.|Institute of Genomic Medicine, Wenzhou Medical University, Wenzhou, China.|Institute of Genomic Medicine, Wenzhou Medical University, Wenzhou, China.|Institute of Molecular Medicine, Department of blood transfusion, Zhejiang Provincial Peoples Hospital, Hangzhou, Zhejiang, China.|Institute of Molecular Medicine, Department of blood transfusion, Zhejiang Provincial Peoples Hospital, Hangzhou, Zhejiang, China.|Institute of Genomic Medicine, Wenzhou Medical University, Wenzhou, China, Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, China.|Institute of Genomic Medicine, Wenzhou Medical University, Wenzhou, China.', 'China|China|China|China|China|China|China|China|China|China', 2, '5-Methylcytosine|Algorithms|Bayes Theorem|High-Throughput Nucleotide Sequencing|Humans|Models, Genetic|Sensitivity and Specificity|Sequence Analysis, DNA|Sulfites', 'Journal Article', '10.1371/journal.pcbi.1003853', '25255082', 'High-throughput bisulfite sequencing technologies have provided a comprehensive and well-fitted way to investigate DNA methylation at single-base resolution. However, there are substantial bioinformatic challenges to distinguish precisely methylcytosines from unconverted cytosines based on bisulfite sequencing data. The challenges arise, at least in part, from cell heterozygosis caused by multicellular sequencing and the still limited number of statistical methods that are available for methylcytosine calling based on bisulfite sequencing data. Here, we present an algorithm, termed Bycom, a new Bayesian model that can perform methylcytosine calling with high accuracy. Bycom considers cell heterozygosis along with sequencing errors and bisulfite conversion efficiency to improve calling accuracy. Bycom performance was compared with the performance of Lister, the method most widely used to identify methylcytosines from bisulfite sequencing data. The results showed that the performance of Bycom was better than that of Lister for data with high methylation levels. Bycom also showed higher sensitivity and specificity for low methylation level samples (<1%) than Lister. A validation experiment based on reduced representation bisulfite sequencing data suggested that Bycom had a false positive rate of about 4% while maintaining an accuracy of close to 94%. This study demonstrated that Bycom had a low false calling rate at any methylation level and accurate methylcytosine calling at high methylation levels. Bycom will contribute significantly to studies aimed at recalibrating the methylation level of genomic regions based on the presence of methylcytosines. '),
(86800, 2014, 'plos', 'Synchronization of isolated downstates (K-complexes) may be caused by cortically-induced disruption of thalamic spindling', 'Mak-McCully, Rachel A|Deiss, Stephen R|Rosen, Burke Q|Jung, Ki-Young|Sejnowski, Terrence J|Bastuji, Hélène|Rey, Marc|Cash, Sydney S|Bazhenov, Maxim|Halgren, Eric', 'Department of Neurosciences, University of California, San Diego, San Diego, California, United States of America.|Computer Science and Engineering Department, University of California, San Diego, San Diego, California, United States of America.|Department of Radiology, University of California, San Diego, San Diego, California, United States of America.|Department of Neurology, Seoul National University College of Medicine, Seoul, Korea.|Division of Biological Sciences, University of California, San Diego, San Diego, California, United States of America, Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, California, United States of America.|Central Integration of Pain, Lyon Neuroscience Research Center, INSERM, U1028, CNRS, UMR5292, Université Claude Bernard, Lyon, Bron, France, Unité dHypnologie, Service de Neurologie Fonctionnelle et dÉpileptologie, Hôpital Neurologique, Hospices Civils de Lyon, Bron, France.|Clinical Neurophysiology Sleep Unit, APHM, Timone Hospital, Aix Marseille Université, Marseille, France.|Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Harvard University, Boston, Massachusetts, United States of America.|Department of Cell Biology and Neuroscience, University of California, Riverside, Riverside, California, United States of America.|Department of Neurosciences, University of California, San Diego, San Diego, California, United States of America, Department of Radiology, University of California, San Diego, San Diego, California, United States of America, Department of Psychiatry, University of California, San Diego, San Diego, California, United States of America.', 'United States|United States|United States|-|United States|France|France|United States|United States|', 0, 'Adolescent|Adult|Aged|Cerebral Cortex|Computational Biology|Computer Simulation|Cortical Synchronization|Electroencephalography|Epilepsy|Female|Humans|Male|Middle Aged|Models, Neurological|Neurons|Thalamus|Young Adult', 'Journal Article', '10.1371/journal.pcbi.1003855', '25255217', 'Sleep spindles and K-complexes (KCs) define stage 2 NREM sleep (N2) in humans. We recently showed that KCs are isolated downstates characterized by widespread cortical silence. We demonstrate here that KCs can be quasi-synchronous across scalp EEG and across much of the cortex using electrocorticography (ECOG) and localized transcortical recordings (bipolar SEEG). We examine the mechanism of synchronous KC production by creating the first conductance based thalamocortical network model of N2 sleep to generate both spontaneous spindles and KCs. Spontaneous KCs are only observed when the model includes diffuse projections from restricted prefrontal areas to the thalamic reticular nucleus (RE), consistent with recent anatomical findings in rhesus monkeys. Modeled KCs begin with a spontaneous focal depolarization of the prefrontal neurons, followed by depolarization of the RE. Surprisingly, the RE depolarization leads to decreased firing due to disrupted spindling, which in turn is due to depolarization-induced inactivation of the low-threshold Ca2+ current (IT). Further, although the RE inhibits thalamocortical (TC) neurons, decreased RE firing causes decreased TC cell firing, again because of disrupted spindling. The resulting abrupt removal of excitatory input to cortical pyramidal neurons then leads to the downstate. Empirically, KCs may also be evoked by sensory stimuli while maintaining sleep. We reproduce this phenomenon in the model by depolarization of either the RE or the widely-projecting prefrontal neurons. Again, disruption of thalamic spindling plays a key role. Higher levels of RE stimulation also cause downstates, but by directly inhibiting the TC neurons. SEEG recordings from the thalamus and cortex in a single patient demonstrated the model prediction that thalamic spindling significantly decreases before KC onset. In conclusion, we show empirically that KCs can be widespread quasi-synchronous cortical downstates, and demonstrate with the first model of stage 2 NREM sleep a possible mechanism whereby this widespread synchrony may arise. '),
(86801, 2014, 'plos', 'Quantifying the impact and extent of undocumented biomedical synonymy', 'Blair, David R|Wang, Kanix|Nestorov, Svetlozar|Evans, James A|Rzhetsky, Andrey', 'Institute for Genomics and Systems Biology, University of Chicago, Chicago, Illinois, United States of America, Committee on Genetics, Genomics, and Systems Biology, University of Chicago, Chicago, Illinois, United States of America.|Institute for Genomics and Systems Biology, University of Chicago, Chicago, Illinois, United States of America, Committee on Genetics, Genomics, and Systems Biology, University of Chicago, Chicago, Illinois, United States of America.|Computation Institute, University of Chicago, Chicago, Illinois, United States of America.|Computation Institute, University of Chicago, Chicago, Illinois, United States of America, Department of Sociology, University of Chicago, Chicago, Illinois, United States of America.|Institute for Genomics and Systems Biology, University of Chicago, Chicago, Illinois, United States of America, Committee on Genetics, Genomics, and Systems Biology, University of Chicago, Chicago, Illinois, United States of America, Computation Institute, University of Chicago, Chicago, Illinois, United States of America, Departments of Medicine and Human Genetics, University of Chicago, Chicago, Illinois, United States of America.', 'United States|United States|United States|United States|United States', 7, 'Computational Biology|Data Mining|Humans|Models, Statistical|Models, Theoretical|Vocabulary, Controlled', 'Journal Article', '10.1371/journal.pcbi.1003799', '25255227', 'Synonymous relationships among biomedical terms are extensively annotated within specialized terminologies, implying that synonymy is important for practical computational applications within this field. It remains unclear, however, whether text mining actually benefits from documented synonymy and whether existing biomedical thesauri provide adequate coverage of these linguistic relationships. In this study, we examine the impact and extent of undocumented synonymy within a very large compendium of biomedical thesauri. First, we demonstrate that missing synonymy has a significant negative impact on named entity normalization, an important problem within the field of biomedical text mining. To estimate the amount synonymy currently missing from thesauri, we develop a probabilistic model for the construction of synonym terminologies that is capable of handling a wide range of potential biases, and we evaluate its performance using the broader domain of near-synonymy among general English words. Our model predicts that over 90% of these relationships are currently undocumented, a result that we support experimentally through crowd-sourcing. Finally, we apply our model to biomedical terminologies and predict that they are missing the vast majority (>90%) of the synonymous relationships they intend to document. Overall, our results expose the dramatic incompleteness of current biomedical thesauri and suggest the need for next-generation, high-coverage lexical terminologies. '),
(86802, 2014, 'plos', 'Characterizing protein interactions employing a genome-wide siRNA cellular phenotyping screen', 'Suratanee, Apichat|Schaefer, Martin H|Betts, Matthew J|Soons, Zita|Mannsperger, Heiko|Harder, Nathalie|Oswald, Marcus|Gipp, Markus|Ramminger, Ellen|Marcus, Guillermo|Männer, Reinhard|Rohr, Karl|Wanker, Erich|Russell, Robert B|Andrade-Navarro, Miguel A|Eils, Roland|König, Rainer', 'Department of Mathematics, Faculty of Applied Science, King Mongkuts University of Technology North Bangkok, Bangsue, Bangkok, Thailand.|EMBL/CRG Systems Biology Research Unit, Center for Genomic Regulation, Barcelona, Spain.|Robert B. Russell, Cell Networks Protein Evolution, BioQuant, University of Heidelberg, Heidelberg, Germany.|Network Modeling, Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute Jena, Jena, Germany, Department of Knowledge Engineering, Maastricht University, Maastricht, The Netherlands, Theoretical Bioinformatics, German Cancer Research Center, Heidelberg, Germany.|Theoretical Bioinformatics, German Cancer Research Center, Heidelberg, Germany.|Theoretical Bioinformatics, German Cancer Research Center, Heidelberg, Germany, Department of Bioinformatics and Functional Genomics, Institute of Pharmacy and Molecular Biotechnology, BioQuant, University of Heidelberg, Heidelberg, Germany.|Network Modeling, Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute Jena, Jena, Germany, Integrated Research and Treatment Center, Center for Sepsis Control and Care (CSCC), Jena University Hospital, Jena, Germany, Theoretical Bioinformatics, German Cancer Research Center, Heidelberg, Germany.|Department of Computer Science V, Institute of Computer Engineering, University of Mannheim, Mannheim, Germany.|AG Neuroproteomics, Max Delbrueck Center for Molecular Medicine, Berlin, Germany.|Department of Computer Science V, Institute of Computer Engineering, University of Mannheim, Mannheim, Germany.|Department of Computer Science V, Institute of Computer Engineering, University of Mannheim, Mannheim, Germany.|Theoretical Bioinformatics, German Cancer Research Center, Heidelberg, Germany, Department of Bioinformatics and Functional Genomics, Institute of Pharmacy and Molecular Biotechnology, BioQuant, University of Heidelberg, Heidelberg, Germany.|AG Neuroproteomics, Max Delbrueck Center for Molecular Medicine, Berlin, Germany.|Robert B. Russell, Cell Networks Protein Evolution, BioQuant, University of Heidelberg, Heidelberg, Germany.|Computational Biology and Data Mining Group, Max Delbrueck Center for Molecular Medicine, Berlin, Germany.|Theoretical Bioinformatics, German Cancer Research Center, Heidelberg, Germany, Department of Bioinformatics and Functional Genomics, Institute of Pharmacy and Molecular Biotechnology, BioQuant, University of Heidelberg, Heidelberg, Germany.|Network Modeling, Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute Jena, Jena, Germany, Integrated Research and Treatment Center, Center for Sepsis Control and Care (CSCC), Jena University Hospital, Jena, Germany, Theoretical Bioinformatics, German Cancer Research Center, Heidelberg, Germany.', 'Thailand|Spain|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germa', 0, 'Cluster Analysis|Databases, Protein|Gene Knockdown Techniques|Genomics|HeLa Cells|Humans|Phenotype|Protein Interaction Maps|Proteins|RNA, Small Interfering|ROC Curve', 'Journal Article', '10.1371/journal.pcbi.1003814', '25255318', 'Characterizing the activating and inhibiting effect of protein-protein interactions (PPI) is fundamental to gain insight into the complex signaling system of a human cell. A plethora of methods has been suggested to infer PPI from data on a large scale, but none of them is able to characterize the effect of this interaction. Here, we present a novel computational development that employs mitotic phenotypes of a genome-wide RNAi knockdown screen and enables identifying the activating and inhibiting effects of PPIs. Exemplarily, we applied our technique to a knockdown screen of HeLa cells cultivated at standard conditions. Using a machine learning approach, we obtained high accuracy (82% AUC of the receiver operating characteristics) by cross-validation using 6,870 known activating and inhibiting PPIs as gold standard. We predicted de novo unknown activating and inhibiting effects for 1,954 PPIs in HeLa cells covering the ten major signaling pathways of the Kyoto Encyclopedia of Genes and Genomes, and made these predictions publicly available in a database. We finally demonstrate that the predicted effects can be used to cluster knockdown genes of similar biological processes in coherent subgroups. The characterization of the activating or inhibiting effect of individual PPIs opens up new perspectives for the interpretation of large datasets of PPIs and thus considerably increases the value of PPIs as an integrated resource for studying the detailed function of signaling pathways of the cellular system of interest. '),
(86803, 2014, 'plos', 'Clique of functional hubs orchestrates population bursts in developmentally regulated neural networks', 'Luccioli, Stefano|Ben-Jacob, Eshel|Barzilai, Ari|Bonifazi, Paolo|Torcini, Alessandro', 'Consiglio Nazionale delle Ricerche, Istituto dei Sistemi Complessi, Sesto Fiorentino, Italy, Joint Italian-Israeli Laboratory on Integrative Network Neuroscience, Tel Aviv University, Ramat Aviv, Israel.|Joint Italian-Israeli Laboratory on Integrative Network Neuroscience, Tel Aviv University, Ramat Aviv, Israel, Beverly and Sackler Faculty of Exact Sciences School of Physics and Astronomy, Tel Aviv University, Ramat Aviv, Israel.|Joint Italian-Israeli Laboratory on Integrative Network Neuroscience, Tel Aviv University, Ramat Aviv, Israel, Department of Neurobiology, George S. Wise Faculty of Life Sciences and Sagol School of Neuroscience, Tel Aviv University, Ramat Aviv, Israel.|Joint Italian-Israeli Laboratory on Integrative Network Neuroscience, Tel Aviv University, Ramat Aviv, Israel, Beverly and Sackler Faculty of Exact Sciences School of Physics and Astronomy, Tel Aviv University, Ramat Aviv, Israel, Department of Neurobiology, George S. Wise Faculty of Life Sciences and Sagol School of Neuroscience, Tel Aviv University, Ramat Aviv, Israel.|Consiglio Nazionale delle Ricerche, Istituto dei Sistemi Complessi, Sesto Fiorentino, Italy, Joint Italian-Israeli Laboratory on Integrative Network Neuroscience, Tel Aviv University, Ramat Aviv, Israel, INFN - Sezione di Firenze and CSDC, Sesto Fiorentino, Italy.', 'Israel|Israel|Israel|Israel|Israel', 0, 'Animals|Computational Biology|Hippocampus|Models, Neurological|Nerve Net|Neurons|Rats|Synapses', 'Journal Article', '10.1371/journal.pcbi.1003823', '25255443', 'It has recently been discovered that single neuron stimulation can impact network dynamics in immature and adult neuronal circuits. Here we report a novel mechanism which can explain in neuronal circuits, at an early stage of development, the peculiar role played by a few specific neurons in promoting/arresting the population activity. For this purpose, we consider a standard neuronal network model, with short-term synaptic plasticity, whose population activity is characterized by bursting behavior. The addition of developmentally inspired constraints and correlations in the distribution of the neuronal connectivities and excitabilities leads to the emergence of functional hub neurons, whose stimulation/deletion is critical for the network activity. Functional hubs form a clique, where a precise sequential activation of the neurons is essential to ignite collective events without any need for a specific topological architecture. Unsupervised time-lagged firings of supra-threshold cells, in connection with coordinated entrainments of near-threshold neurons, are the key ingredients to orchestrate population activity. '),
(86804, 2014, 'plos', 'Natural isotopic signatures of variations in body nitrogen fluxes: a compartmental model analysis', 'Poupin, Nathalie|Mariotti, Franxc3xa7ois|Huneau, Jean-Franxc3xa7ois|Hermier, Dominique|Fouillet, Hxc3xa9lxc3xa8ne', 'INRA, CRNH-IdF, UMR914 Nutrition Physiology and Ingestive Behavior, Paris, France, AgroParisTech, CRNH-IdF, UMR914 Nutrition Physiology and Ingestive Behavior, Paris, France.|INRA, CRNH-IdF, UMR914 Nutrition Physiology and Ingestive Behavior, Paris, France, AgroParisTech, CRNH-IdF, UMR914 Nutrition Physiology and Ingestive Behavior, Paris, France.|INRA, CRNH-IdF, UMR914 Nutrition Physiology and Ingestive Behavior, Paris, France, AgroParisTech, CRNH-IdF, UMR914 Nutrition Physiology and Ingestive Behavior, Paris, France.|INRA, CRNH-IdF, UMR914 Nutrition Physiology and Ingestive Behavior, Paris, France, AgroParisTech, CRNH-IdF, UMR914 Nutrition Physiology and Ingestive Behavior, Paris, France.|INRA, CRNH-IdF, UMR914 Nutrition Physiology and Ingestive Behavior, Paris, France, AgroParisTech, CRNH-IdF, UMR914 Nutrition Physiology and Ingestive Behavior, Paris, France.', 'France|France|France|France|France', 26, 'Animals|Computational Biology|Humans|Liver|Male|Models, Biological|Muscles|Nitrogen|Nitrogen Isotopes|Rats|Rats, Wistar', 'Journal Article', '10.1371/journal.pcbi.1003865', '25275306', 'Body tissues are generally 15N-enriched over the diet, with a discrimination factor (xcex9415N) that varies among tissues and individuals as a function of their nutritional and physiopathological condition. However, both 15N bioaccumulation and intra- and inter-individual xcex9415N variations are still poorly understood, so that theoretical models are required to understand their underlying mechanisms. Using experimental xcex9415N measurements in rats, we developed a multi-compartmental model that provides the first detailed representation of the complex functioning of the bodys xcex9415N system, by explicitly linking the sizes and xcex9415N values of 21 nitrogen pools to the rates and isotope effects of 49 nitrogen metabolic fluxes. We have shown that (i) besides urea production, several metabolic pathways (e.g., protein synthesis, amino acid intracellular metabolism, urea recycling and intestinal absorption or secretion) are most probably associated with isotope fractionation and together contribute to 15N accumulation in tissues, (ii) the xcex9415N of a tissue at steady-state is not affected by variations of its P turnover rate, but can vary according to the relative orientation of tissue free amino acids towards oxidation vs. protein synthesis, (iii) at the whole-body level, xcex9415N variations result from variations in the body partitioning of nitrogen fluxes (e.g., urea production, urea recycling and amino acid exchanges), with or without changes in nitrogen balance, (iv) any deviation from the optimal amino acid intake, in terms of both quality and quantity, causes a global rise in tissue xcex9415N, and (v) xcex9415N variations differ between tissues depending on the metabolic changes involved, which can therefore be identified using simultaneous multi-tissue xcex9415N measurements. This work provides proof of concept that xcex9415N measurements constitute a new promising tool to investigate how metabolic fluxes are nutritionally or physiopathologically reorganized or altered. The existence of such natural and interpretable isotopic biomarkers promises interesting applications in nutrition and health. ');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(86805, 2014, 'plos', 'Memory maintenance in synapses with calcium-based plasticity in the presence of background activity', 'Higgins, David|Graupner, Michael|Brunel, Nicolas', 'IBENS, École Normale Supérieure, Paris, France, Departments of Statistics and Neurobiology, University of Chicago, Chicago, Illinois, United States of America.|Center for Neural Science, New York University, New York, New York, United States of America.|Departments of Statistics and Neurobiology, University of Chicago, Chicago, Illinois, United States of America.', 'France|United States|United States', 0, 'Animals|Calcium|Hippocampus|Models, Neurological|Neuronal Plasticity|Neurons|Rats|Synapses', 'Journal Article', '10.1371/journal.pcbi.1003834', '25275319', 'Most models of learning and memory assume that memories are maintained in neuronal circuits by persistent synaptic modifications induced by specific patterns of pre- and postsynaptic activity. For this scenario to be viable, synaptic modifications must survive the ubiquitous ongoing activity present in neural circuits in vivo. In this paper, we investigate the time scales of memory maintenance in a calcium-based synaptic plasticity model that has been shown recently to be able to fit different experimental data-sets from hippocampal and neocortical preparations. We find that in the presence of background activity on the order of 1 Hz parameters that fit pyramidal layer 5 neocortical data lead to a very fast decay of synaptic efficacy, with time scales of minutes. We then identify two ways in which this memory time scale can be extended: (i) the extracellular calcium concentration in the experiments used to fit the model are larger than estimated concentrations in vivo. Lowering extracellular calcium concentration to in vivo levels leads to an increase in memory time scales of several orders of magnitude, (ii) adding a bistability mechanism so that each synapse has two stable states at sufficiently low background activity leads to a further boost in memory time scale, since memory decay is no longer described by an exponential decay from an initial state, but by an escape from a potential well. We argue that both features are expected to be present in synapses in vivo. These results are obtained first in a single synapse connecting two independent Poisson neurons, and then in simulations of a large network of excitatory and inhibitory integrate-and-fire neurons. Our results emphasise the need for studying plasticity at physiological extracellular calcium concentration, and highlight the role of synaptic bi- or multistability in the stability of learned synaptic structures. '),
(86806, 2014, 'plos', 'Tracking the sleep onset process: an empirical model of behavioral and physiological dynamics', 'Prerau, Michael J|Hartnack, Katie E|Obregon-Henao, Gabriel|Sampson, Aaron|Merlino, Margaret|Gannon, Karen|Bianchi, Matt T|Ellenbogen, Jeffrey M|Purdon, Patrick L', 'Massachusetts General Hospital, Department of Anesthesia, Critical Care, and Pain Medicine, Charlestown, Massachusetts, United States of America.|Massachusetts General Hospital, Department of Anesthesia, Critical Care, and Pain Medicine, Charlestown, Massachusetts, United States of America.|Massachusetts General Hospital, Department of Anesthesia, Critical Care, and Pain Medicine, Charlestown, Massachusetts, United States of America.|Massachusetts General Hospital, Department of Anesthesia, Critical Care, and Pain Medicine, Charlestown, Massachusetts, United States of America.|Massachusetts General Hospital, Department of Neurology, Massachusetts, United States of America.|Massachusetts General Hospital, Department of Neurology, Massachusetts, United States of America.|Massachusetts General Hospital, Department of Neurology, Massachusetts, United States of America.|Johns Hopkins University, Department of Neurology, Baltimore, Maryland, United States of America.|Massachusetts General Hospital, Department of Anesthesia, Critical Care, and Pain Medicine, Charlestown, Massachusetts, United States of America.', 'United States|United States|United States|United States|United States|United States|United States|Un', 27, 'Adult|Computational Biology|Electroencephalography|Female|Humans|Male|Models, Biological|Sleep|Task Performance and Analysis|Wakefulness|Young Adult', 'Journal Article', '10.1371/journal.pcbi.1003866', '25275376', 'The sleep onset process (SOP) is a dynamic process correlated with a multitude of behavioral and physiological markers. A principled analysis of the SOP can serve as a foundation for answering questions of fundamental importance in basic neuroscience and sleep medicine. Unfortunately, current methods for analyzing the SOP fail to account for the overwhelming evidence that the wake/sleep transition is governed by continuous, dynamic physiological processes. Instead, current practices coarsely discretize sleep both in terms of state, where it is viewed as a binary (wake or sleep) process, and in time, where it is viewed as a single time point derived from subjectively scored stages in 30-second epochs, effectively eliminating SOP dynamics from the analysis. These methods also fail to integrate information from both behavioral and physiological data. It is thus imperative to resolve the mismatch between the physiological evidence and analysis methodologies. In this paper, we develop a statistically and physiologically principled dynamic framework and empirical SOP model, combining simultaneously-recorded physiological measurements with behavioral data from a novel breathing task requiring no arousing external sensory stimuli. We fit the model using data from healthy subjects, and estimate the instantaneous probability that a subject is awake during the SOP. The model successfully tracked physiological and behavioral dynamics for individual nights, and significantly outperformed the instantaneous transition models implicit in clinical definitions of sleep onset. Our framework also provides a principled means for cross-subject data alignment as a function of wake probability, allowing us to characterize and compare SOP dynamics across different populations. This analysis enabled us to quantitatively compare the EEG of subjects showing reduced alpha power with the remaining subjects at identical response probabilities. Thus, by incorporating both physiological and behavioral dynamics into our model framework, the dynamics of our analyses can finally match those observed during the SOP. '),
(86807, 2014, 'plos', 'Mesoscopic model and free energy landscape for protein-DNA binding sites: analysis of cyanobacterial promoters', 'Tapia-Rojo, Rafael|Mazo, Juan José|Hernández, José Ángel|Peleato, María Luisa|Fillat, María F|Falo, Fernando', 'Dpto. de Física de la Materia Condensada, Universidad de Zaragoza, Zaragoza, Spain, Institute for Biocomputation and Physics of Complex Systems, Zaragoza, Spain.|Dpto. de Física de la Materia Condensada, Universidad de Zaragoza, Zaragoza, Spain, Instituto de Ciencia de Materiales de Aragón, C.S.I.C.-Universidad de Zaragoza, Zaragoza, Spain.|Department of Biochemistry, Midwestern University, Glendale, Arizona, United States of America.|Institute for Biocomputation and Physics of Complex Systems, Zaragoza, Spain, Departamento de Bioquímica y Biología Molecular y Celular, Universidad de Zaragoza, Zaragoza, Spain.|Institute for Biocomputation and Physics of Complex Systems, Zaragoza, Spain, Departamento de Bioquímica y Biología Molecular y Celular, Universidad de Zaragoza, Zaragoza, Spain.|Dpto. de Física de la Materia Condensada, Universidad de Zaragoza, Zaragoza, Spain, Institute for Biocomputation and Physics of Complex Systems, Zaragoza, Spain.', 'Spain|Spain|United States|Spain|Spain|Spain', 6, 'Binding Sites|Computational Biology|Cyanobacteria|DNA, Bacterial|DNA-Binding Proteins|Models, Genetic|Models, Molecular|Principal Component Analysis|Promoter Regions, Genetic|Thermodynamics', 'Journal Article', '10.1371/journal.pcbi.1003835', '25275384', 'The identification of protein binding sites in promoter sequences is a key problem to understand and control regulation in biochemistry and biotechnological processes. We use a computational method to analyze promoters from a given genome. Our approach is based on a physical model at the mesoscopic level of protein-DNA interaction based on the influence of DNA local conformation on the dynamics of a general particle along the chain. Following the proposed model, the joined dynamics of the protein particle and the DNA portion of interest, only characterized by its base pair sequence, is simulated. The simulation output is analyzed by generating and analyzing the Free Energy Landscape of the system. In order to prove the capacity of prediction of our computational method we have analyzed nine promoters of Anabaena PCC 7120. We are able to identify the transcription starting site of each of the promoters as the most populated macrostate in the dynamics. The developed procedure allows also to characterize promoter macrostates in terms of thermo-statistical magnitudes (free energy and entropy), with valuable biological implications. Our results agree with independent previous experimental results. Thus, our methods appear as a powerful complementary tool for identifying protein binding sites in promoter sequences. '),
(86808, 2014, 'plos', 'Evidence of conformational selection driving the formation of ligand binding sites in protein-protein interfaces', 'Bohnuud, Tanggis|Kozakov, Dima|Vajda, Sandor', 'Program in Bioinformatics, Boston University, Boston, Massachusetts, United States of America.|Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States of America.|Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States of America, Department of Chemistry, Boston University, Boston, Massachusetts, United States of America.', 'United States|United States|United States', 0, 'Binding Sites|Computational Biology|Databases, Protein|Models, Molecular|Nuclear Magnetic Resonance, Biomolecular|Protein Binding|Protein Conformation|Proteins', 'Journal Article', '10.1371/journal.pcbi.1003872', '25275445', 'Many protein-protein interactions (PPIs) are compelling targets for drug discovery, and in a number of cases can be disrupted by small molecules. The main goal of this study is to examine the mechanism of binding site formation in the interface region of proteins that are PPI targets by comparing ligand-free and ligand-bound structures. To avoid any potential bias, we focus on ensembles of ligand-free protein conformations obtained by nuclear magnetic resonance (NMR) techniques and deposited in the Protein Data Bank, rather than on ensembles specifically generated for this study. The measures used for structure comparison are based on detecting binding hot spots, i.e., protein regions that are major contributors to the binding free energy. The main tool of the analysis is computational solvent mapping, which explores the surface of proteins by docking a large number of small probe molecules. Although we consider conformational ensembles obtained by NMR techniques, the analysis is independent of the method used for generating the structures. Finding the energetically most important regions, mapping can identify binding site residues using ligand-free models based on NMR data. In addition, the method selects conformations that are similar to some peptide-bound or ligand-bound structure in terms of the properties of the binding site. This agrees with the conformational selection model of molecular recognition, which assumes such pre-existing conformations. The analysis also shows the maximum level of similarity between unbound and bound states that is achieved without any influence from a ligand. Further shift toward the bound structure assumes protein-peptide or protein-ligand interactions, either selecting higher energy conformations that are not part of the NMR ensemble, or leading to induced fit. Thus, forming the sites in protein-protein interfaces that bind peptides and can be targeted by small ligands always includes conformational selection, although other recognition mechanisms may also be involved. '),
(86809, 2014, 'plos', 'Fast synchronization of ultradian oscillators controlled by delta-notch signaling with cis-inhibition', 'Tiedemann, Hendrik B|Schneltzer, Elida|Zeiser, Stefan|Wurst, Wolfgang|Beckers, Johannes|Przemeck, Gerhard K H|Hrabxc4x9b de Angelis, Martin', 'Institute of Experimental Genetics, Helmholtz Zentrum Mxc3xbcnchen - German Research Center for Environmental Health, Neuherberg, Germany.|Institute of Experimental Genetics, Helmholtz Zentrum Mxc3xbcnchen - German Research Center for Environmental Health, Neuherberg, Germany.|Kinesis Pharma BV, Breda, The Netherlands.|Institute of Developmental Genetics, Helmholtz Zentrum Mxc3xbcnchen - German Research Center for Environmental Health, Neuherberg, Germany, Technische Universitxc3xa4t Mxc3xbcnchen, Center of Life and Food Sciences Weihenstephan, Chair of Developmental Genetics, Freising, Germany.|Institute of Experimental Genetics, Helmholtz Zentrum Mxc3xbcnchen - German Research Center for Environmental Health, Neuherberg, Germany, Technische Universitxc3xa4t Mxc3xbcnchen, Center of Life and Food Sciences Weihenstephan, Chair of Experimental Genetics, Freising, Germany.|Institute of Experimental Genetics, Helmholtz Zentrum Mxc3xbcnchen - German Research Center for Environmental Health, Neuherberg, Germany.|Institute of Experimental Genetics, Helmholtz Zentrum Mxc3xbcnchen - German Research Center for Environmental Health, Neuherberg, Germany, Technische Universitxc3xa4t Mxc3xbcnchen, Center of Life and Food Sciences Weihenstephan, Chair of Experimental Genetics, Freising, Germany.', 'Germany|Germany|Netherlands|Germany|Germany|Germany|Germany', 2, 'Animals|Basic Helix-Loop-Helix Transcription Factors|Biological Clocks|Computational Biology|Gene Regulatory Networks|Intracellular Signaling Peptides and Proteins|Membrane Proteins|Mice|Models, Genetic|RNA, Messenger|Receptor, Notch1|Signal Transduction', 'Journal Article', '10.1371/journal.pcbi.1003843', '25275459', 'While it is known that a large fraction of vertebrate genes are under the control of a gene regulatory network (GRN) forming a clock with circadian periodicity, shorter period oscillatory genes like the Hairy-enhancer-of split (Hes) genes are discussed mostly in connection with the embryonic process of somitogenesis. They form the core of the somitogenesis-clock, which orchestrates the periodic separation of somites from the presomitic mesoderm (PSM). The formation of sharp boundaries between the blocks of many cells works only when the oscillators in the cells forming the boundary are synchronized. It has been shown experimentally that Delta-Notch (D/N) signaling is responsible for this synchronization. This process has to happen rather fast as a cell experiences at most five oscillations from its birth to its incorporation into a somite. Computer simulations describing synchronized oscillators with classical modes of D/N-interaction have difficulties to achieve synchronization in an appropriate time. One approach to solving this problem of modeling fast synchronization in the PSM was the consideration of cell movements. Here we show that fast synchronization of Hes-type oscillators can be achieved without cell movements by including D/N cis-inhibition, wherein the mutual interaction of DELTA and NOTCH in the same cell leads to a titration of ligand against receptor so that only one sort of molecule prevails. Consequently, the symmetry between sender and receiver is partially broken and one cell becomes preferentially sender or receiver at a given moment, which leads to faster entrainment of oscillators. Although not yet confirmed by experiment, the proposed mechanism of enhanced synchronization of mesenchymal cells in the PSM would be a new distinct developmental mechanism employing D/N cis-inhibition. Consequently, the way in which Delta-Notch signaling was modeled so far should be carefully reconsidered. '),
(86810, 2014, 'plos', 'Synaptic size dynamics as an effectively stochastic process', 'Statman, Adiel|Kaufman, Maya|Minerbi, Amir|Ziv, Noam E|Brenner, Naama', 'Department of Chemical Engineering, Technion, Haifa, Israel, Network Biology Research Laboratories, Lorry Lokey Center for Life Sciences and Engineering, Technion, Haifa, Israel.|Network Biology Research Laboratories, Lorry Lokey Center for Life Sciences and Engineering, Technion, Haifa, Israel, Faculty of Medicine, Technion, Haifa, Israel.|Network Biology Research Laboratories, Lorry Lokey Center for Life Sciences and Engineering, Technion, Haifa, Israel, Faculty of Medicine, Technion, Haifa, Israel.|Network Biology Research Laboratories, Lorry Lokey Center for Life Sciences and Engineering, Technion, Haifa, Israel, Faculty of Medicine, Technion, Haifa, Israel.|Department of Chemical Engineering, Technion, Haifa, Israel, Network Biology Research Laboratories, Lorry Lokey Center for Life Sciences and Engineering, Technion, Haifa, Israel.', 'Israel|Israel|Israel|Israel|Israel', 0, 'Animals|Cells, Cultured|Cerebral Cortex|Computational Biology|Female|Male|Models, Neurological|Neurons|Rats|Stochastic Processes|Synapses', 'Journal Article', '10.1371/journal.pcbi.1003846', '25275505', 'Long-term, repeated measurements of individual synaptic properties have revealed that synapses can undergo significant directed and spontaneous changes over time scales of minutes to weeks. These changes are presumably driven by a large number of activity-dependent and independent molecular processes, yet how these processes integrate to determine the totality of synaptic size remains unknown. Here we propose, as an alternative to detailed, mechanistic descriptions, a statistical approach to synaptic size dynamics. The basic premise of this approach is that the integrated outcome of the myriad of processes that drive synaptic size dynamics are effectively described as a combination of multiplicative and additive processes, both of which are stochastic and taken from distributions parametrically affected by physiological signals. We show that this seemingly simple model, known in probability theory as the Kesten process, can generate rich dynamics which are qualitatively similar to the dynamics of individual glutamatergic synapses recorded in long-term time-lapse experiments in ex-vivo cortical networks. Moreover, we show that this stochastic model, which is insensitive to many of its underlying details, quantitatively captures the distributions of synaptic sizes measured in these experiments, the long-term stability of such distributions and their scaling in response to pharmacological manipulations. Finally, we show that the average kinetics of new postsynaptic density formation measured in such experiments is also faithfully captured by the same model. The model thus provides a useful framework for characterizing synapse size dynamics at steady state, during initial formation of such steady states, and during their convergence to new steady states following perturbations. These findings show the strength of a simple low dimensional statistical model to quantitatively describe synapse size dynamics as the integrated result of many underlying complex processes. '),
(86811, 2014, 'plos', 'A reappraisal of how to build modular, reusable models of biological systems', 'Neal, Maxwell L|Cooling, Michael T|Smith, Lucian P|Thompson, Christopher T|Sauro, Herbert M|Carlson, Brian E|Cook, Daniel L|Gennari, John H', 'Department of Bioengineering, University of Washington, Seattle, Washington, United States of America.|Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand.|Department of Bioengineering, University of Washington, Seattle, Washington, United States of America.|Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America.|Department of Bioengineering, University of Washington, Seattle, Washington, United States of America.|Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, Michigan, United States of America.|Department of Physiology and Biophysics, University of Washington, Seattle, Washington, United States of America.|Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington, United States of America.', 'United States|New Zealand|United States|United States|United States|United States|United States|Unit', 0, 'Metabolic Networks and Pathways|Models, Biological|Software|Systems Biology', 'Journal Article', '10.1371/journal.pcbi.1003849', '25275523', '-'),
(86812, 2014, 'plos', 'Expression profile of the Schistosoma japonicum degradome reveals differential protease expression patterns and potential anti-schistosomal intervention targets', 'Liu, Shuai|Cai, Pengfei|Piao, Xianyu|Hou, Nan|Zhou, Xiaosu|Wu, Chuang|Wang, Heng|Chen, Qijun', 'MOH Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.|MOH Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.|MOH Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.|MOH Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.|MOH Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.|MOH Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.|Department of Microbiology and Parasitology, Institute of Basic Medicine, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.|MOH Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China, Key Laboratory of Zoonosis, Jilin University, Changchun, China.', 'China|China|China|China|China|China|China|China', 29, 'Animals|Cathepsins|Cluster Analysis|Female|Gene Expression Profiling|Helminth Proteins|Humans|Male|Oligonucleotide Array Sequence Analysis|Peptide Hydrolases|Phylogeny|Platyhelminths|Schistosoma japonicum', 'Journal Article', '10.1371/journal.pcbi.1003856', '25275570', 'Blood fluke proteases play pivotal roles in the processes of invasion, nutrition acquisition, immune evasion, and other host-parasite interactions. Hundreds of genes encoding putative proteases have been identified in the recently published schistosome genomes. However, the expression profiles of these proteases in Schistosoma species have not yet been systematically analyzed. We retrieved and culled the redundant protease sequences of Schistosoma japonicum, Schistosoma mansoni, Echinococcus multilocularis, and Clonorchis sinensis from public databases utilizing bioinformatic approaches. The degradomes of the four parasitic organisms and Homo sapiens were then comparatively analyzed. A total of 262 S. japonicum protease sequences were obtained and the expression profiles generated using whole-genome microarray. Four main clusters of protease genes with different expression patterns were identified: proteases up-regulated in hepatic schistosomula and adult worms, egg-specific or predominantly expressed proteases, cercaria-specific or predominantly expressed proteases, and constantly expressed proteases. A subset of protease genes with different expression patterns were further validated using real-time quantitative PCR. The present study represents the most comprehensive analysis of a degradome in Schistosoma species to date. These results provide a firm foundation for future research on the specific function(s) of individual proteases and may help to refine anti-proteolytic strategies in blood flukes. '),
(86813, 2014, 'plos', 'Cell type specific alterations in interchromosomal networks across the cell cycle', 'Fritz, Andrew J|Stojkovic, Branislav|Ding, Hu|Xu, Jinhui|Bhattacharya, Sambit|Berezney, Ronald', 'Department of Biological Sciences, University at Buffalo, State University of New York, Buffalo, New York, United States of America.|Department of Computer Science and Engineering, University at Buffalo, State University of New York, Buffalo, New York, United States of America.|Department of Computer Science and Engineering, University at Buffalo, State University of New York, Buffalo, New York, United States of America.|Department of Computer Science and Engineering, University at Buffalo, State University of New York, Buffalo, New York, United States of America.|Department of Computer Sciences, Fayetteville State University, Fayetteville, North Carolina, United States of America.|Department of Biological Sciences, University at Buffalo, State University of New York, Buffalo, New York, United States of America.', 'United States|United States|United States|United States|United States|United States', 0, 'Cell Cycle|Cell Line|Chromosomes, Human|Humans|In Situ Hybridization, Fluorescence|Models, Genetic|Models, Statistical', 'Journal Article', '10.1371/journal.pcbi.1003857', '25275626', 'The interchromosomal organization of a subset of human chromosomes (#1, 4, 11, 12, 16, 17, and 18) was examined in G1 and S phase of human WI38 lung fibroblast and MCF10A breast epithelial cells. Radial positioning of the chromosome territories (CTs) was independent of gene density, but size dependent. While no changes in radial positioning during the cell cycle were detected, there were stage-specific differences between cell types. Each CT was in close proximity (interaction) with a similar number of other CT except the gene rich CT17 which had significantly more interactions. Furthermore, CT17 was a member of the highest pairwise CT combinations with multiple interactions. Major differences were detected in the pairwise interaction profiles of MCF10A versus WI38 including cell cycle alterations from G1 to S. These alterations in interaction profiles were subdivided into five types: overall increase, overall decrease, switching from 1 to xe2x89xa52 interactions, vice versa, or no change. A global data mining program termed the chromatic median determined the most probable overall association network for the entire subset of CT. This probabilistic interchromosomal network was nearly completely different between the two cell lines. It was also strikingly altered across the cell cycle in MCF10A, but only slightly in WI38. We conclude that CT undergo multiple and preferred interactions with other CT in the nucleus and form preferred -albeit probabilistic- interchromosomal networks. This network of interactions is altered across the cell cycle and between cell types. It is intriguing to consider the relationship of these alterations to the corresponding changes in the gene expression program across the cell cycle and in different cell types. '),
(86814, 2014, 'plos', 'Confidence sharing: an economic strategy for efficient information flows in animal groups', 'Korman, Amos|Greenwald, Efrat|Feinerman, Ofer', 'Laboratoire dInformatique Algorithmique: Fondements et Applications (LIAFA), CNRS & University Paris Diderot, Paris, France.|Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot, Israel.|Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot, Israel.', 'France|Israel|Israel', 12, 'Algorithms|Animals|Communication|Computational Biology|Information Dissemination|Information Theory|Models, Biological', 'Journal Article', '10.1371/journal.pcbi.1003862', '25275649', 'Social animals may share information to obtain a more complete and accurate picture of their surroundings. However, physical constraints on communication limit the flow of information between interacting individuals in a way that can cause an accumulation of errors and deteriorated collective behaviors. Here, we theoretically study a general model of information sharing within animal groups. We take an algorithmic perspective to identify efficient communication schemes that are, nevertheless, economic in terms of communication, memory and individual internal computation. We present a simple and natural algorithm in which each agent compresses all information it has gathered into a single parameter that represents its confidence in its behavior. Confidence is communicated between agents by means of active signaling. We motivate this model by novel and existing empirical evidences for confidence sharing in animal groups. We rigorously show that this algorithm competes extremely well with the best possible algorithm that operates without any computational constraints. We also show that this algorithm is minimal, in the sense that further reduction in communication may significantly reduce performances. Our proofs rely on the Cramér-Rao bound and on our definition of a Fisher Channel Capacity. We use these concepts to quantify information flows within the group which are then used to obtain lower bounds on collective performance. The abstract nature of our model makes it rigorously solvable and its conclusions highly general. Indeed, our results suggest confidence sharing as a central notion in the context of animal communication. '),
(86815, 2014, 'plos', 'Resolving anatomical and functional structure in human brain organization: identifying mesoscale organization in weighted network representations', 'Lohse, Christian|Bassett, Danielle S|Lim, Kelvin O|Carlson, Jean M', 'Kirchhoff Institute for Physics, University of Heidelberg, Heidelberg, Germany.|Department of Physics, University of California, Santa Barbara, California, United States of America, Sage Center for the Study of the Mind, University of California, Santa Barbara, California, United States of America, Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.|Department of Psychiatry, University of Minnesota, Minneapolis, Minnesota, United States of America.|Department of Physics, University of California, Santa Barbara, California, United States of America.', 'Germany|United States|United States|United States', 38, 'Adult|Brain|Computational Biology|Female|Humans|Male|Middle Aged|Models, Neurological|Nerve Net|Neural Pathways|Schizophrenia', 'Journal Article', '10.1371/journal.pcbi.1003712', '25275860', 'Human brain anatomy and function display a combination of modular and hierarchical organization, suggesting the importance of both cohesive structures and variable resolutions in the facilitation of healthy cognitive processes. However, tools to simultaneously probe these features of brain architecture require further development. We propose and apply a set of methods to extract cohesive structures in network representations of brain connectivity using multi-resolution techniques. We employ a combination of soft thresholding, windowed thresholding, and resolution in community detection, that enable us to identify and isolate structures associated with different weights. One such mesoscale structure is bipartivity, which quantifies the extent to which the brain is divided into two partitions with high connectivity between partitions and low connectivity within partitions. A second, complementary mesoscale structure is modularity, which quantifies the extent to which the brain is divided into multiple communities with strong connectivity within each community and weak connectivity between communities. Our methods lead to multi-resolution curves of these network diagnostics over a range of spatial, geometric, and structural scales. For statistical comparison, we contrast our results with those obtained for several benchmark null models. Our work demonstrates that multi-resolution diagnostic curves capture complex organizational profiles in weighted graphs. We apply these methods to the identification of resolution-specific characteristics of healthy weighted graph architecture and altered connectivity profiles in psychiatric disease. '),
(86816, 2014, 'plos', 'A model for sigma factor competition in bacterial cells', 'Mauri, Marco|Klumpp, Stefan', 'Max Planck Institute of Colloids and Interfaces, Potsdam, Germany.|Max Planck Institute of Colloids and Interfaces, Potsdam, Germany.', 'Germany|Germany', 0, 'Bacteria|Bacterial Proteins|Computational Biology|Holoenzymes|Models, Biological|Sigma Factor|Transcription, Genetic', 'Journal Article', '10.1371/journal.pcbi.1003845', '25299042', 'Sigma factors control global switches of the genetic expression program in bacteria. Different sigma factors compete for binding to a limited pool of RNA polymerase (RNAP) core enzymes, providing a mechanism for cross-talk between genes or gene classes via the sharing of expression machinery. To analyze the contribution of sigma factor competition to global changes in gene expression, we develop a theoretical model that describes binding between sigma factors and core RNAP, transcription, non-specific binding to DNA and the modulation of the availability of the molecular components. The model is validated by comparison with in vitro competition experiments, with which excellent agreement is found. Transcription is affected via the modulation of the concentrations of the different types of holoenzymes, so saturated promoters are only weakly affected by sigma factor competition. However, in case of overlapping promoters or promoters recognized by two types of sigma factors, we find that even saturated promoters are strongly affected. Active transcription effectively lowers the affinity between the sigma factor driving it and the core RNAP, resulting in complex cross-talk effects. Sigma factor competition is not strongly affected by non-specific binding of core RNAPs, sigma factors and holoenzymes to DNA. Finally, we analyze the role of increased core RNAP availability upon the shut-down of ribosomal RNA transcription during the stringent response. We find that passive up-regulation of alternative sigma-dependent transcription is not only possible, but also displays hypersensitivity based on the sigma factor competition. Our theoretical analysis thus provides support for a significant role of passive control during that global switch of the gene expression program. '),
(86817, 2014, 'plos', 'Complete mapping of substrate translocation highlights the role of LeuT N-terminal segment in regulating transport cycle', 'Cheng, Mary Hongying|Bahar, Ivet', 'Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, United States of America.|Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, United States of America.', 'United States|United States', 44, 'Bacterial Proteins|Computational Biology|Extracellular Space|Intracellular Space|Ion Transport|Leucine|Models, Biological|Molecular Dynamics Simulation|Protein Conformation|Symporters', 'Journal Article', '10.1371/journal.pcbi.1003879', '25299050', 'Neurotransmitter: sodium symporters (NSSs) regulate neuronal signal transmission by clearing excess neurotransmitters from the synapse, assisted by the co-transport of sodium ions. Extensive structural data have been collected in recent years for several members of the NSS family, which opened the way to structure-based studies for a mechanistic understanding of substrate transport. Leucine transporter (LeuT), a bacterial orthologue, has been broadly adopted as a prototype in these studies. This goal has been elusive, however, due to the complex interplay of global and local events as well as missing structural data on LeuT N-terminal segment. We provide here for the first time a comprehensive description of the molecular events leading to substrate/Na+ release to the postsynaptic cell, including the structure and dynamics of the N-terminal segment using a combination of molecular simulations. Substrate and Na+-release follows an influx of water molecules into the substrate/Na+-binding pocket accompanied by concerted rearrangements of transmembrane helices. A redistribution of salt bridges and cation-xcfx80 interactions at the N-terminal segment prompts substrate release. Significantly, substrate release is followed by the closure of the intracellular gate and a global reconfiguration back to outward-facing state to resume the transport cycle. Two minimally hydrated intermediates, not structurally resolved to date, are identified: one, substrate-bound, stabilized during the passage from outward- to inward-facing state (holo-occluded), and another, substrate-free, along the reverse transition (apo-occluded). '),
(86818, 2014, 'plos', 'Improving contact prediction along three dimensions', 'Feinauer, Christoph|Skwark, Marcin J|Pagnani, Andrea|Aurell, Erik', 'DISAT and Center for Computational Sciences, Politecnico Torino, Torino, Italy.|Department of Information and Computer Science, Aalto University, Aalto, Finland, Aalto Science Institute (AScI), Aalto University, Aalto, Finland.|DISAT and Center for Computational Sciences, Politecnico Torino, Torino, Italy, Human Genetics Foundation-Torino, Molecular Biotechnology Center, Torino, Italy.|Department of Information and Computer Science, Aalto University, Aalto, Finland, Aalto Science Institute (AScI), Aalto University, Aalto, Finland, Department of Computational Biology, Royal Institute of Technology, AlbaNova University Centre, Stockholm, Sweden.', 'Italy|Finland|Italy|Finland', 46, 'Computational Biology|Models, Statistical|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1371/journal.pcbi.1003847', '25299132', 'Correlation patterns in multiple sequence alignments of homologous proteins can be exploited to infer information on the three-dimensional structure of their members. The typical pipeline to address this task, which we in this paper refer to as the three dimensions of contact prediction, is to (i) filter and align the raw sequence data representing the evolutionarily related proteins, (ii) choose a predictive model to describe a sequence alignment, (iii) infer the model parameters and interpret them in terms of structural properties, such as an accurate contact map. We show here that all three dimensions are important for overall prediction success. In particular, we show that it is possible to improve significantly along the second dimension by going beyond the pair-wise Potts models from statistical physics, which have hitherto been the focus of the field. These (simple) extensions are motivated by multiple sequence alignments often containing long stretches of gaps which, as a data feature, would be rather untypical for independent samples drawn from a Potts model. Using a large test set of proteins we show that the combined improvements along the three dimensions are as large as any reported to date. '),
(86819, 2014, 'plos', 'Linear motif-mediated interactions have contributed to the evolution of modularity in complex protein interaction networks', 'Kim, Inhae|Lee, Heetak|Han, Seong Kyu|Kim, Sanguk', 'Department of Life Sciences, Pohang University of Science and Technology, Pohang, Korea.|Department of Life Sciences, Pohang University of Science and Technology, Pohang, Korea.|Department of Life Sciences, Pohang University of Science and Technology, Pohang, Korea.|Department of Life Sciences, Pohang University of Science and Technology, Pohang, Korea, School of Interdisciplinary Bioscience and Bioengineering, Pohang University of Science and Technology, Pohang, Korea.', '-|-|-|-', 0, 'Animals|Biological Evolution|Computational Biology|Humans|Mice|Models, Biological|Protein Interaction Maps|Protein Structure, Tertiary', 'Journal Article', '10.1371/journal.pcbi.1003881', '25299147', 'The modular architecture of protein-protein interaction (PPI) networks is evident in diverse species with a wide range of complexity. However, the molecular components that lead to the evolution of modularity in PPI networks have not been clearly identified. Here, we show that weak domain-linear motif interactions (DLIs) are more likely to connect different biological modules than strong domain-domain interactions (DDIs). This molecular division of labor is essential for the evolution of modularity in the complex PPI networks of diverse eukaryotic species. In particular, DLIs may compensate for the reduction in module boundaries that originate from increased connections between different modules in complex PPI networks. In addition, we show that the identification of biological modules can be greatly improved by including molecular characteristics of protein interactions. Our findings suggest that transient interactions have played a unique role in shaping the architecture and modularity of biological networks over the course of evolution. '),
(86820, 2014, 'plos', 'Exploring the miRNA regulatory network using evolutionary correlations', 'Obermayer, Benedikt|Levine, Erel', 'Systems Biology of Gene Regulatory Elements, Max-Delbrück Center for Molecular Medicine, Berlin, Germany, Department of Physics and Center for Systems Biology, Harvard University, Cambridge, United Kingdom.|Systems Biology of Gene Regulatory Elements, Max-Delbrück Center for Molecular Medicine, Berlin, Germany, Department of Physics and Center for Systems Biology, Harvard University, Cambridge, United Kingdom.', 'Germany|Germany', 6, 'Animals|Base Sequence|Bayes Theorem|Computational Biology|Evolution, Molecular|Gene Regulatory Networks|Humans|Mice|MicroRNAs|Models, Genetic|Molecular Sequence Data|Phylogeny|Sequence Alignment|Zebrafish', 'Journal Article', '10.1371/journal.pcbi.1003860', '25299225', 'Post-transcriptional regulation by miRNAs is a widespread and highly conserved phenomenon in metazoans, with several hundreds to thousands of conserved binding sites for each miRNA, and up to two thirds of all genes under miRNA regulation. At the same time, the effect of miRNA regulation on mRNA and protein levels is usually quite modest and associated phenotypes are often weak or subtle. This has given rise to the notion that the highly interconnected miRNA regulatory network exerts its function less through any individual link and more via collective effects that lead to a functional interdependence of network links. We present a Bayesian framework to quantify conservation of miRNA target sites using vertebrate whole-genome alignments. The increased statistical power of our phylogenetic model allows detection of evolutionary correlation in the conservation patterns of site pairs. Such correlations could result from collective functions in the regulatory network. For instance, co-conservation of target site pairs supports a selective benefit of combinatorial regulation by multiple miRNAs. We find that some miRNA families are under pronounced co-targeting constraints, indicating a high connectivity in the regulatory network, while others appear to function in a more isolated way. By analyzing coordinated targeting of different curated gene sets, we observe distinct evolutionary signatures for protein complexes and signaling pathways that could reflect differences in control strategies. Our method is easily scalable to analyze upcoming larger data sets, and readily adaptable to detect high-level selective constraints between other genomic loci. We thus provide a proof-of-principle method to understand regulatory networks from an evolutionary perspective. '),
(86821, 2014, 'plos', 'Reconstruction of the gene regulatory network involved in the sonic hedgehog pathway with a potential role in early development of the mouse brain', 'Liu, Jinhua|Wang, Xuelong|Li, Juan|Wang, Haifang|Wei, Gang|Yan, Jun', 'CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes of Biological Sciences, Chinese Academy of Sciences, Shanghai, China.|CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes of Biological Sciences, Chinese Academy of Sciences, Shanghai, China.|CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes of Biological Sciences, Chinese Academy of Sciences, Shanghai, China.|CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes of Biological Sciences, Chinese Academy of Sciences, Shanghai, China.|CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes of Biological Sciences, Chinese Academy of Sciences, Shanghai, China.|CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes of Biological Sciences, Chinese Academy of Sciences, Shanghai, China.', 'China|China|China|China|China|China', 10, 'Animals|Binding Sites|Brain|Brain Chemistry|GATA3 Transcription Factor|Gene Regulatory Networks|Hedgehog Proteins|Mice|Organ Specificity|PC12 Cells|Patched Receptors|Patched-1 Receptor|Rats|Receptors, Cell Surface', 'Journal Article', '10.1371/journal.pcbi.1003884', '25299227', 'The Sonic hedgehog (Shh) signaling pathway is crucial for pattern formation in early central nervous system development. By systematically analyzing high-throughput in situ hybridization data of E11.5 mouse brain, we found that Shh and its receptor Ptch1 define two adjacent mutually exclusive gene expression domains: Shh+Ptch1- and Shh-Ptch1+. These two domains are associated respectively with Foxa2 and Gata3, two transcription factors that play key roles in specifying them. Gata3 ChIP-seq experiments and RNA-seq assays on Gata3-knockdown cells revealed that Gata3 up-regulates the genes that are enriched in the Shh-Ptch1+ domain. Important Gata3 targets include Slit2 and Slit3, which are involved in the process of axon guidance, as well as Slc18a1, Th and Qdpr, which are associated with neurotransmitter synthesis and release. By contrast, Foxa2 both up-regulates the genes expressed in the Shh+Ptch1- domain and down-regulates the genes characteristic of the Shh-Ptch1+ domain. From these and other data, we were able to reconstruct a gene regulatory network governing both domains. Our work provides the first genome-wide characterization of the gene regulatory network involved in the Shh pathway that underlies pattern formation in the early mouse brain. '),
(86822, 2014, 'plos', 'The SH2 domain regulates c-Abl kinase activation by a cyclin-like mechanism and remodulation of the hinge motion', 'Dxc3xb6lker, Nicole|Gxc3xb3rna, Maria W|Sutto, Ludovico|Torralba, Antonio S|Superti-Furga, Giulio|Gervasio, Francesco L', 'Structural Biology and Biocomputing Programme, Spanish National Cancer Research Center (CNIO), Madrid, Spain.|CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria.|Institute of Structural and Molecular Biology, University College London, London, United Kingdom.|Structural Biology and Biocomputing Programme, Spanish National Cancer Research Center (CNIO), Madrid, Spain.|CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria.|Institute of Structural and Molecular Biology, University College London, London, United Kingdom, Chemistry Department, University College London (UCL), London, United Kingdom.', 'Spain|Austria|United Kingdom|Spain|Austria|United Kingdom', 17, 'Computer Simulation|Cyclins|HEK293 Cells|Humans|Models, Molecular|Protein Structure, Tertiary|Proto-Oncogene Proteins c-abl|Thermodynamics|src Homology Domains', 'Journal Article', '10.1371/journal.pcbi.1003863', '25299346', 'Regulation of the c-Abl (ABL1) tyrosine kinase is important because of its role in cellular signaling, and its relevance in the leukemiogenic counterpart (BCR-ABL). Both auto-inhibition and full activation of c-Abl are regulated by the interaction of the catalytic domain with the Src Homology 2 (SH2) domain. The mechanism by which this interaction enhances catalysis is not known. We combined computational simulations with mutagenesis and functional analysis to find that the SH2 domain conveys both local and global effects on the dynamics of the catalytic domain. Locally, it regulates the flexibility of the xcexb1C helix in a fashion reminiscent of cyclins in cyclin-dependent kinases, reorienting catalytically important motifs. At a more global level, SH2 binding redirects the hinge motion of the N and C lobes and changes the conformational equilibrium of the activation loop. The complex network of subtle structural shifts that link the SH2 domain with the activation loop and the active site may be partially conserved with other SH2-domain containing kinases and therefore offer additional parameters for the design of conformation-specific inhibitors. ');
