INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(89613, 2019, 'plos', 'Approximate Bayesian estimation of coevolutionary arms races', 'Nuismer, Scott L|Week, Bob', 'Department of Biological Sciences, University of Idaho, Moscow, Idaho, United States of America.|Bioinformatics and Computational Biology, University of Idaho, Moscow, Idaho, United States of America.', 'United States|United States', 1, 'Animals|Bayes Theorem|Biological Coevolution|Biological Evolution|Camellia|Computational Biology|Computer Simulation|Ecosystem|Genetic Variation|Genetics, Population|Likelihood Functions|Phenotype|Selection, Genetic|Weevils', 'Journal Article', '10.1371/journal.pcbi.1006988', '30986245', 'Exaggerated traits involved in species interactions have long captivated the imagination of evolutionary biologists and inspired the durable metaphor of the coevolutionary arms race. Despite decades of research, however, we have only a handful of examples where reciprocal coevolutionary change has been rigorously established as the cause of trait exaggeration. Support for a coevolutionary mechanism remains elusive because we lack generally applicable tools for quantifying the intensity of coevolutionary selection. Here we develop an approximate Bayesian computation (ABC) approach for estimating the intensity of coevolutionary selection using population mean phenotypes of traits mediating interspecific interactions. Our approach relaxes important assumptions of a previous maximum likelihood approach by allowing gene flow among populations, variable abiotic environments, and strong coevolutionary selection. Using simulated data, we show that our ABC method accurately infers the strength of coevolutionary selection if reliable estimates are available for key background parameters and ten or more populations are sampled. Applying our approach to the putative arms race between the plant Camellia japonica and its seed predatory weevil, Curculio camelliae, provides support for a coevolutionary hypothesis but fails to preclude the possibility of unilateral evolution. Comparing independently estimated selection gradients acting on Camellia pericarp thickness with values simulated by our model reveals a correlation between predicted and observed selection gradients of 0.941. The strong agreement between predicted and observed selection gradients validates our method.'),
(89614, 2019, 'plos', 'MAPS: Model-based analysis of long-range chromatin interactions from PLAC-seq and HiChIP experiments', 'Juric, Ivan|Yu, Miao|Abnousi, Armen|Raviram, Ramya|Fang, Rongxin|Zhao, Yuan|Zhang, Yanxiao|Qiu, Yunjiang|Yang, Yuchen|Li, Yun|Ren, Bing|Hu, Ming', 'Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, United States of America.|Ludwig Institute for Cancer Research, La Jolla, CA, United States of America.|Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, United States of America.|Ludwig Institute for Cancer Research, La Jolla, CA, United States of America.|Ludwig Institute for Cancer Research, La Jolla, CA, United States of America.|Ludwig Institute for Cancer Research, La Jolla, CA, United States of America.|Ludwig Institute for Cancer Research, La Jolla, CA, United States of America.|Ludwig Institute for Cancer Research, La Jolla, CA, United States of America.|Departments of Genetics, Biostatistics, and Computer Science, University of North Carolina, Chapel Hill, NC, United States of America.|Departments of Genetics, Biostatistics, and Computer Science, University of North Carolina, Chapel Hill, NC, United States of America.|Ludwig Institute for Cancer Research, La Jolla, CA, United States of America.|Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, United States of America.', 'United States|United States|United States|United States|United States|United States|United States|Un', 8, 'Chromatin|Chromatin Assembly and Disassembly|Chromatin Immunoprecipitation|Chromosome Mapping|Computational Biology|Computer Simulation|Genome|Genomics|High-Throughput Nucleotide Sequencing|Histone Code|Humans|Sequence Analysis, DNA|Software', 'Journal Article', '10.1371/journal.pcbi.1006982', '30986246', 'Hi-C and chromatin immunoprecipitation (ChIP) have been combined to identify long-range chromatin interactions genome-wide at reduced cost and enhanced resolution, but extracting information from the resulting datasets has been challenging. Here we describe a computational method, MAPS, Model-based Analysis of PLAC-seq and HiChIP, to process the data from such experiments and identify long-range chromatin interactions. MAPS adopts a zero-truncated Poisson regression framework to explicitly remove systematic biases in the PLAC-seq and HiChIP datasets, and then uses the normalized chromatin contact frequencies to identify significant chromatin interactions anchored at genomic regions bound by the protein of interest. MAPS shows superior performance over existing software tools in the analysis of chromatin interactions from multiple PLAC-seq and HiChIP datasets centered on different transcriptional factors and histone marks. MAPS is freely available at https://github.com/ijuric/MAPS.'),
(89615, 2019, 'plos', 'Neural control of body-plan axis in regenerating planaria', 'Pietak, Alexis|Bischof, Johanna|LaPalme, Joshua|Morokuma, Junji|Levin, Michael', 'Allen Discovery Center, Tufts University, Medford, Massachusetts, United States of America.|Allen Discovery Center, Tufts University, Medford, Massachusetts, United States of America.|Allen Discovery Center, Tufts University, Medford, Massachusetts, United States of America.|Allen Discovery Center, Tufts University, Medford, Massachusetts, United States of America.|Allen Discovery Center, Tufts University, Medford, Massachusetts, United States of America.', 'United States|United States|United States|United States|United States', 12, 'Animals|Body Patterning|Computational Biology|Markov Chains|Metabolic Networks and Pathways|Models, Biological|Models, Neurological|Nervous System Physiological Phenomena|Planarians|RNA Interference|Regeneration|Signal Transduction', 'Journal Article', '10.1371/journal.pcbi.1006904', '30990801', 'Control of axial polarity during regeneration is a crucial open question. We developed a quantitative model of regenerating planaria, which elucidates self-assembly mechanisms of morphogen gradients required for robust body-plan control. The computational model has been developed to predict the fraction of heteromorphoses expected in a population of regenerating planaria fragments subjected to different treatments, and for fragments originating from different regions along the anterior-posterior and medio-lateral axis. This allows for a direct comparison between computational and experimental regeneration outcomes. Vector transport of morphogens was identified as a fundamental requirement to account for virtually scale-free self-assembly of the morphogen gradients observed in planarian homeostasis and regeneration. The model correctly describes altered body-plans following many known experimental manipulations, and accurately predicts outcomes of novel cutting scenarios, which we tested. We show that the vector transport field coincides with the alignment of nerve axons distributed throughout the planarian tissue, and demonstrate that the head-tail axis is controlled by the net polarity of neurons in a regenerating fragment. This model provides a comprehensive framework for mechanistically understanding fundamental aspects of body-plan regulation, and sheds new light on the role of the nervous system in directing growth and form.'),
(89616, 2019, 'plos', 'Learning and forgetting using reinforced Bayesian change detection', 'Moens, Vincent|Zénon, Alexandre', 'CoAction Lab, Institue of Neuroscience, Université Catholique de Louvain, Bruxelles, Belgium.|CoAction Lab, Institue of Neuroscience, Université Catholique de Louvain, Bruxelles, Belgium.', 'Belgium|Belgium', 3, 'Adaptation, Psychological|Algorithms|Animals|Bayes Theorem|Behavior, Animal|Computational Biology|Computer Simulation|Decision Making|Decision Support Techniques|Humans|Learning|Models, Psychological|Reinforcement (Psychology)|Reward', 'Journal Article', '10.1371/journal.pcbi.1006713', '30995214', 'Agents living in volatile environments must be able to detect changes in contingencies while refraining to adapt to unexpected events that are caused by noise. In Reinforcement Learning (RL) frameworks, this requires learning rates that adapt to past reliability of the model. The observation that behavioural flexibility in animals tends to decrease following prolonged training in stable environment provides experimental evidence for such adaptive learning rates. However, in classical RL models, learning rate is either fixed or scheduled and can thus not adapt dynamically to environmental changes. Here, we propose a new Bayesian learning model, using variational inference, that achieves adaptive change detection by the use of Stabilized Forgetting, updating its current belief based on a mixture of fixed, initial priors and previous posterior beliefs. The weight given to these two sources is optimized alongside the other parameters, allowing the model to adapt dynamically to changes in environmental volatility and to unexpected observations. This approach is used to implement the critic of an actor-critic RL model, while the actor samples the resulting value distributions to choose which action to undertake. We show that our model can emulate different adaptation strategies to contingency changes, depending on its prior assumptions of environmental stability, and that model parameters can be fit to real data with high accuracy. The model also exhibits trade-offs between flexibility and computational costs that mirror those observed in real data. Overall, the proposed method provides a general framework to study learning flexibility and decision making in RL contexts.'),
(89617, 2019, 'plos', 'Predicting synthetic lethal interactions using conserved patterns in protein interaction networks', 'Benstead-Hume, Graeme|Chen, Xiangrong|Hopkins, Suzanna R|Lane, Karen A|Downs, Jessica A|Pearl, Frances M G', 'Bioinformatics Lab, School of Life Sciences, University of Sussex, Falmer, Brighton, United Kingdom.|Bioinformatics Lab, School of Life Sciences, University of Sussex, Falmer, Brighton, United Kingdom.|Division of Cancer Biology, Institute of Cancer Research, Chester Beatty Laboratories, London, United Kingdom.|Division of Cancer Biology, Institute of Cancer Research, Chester Beatty Laboratories, London, United Kingdom.|Division of Cancer Biology, Institute of Cancer Research, Chester Beatty Laboratories, London, United Kingdom.|Bioinformatics Lab, School of Life Sciences, University of Sussex, Falmer, Brighton, United Kingdom.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 3, 'Algorithms|Animals|Artificial Intelligence|Computational Biology|Drug Discovery|Gene Ontology|Genes, Essential|Humans|Models, Biological|Molecular Targeted Therapy|Multigene Family|Neoplasms|Protein Interaction Mapping|Protein Interaction Maps|Synthetic Biology|Synthetic Lethal Mutations|Tumor Suppressor Proteins', 'Journal Article', '10.1371/journal.pcbi.1006888', '30995217', 'In response to a need for improved treatments, a number of promising novel targeted cancer therapies are being developed that exploit human synthetic lethal interactions. This is facilitating personalised medicine strategies in cancers where specific tumour suppressors have become inactivated. Mainly due to the constraints of the experimental procedures, relatively few human synthetic lethal interactions have been identified. Here we describe SLant (Synthetic Lethal analysis via Network topology), a computational systems approach to predicting human synthetic lethal interactions that works by identifying and exploiting conserved patterns in protein interaction network topology both within and across species. SLant out-performs previous attempts to classify human SSL interactions and experimental validation of the models predictions suggests it may provide useful guidance for future SSL screenings and ultimately aid targeted cancer therapy development.'),
(89618, 2019, 'plos', 'Toward a 3D model of phyllotaxis based on a biochemically plausible auxin-transport mechanism', 'Hartmann, Félix P|Barbier de Reuille, Pierre|Kuhlemeier, Cris', 'Institute of Plant Sciences, University of Bern, Bern, Switzerland.|Institute of Plant Sciences, University of Bern, Bern, Switzerland.|Institute of Plant Sciences, University of Bern, Bern, Switzerland.', 'Switzerland|Switzerland|Switzerland', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1006896', '30998674', 'Polar auxin transport lies at the core of many self-organizing phenomena sustaining continuous plant organogenesis. In angiosperms, the shoot apical meristem is a potentially unique system in which the two main modes of auxin-driven patterning-convergence and canalization-co-occur in a coordinated manner and in a fully three-dimensional geometry. In the epidermal layer, convergence points form, from which auxin is canalized towards inner tissue. Each of these two patterning processes has been extensively investigated separately, but the integration of both in the shoot apical meristem remains poorly understood. We present here a first attempt of a three-dimensional model of auxin-driven patterning during phyllotaxis. We base our simulations on a biochemically plausible mechanism of auxin transport proposed by Cieslak et al. (2015) which generates both convergence and canalization patterns. We are able to reproduce most of the dynamics of PIN1 polarization in the meristem, and we explore how the epidermal and inner cell layers act in concert during phyllotaxis. In addition, we discuss the mechanism by which initiating veins connect to the already existing vascular system.'),
(89619, 2019, 'plos', 'Imperfect Bayesian inference in visual perception', 'Stengård, Elina|van den Berg, Ronald', 'Department of Psychology, University of Uppsala, Uppsala, Sweden.|Department of Psychology, University of Uppsala, Uppsala, Sweden.', 'Sweden|Sweden', 4, 'Bayes Theorem|Computational Biology|Decision Making|Decision Theory|Discrimination (Psychology)|Heuristics|Humans|Models, Psychological|Photic Stimulation|Uncertainty|Visual Perception', 'Journal Article', '10.1371/journal.pcbi.1006465', '30998675', 'Optimal Bayesian models have been highly successful in describing human performance on perceptual decision-making tasks, such as cue combination and visual search. However, recent studies have argued that these models are often overly flexible and therefore lack explanatory power. Moreover, there are indications that neural computation is inherently imprecise, which makes it implausible that humans would perform optimally on any non-trivial task. Here, we reconsider human performance on a visual-search task by using an approach that constrains model flexibility and tests for computational imperfections. Subjects performed a target detection task in which targets and distractors were tilted ellipses with orientations drawn from Gaussian distributions with different means. We varied the amount of overlap between these distributions to create multiple levels of external uncertainty. We also varied the level of sensory noise, by testing subjects under both short and unlimited display times. On average, empirical performance-measured as d-fell 18.1% short of optimal performance. We found no evidence that the magnitude of this suboptimality was affected by the level of internal or external uncertainty. The data were well accounted for by a Bayesian model with imperfections in its computations. This imperfect Bayesian model convincingly outperformed the flawless Bayesian model as well as all ten heuristic models that we tested. These results suggest that perception is founded on Bayesian principles, but with suboptimalities in the implementation of these principles. The view of perception as imperfect Bayesian inference can provide a middle ground between traditional Bayesian and anti-Bayesian views.'),
(89620, 2019, 'plos', 'Bet-hedging strategies in expanding populations', 'Villa Martín, Paula|Muñoz, Miguel A|Pigolotti, Simone', 'Biological Complexity Unit, Okinawa Institute for Science and Technology Graduate University, Onna, Okinawa 904-0495, Japan.|Departamento de Electromagnetismo y Física de la Materia and Instituto Carlos I de Física Teórica y Computacional, Universidad de Granada, Granada, Spain.|Biological Complexity Unit, Okinawa Institute for Science and Technology Graduate University, Onna, Okinawa 904-0495, Japan.', 'Japan|Spain|Japan', 7, '-', 'Journal Article', '10.1371/journal.pcbi.1006529', '30998676', 'In ecology, species can mitigate their extinction risks in uncertain environments by diversifying individual phenotypes. This observation is quantified by the theory of bet-hedging, which provides a reason for the degree of phenotypic diversity observed even in clonal populations. Bet-hedging in well-mixed populations is rather well understood. However, many species underwent range expansions during their evolutionary history, and the importance of phenotypic diversity in such scenarios still needs to be understood. In this paper, we develop a theory of bet-hedging for populations colonizing new, unknown environments that fluctuate either in space or time. In this case, we find that bet-hedging is a more favorable strategy than in well-mixed populations. For slow rates of variation, temporal and spatial fluctuations lead to different outcomes. In spatially fluctuating environments, bet-hedging is favored compared to temporally fluctuating environments. In the limit of frequent environmental variation, no opportunity for bet-hedging exists, regardless of the nature of the environmental fluctuations. For the same model, bet-hedging is never an advantageous strategy in the well-mixed case, supporting the view that range expansions strongly promote diversification. These conclusions are robust against stochasticity induced by finite population sizes. Our findings shed light on the importance of phenotypic heterogeneity in range expansions, paving the way to novel approaches to understand how biodiversity emerges and is maintained.'),
(89621, 2019, 'plos', 'SPINDLE: End-to-end learning from EEG/EMG to extrapolate animal sleep scoring across experimental settings, labs and species', 'Miladinovixc4x87, xc4x90orxc4x91e|Muheim, Christine|Bauer, Stefan|Spinnler, Andrea|Noain, Daniela|Bandarabadi, Mojtaba|Gallusser, Benjamin|Krummenacher, Gabriel|Baumann, Christian|Adamantidis, Antoine|Brown, Steven A|Buhmann, Joachim M', 'Department of Computer Science, ETH Zurich, Zxc3xbcrich, Switzerland.|Chronobiology and Sleep Research Group, University of Zurich, Zxc3xbcrich, Switzerland.|Department of Computer Science, ETH Zurich, Zxc3xbcrich, Switzerland.|Chronobiology and Sleep Research Group, University of Zurich, Zxc3xbcrich, Switzerland.|Department of Neurology, University Hospital Zurich, Zxc3xbcrich, Switzerland.|Department of Neurology, University of Bern, Bern, Switzerland.|Department of Computer Science, ETH Zurich, Zxc3xbcrich, Switzerland.|Department of Computer Science, ETH Zurich, Zxc3xbcrich, Switzerland.|Department of Neurology, University Hospital Zurich, Zxc3xbcrich, Switzerland.|Department of Neurology, University of Bern, Bern, Switzerland.|Chronobiology and Sleep Research Group, University of Zurich, Zxc3xbcrich, Switzerland.|Department of Computer Science, ETH Zurich, Zxc3xbcrich, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Swit', 3, '-', 'Journal Article', '10.1371/journal.pcbi.1006968', '30998681', 'Understanding sleep and its perturbation by environment, mutation, or medication remains a central problem in biomedical research. Its examination in animal models rests on brain state analysis via classification of electroencephalographic (EEG) signatures. Traditionally, these states are classified by trained human experts by visual inspection of raw EEG recordings, which is a laborious task prone to inter-individual variability. Recently, machine learning approaches have been developed to automate this process, but their generalization capabilities are often insufficient, especially across animals from different experimental studies. To address this challenge, we crafted a convolutional neural network-based architecture to produce domain invariant predictions, and furthermore integrated a hidden Markov model to constrain state dynamics based upon known sleep physiology. Our method, which we named SPINDLE (Sleep Phase Identification with Neural networks for Domain-invariant LEearning) was validated using data of four animal cohorts from three independent sleep labs, and achieved average agreement rates of 99%, 98%, 93%, and 97% with scorings from five human experts from different labs, essentially duplicating human capability. It generalized across different genetic mutants, surgery procedures, recording setups and even different species, far exceeding state-of-the-art solutions that we tested in parallel on this task. Moreover, we show that these scored data can be processed for downstream analyzes identical to those from human-scored data, in particular by demonstrating the ability to detect mutation-induced sleep alteration. We provide to the scientific community free usage of SPINDLE and benchmarking datasets as an online server at https://sleeplearning.ethz.ch. Our aim is to catalyze high-throughput and well-standardized experimental studies in order to improve our understanding of sleep.'),
(89622, 2019, 'plos', 'Inferring neural circuit structure from datasets of heterogeneous tuning curves', 'Arakaki, Takafumi|Barello, G|Ahmadian, Yashar', 'Institute of Neuroscience, University of Oregon, Eugene, Oregon, USA.|Institute of Neuroscience, University of Oregon, Eugene, Oregon, USA.|Institute of Neuroscience, University of Oregon, Eugene, Oregon, USA.', 'United States|United States|United States', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1006816', '31002660', 'Tuning curves characterizing the response selectivities of biological neurons can exhibit large degrees of irregularity and diversity across neurons. Theoretical network models that feature heterogeneous cell populations or partially random connectivity also give rise to diverse tuning curves. Empirical tuning curve distributions can thus be utilized to make model-based inferences about the statistics of single-cell parameters and network connectivity. However, a general framework for such an inference or fitting procedure is lacking. We address this problem by proposing to view mechanistic network models as implicit generative models whose parameters can be optimized to fit the distribution of experimentally measured tuning curves. A major obstacle for fitting such models is that their likelihood function is not explicitly available or is highly intractable. Recent advances in machine learning provide ways for fitting implicit generative models without the need to evaluate the likelihood and its gradient. Generative Adversarial Networks (GANs) provide one such framework which has been successful in traditional machine learning tasks. We apply this approach in two separate experiments, showing how GANs can be used to fit commonly used mechanistic circuit models in theoretical neuroscience to datasets of tuning curves. This fitting procedure avoids the computationally expensive step of inferring latent variables, such as the biophysical parameters of, or synaptic connections between, particular recorded cells. Instead, it directly learns generalizable model parameters characterizing the networks statistical structure such as the statistics of strength and spatial range of connections between different cell types. Another strength of this approach is that it fits the joint high-dimensional distribution of tuning curves, instead of matching a few summary statistics picked a priori by the user, resulting in a more accurate inference of circuit properties. More generally, this framework opens the door to direct model-based inference of circuit structure from data beyond single-cell tuning curves, such as simultaneous population recordings.'),
(89623, 2019, 'plos', 'Investigating the deregulation of metabolic tasks via Minimum Network Enrichment Analysis (MiNEA) as applied to nonalcoholic fatty liver disease using mouse and human omics data', 'Pandey, Vikash|Hatzimanikatis, Vassily', 'Laboratory of Computational Systems Biotechnology, EPFL, Lausanne, Switzerland.|Laboratory of Computational Systems Biotechnology, EPFL, Lausanne, Switzerland.', 'Switzerland|Switzerland', 2, '-', 'Journal Article', '10.1371/journal.pcbi.1006760', '31002661', 'Nonalcoholic fatty liver disease (NAFLD) is associated with metabolic syndromes spanning a wide spectrum of diseases, from simple steatosis to the more complex nonalcoholic steatohepatitis. To identify the deregulation that occurs in metabolic processes at the molecular level that give rise to these various NAFLD phenotypes, algorithms such as pathway enrichment analysis (PEA) can be used. These analyses require the use of predefined pathway maps, which are composed of reactions describing metabolic processes/subsystems. Unfortunately, the annotation of the metabolic subsystems can differ depending on the pathway database used, making these approaches subject to biases associated with different pathway annotations, and these methods cannot capture the balancing of cofactors and byproducts through the complex nature and interactions of genome-scale metabolic networks (GEMs). Here, we introduce a framework entitled Minimum Network Enrichment Analysis (MiNEA) that is applied to GEMs to generate all possible alternative minimal networks (MiNs), which are possible and feasible networks composed of all the reactions pertaining to various metabolic subsystems that can synthesize a target metabolite. We applied MiNEA to investigate deregulated MiNs and to identify key regulators in different NAFLD phenotypes, such as a fatty liver and liver inflammation, in both humans and mice by integrating condition-specific transcriptomics data from liver samples. We identified key deregulations in the synthesis of cholesteryl esters, cholesterol, and hexadecanoate in both humans and mice, and we found that key regulators of the hydrogen peroxide synthesis network were regulated differently in humans and mice. We further identified which MiNs demonstrate the general and specific characteristics of the different NAFLD phenotypes. MiNEA is applicable to any GEM and to any desired target metabolite, making MiNEA flexible enough to study condition-specific metabolism for any given disease or organism.'),
(89624, 2019, 'plos', 'Fluctuating environments select for short-term phenotypic variation leading to long-term exploration', 'Canino-Koning, Rosangela|Wiser, Michael J|Ofria, Charles', 'Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA.|BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, MI, USA.|Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA.', 'United States|United States|United States', 1, 'Biological Variation, Population|Computational Biology|Computer Simulation|Environment|Evolution, Molecular|Gene-Environment Interaction|Genetic Fitness|Genetics, Population|Models, Genetic|Mutation|Phylogeny|Software', 'Journal Article', '10.1371/journal.pcbi.1006445', '31002665', 'Genetic spaces are often described in terms of fitness landscapes or genotype-to-phenotype maps, where each genetic sequence is associated with phenotypic properties and linked to other genotypes that are a single mutational step away. The positions close to a genotype make up its mutational landscape and, in aggregate, determine the short-term evolutionary potential of a population. Populations with wider ranges of phenotypes in their mutational neighborhood are known to be more evolvable. Likewise, those with fewer phenotypic changes available in their local neighborhoods are more mutationally robust. Here, we examine whether forces that change the distribution of phenotypes available by mutation profoundly alter subsequent evolutionary dynamics. We compare evolved populations of digital organisms that were subject to either static or cyclically-changing environments. For each of these, we examine diversity of the phenotypes that are produced through mutations in order to characterize the local genotype-phenotype map. We demonstrate that environmental change can push populations toward more evolvable mutational landscapes where many alternate phenotypes are available, though purely deleterious mutations remain suppressed. Further, we show that populations in environments with harsh changes switch phenotypes more readily than those in environments with more benign changes. We trace this effect to repeated population bottlenecks in the harsh environments, which result in shorter coalescence times and keep populations in regions of the mutational landscape where the phenotypic shifts in question are more likely to occur. Typically, static environments select solely for immediate optimization, at the expensive of long-term evolvability. In contrast, we show that with changing environments, short-term pressures to deal with immediate challenges can align with long-term pressures to explore a more productive portion of the mutational landscape.'),
(89625, 2018, 'plos', 'CancerInSilico: An R/Bioconductor package for combining mathematical and statistical modeling to simulate time course bulk and single cell gene expression data in cancer', 'Sherman, Thomas D|Kagohara, Luciane T|Cao, Raymon|Cheng, Raymond|Satriano, Matthew|Considine, Michael|Krigsfeld, Gabriel|Ranaweera, Ruchira|Tang, Yong|Jablonski, Sandra A|Stein-OBrien, Genevieve|Gaykalova, Daria A|Weiner, Louis M|Chung, Christine H|Fertig, Elana J', 'Department of Oncology, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD United States of America.|Department of Oncology, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD United States of America.|Department of Oncology, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD United States of America.|Science, Math and Computer Science Magnet Program, Poolesville High School, Poolesville, MD United States of America.|Department of Mathematics, University of Waterloo, Waterloo, Ontario, Canada.|Department of Oncology, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD United States of America.|Department of Oncology, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD United States of America.|Moffitt Cancer Center, Tampa, FL, United States of America.|Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC United States of America.|Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC United States of America.|Department of Oncology, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD United States of America.|Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, MD United States of America.|Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC United States of America.|Moffitt Cancer Center, Tampa, FL, United States of America.|Department of Oncology, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD United States of America.', 'United States|United States|United States|United States|Canada|United States|United States|United St', 0, 'Algorithms|Computational Biology|Computer Simulation|Gene Expression|Humans|Neoplasms', 'Journal Article', '10.1371/journal.pcbi.1006935', '31002670', 'Bioinformatics techniques to analyze time course bulk and single cell omics data are advancing. The absence of a known ground truth of the dynamics of molecular changes challenges benchmarking their performance on real data. Realistic simulated time-course datasets are essential to assess the performance of time course bioinformatics algorithms. We develop an R/Bioconductor package, CancerInSilico, to simulate bulk and single cell transcriptional data from a known ground truth obtained from mathematical models of cellular systems. This package contains a general R infrastructure for running cell-based models and simulating gene expression data based on the model states. We show how to use this package to simulate a gene expression data set and consequently benchmark analysis methods on this data set with a known ground truth. The package is freely available via Bioconductor: http://bioconductor.org/packages/CancerInSilico/.'),
(89626, 2019, 'plos', 'Stability of working memory in continuous attractor networks under the control of short-term plasticity', 'Seeholzer, Alexander|Deger, Moritz|Gerstner, Wulfram', 'School of Computer and Communication Sciences and School of Life Sciences, Brain Mind Institute, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.|School of Computer and Communication Sciences and School of Life Sciences, Brain Mind Institute, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.|School of Computer and Communication Sciences and School of Life Sciences, Brain Mind Institute, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.', 'Switzerland|Switzerland|Switzerland', 10, '-', 'Journal Article', '10.1371/journal.pcbi.1006928', '31002672', 'Continuous attractor models of working-memory store continuous-valued information in continuous state-spaces, but are sensitive to noise processes that degrade memory retention. Short-term synaptic plasticity of recurrent synapses has previously been shown to affect continuous attractor systems: short-term facilitation can stabilize memory retention, while short-term depression possibly increases continuous attractor volatility. Here, we present a comprehensive description of the combined effect of both short-term facilitation and depression on noise-induced memory degradation in one-dimensional continuous attractor models. Our theoretical description, applicable to rate models as well as spiking networks close to a stationary state, accurately describes the slow dynamics of stored memory positions as a combination of two processes: (i) diffusion due to variability caused by spikes, and (ii) drift due to random connectivity and neuronal heterogeneity. We find that facilitation decreases both diffusion and directed drifts, while short-term depression tends to increase both. Using mutual information, we evaluate the combined impact of short-term facilitation and depression on the ability of networks to retain stable working memory. Finally, our theory predicts the sensitivity of continuous working memory to distractor inputs and provides conditions for stability of memory.'),
(89627, 2019, 'plos', 'Finding the balance between model complexity and performance: Using ventral striatal oscillations to classify feeding behavior in rats', 'Dwiel, Lucas L|Khokhar, Jibran Y|Connerney, Michael A|Green, Alan I|Doucette, Wilder T', 'Department of Psychiatry, Geisel School of Medicine at Dartmouth, Dartmouth College, Hanover, New Hampshire, United States of America.|Department of Psychiatry, Geisel School of Medicine at Dartmouth, Dartmouth College, Hanover, New Hampshire, United States of America.|Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire, United States of America.|Department of Psychiatry, Geisel School of Medicine at Dartmouth, Dartmouth College, Hanover, New Hampshire, United States of America.|Department of Psychiatry, Geisel School of Medicine at Dartmouth, Dartmouth College, Hanover, New Hampshire, United States of America.', 'United States|United States|United States|United States|United States', 3, '-', 'Journal Article', '10.1371/journal.pcbi.1006838', '31009448', 'The ventral striatum (VS) is a central node within a distributed network that controls appetitive behavior, and neuromodulation of the VS has demonstrated therapeutic potential for appetitive disorders. Local field potential (LFP) oscillations recorded from deep brain stimulation (DBS) electrodes within the VS are a pragmatic source of neural systems-level information about appetitive behavior that could be used in responsive neuromodulation systems. Here, we recorded LFPs from the bilateral nucleus accumbens core and shell (subregions of the VS) during limited access to palatable food across varying conditions of hunger and food palatability in male rats. We used standard statistical methods (logistic regression) as well as the machine learning algorithm lasso to predict aspects of feeding behavior using VS LFPs. We were able to predict the amount of food eaten, the increase in consumption following food deprivation, and the type of food eaten. Further, we were able to predict whether the initiation of feeding was imminent up to 42.5 seconds before feeding began and classify current behavior as either feeding or not-feeding. In classifying feeding behavior, we found an optimal balance between model complexity and performance with models using 3 LFP features primarily from the alpha and high gamma frequencies. As shown here, unbiased methods can identify systems-level neural activity linked to domains of mental illness with potential application to the development and personalization of novel treatments.'),
(89628, 2019, 'plos', 'Isolating and quantifying the role of developmental noise in generating phenotypic variation', 'Kiskowski, Maria|Glimm, Tilmann|Moreno, Nickolas|Gamble, Tony|Chiari, Ylenia', 'University of South Alabama, Department of Mathematics and Statistics, Mobile, AL, United States of America.|Western Washington University, Department of Mathematics, Bellingham, WA, United States of America.|University of South Alabama, Department of Biology, Mobile, AL, United States of America.|Department of Biological Sciences, Marquette University, Milwaukee, WI, United States of America.|University of South Alabama, Department of Biology, Mobile, AL, United States of America.', 'United States|United States|United States|United States|United States', 1, '-', 'Journal Article', '10.1371/journal.pcbi.1006943', '31009449', 'Genotypic variation, environmental variation, and their interaction may produce variation in the developmental process and cause phenotypic differences among individuals. Developmental noise, which arises during development from stochasticity in cellular and molecular processes when genotype and environment are fixed, also contributes to phenotypic variation. While evolutionary biology has long focused on teasing apart the relative contribution of genes and environment to phenotypic variation, our understanding of the role of developmental noise has lagged due to technical difficulties in directly measuring the contribution of developmental noise. The influence of developmental noise is likely underestimated in studies of phenotypic variation due to intrinsic mechanisms within organisms that stabilize phenotypes and decrease variation. Since we are just beginning to appreciate the extent to which phenotypic variation due to stochasticity is potentially adaptive, the contribution of developmental noise to phenotypic variation must be separated and measured to fully understand its role in evolution. Here, we show that variation in the component of the developmental process corresponding to environmental and genetic factors (here treated together as a unit called the LALI-type) versus the contribution of developmental noise, can be distinguished for leopard gecko (Eublepharis macularius) head color patterns using mathematical simulations that model the role of random variation (corresponding to developmental noise) in patterning. Specifically, we modified the parameters of simulations corresponding to variation in the LALI-type to generate the full range of phenotypic variation in color pattern seen on the heads of eight leopard geckos. We observed that over the range of these parameters, variation in color pattern due to LALI-type variation exceeds that due to developmental noise in the studied gecko cohort. However, the effect of developmental noise on patterning is also substantial. Our approach addresses one of the major goals of evolutionary biology: to quantify the role of stochasticity in shaping phenotypic variation.'),
(89629, 2019, 'plos', 'BOFdat: Generating biomass objective functions for genome-scale metabolic models from experimental data', 'Lachance, Jean-Christophe|Lloyd, Colton J|Monk, Jonathan M|Yang, Laurence|Sastry, Anand V|Seif, Yara|Palsson, Bernhard O|Rodrigue, Sébastien|Feist, Adam M|King, Zachary A|Jacques, Pierre-Étienne', 'Département de Biologie, Université de Sherbrooke, Sherbrooke, Québec, Canada.|Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States of America.|Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States of America.|Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States of America.|Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States of America.|Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States of America.|Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States of America.|Département de Biologie, Université de Sherbrooke, Sherbrooke, Québec, Canada.|Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States of America.|Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States of America.|Département de Biologie, Université de Sherbrooke, Sherbrooke, Québec, Canada.', 'Canada|United States|United States|United States|United States|United States|United States|Canada|Un', 9, '-', 'Journal Article', '10.1371/journal.pcbi.1006971', '31009451', 'Genome-scale metabolic models (GEMs) are mathematically structured knowledge bases of metabolism that provide phenotypic predictions from genomic information. GEM-guided predictions of growth phenotypes rely on the accurate definition of a biomass objective function (BOF) that is designed to include key cellular biomass components such as the major macromolecules (DNA, RNA, proteins), lipids, coenzymes, inorganic ions and species-specific components. Despite its importance, no standardized computational platform is currently available to generate species-specific biomass objective functions in a data-driven, unbiased fashion. To fill this gap in the metabolic modeling software ecosystem, we implemented BOFdat, a Python package for the definition of a Biomass Objective Function from experimental data. BOFdat has a modular implementation that divides the BOF definition process into three independent modules defined here as steps: 1) the coefficients for major macromolecules are calculated, 2) coenzymes and inorganic ions are identified and their stoichiometric coefficients estimated, 3) the remaining species-specific metabolic biomass precursors are algorithmically extracted in an unbiased way from experimental data. We used BOFdat to reconstruct the BOF of the Escherichia coli model iML1515, a gold standard in the field. The BOF generated by BOFdat resulted in the most concordant biomass composition, growth rate, and gene essentiality prediction accuracy when compared to other methods. Installation instructions for BOFdat are available in the documentation and the source code is available on GitHub (https://github.com/jclachance/BOFdat).'),
(89630, 2019, 'plos', 'A Bayesian model of acquisition and clearance of bacterial colonization incorporating within-host variation', 'Järvenpää, Marko|Sater, Mohamad R Abdul|Lagoudas, Georgia K|Blainey, Paul C|Miller, Loren G|McKinnell, James A|Huang, Susan S|Grad, Yonatan H|Marttinen, Pekka', 'Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Espoo, Finland.|Department of Immunology and Infectious Diseases, Harvard TH Chan School of Public Health, Boston, MA, USA.|Department of Biological Engineering, MIT, Cambridge, MA, USA.|Department of Biological Engineering, MIT, Cambridge, MA, USA.|Infectious Disease Clinical Outcomes Research Unit, Division of Infectious Diseases, LA Biomed Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA.|Infectious Disease Clinical Outcomes Research Unit, Division of Infectious Diseases, LA Biomed Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA.|Division of Infectious Diseases and Health Policy Research Institute, University of California, Irvine School of Medicine, Irvine, CA, USA.|Department of Immunology and Infectious Diseases, Harvard TH Chan School of Public Health, Boston, MA, USA.|Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Espoo, Finland.', 'Finland|United States|United States|United States|United States|United States|United States|United States|Finland', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1006534', '31009452', 'Bacterial populations that colonize a host can play important roles in host health, including serving as a reservoir that transmits to other hosts and from which invasive strains emerge, thus emphasizing the importance of understanding rates of acquisition and clearance of colonizing populations. Studies of colonization dynamics have been based on assessment of whether serial samples represent a single population or distinct colonization events. With the use of whole genome sequencing to determine genetic distance between isolates, a common solution to estimate acquisition and clearance rates has been to assume a fixed genetic distance threshold below which isolates are considered to represent the same strain. However, this approach is often inadequate to account for the diversity of the underlying within-host evolving population, the time intervals between consecutive measurements, and the uncertainty in the estimated acquisition and clearance rates. Here, we present a fully Bayesian model that provides probabilities of whether two strains should be considered the same, allowing us to determine bacterial clearance and acquisition from genomes sampled over time. Our method explicitly models the within-host variation using population genetic simulation, and the inference is done using a combination of Approximate Bayesian Computation (ABC) and Markov Chain Monte Carlo (MCMC). We validate the method with multiple carefully conducted simulations and demonstrate its use in practice by analyzing a collection of methicillin resistant Staphylococcus aureus (MRSA) isolates from a large recently completed longitudinal clinical study. An R-code implementation of the method is freely available at: https://github.com/mjarvenpaa/bacterial-colonization-model.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(89631, 2019, 'plos', 'Analyzing the symmetrical arrangement of structural repeats in proteins with CE-Symm', 'Bliven, Spencer E|Lafita, Aleix|Rose, Peter W|Capitani, Guido|Prlixc4x87, Andreas|Bourne, Philip E', 'Laboratory of Biomolecular Research, Paul Scherrer Institute, Villigen, Switzerland.|Laboratory of Biomolecular Research, Paul Scherrer Institute, Villigen, Switzerland.|RCSB Protein Data Bank, San Diego Supercomputing Center, University of California San Diego, La Jolla, California, United States of America.|Laboratory of Biomolecular Research, Paul Scherrer Institute, Villigen, Switzerland.|RCSB Protein Data Bank, San Diego Supercomputing Center, University of California San Diego, La Jolla, California, United States of America.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, United States of America.', 'Switzerland|Switzerland|United States|Switzerland|United States|United States', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1006842', '31009453', 'Many proteins fold into highly regular and repetitive three dimensional structures. The analysis of structural patterns and repeated elements is fundamental to understand protein function and evolution. We present recent improvements to the CE-Symm tool for systematically detecting and analyzing the internal symmetry and structural repeats in proteins. In addition to the accurate detection of internal symmetry, the tool is now capable of i) reporting the type of symmetry, ii) identifying the smallest repeating unit, iii) describing the arrangement of repeats with transformation operations and symmetry axes, and iv) comparing the similarity of all the internal repeats at the residue level. CE-Symm 2.0 helps the user investigate proteins with a robust and intuitive sequence-to-structure analysis, with many applications in protein classification, functional annotation and evolutionary studies. We describe the algorithmic extensions of the method and demonstrate its applications to the study of interesting cases of protein evolution.'),
(89632, 2019, 'plos', 'Weak electric fields promote resonance in neuronal spiking activity: Analytical results from two-compartment cell and network models', 'Ladenbauer, Josef|Obermayer, Klaus', 'Laboratoire de Neurosciences Cognitives et Computationnelles, École Normale Supérieure - PSL Research University, Paris, France.|Department of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, Germany.', 'France|Germany', 0, 'Action Potentials|Animals|Cerebral Cortex|Computational Biology|Dendrites|Electric Stimulation|Humans|Interneurons|Models, Neurological|Neurons|Pyramidal Cells', 'Journal Article', '10.1371/journal.pcbi.1006974', '31009455', 'Transcranial brain stimulation and evidence of ephaptic coupling have sparked strong interests in understanding the effects of weak electric fields on the dynamics of neuronal populations. While their influence on the subthreshold membrane voltage can be biophysically well explained using spatially extended neuron models, mechanistic analyses of neuronal spiking and network activity have remained a methodological challenge. More generally, this challenge applies to phenomena for which single-compartment (point) neuron models are oversimplified. Here we employ a pyramidal neuron model that comprises two compartments, allowing to distinguish basal-somatic from apical dendritic inputs and accounting for an extracellular field in a biophysically minimalistic way. Using an analytical approach we fit its parameters to reproduce the response properties of a canonical, spatial model neuron and dissect the stochastic spiking dynamics of single cells and large networks. We show that oscillatory weak fields effectively mimic anti-correlated inputs at the soma and dendrite and strongly modulate neuronal spiking activity in a rather narrow frequency band. This effect carries over to coupled populations of pyramidal cells and inhibitory interneurons, boosting network-induced resonance in the beta and gamma frequency bands. Our work contributes a useful theoretical framework for mechanistic analyses of population dynamics going beyond point neuron models, and provides insights on modulation effects of extracellular fields due to the morphology of pyramidal cells.'),
(89633, 2019, 'plos', 'Neural crest streaming as an emergent property of tissue interactions during morphogenesis', 'Szabó, András|Theveneau, Eric|Turan, Melissa|Mayor, Roberto', 'Research Department of Cell and Developmental Biology, University College London, London, United Kingdom.|Research Department of Cell and Developmental Biology, University College London, London, United Kingdom.|Research Department of Cell and Developmental Biology, University College London, London, United Kingdom.|Research Department of Cell and Developmental Biology, University College London, London, United Kingdom.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 4, '-', 'Journal Article', '10.1371/journal.pcbi.1007002', '31009457', 'A fundamental question in embryo morphogenesis is how a complex pattern is established in seemingly uniform tissues. During vertebrate development, neural crest cells differentiate as a continuous mass of tissue along the neural tube and subsequently split into spatially distinct migratory streams to invade the rest of the embryo. How these streams are established is not well understood. Inhibitory signals surrounding the migratory streams led to the idea that position and size of streams are determined by a pre-pattern of such signals. While clear evidence for a pre-pattern in the cranial region is still lacking, all computational models of neural crest migration published so far have assumed a pre-pattern of negative signals that channel the neural crest into streams. Here we test the hypothesis that instead of following a pre-existing pattern, the cranial neural crest creates their own migratory pathway by interacting with the surrounding tissue. By combining theoretical modeling with experimentation, we show that streams emerge from the interaction of the hindbrain neural crest and the neighboring epibranchial placodal tissues, without the need for a pre-existing guidance cue. Our model suggests that the initial collective neural crest invasion is based on short-range repulsion and asymmetric attraction between neighboring tissues. The model provides a coherent explanation for the formation of cranial neural crest streams in concert with previously reported findings and our new in vivo observations. Our results point to a general mechanism of inducing collective invasion patterns.'),
(89634, 2019, 'plos', 'A benchmark-driven approach to reconstruct metabolic networks for studying cancer metabolism', 'Jamialahmadi, Oveis|Hashemi-Najafabadi, Sameereh|Motamedian, Ehsan|Romeo, Stefano|Bagheri, Fatemeh', 'Department of Biotechnology, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran.|Department of Biomedical Engineering, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran.|Department of Biotechnology, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran.|Department of Molecular and Clinical Medicine, University of Gothenburg, Gothenburg, Sweden.|Department of Biotechnology, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran.', '-|-|-|Sweden|-', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1006936', '31009458', 'Genome-scale metabolic modeling has emerged as a promising way to study the metabolic alterations underlying cancer by identifying novel drug targets and biomarkers. To date, several computational methods have been developed to integrate high-throughput data with existing human metabolic reconstructions to generate context-specific cancer metabolic models. Despite a number of studies focusing on benchmarking the context-specific algorithms, no quantitative assessment has been made to compare the predictive performance of these methods. Here, we integrated various and different datasets used in previous works to design a quantitative platform to examine functional and consistency performance of several existing genome-scale cancer modeling approaches. Next, we used the results obtained here to develop a method for the reconstruction of context-specific metabolic models. We then compared the predictive power and consistency of networks generated by our method to other computational approaches investigated here. Our results showed a satisfactory performance of the developed method in most of the benchmarks. This benchmarking platform is of particular use in algorithm selection and assessing the performance of newly developed algorithms. More importantly, it can serve as guidelines for designing and developing new methods focusing on weaknesses and strengths of existing algorithms.'),
(89635, 2019, 'plos', 'Temporal pattern separation in hippocampal neurons through multiplexed neural codes', 'Madar, Antoine D|Ewell, Laura A|Jones, Mathew V', 'Department of Neuroscience, University of Wisconsin-Madison, WI, United States of America.|Department of Neuroscience, University of Wisconsin-Madison, WI, United States of America.|Department of Neuroscience, University of Wisconsin-Madison, WI, United States of America.', 'United States|United States|United States', 0, 'Action Potentials|Animals|Computational Biology|Dentate Gyrus|Hippocampus|Male|Memory, Episodic|Mice|Mice, Inbred C57BL|Models, Neurological|Neurons|Pyramidal Cells', 'Journal Article', '10.1371/journal.pcbi.1006932', '31009459', 'Pattern separation is a central concept in current theories of episodic memory: this computation is thought to support our ability to avoid confusion between similar memories by transforming similar cortical input patterns of neural activity into dissimilar output patterns before their long-term storage in the hippocampus. Because there are many ways one can define patterns of neuronal activity and the similarity between them, pattern separation could in theory be achieved through multiple coding strategies. Using our recently developed assay that evaluates pattern separation in isolated tissue by controlling and recording the input and output spike trains of single hippocampal neurons, we explored neural codes through which pattern separation is performed by systematic testing of different similarity metrics and various time resolutions. We discovered that granule cells, the projection neurons of the dentate gyrus, can exhibit both pattern separation and its opposite computation, pattern convergence, depending on the neural code considered and the statistical structure of the input patterns. Pattern separation is favored when inputs are highly similar, and is achieved through spike time reorganization at short time scales (< 100 ms) as well as through variations in firing rate and burstiness at longer time scales. These multiplexed forms of pattern separation are network phenomena, notably controlled by GABAergic inhibition, that involve many celltypes with input-output transformations that participate in pattern separation to different extents and with complementary neural codes: a rate code for dentate fast-spiking interneurons, a burstiness code for hilar mossy cells and a synchrony code at long time scales for CA3 pyramidal cells. Therefore, the isolated hippocampal circuit itself is capable of performing temporal pattern separation using multiplexed coding strategies that might be essential to optimally disambiguate multimodal mnemonic representations.'),
(89636, 2019, 'plos', 'Synchronization dependent on spatial structures of a mesoscopic whole-brain network', 'Choi, Hannah|Mihalas, Stefan', 'Department of Applied Mathematics, University of Washington, Seattle, WA, USA.|Department of Applied Mathematics, University of Washington, Seattle, WA, USA.', 'United States|United States', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1006978', '31013267', 'Complex structural connectivity of the mammalian brain is believed to underlie the versatility of neural computations. Many previous studies have investigated properties of small subsystems or coarse connectivity among large brain regions that are often binarized and lack spatial information. Yet little is known about spatial embedding of the detailed whole-brain connectivity and its functional implications. We focus on closing this gap by analyzing how spatially-constrained neural connectivity shapes synchronization of the brain dynamics based on a system of coupled phase oscillators on a mammalian whole-brain network at the mesoscopic level. This was made possible by the recent development of the Allen Mouse Brain Connectivity Atlas constructed from viral tracing experiments together with a new mapping algorithm. We investigated whether the network can be compactly represented based on the spatial dependence of the network topology. We found that the connectivity has a significant spatial dependence, with spatially close brain regions strongly connected and distal regions weakly connected, following a power law. However, there are a number of residuals above the power-law fit, indicating connections between brain regions that are stronger than predicted by the power-law relationship. By measuring the sensitivity of the network order parameter, we show how these strong connections dispersed across multiple spatial scales of the network promote rapid transitions between partial synchronization and more global synchronization as the global coupling coefficient changes. We further demonstrate the significance of the locations of the residual connections, suggesting a possible link between the network complexity and the brains exceptional ability to swiftly switch computational states depending on stimulus and behavioral context.'),
(89637, 2019, 'plos', 'Deep convolutional models improve predictions of macaque V1 responses to natural images', 'Cadena, Santiago A|Denfield, George H|Walker, Edgar Y|Gatys, Leon A|Tolias, Andreas S|Bethge, Matthias|Ecker, Alexander S', 'Centre for Integrative Neuroscience and Institute for Theoretical Physics, University of Tübingen, Tübingen, Germany.|Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, Texas, United States of America.|Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, Texas, United States of America.|Centre for Integrative Neuroscience and Institute for Theoretical Physics, University of Tübingen, Tübingen, Germany.|Bernstein Center for Computational Neuroscience, Tübingen, Germany.|Centre for Integrative Neuroscience and Institute for Theoretical Physics, University of Tübingen, Tübingen, Germany.|Centre for Integrative Neuroscience and Institute for Theoretical Physics, University of Tübingen, Tübingen, Germany.', 'Germany|United States|United States|Germany|Germany|Germany|Germany', 3, '-', 'Journal Article', '10.1371/journal.pcbi.1006897', '31013278', 'Despite great efforts over several decades, our best models of primary visual cortex (V1) still predict spiking activity quite poorly when probed with natural stimuli, highlighting our limited understanding of the nonlinear computations in V1. Recently, two approaches based on deep learning have emerged for modeling these nonlinear computations: transfer learning from artificial neural networks trained on object recognition and data-driven convolutional neural network models trained end-to-end on large populations of neurons. Here, we test the ability of both approaches to predict spiking activity in response to natural images in V1 of awake monkeys. We found that the transfer learning approach performed similarly well to the data-driven approach and both outperformed classical linear-nonlinear and wavelet-based feature representations that build on existing theories of V1. Notably, transfer learning using a pre-trained feature space required substantially less experimental time to achieve the same performance. In conclusion, multi-layer convolutional neural networks (CNNs) set the new state of the art for predicting neural responses to natural images in primate V1 and deep features learned for object recognition are better explanations for V1 computation than all previous filter bank theories. This finding strengthens the necessity of V1 models that are multiple nonlinearities away from the image domain and it supports the idea of explaining early visual cortex based on high-level functional goals.'),
(89638, 2019, 'plos', 'Revealing evolutionary constraints on proteins through sequence analysis', 'Wang, Shou-Wen|Bitbol, Anne-Florence|Wingreen, Ned S', 'Department of Engineering Physics, Tsinghua University, Beijing, China.|Sorbonne Université, CNRS, Laboratoire Jean Perrin (UMR 8237), F-75005 Paris, France.|Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America.', 'China|France|Jersey', 6, '-', 'Journal Article', '10.1371/journal.pcbi.1007010', '31017888', 'Statistical analysis of alignments of large numbers of protein sequences has revealed sectors of collectively coevolving amino acids in several protein families. Here, we show that selection acting on any functional property of a protein, represented by an additive trait, can give rise to such a sector. As an illustration of a selected trait, we consider the elastic energy of an important conformational change within an elastic network model, and we show that selection acting on this energy leads to correlations among residues. For this concrete example and more generally, we demonstrate that the main signature of functional sectors lies in the small-eigenvalue modes of the covariance matrix of the selected sequences. However, secondary signatures of these functional sectors also exist in the extensively-studied large-eigenvalue modes. Our simple, general model leads us to propose a principled method to identify functional sectors, along with the magnitudes of mutational effects, from sequence data. We further demonstrate the robustness of these functional sectors to various forms of selection, and the robustness of our approach to the identification of multiple selected traits.'),
(89639, 2019, 'plos', 'A kinetic model for Brain-Derived Neurotrophic Factor mediated spike timing-dependent LTP', 'Solinas, Sergio M G|Edelmann, Elke|Leßmann, Volkmar|Migliore, Michele', 'Institute of Biophysics, National Research Council, Palermo, Italy.|Institute of Physiology, Otto-von-Guericke-University, Medical Faculty, Magdeburg, Germany.|Institute of Physiology, Otto-von-Guericke-University, Medical Faculty, Magdeburg, Germany.|Institute of Biophysics, National Research Council, Palermo, Italy.', 'Italy|Germany|Germany|Italy', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1006975', '31017891', 'Across the mammalian nervous system, neurotrophins control synaptic plasticity, neuromodulation, and neuronal growth. The neurotrophin Brain-Derived Neurotrophic Factor (BDNF) is known to promote structural and functional synaptic plasticity in the hippocampus, the cerebral cortex, and many other brain areas. In recent years, a wealth of data has been accumulated revealing the paramount importance of BDNF for neuronal function. BDNF signaling gives rise to multiple complex signaling pathways that mediate neuronal survival and differentiation during development, and formation of new memories. These different roles of BDNF for neuronal function have essential consequences if BDNF signaling in the brain is reduced. Thus, BDNF knock-out mice or mice that are deficient in BDNF receptor signaling via TrkB and p75 receptors show deficits in neuronal development, synaptic plasticity, and memory formation. Accordingly, BDNF signaling dysfunctions are associated with many neurological and neurodegenerative conditions including Alzheimers and Huntingtons disease. However, despite the widespread implications of BDNF-dependent signaling in synaptic plasticity in healthy and pathological conditions, the interplay of the involved different biochemical pathways at the synaptic level remained mostly unknown. In this paper, we investigated the role of BDNF/TrkB signaling in spike-timing dependent plasticity (STDP) in rodent hippocampus CA1 pyramidal cells, by implementing the first subcellular model of BDNF regulated, spike timing-dependent long-term potentiation (t-LTP). The model is based on previously published experimental findings on STDP and accounts for the observed magnitude, time course, stimulation pattern and BDNF-dependence of t-LTP. It allows interpreting the main experimental findings concerning specific biomolecular processes, and it can be expanded to take into account more detailed biochemical reactions. The results point out a few predictions on how to enhance LTP induction in such a way to rescue or improve cognitive functions under pathological conditions.'),
(89640, 2019, 'plos', 'Hydrodynamics of transient cell-cell contact: The role of membrane permeability and active protrusion length', 'Liu, Kai|Chu, Brian|Newby, Jay|Read, Elizabeth L|Lowengrub, John|Allard, Jun', 'Department of Mathematics, University of California Irvine, Irvine, California, United States of America.|Department of Chemical Engineering and Materials Science, University of California Irvine, Irvine, California, United States of America.|Department of Mathematics, University of Alberta, Edmonton, Alberta, Canada.|Department of Chemical Engineering and Materials Science, University of California Irvine, Irvine, California, United States of America.|Department of Mathematics, University of California Irvine, Irvine, California, United States of America.|Department of Mathematics, University of California Irvine, Irvine, California, United States of America.', 'United States|United States|Canada|United States|United States|United States', 3, '-', 'Journal Article', '10.1371/journal.pcbi.1006352', '31022168', 'In many biological settings, two or more cells come into physical contact to form a cell-cell interface. In some cases, the cell-cell contact must be transient, forming on timescales of seconds. One example is offered by the T cell, an immune cell which must attach to the surface of other cells in order to decipher information about disease. The aspect ratio of these interfaces (tens of nanometers thick and tens of micrometers in diameter) puts them into the thin-layer limit, or lubrication limit, of fluid dynamics. A key question is how the receptors and ligands on opposing cells come into contact. What are the relative roles of thermal undulations of the plasma membrane and deterministic forces from active filopodia? We use a computational fluid dynamics algorithm capable of simulating 10-nanometer-scale fluid-structure interactions with thermal fluctuations up to seconds- and microns-scales. We use this to simulate two opposing membranes, variously including thermal fluctuations, active forces, and membrane permeability. In some regimes dominated by thermal fluctuations, proximity is a rare event, which we capture by computing mean first-passage times using a Weighted Ensemble rare-event computational method. Our results demonstrate a parameter regime in which the time it takes for an active force to drive local contact actually increases if the cells are being held closer together (e.g., by nonspecific adhesion), a phenomenon we attribute to the thin-layer effect. This leads to an optimal initial cell-cell separation for fastest receptor-ligand binding, which could have relevance for the role of cellular protrusions like microvilli. We reproduce a previous experimental observation that fluctuation spatial scales are largely unaffected, but timescales are dramatically slowed, by the thin-layer effect. We also find that membrane permeability would need to be above physiological levels to abrogate the thin-layer effect.'),
(89641, 2019, 'plos', 'Details in the evaluation of circular RNA detection tools: Reply to Chen and Chuang', 'Zeng, Xiangxiang|Lin, Wei|Guo, Maozu|Zou, Quan', 'Shenzhen Research Institute of Xiamen University, Shenzhen, China.|School of Information Science and Engineering, Xiamen University, Xiamen, China.|School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China.|Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.', 'China|China|China|China', 1, '-', 'Journal Article', '10.1371/journal.pcbi.1006916', '31022173', '-'),
(89642, 2019, 'plos', 'Ten simple rules for providing a meaningful research experience to high school students', 'Lescak, Emily A|ONeill, Kate M|Collu, Giovanna M|Das, Subhamoy', 'Department of Biological Sciences, University of Alaska Anchorage, Anchorage, Alaska, United States of America.|Institute for Physical Science and Technology, University of Maryland, College Park, Maryland, United States of America.|Department of Cell, Developmental and Regenerative Biology and Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America.|Department of Neurosurgery, Stanford School of Medicine, Stanford, California, United States of America.', 'United States|United States|United States|United States', 0, 'Adolescent|Computational Biology|Humans|Mentoring|Research|Science|Students', 'Editorial', '10.1371/journal.pcbi.1006920', '31022174', '-'),
(89643, 2019, 'plos', 'TASmania: A bacterial Toxin-Antitoxin Systems database', 'Akarsu, Hatice|Bordes, Patricia|Mansour, Moise|Bigot, Donna-Joe|Genevaux, Pierre|Falquet, Laurent', 'Department of Biology, University of Fribourg & Swiss Institute of Bioinformatics, Fribourg, Switzerland.|Laboratoire de Microbiologie et de Génétique Moléculaires (LMGM), Centre de Biologie Intégrative (CBI), Université de Toulouse, CNRS, UPS, Toulouse, France.|Laboratoire de Microbiologie et de Génétique Moléculaires (LMGM), Centre de Biologie Intégrative (CBI), Université de Toulouse, CNRS, UPS, Toulouse, France.|Laboratoire de Microbiologie et de Génétique Moléculaires (LMGM), Centre de Biologie Intégrative (CBI), Université de Toulouse, CNRS, UPS, Toulouse, France.|Laboratoire de Microbiologie et de Génétique Moléculaires (LMGM), Centre de Biologie Intégrative (CBI), Université de Toulouse, CNRS, UPS, Toulouse, France.|Department of Biology, University of Fribourg & Swiss Institute of Bioinformatics, Fribourg, Switzerland.', 'Switzerland|France|France|France|France|Switzerland', 12, '-', 'Journal Article', '10.1371/journal.pcbi.1006946', '31022176', 'Bacterial Toxin-Antitoxin systems (TAS) are involved in key biological functions including plasmid maintenance, defense against phages, persistence and virulence. They are found in nearly all phyla and classified into 6 different types based on the mode of inactivation of the toxin, with the type II TAS being the best characterized so far. We have herein developed a new in silico discovery pipeline named TASmania, which mines the >41K assemblies of the EnsemblBacteria database for known and uncharacterized protein components of type I to IV TAS loci. Our pipeline annotates the proteins based on a list of curated HMMs, which leads to >2.106 loci candidates, including orphan toxins and antitoxins, and organises the candidates in pseudo-operon structures in order to identify new TAS candidates based on a guilt-by-association strategy. In addition, we classify the two-component TAS with an unsupervised method on top of the pseudo-operon (pop) gene structures, leading to 1567 popTA models offering a more robust classification of the TAs families. These results give valuable clues in understanding the toxin/antitoxin modular structures and the TAS phylum specificities. Preliminary in vivo work confirmed six putative new hits in Mycobacterium tuberculosis as promising candidates. The TASmania database is available on the following server https://shiny.bioinformatics.unibe.ch/apps/tasmania/.'),
(89644, 2019, 'plos', 'Chemical features mining provides new descriptive structure-odor relationships', 'Licon, Carmen C|Bosc, Guillaume|Sabri, Mohammed|Mantel, Marylou|Fournel, Arnaud|Bushdid, Caroline|Golebiowski, Jerome|Robardet, Celine|Plantevit, Marc|Kaytoue, Mehdi|Bensafi, Moustafa', 'Lyon Neuroscience Research Center, University Lyon, CNRS UMR5292, France.|INSA Lyon, CNRS, LIRIS UMR5205, France.|Lyon Neuroscience Research Center, University Lyon, CNRS UMR5292, France.|Lyon Neuroscience Research Center, University Lyon, CNRS UMR5292, France.|Lyon Neuroscience Research Center, University Lyon, CNRS UMR5292, France.|Institute of Chemistry of Nice, UMR CNRS 7272, Université Côte dAzur, Nice, France.|Institute of Chemistry of Nice, UMR CNRS 7272, Université Côte dAzur, Nice, France.|INSA Lyon, CNRS, LIRIS UMR5205, France.|Université Lyon 1, CNRS, LIRIS UMR5205, France.|INSA Lyon, CNRS, LIRIS UMR5205, France.|Lyon Neuroscience Research Center, University Lyon, CNRS UMR5292, France.', 'France|France|France|France|France|France|France|France|France|France|France', 3, '-', 'Journal Article', '10.1371/journal.pcbi.1006945', '31022180', 'An important goal in researching the biology of olfaction is to link the perception of smells to the chemistry of odorants. In other words, why do some odorants smell like fruits and others like flowers? While the so-called stimulus-percept issue was resolved in the field of color vision some time ago, the relationship between the chemistry and psycho-biology of odors remains unclear up to the present day. Although a series of investigations have demonstrated that this relationship exists, the descriptive and explicative aspects of the proposed models that are currently in use require greater sophistication. One reason for this is that the algorithms of current models do not consistently consider the possibility that multiple chemical rules can describe a single quality despite the fact that this is the case in reality, whereby two very different molecules can evoke a similar odor. Moreover, the available datasets are often large and heterogeneous, thus rendering the generation of multiple rules without any use of a computational approach overly complex. We considered these two issues in the present paper. First, we built a new database containing 1689 odorants characterized by physicochemical properties and olfactory qualities. Second, we developed a computational method based on a subgroup discovery algorithm that discriminated perceptual qualities of smells on the basis of physicochemical properties. Third, we ran a series of experiments on 74 distinct olfactory qualities and showed that the generation and validation of rules linking chemistry to odor perception was possible. Taken together, our findings provide significant new insights into the relationship between stimulus and percept in olfaction. In addition, by automatically extracting new knowledge linking chemistry of odorants and psychology of smells, our results provide a new computational framework of analysis enabling scientists in the field to test original hypotheses using descriptive or predictive modeling.'),
(89645, 2019, 'plos', 'Leveraging heterogeneity for neural computation with fading memory in layer 2/3 cortical microcircuits', 'Duarte, Renato|Morrison, Abigail', 'Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6) and JARA Institute Brain Structure-Function Relationships (JBI-1 / INM-10), Jülich Research Centre, Jülich, Germany.|Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6) and JARA Institute Brain Structure-Function Relationships (JBI-1 / INM-10), Jülich Research Centre, Jülich, Germany.', 'Germany|Germany', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1006781', '31022182', 'Complexity and heterogeneity are intrinsic to neurobiological systems, manifest in every process, at every scale, and are inextricably linked to the systems emergent collective behaviours and function. However, the majority of studies addressing the dynamics and computational properties of biologically inspired cortical microcircuits tend to assume (often for the sake of analytical tractability) a great degree of homogeneity in both neuronal and synaptic/connectivity parameters. While simplification and reductionism are necessary to understand the brains functional principles, disregarding the existence of the multiple heterogeneities in the cortical composition, which may be at the core of its computational proficiency, will inevitably fail to account for important phenomena and limit the scope and generalizability of cortical models. We address these issues by studying the individual and composite functional roles of heterogeneities in neuronal, synaptic and structural properties in a biophysically plausible layer 2/3 microcircuit model, built and constrained by multiple sources of empirical data. This approach was made possible by the emergence of large-scale, well curated databases, as well as the substantial improvements in experimental methodologies achieved over the last few years. Our results show that variability in single neuron parameters is the dominant source of functional specialization, leading to highly proficient microcircuits with much higher computational power than their homogeneous counterparts. We further show that fully heterogeneous circuits, which are closest to the biophysical reality, owe their response properties to the differential contribution of different sources of heterogeneity.'),
(89646, 2019, 'plos', 'Clonal hematopoiesis of indeterminate potential and its impact on patient trajectories after stem cell transplantation', 'Park, Derek S|Akuffo, Afua A|Muench, David E|Grimes, H Leighton|Epling-Burnette, Pearlie K|Maini, Philip K|Anderson, Alexander R A|Bonsall, Michael B', 'Mathematical Ecology Research Group, Department of Zoology, University of Oxford, Oxford, United Kingdom.|Department of Immunology, H. Lee Moffitt Cancer Center, Tampa, Florida, USA.|Division of Immunobiology and Center for Systems Immunology, Cincinnati Childrens Hospital Medical Center, Cincinnati, Ohio, USA.|Division of Immunobiology and Center for Systems Immunology, Cincinnati Childrens Hospital Medical Center, Cincinnati, Ohio, USA.|Department of Immunology, H. Lee Moffitt Cancer Center, Tampa, Florida, USA.|Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, United Kingdom.|Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center, Tampa, Florida, USA.|Mathematical Ecology Research Group, Department of Zoology, University of Oxford, Oxford, United Kingdom.', 'United Kingdom|United States|United States|United States|United States|United Kingdom|United States|United Kingdom', 4, '-', 'Journal Article', '10.1371/journal.pcbi.1006913', '31026273', 'Clonal hematopoiesis of indeterminate potential (CHIP) is a recently identified process where older patients accumulate distinct subclones defined by recurring somatic mutations in hematopoietic stem cells. CHIPs implications for stem cell transplantation have been harder to identify due to the high degree of mutational heterogeneity that is present within the genetically distinct subclones. In order to gain a better understanding of CHIP and the impact of clonal dynamics on transplantation outcomes, we created a mathematical model of clonal competition dynamics. Our analyses highlight the importance of understanding competition intensity between healthy and mutant clones. Importantly, we highlight the risk that CHIP poses in leading to dominance of precancerous mutant clones and the risk of donor derived leukemia. Furthermore, we estimate the degree of competition intensity and bone marrow niche decline in mice during aging by using our modeling framework. Together, our work highlights the importance of better characterizing the ecological and clonal composition in hematopoietic donor populations at the time of stem cell transplantation.'),
(89647, 2019, 'plos', 'Integrated computational and Drosophila cancer model platform captures previously unappreciated chemicals perturbing a kinase network', 'Ung, Peter M U|Sonoshita, Masahiro|Scopton, Alex P|Dar, Arvin C|Cagan, Ross L|Schlessinger, Avner', 'Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America.|Department of Cell, Developmental and Regenerative Biology, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America.|Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America.|Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America.|Department of Cell, Developmental and Regenerative Biology, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America.|Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America.', 'United States|United States|United States|United States|United States|United States', 2, '-', 'Journal Article', '10.1371/journal.pcbi.1006878', '31026276', 'Drosophila provides an inexpensive and quantitative platform for measuring whole animal drug response. A complementary approach is virtual screening, where chemical libraries can be efficiently screened against protein target(s). Here, we present a unique discovery platform integrating structure-based modeling with Drosophila biology and organic synthesis. We demonstrate this platform by developing chemicals targeting a Drosophila model of Medullary Thyroid Cancer (MTC) characterized by a transformation network activated by oncogenic dRetM955T. Structural models for kinases relevant to MTC were generated for virtual screening to identify unique preliminary hits that suppressed dRetM955T-induced transformation. We then combined features from our hits with those of known inhibitors to create a hybrid molecule with improved suppression of dRetM955T transformation. Our platform provides a framework to efficiently explore novel kinase inhibitors outside of explored inhibitor chemical space that are effective in inhibiting cancer networks while minimizing whole body toxicity.'),
(89648, 2019, 'plos', 'A Bayesian framework for the analysis of systems biology models of the brain', 'Russell-Buckland, Joshua|Barnes, Christopher P|Tachtsidis, Ilias', 'Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom.|Department of Cell and Developmental Biology, University College London, London, United Kingdom.|Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom.', 'United Kingdom|United Kingdom|United Kingdom', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1006631', '31026277', 'Systems biology models are used to understand complex biological and physiological systems. Interpretation of these models is an important part of developing this understanding. These models are often fit to experimental data in order to understand how the system has produced various phenomena or behaviour that are seen in the data. In this paper, we have outlined a framework that can be used to perform Bayesian analysis of complex systems biology models. In particular, we have focussed on analysing a systems biology of the brain using both simulated and measured data. By using a combination of sensitivity analysis and approximate Bayesian computation, we have shown that it is possible to obtain distributions of parameters that can better guard against misinterpretation of results, as compared to a maximum likelihood estimate based approach. This is done through analysis of simulated and experimental data. NIRS measurements were simulated using the same simulated systemic input data for the model in a healthy and impaired state. By analysing both of these datasets, we show that different parameter spaces can be distinguished and compared between different physiological states or conditions. Finally, we analyse experimental data using the new Bayesian framework and the previous maximum likelihood estimate approach, showing that the Bayesian approach provides a more complete understanding of the parameter space.'),
(89649, 2019, 'plos', 'Finding driver mutations in cancer: Elucidating the role of background mutational processes', 'Brown, Anna-Leigh|Li, Minghui|Goncearenco, Alexander|Panchenko, Anna R', 'National Center for Biotechnology Information, NLM, NIH, Bethesda, MD, United States of America.|School of Biology and Basic Medical Sciences, Soochow University, Suzhou, China.|National Center for Biotechnology Information, NLM, NIH, Bethesda, MD, United States of America.|National Center for Biotechnology Information, NLM, NIH, Bethesda, MD, United States of America.', 'United States|China|United States|United States', 10, '-', 'Journal Article', '10.1371/journal.pcbi.1006981', '31034466', 'Identifying driver mutations in cancer is notoriously difficult. To date, recurrence of a mutation in patients remains one of the most reliable markers of mutation driver status. However, some mutations are more likely to occur than others due to differences in background mutation rates arising from various forms of infidelity of DNA replication and repair machinery, endogenous, and exogenous mutagens. We calculated nucleotide and codon mutability to study the contribution of background processes in shaping the observed mutational spectrum in cancer. We developed and tested probabilistic pan-cancer and cancer-specific models that adjust the number of mutation recurrences in patients by background mutability in order to find mutations which may be under selection in cancer. We showed that mutations with higher mutability values had higher observed recurrence frequency, especially in tumor suppressor genes. This trend was prominent for nonsense and silent mutations or mutations with neutral functional impact. In oncogenes, however, highly recurring mutations were characterized by relatively low mutability, resulting in an inversed U-shaped trend. Mutations not yet observed in any tumor had relatively low mutability values, indicating that background mutability might limit mutation occurrence. We compiled a dataset of missense mutations from 58 genes with experimentally validated functional and transforming impacts from various studies. We found that mutability of driver mutations was lower than that of passengers and consequently adjusting mutation recurrence frequency by mutability significantly improved ranking of mutations and driver mutation prediction. Even though no training on existing data was involved, our approach performed similarly or better to the state-of-the-art methods.'),
(89650, 2019, 'plos', 'The nonlinear dynamics and fluctuations of mRNA levels in cell cycle coupled transcription', 'Sun, Qiwen|Jiao, Feng|Lin, Genghong|Yu, Jianshe|Tang, Moxun', 'Center for Applied Mathematics, Guangzhou University, Guangzhou, 510006, China.|Center for Applied Mathematics, Guangzhou University, Guangzhou, 510006, China.|Center for Applied Mathematics, Guangzhou University, Guangzhou, 510006, China.|Center for Applied Mathematics, Guangzhou University, Guangzhou, 510006, China.|Department of Mathematics, Michigan State University, East Lansing, Michigan, United States of America.', 'China|China|China|China|United States', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1007017', '31034470', 'Gene transcription is a noisy process, and cell division cycle is an important source of gene transcription noise. In this work, we develop a mathematical approach by coupling transcription kinetics with cell division cycles to delineate how they are combined to regulate transcription output and noise. In view of gene dosage, a cell cycle is divided into an early stage [Formula: see text] and a late stage [Formula: see text]. The analytical forms for the mean and the noise of mRNA numbers are given in each stage. The analysis based on these formulas predicts precisely the fold change r* of mRNA numbers from [Formula: see text] to [Formula: see text] measured in a mouse embryonic stem cell line. When transcription follows similar kinetics in both stages, r* buffers against DNA dosage variation and r* xe2x88x88 (1, 2). Numerical simulations suggest that increasing cell cycle durations up-regulates transcription with less noise, whereas rapid stage transitions induce highly noisy transcription. A minimization of the transcription noise is observed when transcription homeostasis is attained by varying a single kinetic rate. When the transcription level scales with cellular volume, either by reducing the transcription burst frequency or by increasing the burst size in [Formula: see text], the noise shows only a minor variation over a wide range of cell cycle stage durations. The reduction level in the burst frequency is nearly a constant, whereas the increase in the burst size is conceivably sensitive, when responding to a large random variation of the cell cycle durations and the gene duplication time.'),
(89651, 2019, 'plos', 'Gap junctions set the speed and nucleation rate of stage I retinal waves', 'Kxc3xa4hne, Malte|Rxc3xbcdiger, Sten|Kihara, Alexandre Hiroaki|Lindner, Benjamin', 'Institut fxc3xbcr Physik, Humboldt-Universitxc3xa4t zu Berlin, Berlin, Germany.|Institut fxc3xbcr Physik, Humboldt-Universitxc3xa4t zu Berlin, Berlin, Germany.|Universidade Federal do ABC, Santo Andrxc3xa9, SP, Brazil.|Institut fxc3xbcr Physik, Humboldt-Universitxc3xa4t zu Berlin, Berlin, Germany.', 'Germany|Germany|Brazil|Germany', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1006355', '31034472', 'Spontaneous waves in the developing retina are essential in the formation of the retinotopic mapping in the visual system. From experiments in rabbits, it is known that the earliest type of retinal waves (stage I) is nucleated spontaneously, propagates at a speed of 451xc2xb191 xcexbcm/sec and relies on gap junction coupling between ganglion cells. Because gap junctions (electrical synapses) have short integration times, it has been argued that they cannot set the low speed of stage I retinal waves. Here, we present a theoretical study of a two-dimensional neural network of the ganglion cell layer with gap junction coupling and intrinsic noise. We demonstrate that this model can explain observed nucleation rates as well as the comparatively slow propagation speed of the waves. From the interaction between two coupled neurons, we estimate the wave speed in the model network. Furthermore, using simulations of small networks of neurons (Nxe2x89xa4260), we estimate the nucleation rate in the form of an Arrhenius escape rate. These results allow for informed simulations of a realistically sized network, yielding values of the gap junction coupling and the intrinsic noise level that are in a physiologically plausible range.'),
(89652, 2019, 'plos', 'The life history of learning: Demographic structure changes cultural outcomes', 'Fogarty, Laurel|Creanza, Nicole|Feldman, Marcus W', 'School of Biology, Sir Harold Mitchell Building, Greenside Place, St Andrews, United Kingdom.|Department of Biological Sciences, Vanderbilt University, Nashville, Tennessee, United States of America.|Department of Biology, Stanford University, Stanford, California, United States of America.', 'United Kingdom|United States|United States', 2, '-', 'Journal Article', '10.1371/journal.pcbi.1006821', '31039147', 'Human populations show rich cultural diversity. Underpinning this diversity of tools, rituals, and cultural norms are complex interactions between cultural evolutionary and demographic processes. Most models of cultural change assume that individuals use the same learning modes and methods throughout their lives. However, empirical data on learning life histories-the balance of dominant modes of learning (for example, learning from parents, peers, or unrelated elders) throughout an individuals lifetime-suggest that age structure may play a crucial role in determining learning modes and cultural evolutionary trajectories. Thus, studied in isolation, demographic and cultural evolutionary models show only part of the picture. This paper describes a mathematical and computational framework that combines demographic and cultural evolutionary methods. Using this general framework, we examine interactions between the ways in which culture is spread throughout an individuals lifetime and cultural change across generations. We show that including demographic structure alongside cultural dynamics can help to explain domain-specific patterns of cultural evolution that are a persistent feature of cultural data, and can shed new light on rare but significant demographic events.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(89653, 2019, 'plos', 'A multiscale model of epigenetic heterogeneity-driven cell fate decision-making', 'Folguera-Blasco, Núria|Pérez-Carrasco, Rubén|Cuyàs, Elisabet|Menendez, Javier A|Alarcón, Tomás', 'Centre de Recerca Matemàtica, Edifici C, Campus de Bellaterra, 08193 Bellaterra, Barcelona, Spain.|Department of Mathematics, University College London, Gower Street, London WC1E 6BT, UK.|ProCURE (Program Against Cancer Therapeutic Resistance), Metabolism and Cancer Group, Catalan Institute of Oncology, Girona, Spain.|ProCURE (Program Against Cancer Therapeutic Resistance), Metabolism and Cancer Group, Catalan Institute of Oncology, Girona, Spain.|Centre de Recerca Matemàtica, Edifici C, Campus de Bellaterra, 08193 Bellaterra, Barcelona, Spain.', 'Spain|United Kingdom|Spain|Spain|Spain', 11, '-', 'Journal Article', '10.1371/journal.pcbi.1006592', '31039148', 'The inherent capacity of somatic cells to switch their phenotypic status in response to damage stimuli in vivo might have a pivotal role in ageing and cancer. However, how the entry-exit mechanisms of phenotype reprogramming are established remains poorly understood. In an attempt to elucidate such mechanisms, we herein introduce a stochastic model of combined epigenetic regulation (ER)-gene regulatory network (GRN) to study the plastic phenotypic behaviours driven by ER heterogeneity. To deal with such complex system, we additionally formulate a multiscale asymptotic method for stochastic model reduction, from which we derive an efficient hybrid simulation scheme. Our analysis of the coupled system reveals a regime of tristability in which pluripotent stem-like and differentiated steady-states coexist with a third indecisive state, with ER driving transitions between these states. Crucially, ER heterogeneity of differentiation genes is for the most part responsible for conferring abnormal robustness to pluripotent stem-like states. We formulate epigenetic heterogeneity-based strategies capable of unlocking and facilitating the transit from differentiation-refractory (stem-like) to differentiation-primed epistates. The application of the hybrid numerical method validates the likelihood of such switching involving solely kinetic changes in epigenetic factors. Our results suggest that epigenetic heterogeneity regulates the mechanisms and kinetics of phenotypic robustness of cell fate reprogramming. The occurrence of tunable switches capable of modifying the nature of cell fate reprogramming might pave the way for new therapeutic strategies to regulate reparative reprogramming in ageing and cancer.'),
(89654, 2019, 'plos', 'I_MDS: an inflammatory bowel disease molecular activity score to classify patients with differing disease-driving pathways and therapeutic response to anti-TNF treatment', 'Pavlidis, Stelios|Monast, Calixte|Loza, Matthew J|Branigan, Patrick|Chung, Kiang F|Adcock, Ian M|Guo, Yike|Rowe, Anthony|Baribaud, Frédéric', 'Janssen Research & Development Ltd, High Wycombe, United Kingdom.|Janssen Research & Development LLC, United States of America.|Janssen Research & Development LLC, United States of America.|Janssen Research & Development LLC, United States of America.|National Heart and Lung Institute, Imperial College & Biomedical Research Unit, Royal Brompton & Harefield NHS Trust, London, United Kingdom.|National Heart and Lung Institute, Imperial College & Biomedical Research Unit, Royal Brompton & Harefield NHS Trust, London, United Kingdom.|Data Science Institute, Imperial College London, London, United Kingdom.|Janssen Research & Development LLC, United States of America.|Janssen Research & Development LLC, United States of America.', 'United Kingdom|United States|United States|United States|United Kingdom|United Kingdom|United Kingdo', 3, '-', 'Journal Article', '10.1371/journal.pcbi.1006951', '31039157', 'Crohns disease and ulcerative colitis are driven by both common and distinct underlying mechanisms of pathobiology. Both diseases, exhibit heterogeneity underscored by the variable clinical responses to therapeutic interventions. We aimed to identify disease-driving pathways and classify individuals into subpopulations that differ in their pathobiology and response to treatment. We applied hierarchical clustering of enrichment scores derived from gene set variation analysis of signatures representative of various immunological processes and activated cell types, to a colonic biopsy dataset that included healthy volunteers, Crohns disease and ulcerative colitis patients. Patient stratification at baseline or after anti-TNF treatment in clinical responders and non-responders was queried. Signatures with significantly different enrichment scores were identified using a general linear model. Comparisons to healthy controls were made at baseline in all participants and then separately in responders and non-responders. Fifty-nine percent of the signatures were commonly enriched in both conditions at baseline, supporting the notion of a disease continuum within ulcerative colitis and Crohns disease. Signatures included T cells, macrophages, neutrophil activation and poly:IC signatures, representing acute inflammation and a complex mix of potential disease-driving biology. Collectively, identification of significantly enriched signatures allowed establishment of an inflammatory bowel disease molecular activity score which uses biopsy transcriptomics as a surrogate marker to accurately track disease severity. This score separated diseased from healthy samples, enabled discrimination of clinical responders and non-responders at baseline with 100% specificity and 78.8% sensitivity, and was validated in an independent data set that showed comparable classification. Comparing responders and non-responders separately at baseline to controls, 43% and 70% of signatures were enriched, respectively, suggesting greater molecular dysregulation in TNF non-responders at baseline. This methodological approach could facilitate better targeted design of clinical studies to test therapeutics, concentrating on patient subsets sharing similar underlying pathobiology, therefore increasing the likelihood of clinical response.'),
(89655, 2019, 'plos', 'Uncovering functional signature in neural systems via random matrix theory', 'Almog, Assaf|Buijink, M Renate|Roethler, Ori|Michel, Stephan|Meijer, Johanna H|Rohling, Jos H T|Garlaschelli, Diego', 'The Big Data Lab, Department of Industrial Engineering, Tel-Aviv University, Ramat Aviv, Israel.|Laboratory for Neurophysiology, Department of Molecular Cell Biology, Leiden University Medical Center, Leiden, The Netherlands.|Laboratory for Neurophysiology, Department of Molecular Cell Biology, Leiden University Medical Center, Leiden, The Netherlands.|Laboratory for Neurophysiology, Department of Molecular Cell Biology, Leiden University Medical Center, Leiden, The Netherlands.|Laboratory for Neurophysiology, Department of Molecular Cell Biology, Leiden University Medical Center, Leiden, The Netherlands.|Laboratory for Neurophysiology, Department of Molecular Cell Biology, Leiden University Medical Center, Leiden, The Netherlands.|Instituut-Lorentz for Theoretical Physics, Leiden Institute of Physics, University of Leiden, Leiden, The Netherlands.', 'Israel|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 3, '-', 'Journal Article', '10.1371/journal.pcbi.1006934', '31042698', 'Neural systems are organized in a modular way, serving multiple functionalities. This multiplicity requires that both positive (e.g. excitatory, phase-coherent) and negative (e.g. inhibitory, phase-opposing) interactions take place across brain modules. Unfortunately, most methods to detect modules from time series either neglect or convert to positive, any measured negative correlation. This may leave a significant part of the sign-dependent functional structure undetected. Here we present a novel method, based on random matrix theory, for the identification of sign-dependent modules in the brain. Our method filters out both local (unit-specific) noise and global (system-wide) dependencies that typically obfuscate the presence of such structure. The method is guaranteed to identify an optimally contrasted functional signature, i.e. a partition into modules that are positively correlated internally and negatively correlated across. The method is purely data-driven, does not use any arbitrary threshold or network projection, and outputs only statistically significant structure. In measurements of neuronal gene expression in the biological clock of mice, the method systematically uncovers two otherwise undetectable, negatively correlated modules whose relative size and mutual interaction strength are found to depend on photoperiod.'),
(89656, 2019, 'plos', 'Correction: Demonstrating aspects of multiscale modeling by studying the permeation pathway of the human ZnT2 zinc transporter', 'Golan, Yarden|Alhadeff, Raphael|Glaser, Fabian|Ganoth, Assaf|Warshel, Arieh|Assaraf, Yehuda G', 'Instituut-Lorentz for Theoretical Physics, Leiden Institute of Physics, University of Leiden, Leiden, The Netherlands.|Instituut-Lorentz for Theoretical Physics, Leiden Institute of Physics, University of Leiden, Leiden, The Netherlands.|Instituut-Lorentz for Theoretical Physics, Leiden Institute of Physics, University of Leiden, Leiden, The Netherlands.|Instituut-Lorentz for Theoretical Physics, Leiden Institute of Physics, University of Leiden, Leiden, The Netherlands.|Instituut-Lorentz for Theoretical Physics, Leiden Institute of Physics, University of Leiden, Leiden, The Netherlands.|Instituut-Lorentz for Theoretical Physics, Leiden Institute of Physics, University of Leiden, Leiden, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 0, '-', 'Published Erratum', '10.1371/journal.pcbi.1007021', '31042699', '[This corrects the article DOI: 10.1371/journal.pcbi.1006503.].'),
(89657, 2019, 'plos', 'Dynamic balance between vesicle transport and microtubule growth enables neurite outgrowth', 'Yadaw, Arjun Singh|Siddiq, Mustafa M|Rabinovich, Vera|Tolentino, Rosa|Hansen, Jens|Iyengar, Ravi', 'Department of Pharmacological Sciences and Systems Biology Center New York, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America.|Department of Pharmacological Sciences and Systems Biology Center New York, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America.|Department of Pharmacological Sciences and Systems Biology Center New York, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America.|Department of Pharmacological Sciences and Systems Biology Center New York, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America.|Department of Pharmacological Sciences and Systems Biology Center New York, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America.|Department of Pharmacological Sciences and Systems Biology Center New York, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America.', 'United States|United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1006877', '31042702', 'Whole cell responses involve multiple subcellular processes (SCPs). To understand how balance between SCPs controls the dynamics of whole cell responses we studied neurite outgrowth in rat primary cortical neurons in culture. We used a combination of dynamical models and experiments to understand the conditions that permitted growth at a specified velocity and when aberrant growth could lead to the formation of dystrophic bulbs. We hypothesized that dystrophic bulb formation is due to quantitative imbalances between SCPs. Simulations predict redundancies between lower level sibling SCPs within each type of high level SCP. In contrast, higher level SCPs, such as vesicle transport and exocytosis or microtubule growth characteristic of each type need to be strictly coordinated with each other and imbalances result in stalling of neurite outgrowth. From these simulations, we predicted the effect of changing the activities of SCPs involved in vesicle exocytosis or microtubule growth could lead to formation of dystrophic bulbs. siRNA ablation experiments verified these predictions. We conclude that whole cell dynamics requires balance between the higher-level SCPs involved and imbalances can terminate whole cell responses such as neurite outgrowth.'),
(89658, 2019, 'plos', 'Experimentally guided computational antibody affinity maturation with de novo docking, modelling and rational design', 'Cannon, Daniel A|Shan, Lu|Du, Qun|Shirinian, Lena|Rickert, Keith W|Rosenthal, Kim L|Korade, Martin|van Vlerken-Ysla, Lilian E|Buchanan, Andrew|Vaughan, Tristan J|Damschroder, Melissa M|Popovic, Bojana', 'Department of Antibody Discovery and Protein Engineering, AstraZeneca, Cambridge, United Kingdom.|Department of Antibody Discovery and Protein Engineering, AstraZeneca, Gaithersburg, Maryland, United States of America.|Department of Antibody Discovery and Protein Engineering, AstraZeneca, Gaithersburg, Maryland, United States of America.|Department of Antibody Discovery and Protein Engineering, AstraZeneca, Gaithersburg, Maryland, United States of America.|Department of Antibody Discovery and Protein Engineering, AstraZeneca, Gaithersburg, Maryland, United States of America.|Department of Antibody Discovery and Protein Engineering, AstraZeneca, Gaithersburg, Maryland, United States of America.|Department of Oncology Research, AstraZeneca, Gaithersburg, Maryland, United States of America.|Department of Oncology Research, AstraZeneca, Gaithersburg, Maryland, United States of America.|Department of Antibody Discovery and Protein Engineering, AstraZeneca, Cambridge, United Kingdom.|Department of Antibody Discovery and Protein Engineering, AstraZeneca, Cambridge, United Kingdom.|Department of Antibody Discovery and Protein Engineering, AstraZeneca, Gaithersburg, Maryland, United States of America.|Department of Antibody Discovery and Protein Engineering, AstraZeneca, Cambridge, United Kingdom.', 'United Kingdom|United States|United States|United States|United States|United States|United States|U', 5, '-', 'Journal Article', '10.1371/journal.pcbi.1006980', '31042706', 'Antibodies are an important class of therapeutics that have significant clinical impact for the treatment of severe diseases. Computational tools to support antibody drug discovery have been developing at an increasing rate over the last decade and typically rely upon a predetermined co-crystal structure of the antibody bound to the antigen for structural predictions. Here, we show an example of successful in silico affinity maturation of a hybridoma derived antibody, AB1, using just a homology model of the antibody fragment variable region and a protein-protein docking model of the AB1 antibody bound to the antigen, murine CCL20 (muCCL20). In silico affinity maturation, together with alanine scanning, has allowed us to fine-tune the protein-protein docking model to subsequently enable the identification of two single-point mutations that increase the affinity of AB1 for muCCL20. To our knowledge, this is one of the first examples of the use of homology modelling and protein docking for affinity maturation and represents an approach that can be widely deployed.'),
(89659, 2019, 'plos', 'Correction: The role of the encapsulated cargo in microcompartment assembly', 'PLOS Computational Biology Staff', 'Department of Antibody Discovery and Protein Engineering, AstraZeneca, Cambridge, United Kingdom.', 'United Kingdom', 0, '-', 'Published Erratum', '10.1371/journal.pcbi.1006901', '31048829', '[This corrects the article DOI: 10.1371/journal.pcbi.1006351.].'),
(89660, 2019, 'plos', 'Ten quick tips for biocuration', 'Tang, Y Amy|Pichler, Klemens|Füllgrabe, Anja|Lomax, Jane|Malone, James|Munoz-Torres, Monica C|Vasant, Drashtti V|Williams, Eleanor|Haendel, Melissa', 'Genestack Limited, Cambridge, Cambridgeshire, United Kingdom.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, United Kingdom.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, United Kingdom.|SciBite Limited, BioData Innovation Centre, Hinxton, Cambridgeshire, United Kingdom.|SciBite Limited, BioData Innovation Centre, Hinxton, Cambridgeshire, United Kingdom.|Phoenix Bioinformatics, Fremont, California, United States of America.|Bayer Business Services GmbH, BP Research and Development, Translational Sciences, Berlin, Germany.|Centre for Gene Regulation and Expression, School of Life Sciences, University of Dundee, Dundee, United Kingdom.|Linus Pauling Institute, Oregon State University, Corvallis, Oregon, United States of America.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United States|Germany|Uni', 1, '-', 'Journal Article', '10.1371/journal.pcbi.1006906', '31048830', '-'),
(89661, 2019, 'plos', 'Ten quick tips for using a Raspberry Pi', 'Fletcher, Anthony C|Mura, Cameron', 'Salix Management Consultants Ltd, London, United Kingdom.|Dept of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America.', 'United Kingdom|United States', 1, '-', 'Journal Article', '10.1371/journal.pcbi.1006959', '31048834', '-'),
(89662, 2019, 'plos', 'Modeling of the axon plasma membrane structure and its effects on protein diffusion', 'Zhang, Yihao|Tzingounis, Anastasios V|Lykotrafitis, George', 'Department of Mechanical Engineering, University of Connecticut, Storrs, CT, United States of America.|Department of Physiology and Neurobiology, University of Connecticut, Storrs, CT, United States of America.|Department of Mechanical Engineering, University of Connecticut, Storrs, CT, United States of America.', 'United States|United States|United States', 1, '-', 'Journal Article', '10.1371/journal.pcbi.1007003', '31048841', 'The axon plasma membrane consists of the membrane skeleton, which comprises ring-like actin filaments connected to each other by spectrin tetramers, and the lipid bilayer, which is tethered to the skeleton via, at least, ankyrin. Currently it is unknown whether this unique axon plasma membrane skeleton (APMS) sets the diffusion rules of lipids and proteins in the axon. To answer this question, we developed a coarse-grain molecular dynamics model for the axon that includes the APMS, the phospholipid bilayer, transmembrane proteins (TMPs), and integral monotopic proteins (IMPs) in both the inner and outer lipid layers. We first showed that actin rings limit the longitudinal diffusion of TMPs and the IMPs of the inner leaflet but not of the IMPs of the outer leaflet. To reconcile the experimental observations, which show restricted diffusion of IMPs of the outer leaflet, with our simulations, we conjectured the existence of actin-anchored proteins that form a fence which restricts the longitudinal diffusion of IMPs of the outer leaflet. We also showed that spectrin filaments could modify transverse diffusion of TMPs and IMPs of the inner leaflet, depending on the strength of the association between lipids and spectrin. For instance, in areas where spectrin binds to the lipid bilayer, spectrin filaments would restrict diffusion of proteins within the skeleton corrals. In contrast, in areas where spectrin and lipids are not associated, spectrin modifies the diffusion of TMPs and IMPs of the inner leaflet from normal to confined-hop diffusion. Overall, we showed that diffusion of axon plasma membrane proteins is deeply anisotropic, as longitudinal diffusion is of different type than transverse diffusion. Finally, we investigated how accumulation of TMPs affects diffusion of TMPs and IMPs of both the inner and outer leaflets by changing the density of TMPs. We showed that the APMS structure acts as a fence that restricts the diffusion of TMPs and IMPs of the inner leaflet within the membrane skeleton corrals. Our findings provide insight into how the axon skeleton acts as diffusion barrier and maintains neuronal polarity.'),
(89663, 2019, 'plos', 'Balance of mechanical forces drives endothelial gap formation and may facilitate cancer and immune-cell extravasation', 'Escribano, Jorge|Chen, Michelle B|Moeendarbary, Emad|Cao, Xuan|Shenoy, Vivek|Garcia-Aznar, Jose Manuel|Kamm, Roger D|Spill, Fabian', 'Department of Mechanical Engineering, University of Zaragoza, Zaragoza, Spain.|Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.|Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.|Department of Materials Science and Engineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.|Department of Materials Science and Engineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.|Department of Mechanical Engineering, University of Zaragoza, Zaragoza, Spain.|Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.|Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.', 'Spain|United States|United States|United States|United States|Spain|United States|United States', 5, '-', 'Journal Article', '10.1371/journal.pcbi.1006395', '31048903', 'The formation of gaps in the endothelium is a crucial process underlying both cancer and immune cell extravasation, contributing to the functioning of the immune system during infection, the unfavorable development of chronic inflammation and tumor metastasis. Here, we present a stochastic-mechanical multiscale model of an endothelial cell monolayer and show that the dynamic nature of the endothelium leads to spontaneous gap formation, even without intervention from the transmigrating cells. These gaps preferentially appear at the vertices between three endothelial cells, as opposed to the border between two cells. We quantify the frequency and lifetime of these gaps, and validate our predictions experimentally. Interestingly, we find experimentally that cancer cells also preferentially extravasate at vertices, even when they first arrest on borders. This suggests that extravasating cells, rather than initially signaling to the endothelium, might exploit the autonomously forming gaps in the endothelium to initiate transmigration.'),
(89664, 2019, 'plos', 'Identification of avian flapping motion from non-volant winged dinosaurs based on modal effective mass analysis', 'Talori, Yaser Saffar|Zhao, Jing-Shan|Liu, Yun-Fei|Lu, Wen-Xiu|Li, Zhi-Heng|OConnor, Jingmai Kathleen', 'Department of Mechanical Engineering, Tsinghua University, Beijing, P. R. China.|Department of Mechanical Engineering, Tsinghua University, Beijing, P. R. China.|Department of Mechanical Engineering, Tsinghua University, Beijing, P. R. China.|Department of Mechanical Engineering, Tsinghua University, Beijing, P. R. China.|Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Chinese Academy of Sciences, Beijing, P. R. China.|Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Chinese Academy of Sciences, Beijing, P. R. China.', 'China|China|China|China|China|China', 2, '-', 'Journal Article', '10.1371/journal.pcbi.1006846', '31048911', 'The origin of avian flight is one of the most controversial debates in Paleontology. This paper investigates the wing performance of Caudipteryx, the most basal non-volant dinosaur with pennaceous feathered forelimbs by using modal effective mass theory. From a mechanical standpoint, the forced vibrations excited by hindlimb locomotion stimulate the movement of wings, creating a flapping-like motion in response. This shows that the origin of the avian flight stroke should lie in a completely natural process of active locomotion on the ground. In this regard, flapping in the history of evolution of avian flight should have already occurred when the dinosaurs were equipped with pennaceous remiges and rectrices. The forced vibrations provided the initial training for flapping the feathered wings of theropods similar to Caudipteryx.'),
(89665, 2019, 'plos', 'A system-wide network reconstruction of gene regulation and metabolism in Escherichia coli', 'Grimbs, Anne|Klosik, David F|Bornholdt, Stefan|Hütt, Marc-Thorsten', 'Computational Systems Biology, Department of Life Sciences & Chemistry, Jacobs University, Bremen, Germany.|Institute for Theoretical Physics, University of Bremen, Bremen, Germany.|Institute for Theoretical Physics, University of Bremen, Bremen, Germany.|Computational Systems Biology, Department of Life Sciences & Chemistry, Jacobs University, Bremen, Germany.', 'Germany|Germany|Germany|Germany', 1, '-', 'Journal Article', '10.1371/journal.pcbi.1006962', '31050661', 'Genome-scale metabolic models have become a fundamental tool for examining metabolic principles. However, metabolism is not solely characterized by the underlying biochemical reactions and catalyzing enzymes, but also affected by regulatory events. Since the pioneering work of Covert and co-workers as well as Shlomi and co-workers it is debated, how regulation and metabolism synergistically characterize a coherent cellular state. The first approaches started from metabolic models, which were extended by the regulation of the encoding genes of the catalyzing enzymes. By now, bioinformatics databases in principle allow addressing the challenge of integrating regulation and metabolism on a system-wide level. Collecting information from several databases we provide a network representation of the integrated gene regulatory and metabolic system for Escherichia coli, including major cellular processes, from metabolic processes via protein modification to a variety of regulatory events. Besides transcriptional regulation, we also take into account regulation of translation, enzyme activities and reactions. Our network model provides novel topological characterizations of system components based on their positions in the network. We show that network characteristics suggest a representation of the integrated system as three network domains (regulatory, metabolic and interface networks) instead of two. This new three-domain representation reveals the structural centrality of components with known high functional relevance. This integrated network can serve as a platform for understanding coherent cellular states as active subnetworks and to elucidate crossover effects between metabolism and gene regulation.'),
(89666, 2019, 'plos', 'How Dendrites Affect Online Recognition Memory', 'Wu, Xundong|Mel, Gabriel C|Strouse, D J|Mel, Bartlett W', 'School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China.|Computer Science Department, University of Southern California, Los Angeles, CA, United States.|Physics Department, Princeton University, Princeton, NJ, United States.|Biomedical Engineering Department and Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, United States.', 'China|United States|United States|United States', 2, '-', 'Journal Article', '10.1371/journal.pcbi.1006892', '31050662', 'In order to record the stream of autobiographical information that defines our unique personal history, our brains must form durable memories from single brief exposures to the patterned stimuli that impinge on them continuously throughout life. However, little is known about the computational strategies or neural mechanisms that underlie the brains ability to perform this type of online learning. Based on increasing evidence that dendrites act as both signaling and learning units in the brain, we developed an analytical model that relates online recognition memory capacity to roughly a dozen dendritic, network, pattern, and task-related parameters. We used the model to determine what dendrite size maximizes storage capacity under varying assumptions about pattern density and noise level. We show that over a several-fold range of both of these parameters, and over multiple orders-of-magnitude of memory size, capacity is maximized when dendrites contain a few hundred synapses-roughly the natural number found in memory-related areas of the brain. Thus, in comparison to entire neurons, dendrites increase storage capacity by providing a larger number of better-sized learning units. Our model provides the first normative theory that explains how dendrites increase the brains capacity for online learning, predicts which combinations of parameter settings we should expect to find in the brain under normal operating conditions, leads to novel interpretations of an array of existing experimental results, and provides a tool for understanding which changes associated with neurological disorders, aging, or stress are most likely to produce memory deficits-knowledge that could eventually help in the design of improved clinical treatments for memory loss.'),
(89667, 2019, 'plos', 'Enchained growth and cluster dislocation: A possible mechanism for microbiota homeostasis', 'Bansept, Florence|Schumann-Moor, Kathrin|Diard, Médéric|Hardt, Wolf-Dietrich|Slack, Emma|Loverdo, Claude', 'Laboratoire Jean Perrin, Sorbonne Université / CNRS, Paris, France.|Institute of Microbiology, Department of Biology, ETH Zürich, Switzerland.|Institute of Microbiology, Department of Biology, ETH Zürich, Switzerland.|Institute of Microbiology, Department of Biology, ETH Zürich, Switzerland.|Institute of Microbiology, Department of Biology, ETH Zürich, Switzerland.|Laboratoire Jean Perrin, Sorbonne Université / CNRS, Paris, France.', 'France|Switzerland|Switzerland|Switzerland|Switzerland|France', 5, '-', 'Journal Article', '10.1371/journal.pcbi.1006986', '31050663', 'Immunoglobulin A is a class of antibodies produced by the adaptive immune system and secreted into the gut lumen to fight pathogenic bacteria. We recently demonstrated that the main physical effect of these antibodies is to enchain daughter bacteria, i.e. to cross-link bacteria into clusters as they divide, preventing them from interacting with epithelial cells, thus protecting the host. These links between bacteria may break over time. We study several models using analytical and numerical calculations. We obtain the resulting distribution of chain sizes, that we compare with experimental data. We study the rate of increase in the number of free bacteria as a function of the replication rate of bacteria. Our models show robustly that at higher replication rates, bacteria replicate before the link between daughter bacteria breaks, leading to growing cluster sizes. On the contrary at low growth rates two daughter bacteria have a high probability to break apart. Thus the gut could produce IgA against all the bacteria it has encountered, but the most affected bacteria would be the fast replicating ones, that are more likely to destabilize the microbiota. Linking the effect of the immune effectors (here the clustering) with a property directly relevant to the potential bacterial pathogeneicity (here the replication rate) could avoid to make complex decisions about which bacteria to produce effectors against.'),
(89668, 2019, 'plos', 'Amplitude cancellation influences the association between frequency components in the neural drive to muscle and the rectified EMG signal', 'Dideriksen, Jakob Lund|Farina, Dario', 'Department of Health Science and Technology, Aalborg University, Aalborg, Denmark.|Department of Bioengineering, Imperial College London, London, United Kingdom.', 'Denmark|United Kingdom', 3, '-', 'Journal Article', '10.1371/journal.pcbi.1006985', '31050667', 'The rectified surface EMG signal is commonly used as an estimator of the neural drive to muscles and therefore to infer sources of synaptic input to motor neurons. Loss of EMG amplitude due to the overlap of motor unit action potentials (amplitude cancellation), however, may distort the spectrum of the rectified EMG and thereby its correlation with the neural drive. In this study, we investigated the impact of amplitude cancelation on this correlation using analytical derivations and a computational model of motor neuron activity, force, and the EMG signal. First, we demonstrated analytically that an ideal rectified EMG signal without amplitude cancellation (EMGnc) is superior to the actual rectified EMG signal as estimator of the neural drive to muscle. This observation was confirmed by the simulations, as the average coefficient of determination (r2) between the neural drive in the 1-30 Hz band and EMGnc (0.59±0.08) was matched by the correlation between the rectified EMG and the neural drive only when the level of amplitude cancellation was low (<40%) at low contraction levels (<5% of maximum voluntary contraction force, MVC). This correlation, however, decreased linearly with amplitude cancellation (r = -0.83) to values of r2 <0.2 at amplitude cancellation levels >60% (contraction levels >15% MVC). Moreover, the simulations showed that a stronger (i.e. more variable) neural drive implied a stronger correlation between the rectified EMG and the neural drive and that amplitude cancellation distorted this correlation mainly for low-frequency components (<5 Hz) of the neural drive. In conclusion, the results indicate that amplitude cancellation distorts the spectrum of the rectified EMG signal. This implies that valid use of the rectified EMG as an estimator of the neural drive requires low contraction levels and/or strong common synaptic input to the motor neurons.'),
(89669, 2019, 'plos', 'Correction: How pupil responses track value-based decision-making during and after reinforcement learning', 'Van Slooten, Joanne C|Jahfari, Sara|Knapen, Tomas|Theeuwes, Jan', 'Department of Bioengineering, Imperial College London, London, United Kingdom.|Department of Bioengineering, Imperial College London, London, United Kingdom.|Department of Bioengineering, Imperial College London, London, United Kingdom.|Department of Bioengineering, Imperial College London, London, United Kingdom.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, '-', 'Published Erratum', '10.1371/journal.pcbi.1007031', '31059496', '[This corrects the article DOI: 10.1371/journal.pcbi.1006632.].'),
(89670, 2019, 'plos', 'Quasiperiodic rhythms of the inferior olive', 'Negrello, Mario|Warnaar, Pascal|Romano, Vincenzo|Owens, Cullen B|Lindeman, Sander|Iavarone, Elisabetta|Spanke, Jochen K|Bosman, Laurens W J|De Zeeuw, Chris I', 'Department of Neuroscience, Erasmus MC, Rotterdam, the Netherlands.|Department of Neuroscience, Erasmus MC, Rotterdam, the Netherlands.|Department of Neuroscience, Erasmus MC, Rotterdam, the Netherlands.|Department of Neuroscience, Erasmus MC, Rotterdam, the Netherlands.|Department of Neuroscience, Erasmus MC, Rotterdam, the Netherlands.|Department of Neuroscience, Erasmus MC, Rotterdam, the Netherlands.|Department of Neuroscience, Erasmus MC, Rotterdam, the Netherlands.|Department of Neuroscience, Erasmus MC, Rotterdam, the Netherlands.|Department of Neuroscience, Erasmus MC, Rotterdam, the Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Neth', 5, '-', 'Journal Article', '10.1371/journal.pcbi.1006475', '31059498', 'Inferior olivary activity causes both short-term and long-term changes in cerebellar output underlying motor performance and motor learning. Many of its neurons engage in coherent subthreshold oscillations and are extensively coupled via gap junctions. Studies in reduced preparations suggest that these properties promote rhythmic, synchronized output. However, the interaction of these properties with torrential synaptic inputs in awake behaving animals is not well understood. Here we combine electrophysiological recordings in awake mice with a realistic tissue-scale computational model of the inferior olive to study the relative impact of intrinsic and extrinsic mechanisms governing its activity. Our data and model suggest that if subthreshold oscillations are present in the awake state, the period of these oscillations will be transient and variable. Accordingly, by using different temporal patterns of sensory stimulation, we found that complex spike rhythmicity was readily evoked but limited to short intervals of no more than a few hundred milliseconds and that the periodicity of this rhythmic activity was not fixed but dynamically related to the synaptic input to the inferior olive as well as to motor output. In contrast, in the long-term, the average olivary spiking activity was not affected by the strength and duration of the sensory stimulation, while the level of gap junctional coupling determined the stiffness of the rhythmic activity in the olivary network during its dynamic response to sensory modulation. Thus, interactions between intrinsic properties and extrinsic inputs can explain the variations of spiking activity of olivary neurons, providing a temporal framework for the creation of both the short-term and long-term changes in cerebellar output.'),
(89671, 2019, 'plos', 'A dynamic network model of temporal receptive fields in primary auditory cortex', 'Rahman, Monzilur|Willmore, Ben D B|King, Andrew J|Harper, Nicol S', 'Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom.|Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom.|Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom.|Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1006618', '31059503', 'Auditory neurons encode stimulus history, which is often modelled using a span of time-delays in a spectro-temporal receptive field (STRF). We propose an alternative model for the encoding of stimulus history, which we apply to extracellular recordings of neurons in the primary auditory cortex of anaesthetized ferrets. For a linear-non-linear STRF model (LN model) to achieve a high level of performance in predicting single unit neural responses to natural sounds in the primary auditory cortex, we found that it is necessary to include time delays going back at least 200 ms in the past. This is an unrealistic time span for biological delay lines. We therefore asked how much of this dependence on stimulus history can instead be explained by dynamical aspects of neurons. We constructed a neural-network model whose output is the weighted sum of units whose responses are determined by a dynamic firing-rate equation. The dynamic aspect performs low-pass filtering on each units response, providing an exponentially decaying memory whose time constant is individual to each unit. We find that this dynamic network (DNet) model, when fitted to the neural data using STRFs of only 25 ms duration, can achieve prediction performance on a held-out dataset comparable to the best performing LN model with STRFs of 200 ms duration. These findings suggest that integration due to the membrane time constants or other exponentially-decaying memory processes may underlie linear temporal receptive fields of neurons beyond 25 ms.'),
(89672, 2019, 'plos', 'Fast and robust deconvolution of tumor infiltrating lymphocyte from expression profiles using least trimmed squares', 'Hao, Yuning|Yan, Ming|Heath, Blake R|Lei, Yu L|Xie, Yuying', 'Department of Statistics and Probability, Michigan State University, East Lansing, United States of America.|Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, United States of America.|Department of Periodontics and Oral Medicine, University of Michigan School of Dentistry Ann Arbor, United States of America.|Department of Periodontics and Oral Medicine, University of Michigan School of Dentistry Ann Arbor, United States of America.|Department of Statistics and Probability, Michigan State University, East Lansing, United States of America.', 'United States|United States|United States|United States|United States', 7, '-', 'Journal Article', '10.1371/journal.pcbi.1006976', '31059559', 'Gene-expression deconvolution is used to quantify different types of cells in a mixed population. It provides a highly promising solution to rapidly characterize the tumor-infiltrating immune landscape and identify cold cancers. However, a major challenge is that gene-expression data are frequently contaminated by many outliers that decrease the estimation accuracy. Thus, it is imperative to develop a robust deconvolution method that automatically decontaminates data by reliably detecting and removing outliers. We developed a new machine learning tool, Fast And Robust DEconvolution of Expression Profiles (FARDEEP), to enumerate immune cell subsets from whole tumor tissue samples. To reduce noise in the tumor gene expression datasets, FARDEEP utilizes an adaptive least trimmed square to automatically detect and remove outliers before estimating the cell compositions. We show that FARDEEP is less susceptible to outliers and returns a better estimation of coefficients than the existing methods with both numerical simulations and real datasets. FARDEEP provides an estimate related to the absolute quantity of each immune cell subset in addition to relative percentages. Hence, FARDEEP represents a novel robust algorithm to complement the existing toolkit for the characterization of tissue-infiltrating immune cell landscape. The source code for FARDEEP is implemented in R and available for download at https://github.com/YuningHao/FARDEEP.git.'),
(89673, 2019, 'plos', 'Reward-driven changes in striatal pathway competition shape evidence evaluation in decision-making', 'Dunovan, Kyle|Vich, Catalina|Clapp, Matthew|Verstynen, Timothy|Rubin, Jonathan', 'Dept. of Psychology, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.|Dept. de Matemàtiques i Informàtica, Universitat de les Illes Balears, Palma, Illes Balears, Spain.|Dept. of Biomedical Engineering, University of South Carolina, Columbia, South Carolina, United States of America.|Dept. of Psychology, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.|Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania, United States of America.', 'United States|Spain|United States|United States|United States', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1006998', '31060045', 'Cortico-basal-ganglia-thalamic (CBGT) networks are critical for adaptive decision-making, yet how changes to circuit-level properties impact cognitive algorithms remains unclear. Here we explore how dopaminergic plasticity at corticostriatal synapses alters competition between striatal pathways, impacting the evidence accumulation process during decision-making. Spike-timing dependent plasticity simulations showed that dopaminergic feedback based on rewards modified the ratio of direct and indirect corticostriatal weights within opposing action channels. Using the learned weight ratios in a full spiking CBGT network model, we simulated neural dynamics and decision outcomes in a reward-driven decision task and fit them with a drift diffusion model. Fits revealed that the rate of evidence accumulation varied with inter-channel differences in direct pathway activity while boundary height varied with overall indirect pathway activity. This multi-level modeling approach demonstrates how complementary learning and decision computations can emerge from corticostriatal plasticity.'),
(89674, 2019, 'plos', 'EAT-Rice: A predictive model for flanking gene expression of T-DNA insertion activation-tagged rice mutants by machine learning approaches', 'Liao, Chi-Chou|Chen, Liang-Jwu|Lo, Shuen-Fang|Chen, Chi-Wei|Chu, Yen-Wei', 'Institute of Molecular Biology, National Chung Hsing University, Taichung, Taiwan.|Institute of Molecular Biology, National Chung Hsing University, Taichung, Taiwan.|Agricultural Biotechnology Center, National Chung Hsing University, Taichung, Taiwan.|Department of Computer Science and Engineering, National Chung Hsing University, Taichung, Taiwan.|Institute of Molecular Biology, National Chung Hsing University, Taichung, Taiwan.', '-|-|-|-|-', 2, '-', 'Journal Article', '10.1371/journal.pcbi.1006942', '31067213', 'T-DNA activation-tagging technology is widely used to study rice gene functions. When T-DNA inserts into genome, the flanking gene expression may be altered using CaMV 35S enhancer, but the affected genes still need to be validated by biological experiment. We have developed the EAT-Rice platform to predict the flanking gene expression of T-DNA insertion site in rice mutants. The three kinds of DNA sequences including UPS1K, DISTANCE, and MIDDLE were retrieved to encode and build a forecast model of two-layer machine learning. In the first-layer models, the features nucleotide context (N-gram), cis-regulatory elements (Motif), nucleotide physicochemical properties (NPC), and CG-island (CGI) were used to build SVM models by analysing the concealed information embedded within the three kinds of sequences. Logistic regression was used to estimate the probability of gene activation which as feature-encoding weighting within first-layer model. In the second-layer models, the NaiveBayesUpdateable algorithm was used to integrate these first layer-models, and the system performance was 88.33% on 5-fold cross-validation, and 79.17% on independent-testing finally. In the three kinds of sequences, the model constructed by Middle had the best contribution to the system for identifying the activated genes. The EAT-Rice system provided better performance and gene expression prediction at further distances when compared to the TRIM database. An online server based on EAT-rice is available at http://predictor.nchu.edu.tw/EAT-Rice.'),
(89675, 2019, 'plos', 'Erratum: Notice of Republication: Ten simple rules for organizing a webinar series', 'PLOS Computational Biology Staff', 'Institute of Molecular Biology, National Chung Hsing University, Taichung, Taiwan.', '-', 0, '-', 'Published Erratum', '10.1371/journal.pcbi.1007048', '31067216', '[This corrects the article DOI: 10.1371/journal.pcbi.1006671.].'),
(89676, 2019, 'plos', 'The statistics of epidemic transitions', 'Drake, John M|Brett, Tobias S|Chen, Shiyang|Epureanu, Bogdan I|Ferrari, Matthew J|Marty, Éric|Miller, Paige B|ODea, Eamon B|ORegan, Suzanne M|Park, Andrew W|Rohani, Pejman', 'Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America.|Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America.|Department of Mechanical Engineering, University of Michigan, Ann Arbor, Michigan, United States of America.|Department of Mechanical Engineering, University of Michigan, Ann Arbor, Michigan, United States of America.|Center for Infectious Disease Dynamics, Pennsylvania State University, State College, Pennsylvania, United States of America.|Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America.|Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America.|Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America.|Department of Mathematics, North Carolina A&T State University, Greensboro, North Carolina, United States of America.|Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America.|Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America.', 'Georgia|Georgia|United States|United States|United States|Georgia|Georgia|Georgia|United States|Geor', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1006917', '31067217', 'Emerging and re-emerging pathogens exhibit very complex dynamics, are hard to model and difficult to predict. Their dynamics might appear intractable. However, new statistical approaches-rooted in dynamical systems and the theory of stochastic processes-have yielded insight into the dynamics of emerging and re-emerging pathogens. We argue that these approaches may lead to new methods for predicting epidemics. This perspective views pathogen emergence and re-emergence as a critical transition, and uses the concept of noisy dynamic bifurcation to understand the relationship between the system observables and the distance to this transition. Because the system dynamics exhibit characteristic fluctuations in response to perturbations for a system in the vicinity of a critical point, we propose this information may be harnessed to develop early warning signals. Specifically, the motion of perturbations slows as the system approaches the transition.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(89677, 2019, 'plos', 'An integrative transcriptome analysis framework for drug efficacy and similarity reveals drug-specific signatures of anti-TNF treatment in a mouse model of inflammatory polyarthritis', 'Karagianni, Niki|Kranidioti, Ksanthi|Fikas, Nikolaos|Tsochatzidou, Maria|Chouvardas, Panagiotis|Denis, Maria C|Kollias, George|Nikolaou, Christoforos', 'Biomedcode Hellas SA, Vari, Greece.|Biomedcode Hellas SA, Vari, Greece.|Department of Biology, University of Crete, Heraklion, Greece.|Department of Biology, University of Crete, Heraklion, Greece.|Institute of Immunology, Biomedical Sciences Research Center (BSRC), Alexander Fleming, Vari, Greece.|Biomedcode Hellas SA, Vari, Greece.|Institute of Immunology, Biomedical Sciences Research Center (BSRC), Alexander Fleming, Vari, Greece.|Department of Biology, University of Crete, Heraklion, Greece.', 'Greece|Greece|Greece|Greece|Greece|Greece|Greece|Greece', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1006933', '31071076', 'Anti-TNF agents have been in the first line of treatment of various inflammatory diseases such as Rheumatoid Arthritis and Crohns Disease, with a number of different biologics being currently in use. A detailed analysis of their effect at transcriptome level has nevertheless been lacking. We herein present a concise analysis of an extended transcriptomics profiling of four different anti-TNF biologics upon treatment of the established hTNFTg (Tg197) mouse model of spontaneous inflammatory polyarthritis. We implement a series of computational analyses that include clustering of differentially expressed genes, functional analysis and random forest classification. Taking advantage of our detailed sample structure, we devise metrics of treatment efficiency that take into account changes in gene expression compared to both the healthy and the diseased state. Our results suggest considerable variability in the capacity of different biologics to modulate gene expression that can be attributed to treatment-specific functional pathways and differential preferences to restore over- or under-expressed genes. Early intervention appears to manage inflammation in a more efficient way but is accompanied by increased effects on a number of genes that are seemingly unrelated to the disease. Administration at an early stage is also lacking in capacity to restore healthy expression levels of under-expressed genes. We record quantifiable differences among anti-TNF biologics in their efficiency to modulate over-expressed genes related to immune and inflammatory pathways. More importantly, we find a subset of the tested substances to have quantitative advantages in addressing deregulation of under-expressed genes involved in pathways related to known RA comorbidities. Our study shows the potential of transcriptomic analyses to identify comprehensive and distinct treatment-specific gene signatures combining disease-related and unrelated genes and proposes a generalized framework for the assessment of drug efficacy, the search of biosimilars and the evaluation of the efficacy of TNF small molecule inhibitors.'),
(89678, 2019, 'plos', 'A quick guide for using Microsoft OneNote as an electronic laboratory notebook', 'Guerrero, Santiago|López-Cortés, Andrés|García-Cárdenas, Jennyfer M|Saa, Pablo|Indacochea, Alberto|Armendáriz-Castillo, Isaac|Zambrano, Ana Karina|Yumiceba, Verónica|Pérez-Villa, Andy|Guevara-Ramírez, Patricia|Moscoso-Zea, Oswaldo|Paredes, Joel|Leone, Paola E|Paz-Y-Miño, César', 'Centro de Investigación Genética y Genómica, Facultad de Ciencias de la Salud Eugenio Espejo, Universidad UTE, Quito, Ecuador.|Centro de Investigación Genética y Genómica, Facultad de Ciencias de la Salud Eugenio Espejo, Universidad UTE, Quito, Ecuador.|Centro de Investigación Genética y Genómica, Facultad de Ciencias de la Salud Eugenio Espejo, Universidad UTE, Quito, Ecuador.|Faculty of Engineering Sciences, Universidad UTE, Quito, Ecuador.|Gene Regulation, Stem Cells and Cancer Programme, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain.|Centro de Investigación Genética y Genómica, Facultad de Ciencias de la Salud Eugenio Espejo, Universidad UTE, Quito, Ecuador.|Centro de Investigación Genética y Genómica, Facultad de Ciencias de la Salud Eugenio Espejo, Universidad UTE, Quito, Ecuador.|Centro de Investigación Genética y Genómica, Facultad de Ciencias de la Salud Eugenio Espejo, Universidad UTE, Quito, Ecuador.|Centro de Investigación Genética y Genómica, Facultad de Ciencias de la Salud Eugenio Espejo, Universidad UTE, Quito, Ecuador.|Centro de Investigación Genética y Genómica, Facultad de Ciencias de la Salud Eugenio Espejo, Universidad UTE, Quito, Ecuador.|Faculty of Engineering Sciences, Universidad UTE, Quito, Ecuador.|Centro de Investigación Genética y Genómica, Facultad de Ciencias de la Salud Eugenio Espejo, Universidad UTE, Quito, Ecuador.|Centro de Investigación Genética y Genómica, Facultad de Ciencias de la Salud Eugenio Espejo, Universidad UTE, Quito, Ecuador.|Centro de Investigación Genética y Genómica, Facultad de Ciencias de la Salud Eugenio Espejo, Universidad UTE, Quito, Ecuador.', 'Ecuador|Ecuador|Ecuador|Ecuador|Spain|Ecuador|Ecuador|Ecuador|Ecuador|Ecuador|Ecuador|Ecuador|Ecuado', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1006918', '31071077', 'Scientific data recording and reporting systems are of a great interest for endorsing reproducibility and transparency practices among the scientific community. Current research generates large datasets that can no longer be documented using paper lab notebooks (PLNs). In this regard, electronic laboratory notebooks (ELNs) could be a promising solution to replace PLNs and promote scientific reproducibility and transparency. We previously analyzed five ELNs and performed two survey-based studies to implement an ELN in a biomedical research institute. Among the ELNs tested, we found that Microsoft OneNote presents numerous features related to ELN best functionalities. In addition, both surveyed groups preferred OneNote over a scientifically designed ELN (PerkinElmer Elements). However, OneNote remains a general note-taking application and has not been designed for scientific purposes. We therefore provide a quick guide to adapt OneNote to an ELN workflow that can also be adjusted to other nonscientific ELNs.'),
(89679, 2019, 'plos', 'A neuromechanistic model for rhythmic beat generation', 'Bose, Amitabha|Byrne, Áine|Rinzel, John', 'Department of Mathematical Sciences, New Jersey Institute of Technology, Newark, New Jersey, United States of America.|Center for Neural Science, New York University, New York, New York, United States of America.|Center for Neural Science, New York University, New York, New York, United States of America.', 'Jersey|United States|United States', 3, '-', 'Journal Article', '10.1371/journal.pcbi.1006450', '31071078', 'When listening to music, humans can easily identify and move to the beat. Numerous experimental studies have identified brain regions that may be involved with beat perception and representation. Several theoretical and algorithmic approaches have been proposed to account for this ability. Related to, but different from the issue of how we perceive a beat, is the question of how we learn to generate and hold a beat. In this paper, we introduce a neuronal framework for a beat generator that is capable of learning isochronous rhythms over a range of frequencies that are relevant to music and speech. Our approach combines ideas from error-correction and entrainment models to investigate the dynamics of how a biophysically-based neuronal network model synchronizes its period and phase to match that of an external stimulus. The model makes novel use of on-going faster gamma rhythms to form a set of discrete clocks that provide estimates, but not exact information, of how well the beat generator spike times match those of a stimulus sequence. The beat generator is endowed with plasticity allowing it to quickly learn and thereby adjust its spike times to achieve synchronization. Our model makes generalizable predictions about the existence of asymmetries in the synchronization process, as well as specific predictions about resynchronization times after changes in stimulus tempo or phase. Analysis of the model demonstrates that accurate rhythmic time keeping can be achieved over a range of frequencies relevant to music, in a manner that is robust to changes in parameters and to the presence of noise.'),
(89680, 2019, 'plos', 'Macroscopic phase resetting-curves determine oscillatory coherence and signal transfer in inter-coupled neural circuits', 'Dumont, Grégory|Gutkin, Boris', 'Group for Neural Theory, LNC INSERM U960, DEC, Ecole Normale Supérieure PSL* University, Paris, France.|Group for Neural Theory, LNC INSERM U960, DEC, Ecole Normale Supérieure PSL* University, Paris, France.', 'France|France', 5, '-', 'Journal Article', '10.1371/journal.pcbi.1007019', '31071085', 'Macroscopic oscillations of different brain regions show multiple phase relationships that are persistent across time and have been implicated in routing information. While multiple cellular mechanisms influence the network oscillatory dynamics and structure the macroscopic firing motifs, one of the key questions is to identify the biophysical neuronal and synaptic properties that permit such motifs to arise. A second important issue is how the different neural activity coherence states determine the communication between the neural circuits. Here we analyse the emergence of phase-locking within bidirectionally delayed-coupled spiking circuits in which global gamma band oscillations arise from synaptic coupling among largely excitable neurons. We consider both the interneuronal (ING) and the pyramidal-interneuronal (PING) population gamma rhythms and the inter coupling targeting the pyramidal or the inhibitory neurons. Using a mean-field approach together with an exact reduction method, we reduce each spiking network to a low dimensional nonlinear system and derive the macroscopic phase resetting-curves (mPRCs) that determine how the phase of the global oscillation responds to incoming perturbations. This is made possible by the use of the quadratic integrate-and-fire model together with a Lorentzian distribution of the bias current. Depending on the type of gamma (PING vs. ING), we show that incoming excitatory inputs can either speed up the macroscopic oscillation (phase advance, type I PRC) or induce both a phase advance and a delay (type II PRC). From there we determine the structure of macroscopic coherence states (phase-locking) of two weakly synaptically-coupled networks. To do so we derive a phase equation for the coupled system which links the synaptic mechanisms to the coherence states of the system. We show that a synaptic transmission delay is a necessary condition for symmetry breaking, i.e. a non-symmetric phase lag between the macroscopic oscillations. This potentially provides an explanation to the experimentally observed variety of gamma phase-locking modes. Our analysis further shows that symmetry-broken coherence states can lead to a preferred direction of signal transfer between the oscillatory networks where this directionality also depends on the timing of the signal. Hence we suggest a causal theory for oscillatory modulation of functional connectivity between cortical circuits.'),
(89681, 2019, 'plos', 'Pathway-specific protein domains are predictive for human diseases', 'Shim, Jung Eun|Kim, Ji Hyun|Shin, Junha|Lee, Ji Eun|Lee, Insuk', 'Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, Korea.|Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea.|Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, Korea.|Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea.|Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, Korea.', '-|-|-|-|-', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1007052', '31075101', 'Protein domains are basic functional units of proteins. Many protein domains are pervasive among diverse biological processes, yet some are associated with specific pathways. Human complex diseases are generally viewed as pathway-level disorders. Therefore, we hypothesized that pathway-specific domains could be highly informative for human diseases. To test the hypothesis, we developed a network-based scoring scheme to quantify specificity of domain-pathway associations. We first generated domain profiles for human proteins, then constructed a co-pathway protein network based on the associations between domain profiles. Based on the score, we classified human protein domains into pathway-specific domains (PSDs) and non-specific domains (NSDs). We found that PSDs contained more pathogenic variants than NSDs. PSDs were also enriched for disease-associated mutations that disrupt protein-protein interactions (PPIs) and tend to have a moderate number of domain interactions. These results suggest that mutations in PSDs are likely to disrupt within-pathway PPIs, resulting in functional failure of pathways. Finally, we demonstrated the prediction capacity of PSDs for disease-associated genes with experimental validations in zebrafish. Taken together, the network-based quantitative method of modeling domain-pathway associations presented herein suggested underlying mechanisms of how protein domains associated with specific pathways influence mutational impacts on diseases via perturbations in within-pathway PPIs, and provided a novel genomic feature for interpreting genetic variants to facilitate the discovery of human disease genes.'),
(89682, 2019, 'plos', 'Beyond Boumas window: How to explain global aspects of crowding?', 'Doerig, Adrien|Bornet, Alban|Rosenholtz, Ruth|Francis, Gregory|Clarke, Aaron M|Herzog, Michael H', 'Laboratory of Psychophysics, Brain Mind Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.|Laboratory of Psychophysics, Brain Mind Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.|Department of Brain and Cognitive Sciences, Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, United States of America.|Department of Psychological Sciences, Purdue University, West Lafayette, IN, United States of America.|Laboratory of Computational Vision, Psychology Department, Bilkent University, Ankara, Turkey.|Laboratory of Psychophysics, Brain Mind Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.', 'Switzerland|Switzerland|United States|United States|Turkey|Switzerland', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1006580', '31075131', 'In crowding, perception of an object deteriorates in the presence of nearby elements. Although crowding is a ubiquitous phenomenon, since elements are rarely seen in isolation, to date there exists no consensus on how to model it. Previous experiments showed that the global configuration of the entire stimulus must be taken into account. These findings rule out simple pooling or substitution models and favor models sensitive to global spatial aspects. In order to investigate how to incorporate global aspects into models, we tested a large number of models with a database of forty stimuli tailored for the global aspects of crowding. Our results show that incorporating grouping like components strongly improves model performance.'),
(89683, 2019, 'plos', 'DoGNet: A deep architecture for synapse detection in multiplexed fluorescence images', 'Kulikov, Victor|Guo, Syuan-Ming|Stone, Matthew|Goodman, Allen|Carpenter, Anne|Bathe, Mark|Lempitsky, Victor', 'CDISE, Skoltech, Moscow, Russian Federation.|Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.|Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.|Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America.|Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America.|Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.|CDISE, Skoltech, Moscow, Russian Federation.', 'Russian Federation|United States|United States|United States|United States|United States|Russian Fed', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1007012', '31083649', 'Neuronal synapses transmit electrochemical signals between cells through the coordinated action of presynaptic vesicles, ion channels, scaffolding and adapter proteins, and membrane receptors. In situ structural characterization of numerous synaptic proteins simultaneously through multiplexed imaging facilitates a bottom-up approach to synapse classification and phenotypic description. Objective automation of efficient and reliable synapse detection within these datasets is essential for the high-throughput investigation of synaptic features. Convolutional neural networks can solve this generalized problem of synapse detection, however, these architectures require large numbers of training examples to optimize their thousands of parameters. We propose DoGNet, a neural network architecture that closes the gap between classical computer vision blob detectors, such as Difference of Gaussians (DoG) filters, and modern convolutional networks. DoGNet is optimized to analyze highly multiplexed microscopy data. Its small number of training parameters allows DoGNet to be trained with few examples, which facilitates its application to new datasets without overfitting. We evaluate the method on multiplexed fluorescence imaging data from both primary mouse neuronal cultures and mouse cortex tissue slices. We show that DoGNet outperforms convolutional networks with a low-to-moderate number of training examples, and DoGNet is efficiently transferred between datasets collected from separate research groups. DoGNet synapse localizations can then be used to guide the segmentation of individual synaptic protein locations and spatial extents, revealing their spatial organization and relative abundances within individual synapses. The source code is publicly available: https://github.com/kulikovv/dognet.'),
(89684, 2019, 'plos', 'Enhanced flux prediction by integrating relative expression and relative metabolite abundance into thermodynamically consistent metabolic models', 'Pandey, Vikash|Hadadi, Noushin|Hatzimanikatis, Vassily', 'Laboratory of Computational Systems Biotechnology, EPFL, Lausanne, Switzerland.|Laboratory of Computational Systems Biotechnology, EPFL, Lausanne, Switzerland.|Laboratory of Computational Systems Biotechnology, EPFL, Lausanne, Switzerland.', 'Switzerland|Switzerland|Switzerland', 1, '-', 'Journal Article', '10.1371/journal.pcbi.1007036', '31083653', 'The ever-increasing availability of transcriptomic and metabolomic data can be used to deeply analyze and make ever-expanding predictions about biological processes, as changes in the reaction fluxes through genome-wide pathways can now be tracked. Currently, constraint-based metabolic modeling approaches, such as flux balance analysis (FBA), can quantify metabolic fluxes and make steady-state flux predictions on a genome-wide scale using optimization principles. However, relating the differential gene expression or differential metabolite abundances in different physiological states to the differential flux profiles remains a challenge. Here we present a novel method, named REMI (Relative Expression and Metabolomic Integrations), that employs genome-scale metabolic models (GEMs) to translate differential gene expression and metabolite abundance data obtained through genetic or environmental perturbations into differential fluxes to analyze the altered physiology for any given pair of conditions. REMI allows for gene-expression, metabolite abundance, and thermodynamic data to be integrated into a single framework, then uses optimization principles to maximize the consistency between the differential gene-expression levels and metabolite abundance data and the estimated differential fluxes and thermodynamic constraints. We applied REMI to integrate into the Escherichia coli GEM publicly available sets of expression and metabolomic data obtained from two independent studies and under wide-ranging conditions. The differential flux distributions obtained from REMI corresponding to the various perturbations better agreed with the measured fluxomic data, and thus better reflected the different physiological states, than a traditional model. Compared to the similar alternative method that provides one solution from the solution space, REMI was able to enumerate several alternative flux profiles using a mixed-integer linear programming approach. Using this important advantage, we performed a high-frequency analysis of common genes and their associated reactions in the obtained alternative solutions and identified the most commonly regulated genes across any two given conditions. We illustrate that this new implementation provides more robust and biologically relevant results for a better understanding of the system physiology.'),
(89685, 2019, 'plos', 'The ability of transcription factors to differentially regulate gene expression is a crucial component of the mechanism underlying inversion, a frequently observed genetic interaction pattern', 'Amini, Saman|Jacobsen, Annika|Ivanova, Olga|Lijnzaad, Philip|Heringa, Jaap|Holstege, Frank C P|Feenstra, K Anton|Kemmeren, Patrick', 'Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands.|Centre for Integrative Bioinformatics (IBIVU), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.|Centre for Integrative Bioinformatics (IBIVU), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.|Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands.|Centre for Integrative Bioinformatics (IBIVU), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.|Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands.|Centre for Integrative Bioinformatics (IBIVU), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.|Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 3, '-', 'Journal Article', '10.1371/journal.pcbi.1007061', '31083661', 'Genetic interactions, a phenomenon whereby combinations of mutations lead to unexpected effects, reflect how cellular processes are wired and play an important role in complex genetic diseases. Understanding the molecular basis of genetic interactions is crucial for deciphering pathway organization as well as understanding the relationship between genetic variation and disease. Several hypothetical molecular mechanisms have been linked to different genetic interaction types. However, differences in genetic interaction patterns and their underlying mechanisms have not yet been compared systematically between different functional gene classes. Here, differences in the occurrence and types of genetic interactions are compared for two classes, gene-specific transcription factors (GSTFs) and signaling genes (kinases and phosphatases). Genome-wide gene expression data for 63 single and double deletion mutants in bakers yeast reveals that the two most common genetic interaction patterns are buffering and inversion. Buffering is typically associated with redundancy and is well understood. In inversion, genes show opposite behavior in the double mutant compared to the corresponding single mutants. The underlying mechanism is poorly understood. Although both classes show buffering and inversion patterns, the prevalence of inversion is much stronger in GSTFs. To decipher potential mechanisms, a Petri Net modeling approach was employed, where genes are represented as nodes and relationships between genes as edges. This allowed over 9 million possible three and four node models to be exhaustively enumerated. The models show that a quantitative difference in interaction strength is a strict requirement for obtaining inversion. In addition, this difference is frequently accompanied with a second gene that shows buffering. Taken together, these results provide a mechanistic explanation for inversion. Furthermore, the ability of transcription factors to differentially regulate expression of their targets provides a likely explanation why inversion is more prevalent for GSTFs compared to kinases and phosphatases.'),
(89686, 2019, 'plos', 'Spatial neuronal synchronization and the waveform of oscillations: Implications for EEG and MEG', 'Schaworonkow, Natalie|Nikulin, Vadim V', 'Frankfurt Institute for Advanced Studies, Johann Wolfgang Goethe University, Frankfurt am Main, Germany.|Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.', 'Germany|Germany', 1, '-', 'Journal Article', '10.1371/journal.pcbi.1007055', '31086368', 'Neuronal oscillations are ubiquitous in the human brain and are implicated in virtually all brain functions. Although they can be described by a prominent peak in the power spectrum, their waveform is not necessarily sinusoidal and shows rather complex morphology. Both frequency and temporal descriptions of such non-sinusoidal neuronal oscillations can be utilized. However, in non-invasive EEG/MEG recordings the waveform of oscillations often takes a sinusoidal shape which in turn leads to a rather oversimplified view on oscillatory processes. In this study, we show in simulations how spatial synchronization can mask non-sinusoidal features of the underlying rhythmic neuronal processes. Consequently, the degree of non-sinusoidality can serve as a measure of spatial synchronization. To confirm this empirically, we show that a mixture of EEG components is indeed associated with more sinusoidal oscillations compared to the waveform of oscillations in each constituent component. Using simulations, we also show that the spatial mixing of the non-sinusoidal neuronal signals strongly affects the amplitude ratio of the spectral harmonics constituting the waveform. Finally, our simulations show how spatial mixing can affect the strength and even the direction of the amplitude coupling between constituent neuronal harmonics at different frequencies. Validating these simulations, we also demonstrate these effects in real EEG recordings. Our findings have far reaching implications for the neurophysiological interpretation of spectral profiles, cross-frequency interactions, as well as for the unequivocal determination of oscillatory phase.'),
(89687, 2019, 'plos', 'Interacting cells driving the evolution of multicellular life cycles', 'Gao, Yuanxiao|Traulsen, Arne|Pichugin, Yuriy', 'Max Planck Institute for Evolutionary Biology, August-Thienemann-Str. 2, 24306 Plön, Germany.|Max Planck Institute for Evolutionary Biology, August-Thienemann-Str. 2, 24306 Plön, Germany.|Max Planck Institute for Evolutionary Biology, August-Thienemann-Str. 2, 24306 Plön, Germany.', 'Germany|Germany|Germany', 1, '-', 'Journal Article', '10.1371/journal.pcbi.1006987', '31086369', 'Evolution of complex multicellular life began from the emergence of a life cycle involving the formation of cell clusters. The opportunity for cells to interact within clusters provided them with an advantage over unicellular life forms. However, what kind of interactions may lead to the evolution of multicellular life cycles? Here, we combine evolutionary game theory with a model for the emergence of multicellular groups to investigate how cell interactions can influence reproduction modes during the early stages of the evolution of multicellularity. In our model, the presence of both cell types is maintained by stochastic phenotype switching during cell division. We identify evolutionary optimal life cycles as those which maximize the population growth rate. Among all interactions captured by two-player games, the vast majority promotes two classes of life cycles: (i) splitting into unicellular propagules or (ii) fragmentation into two offspring clusters of equal (or almost equal) size. Our findings indicate that the three most important characteristics, determining whether multicellular life cycles will evolve, are the average performance of homogeneous groups, heterogeneous groups, and solitary cells.'),
(89688, 2019, 'plos', 'Human representation of multimodal distributions as clusters of samples', 'Sun, Jingwei|Li, Jian|Zhang, Hang', 'School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China.|School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China.|School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China.', 'China|China|China', 1, '-', 'Journal Article', '10.1371/journal.pcbi.1007047', '31086374', 'Behavioral and neuroimaging evidence shows that human decisions are sensitive to the statistical regularities (mean, variance, skewness, etc.) of reward distributions. However, it is unclear what representations human observers form to approximate reward distributions, or probability distributions in general. When the possible values of a probability distribution are numerous, it is cognitively costly and perhaps unrealistic to maintain in mind the probability of each possible value. Here we propose a Clusters of Samples (CoS) representation model: The samples of the to-be-represented distribution are classified into a small number of clusters and only the centroids and relative weights of the clusters are retained for future use. We tested the behavioral relevance of CoS in four experiments. On each trial, human subjects reported the mean and mode of a sequentially presented multimodal distribution of spatial positions or orientations. By varying the global and local features of the distributions, we observed systematic errors in the reported mean and mode. We found that our CoS representation of probability distributions outperformed alternative models in accounting for subjects response patterns. The ostensible influence of positive/negative skewness on the over/under estimation of the reported mean, analogous to the skewness preference phenomenon in decisions, could be well explained by models based on CoS.'),
(89689, 2019, 'plos', 'DigChem: Identification of disease-gene-chemical relationships from Medline abstracts', 'Kim, Jeongkyun|Kim, Jung-Jae|Lee, Hyunju', 'Gwangju Institute of Science and Technology, School of Electrical Engineering and Computer Science, Gwangju, Korea.|Institute for Infocomm Research, A-STAR, 138632, Singapore.|Gwangju Institute of Science and Technology, School of Electrical Engineering and Computer Science, Gwangju, Korea.', '-|Singapore|-', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1007022', '31091224', 'Chemicals interact with genes in the process of disease development and treatment. Although much biomedical research has been performed to understand relationships among genes, chemicals, and diseases, which have been reported in biomedical articles in Medline, there are few studies that extract disease-gene-chemical relationships from biomedical literature at a PubMed scale. In this study, we propose a deep learning model based on bidirectional long short-term memory to identify the evidence sentences of relationships among genes, chemicals, and diseases from Medline abstracts. Then, we develop the search engine DigChem to enable disease-gene-chemical relationship searches for 35,124 genes, 56,382 chemicals, and 5,675 diseases. We show that the identified relationships are reliable by comparing them with manual curation and existing databases. DigChem is available at http://gcancer.org/digchem.'),
(89690, 2019, 'plos', 'Beyond core object recognition: Recurrent processes account for object recognition under occlusion', 'Rajaei, Karim|Mohsenzadeh, Yalda|Ebrahimpour, Reza|Khaligh-Razavi, Seyed-Mahdi', 'School of Cognitive Sciences (SCS), Institute for Research in Fundamental Sciences (IPM), Niavaran, Tehran, Iran.|Computer Science and AI Lab (CSAIL), MIT, Cambridge, Massachusetts, United States of America.|School of Cognitive Sciences (SCS), Institute for Research in Fundamental Sciences (IPM), Niavaran, Tehran, Iran.|Computer Science and AI Lab (CSAIL), MIT, Cambridge, Massachusetts, United States of America.', '-|United States|-|United States', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1007001', '31091234', 'Core object recognition, the ability to rapidly recognize objects despite variations in their appearance, is largely solved through the feedforward processing of visual information. Deep neural networks are shown to achieve human-level performance in these tasks, and explain the primate brain representation. On the other hand, object recognition under more challenging conditions (i.e. beyond the core recognition problem) is less characterized. One such example is object recognition under occlusion. It is unclear to what extent feedforward and recurrent processes contribute in object recognition under occlusion. Furthermore, we do not know whether the conventional deep neural networks, such as AlexNet, which were shown to be successful in solving core object recognition, can perform similarly well in problems that go beyond the core recognition. Here, we characterize neural dynamics of object recognition under occlusion, using magnetoencephalography (MEG), while participants were presented with images of objects with various levels of occlusion. We provide evidence from multivariate analysis of MEG data, behavioral data, and computational modelling, demonstrating an essential role for recurrent processes in object recognition under occlusion. Furthermore, the computational model with local recurrent connections, used here, suggests a mechanistic explanation of how the human brain might be solving this problem.'),
(89691, 2019, 'plos', 'Experimentally-constrained biophysical models of tonic and burst firing modes in thalamocortical neurons', 'Iavarone, Elisabetta|Yi, Jane|Shi, Ying|Zandt, Bas-Jan|OReilly, Christian|Van Geit, Werner|Rössert, Christian|Markram, Henry|Hill, Sean L', 'Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, Geneva, Switzerland.|Laboratory of Neural Microcircuitry, Brain Mind Institute, École polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland.|Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, Geneva, Switzerland.|Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, Geneva, Switzerland.|Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, Geneva, Switzerland.|Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, Geneva, Switzerland.|Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, Geneva, Switzerland.|Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, Geneva, Switzerland.|Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, Geneva, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Swit', 1, '-', 'Journal Article', '10.1371/journal.pcbi.1006753', '31095552', 'Somatosensory thalamocortical (TC) neurons from the ventrobasal (VB) thalamus are central components in the flow of sensory information between the periphery and the cerebral cortex, and participate in the dynamic regulation of thalamocortical states including wakefulness and sleep. This property is reflected at the cellular level by the ability to generate action potentials in two distinct firing modes, called tonic firing and low-threshold bursting. Although the general properties of TC neurons are known, we still lack a detailed characterization of their morphological and electrical properties in the VB thalamus. The aim of this study was to build biophysically-detailed models of VB TC neurons explicitly constrained with experimental data from rats. We recorded the electrical activity of VB neurons (N = 49) and reconstructed morphologies in 3D (N = 50) by applying standardized protocols. After identifying distinct electrical types, we used a multi-objective optimization to fit single neuron electrical models (e-models), which yielded multiple solutions consistent with the experimental data. The models were tested for generalization using electrical stimuli and neuron morphologies not used during fitting. A local sensitivity analysis revealed that the e-models are robust to small parameter changes and that all the parameters were constrained by one or more features. The e-models, when tested in combination with different morphologies, showed that the electrical behavior is substantially preserved when changing dendritic structure and that the e-models were not overfit to a specific morphology. The models and their analysis show that automatic parameter search can be applied to capture complex firing behavior, such as co-existence of tonic firing and low-threshold bursting over a wide range of parameter sets and in combination with different neuron morphologies.'),
(89692, 2019, 'plos', 'Ten simple rules for carrying out and writing meta-analyses', 'Forero, Diego A|Lopez-Leon, Sandra|González-Giraldo, Yeimy|Bagos, Pantelis G', 'Laboratory of NeuroPsychiatric Genetics, Biomedical Sciences Research Group, School of Medicine, Universidad Antonio Nariño, Bogotá, Colombia.|Novartis Pharmaceuticals Corporation, East Hanover, New Jersey, United States of America.|Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Bogotá., Colombia.|Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece.', 'Colombia|Jersey|Colombia|Greece', 0, '-', 'Editorial', '10.1371/journal.pcbi.1006922', '31095553', '-'),
(89693, 2019, 'plos', 'Securing the future of research computing in the biosciences', 'Leng, Joanna|Shoura, Massa|McLeish, Tom C B|Real, Alan N|Hardey, Mariann|McCafferty, James|Ranson, Neil A|Harris, Sarah A', 'School of Computing, University of Leeds, Leeds, United Kingdom.|School of Pathology, Stanford University, Palo Alto, California, United States of America.|Department of Physics, University of York, York, United Kingdom.|Advanced Research Computing, University of Durham, Durham, United Kingdom.|Advanced Research Computing, University of Durham, Durham, United Kingdom.|Information Services Division, UCL, London, United Kingdom.|Astbury Centre for Structural and Molecular Biology, University of Leeds, Leeds, United Kingdom.|Astbury Centre for Structural and Molecular Biology, University of Leeds, Leeds, United Kingdom.', 'United Kingdom|United States|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United King', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1006958', '31095554', 'Improvements in technology often drive scientific discovery. Therefore, research requires sustained investment in the latest equipment and training for the researchers who are going to use it. Prioritising and administering infrastructure investment is challenging because future needs are difficult to predict. In the past, highly computationally demanding research was associated primarily with particle physics and astronomy experiments. However, as biology becomes more quantitative and bioscientists generate more and more data, their computational requirements may ultimately exceed those of physical scientists. Computation has always been central to bioinformatics, but now imaging experiments have rapidly growing data processing and storage requirements. There is also an urgent need for new modelling and simulation tools to provide insight and understanding of these biophysical experiments. Bioscience communities must work together to provide the software and skills training needed in their areas. Research-active institutions need to recognise that computation is now vital in many more areas of discovery and create an environment where it can be embraced. The public must also become aware of both the power and limitations of computing, particularly with respect to their health and personal data.'),
(89694, 2019, 'plos', 'Evolution of major histocompatibility complex gene copy number', 'Bentkowski, Piotr|Radwan, Jacek', 'Evolutionary Biology Group, Faculty of Biology, Adam Mickiewicz University in Poznaxc5x84, Poland.|Evolutionary Biology Group, Faculty of Biology, Adam Mickiewicz University in Poznaxc5x84, Poland.', 'Poland|Poland', 2, '-', 'Journal Article', '10.1371/journal.pcbi.1007015', '31095555', 'MHC genes, which code for proteins responsible for presenting pathogen-derived antigens to the host immune system, show remarkable copy-number variation both between and within species. However, the evolutionary forces driving this variation are poorly understood. Here, we use computer simulations to investigate whether evolution of the number of MHC variants in the genome can be shaped by the number of pathogen species the host population encounters (pathogen richness). Our model assumed that while increasing a range of pathogens recognised, expressing additional MHC variants also incurs costs such as an increased risk of autoimmunity. We found that pathogen richness selected for high MHC copy number only when the costs were low. Furthermore, the shape of the association was modified by the rate of pathogen evolution, with faster pathogen mutation rates selecting for increased host MHC copy number, but only when pathogen richness was low to moderate. Thus, taking into account factors other than pathogen richness may help explain wide variation between vertebrate species in the number of MHC genes. Within population, variation in the number of unique MHC variants carried by individuals (INV) was observed under most parameter combinations, except at low pathogen richness. This variance gave rise to positive correlations between INV and host immunocompetence (proportion of pathogens recognised). However, within-population variation in host immunocompetence declined with pathogen richness. Thus, counterintuitively, pathogens can contribute more to genetic variance for host fitness in species exposed to fewer pathogen species, with consequences to predictions from Hamilton-Zuk theory of sexual selection.'),
(89695, 2019, 'plos', 'Amplifying the redistribution of somato-dendritic inhibition by the interplay of three interneuron types', 'Hertäg, Loreen|Sprekeler, Henning', 'Modelling of Cognitive Processes, Berlin Institute of Technology, Berlin, Germany.|Modelling of Cognitive Processes, Berlin Institute of Technology, Berlin, Germany.', 'Germany|Germany', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1006999', '31095556', 'GABAergic interneurons play an important role in shaping the activity of excitatory pyramidal cells (PCs). How the various inhibitory cell types contribute to neuronal information processing, however, is not resolved. Here, we propose a functional role for a widespread network motif consisting of parvalbumin- (PV), somatostatin- (SOM) and vasoactive intestinal peptide (VIP)-expressing interneurons. Following the idea that PV and SOM interneurons control the distribution of somatic and dendritic inhibition onto PCs, we suggest that mutual inhibition between VIP and SOM cells translates weak inputs to VIP interneurons into large changes of somato-dendritic inhibition of PCs. Using a computational model, we show that the neuronal and synaptic properties of the circuit support this hypothesis. Moreover, we demonstrate that the SOM-VIP motif allows transient inputs to persistently switch the circuit between two processing modes, in which top-down inputs onto apical dendrites of PCs are either integrated or cancelled.'),
(89696, 2019, 'plos', 'Drug combination sensitivity scoring facilitates the discovery of synergistic and efficacious drug combinations in cancer', 'Malyutina, Alina|Majumder, Muntasir Mamun|Wang, Wenyu|Pessia, Alberto|Heckman, Caroline A|Tang, Jing', 'Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland.|Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland.|Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland.|Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland.|Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland.|Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland.', 'Finland|Finland|Finland|Finland|Finland|Finland', 5, '-', 'Journal Article', '10.1371/journal.pcbi.1006752', '31107860', 'High-throughput drug screening has facilitated the discovery of drug combinations in cancer. Many existing studies adopted a full matrix design, aiming for the characterization of drug pair effects for cancer cells. However, the full matrix design may be suboptimal as it requires a drug pair to be combined at multiple concentrations in a full factorial manner. Furthermore, many of the computational tools assess only the synergy but not the sensitivity of drug combinations, which might lead to false positive discoveries. We proposed a novel cross design to enable a more cost-effective and simultaneous testing of drug combination sensitivity and synergy. We developed a drug combination sensitivity score (CSS) to determine the sensitivity of a drug pair, and showed that the CSS is highly reproducible between the replicates and thus supported its usage as a robust metric. We further showed that CSS can be predicted using machine learning approaches which determined the top pharmaco-features to cluster cancer cell lines based on their drug combination sensitivity profiles. To assess the degree of drug interactions using the cross design, we developed an S synergy score based on the difference between the drug combination and the single drug dose-response curves. We showed that the S score is able to detect true synergistic and antagonistic drug combinations at an accuracy level comparable to that using the full matrix design. Taken together, we showed that the cross design coupled with the CSS sensitivity and S synergy scoring methods may provide a robust and accurate characterization of both drug combination sensitivity and synergy levels, with minimal experimental materials required. Our experimental-computational approach could be utilized as an efficient pipeline for improving the discovery rate in high-throughput drug combination screening, particularly for primary patient samples which are difficult to obtain.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(89697, 2019, 'plos', 'Reduced level of docosahexaenoic acid shifts GPCR neuroreceptors to less ordered membrane regions', 'Javanainen, Matti|Enkavi, Giray|Guixà-Gonzaléz, Ramon|Kulig, Waldemar|Martinez-Seara, Hector|Levental, Ilya|Vattulainen, Ilpo', 'Computational Physics Laboratory, Tampere University, Tampere, Finland.|Computational Physics Laboratory, Tampere University, Tampere, Finland.|Laboratory of Computational Medicine, Biostatistics Unit, Faculty of Medicine, Autonomous University of Barcelona, Bellaterra, Spain.|Computational Physics Laboratory, Tampere University, Tampere, Finland.|Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic.|Department of Integrated Biology and Pharmacology, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, United States of America.|Computational Physics Laboratory, Tampere University, Tampere, Finland.', 'Finland|Finland|Spain|Finland|-|United States|Finland', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1007033', '31107861', 'G protein-coupled receptors (GPCRs) control cellular signaling and responses. Many of these GPCRs are modulated by cholesterol and polyunsaturated fatty acids (PUFAs) which have been shown to co-exist with saturated lipids in ordered membrane domains. However, the lipid compositions of such domains extracted from the brain cortex tissue of individuals suffering from GPCR-associated neurological disorders show drastically lowered levels of PUFAs. Here, using free energy techniques and multiscale simulations of numerous membrane proteins, we show that the presence of the PUFA DHA helps helical multi-pass proteins such as GPCRs partition into ordered membrane domains. The mechanism is based on hybrid lipids, whose PUFA chains coat the rough protein surface, while the saturated chains face the raft environment, thus minimizing perturbations therein. Our findings suggest that the reduction of GPCR partitioning to their native ordered environments due to PUFA depletion might affect the function of these receptors in numerous neurodegenerative diseases, where the membrane PUFA levels in the brain are decreased. We hope that this work inspires experimental studies on the connection between membrane PUFA levels and GPCR signaling.'),
(89698, 2019, 'plos', 'Detecting interaction networks in the human microbiome with conditional Granger causality', 'Mainali, Kumar|Bewick, Sharon|Vecchio-Pagan, Briana|Karig, David|Fagan, William F', 'Department of Biology, University of Maryland, College Park, Maryland, United States of America.|Department of Biology, University of Maryland, College Park, Maryland, United States of America.|Research and Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, Laurel, Maryland, United States of America.|Research and Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, Laurel, Maryland, United States of America.|Department of Biology, University of Maryland, College Park, Maryland, United States of America.', 'United States|United States|United States|United States|United States', 2, '-', 'Journal Article', '10.1371/journal.pcbi.1007037', '31107866', 'Human microbiome research is rife with studies attempting to deduce microbial correlation networks from sequencing data. Standard correlation and/or network analyses may be misleading when taken as an indication of taxon interactions because correlation is neither necessary nor sufficient to establish causation, environmental filtering can lead to correlation between non-interacting taxa. Unfortunately, microbial ecologists have generally used correlation as a proxy for causality although there is a general consensus about what constitutes a causal relationship: causes both precede and predict effects. We apply one of the first causal models for detecting interactions in human microbiome samples. Specifically, we analyze a long duration, high resolution time series of the human microbiome to decipher the networks of correlation and causation of human-associated microbial genera. We show that correlation is not a good proxy for biological interaction, we observed a weak negative relationship between correlation and causality. Strong interspecific interactions are disproportionately positive, whereas almost all strong intraspecific interactions are negative. Interestingly, intraspecific interactions also appear to act at a short timescale causing vast majority of the effects within 1-3 days. We report how different taxa are involved in causal relationships with others, and show that strong interspecific interactions are rarely conserved across two body sites whereas strong intraspecific interactions are much more conserved, ranging from 33% between the gut and right-hand to 70% between the two hands. Therefore, in the absence of guiding assumptions about ecological interactions, Granger causality and related techniques may be particularly helpful for understanding the driving factors governing microbiome composition and structure.'),
(89699, 2019, 'plos', 'Host contact dynamics shapes richness and dominance of pathogen strains', 'Pinotti, Francesco|Fleury, Éric|Guillemot, Didier|Böelle, Pierre-Yves|Poletto, Chiara', 'INSERM, Sorbonne Université, Institut Pierre Louis dÉpidémiologie et de Santé Publique (IPLESP), 75012 Paris, France.|INRIA, Paris, France.|Inserm, UVSQ, Institut Pasteur, Université Paris-Saclay, Biostatistics, Biomathematics, Pharmacoepidemiology and Infectious Diseases (B2PHI), Paris, France.|INSERM, Sorbonne Université, Institut Pierre Louis dÉpidémiologie et de Santé Publique (IPLESP), 75012 Paris, France.|INSERM, Sorbonne Université, Institut Pierre Louis dÉpidémiologie et de Santé Publique (IPLESP), 75012 Paris, France.', 'France|France|France|France|France', 2, '-', 'Journal Article', '10.1371/journal.pcbi.1006530', '31112541', 'The interaction among multiple microbial strains affects the spread of infectious diseases and the efficacy of interventions. Genomic tools have made it increasingly easy to observe pathogenic strains diversity, but the best interpretation of such diversity has remained difficult because of relationships with host and environmental factors. Here, we focus on host-to-host contact behavior and study how it changes populations of pathogens in a minimal model of multi-strain interaction. We simulated a population of identical strains competing by mutual exclusion and spreading on a dynamical network of hosts according to a stochastic susceptible-infectious-susceptible model. We computed ecological indicators of diversity and dominance in strain populations for a collection of networks illustrating various properties found in real-world examples. Heterogeneities in the number of contacts among hosts were found to reduce diversity and increase dominance by making the repartition of strains among infected hosts more uneven, while strong community structure among hosts increased strain diversity. We found that the introduction of strains associated with hosts entering and leaving the system led to the highest pathogenic richness at intermediate turnover levels. These results were finally illustrated using the spread of Staphylococcus aureus in a long-term health-care facility where close proximity interactions and strain carriage were collected simultaneously. We found that network structural and temporal properties could account for a large part of the variability observed in strain diversity. These results show how stochasticity and network structure affect the population ecology of pathogens and warn against interpreting observations as unambiguous evidence of epidemiological differences between strains.'),
(89700, 2019, 'plos', 'Noise-precision tradeoff in predicting combinations of mutations and drugs', 'Tendler, Avichai|Zimmer, Anat|Mayo, Avi|Alon, Uri', 'Dept. Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.|Dept. Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.|Dept. Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.|Dept. Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.', 'Israel|Israel|Israel|Israel', 3, '-', 'Journal Article', '10.1371/journal.pcbi.1006956', '31116755', 'Many biological problems involve the response to multiple perturbations. Examples include response to combinations of many drugs, and the effects of combinations of many mutations. Such problems have an exponentially large space of combinations, which makes it infeasible to cover the entire space experimentally. To overcome this problem, several formulae that predict the effect of drug combinations or fitness landscape values have been proposed. These formulae use the effects of single perturbations and pairs of perturbations to predict triplets and higher order combinations. Interestingly, different formulae perform best on different datasets. Here we use Pareto optimality theory to quantitatively explain why no formula is optimal for all datasets, due to an inherent bias-variance (noise-precision) tradeoff. We calculate the Pareto front of log-linear formulae and find that the optimal formula depends on properties of the dataset: the typical interaction strength and the experimental noise. This study provides an approach to choose a suitable prediction formula for a given dataset, in order to best overcome the combinatorial explosion problem.'),
(89701, 2019, 'plos', 'Efficient algorithms to discover alterations with complementary functional association in cancer', 'Sarto Basso, Rebecca|Hochbaum, Dorit S|Vandin, Fabio', 'Department of Industrial Engineering and Operations Research, University of California at Berkeley, Berkeley, CA, USA.|Department of Industrial Engineering and Operations Research, University of California at Berkeley, Berkeley, CA, USA.|Department of Information Engineering, University of Padova, Padova, Italy.', 'United States|United States|Italy', 1, '-', 'Journal Article', '10.1371/journal.pcbi.1006802', '31120875', 'Recent large cancer studies have measured somatic alterations in an unprecedented number of tumours. These large datasets allow the identification of cancer-related sets of genetic alterations by identifying relevant combinatorial patterns. Among such patterns, mutual exclusivity has been employed by several recent methods that have shown its effectiveness in characterizing gene sets associated to cancer. Mutual exclusivity arises because of the complementarity, at the functional level, of alterations in genes which are part of a group (e.g., a pathway) performing a given function. The availability of quantitative target profiles, from genetic perturbations or from clinical phenotypes, provides additional information that can be leveraged to improve the identification of cancer related gene sets by discovering groups with complementary functional associations with such targets. In this work we study the problem of finding groups of mutually exclusive alterations associated with a quantitative (functional) target. We propose a combinatorial formulation for the problem, and prove that the associated computational problem is computationally hard. We design two algorithms to solve the problem and implement them in our tool UNCOVER. We provide analytic evidence of the effectiveness of UNCOVER in finding high-quality solutions and show experimentally that UNCOVER finds sets of alterations significantly associated with functional targets in a variety of scenarios. In particular, we show that our algorithms find sets which are better than the ones obtained by the state-of-the-art method, even when sets are evaluated using the statistical score employed by the latter. In addition, our algorithms are much faster than the state-of-the-art, allowing the analysis of large datasets of thousands of target profiles from cancer cell lines. We show that on two such datasets, one from project Achilles and one from the Genomics of Drug Sensitivity in Cancer project, UNCOVER identifies several significant gene sets with complementary functional associations with targets. Software available at: https://github.com/VandinLab/UNCOVER.'),
(89702, 2019, 'plos', 'Epidemic prevalence information on social networks can mediate emergent collective outcomes in voluntary vaccine schemes', 'Sharma, Anupama|Menon, Shakti N|Sasidevan, V|Sinha, Sitabhra', 'The Institute of Mathematical Sciences, CIT Campus, Taramani, Chennai, India.|The Institute of Mathematical Sciences, CIT Campus, Taramani, Chennai, India.|The Institute of Mathematical Sciences, CIT Campus, Taramani, Chennai, India.|The Institute of Mathematical Sciences, CIT Campus, Taramani, Chennai, India.', 'India|India|India|India', 2, '-', 'Journal Article', '10.1371/journal.pcbi.1006977', '31120877', 'The effectiveness of a mass vaccination program can engender its own undoing if individuals choose to not get vaccinated believing that they are already protected by herd immunity. This would appear to be the optimal decision for an individual, based on a strategic appraisal of her costs and benefits, even though she would be vulnerable during subsequent outbreaks if the majority of the population argues in this manner. We investigate how voluntary vaccination can nevertheless emerge in a social network of rational agents, who make informed decisions whether to be vaccinated, integrated with a model of epidemic dynamics. The information available to each agent includes the prevalence of the disease in their local network neighborhood and/or globally in the population, as well as the fraction of their neighbors that are protected against the disease. Crucially, the payoffs governing the decision of agents vary with disease prevalence, resulting in the vaccine uptake behavior changing in response to contagion spreading. The collective behavior of the agents responding to local prevalence can lead to a significant reduction in the final epidemic size, particularly for less contagious diseases having low basic reproduction number [Formula: see text]. Near the epidemic threshold ([Formula: see text]) the use of local prevalence information can result in divergent responses in the final vaccine coverage. Our results suggest that heterogeneity in the risk perception resulting from the spatio-temporal evolution of an epidemic differentially affects agents payoffs, which is a critical determinant of the success of voluntary vaccination schemes.'),
(89703, 2019, 'plos', 'Wisdom of crowds in computational biology', 'Papin, Jason A|Mac Gabhann, Feilim', 'University of Virginia, Charlottesville, Virginia, United States of America.|Johns Hopkins University, Baltimore, Maryland, United States of America.', 'United States|United States', 1, '-', 'Editorial', '10.1371/journal.pcbi.1007032', '31120879', '-'),
(89704, 2019, 'plos', 'A chemical kinetic basis for measuring translation initiation and elongation rates from ribosome profiling data', 'Sharma, Ajeet K|Sormanni, Pietro|Ahmed, Nabeel|Ciryam, Prajwal|Friedrich, Ulrike A|Kramer, Günter|OBrien, Edward P', 'Department of Chemistry, Pennsylvania State University, University Park, Pennsylvania, United States of America.|Centre for Misfolding Diseases, Department of Chemistry, University of Cambridge, Cambridge, United Kingdom.|Bioinformatics and Genomics Graduate Program, The Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, Pennsylvania, United States of America.|Centre for Misfolding Diseases, Department of Chemistry, University of Cambridge, Cambridge, United Kingdom.|Center for Molecular Biology of the Heidelberg University (ZMBH), DKFZ-ZMBH Alliance, Heidelberg, Germany.|Center for Molecular Biology of the Heidelberg University (ZMBH), DKFZ-ZMBH Alliance, Heidelberg, Germany.|Department of Chemistry, Pennsylvania State University, University Park, Pennsylvania, United States of America.', 'United States|United Kingdom|United States|United Kingdom|Germany|Germany|United States', 8, '-', 'Journal Article', '10.1371/journal.pcbi.1007070', '31120880', 'Analysis methods based on simulations and optimization have been previously developed to estimate relative translation rates from next-generation sequencing data. Translation involves molecules and chemical reactions, hence bioinformatics methods consistent with the laws of chemistry and physics are more likely to produce accurate results. Here, we derive simple equations based on chemical kinetic principles to measure the translation-initiation rate, transcriptome-wide elongation rate, and individual codon translation rates from ribosome profiling experiments. Our methods reproduce the known rates from ribosome profiles generated from detailed simulations of translation. By applying our methods to data from S. cerevisiae and mouse embryonic stem cells, we find that the extracted rates reproduce expected correlations with various molecular properties, and we also find that mouse embryonic stem cells have a global translation speed of 5.2 AA/s, in agreement with previous reports that used other approaches. Our analysis further reveals that a codon can exhibit up to 26-fold variability in its translation rate depending upon its context within a transcript. This broad distribution means that the average translation rate of a codon is not representative of the rate at which most instances of that codon are translated, and it suggests that translational regulation might be used by cells to a greater degree than previously thought.'),
(89705, 2019, 'plos', 'Forecasting national and regional influenza-like illness for the USA', 'Ben-Nun, Michal|Riley, Pete|Turtle, James|Bacon, David P|Riley, Steven', 'Predictive Science Inc., San Diego, CA, USA.|Predictive Science Inc., San Diego, CA, USA.|Predictive Science Inc., San Diego, CA, USA.|Leidos, McLean, VA, USA.|MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 1, '-', 'Journal Article', '10.1371/journal.pcbi.1007013', '31120881', 'Health planners use forecasts of key metrics associated with influenza-like illness (ILI), near-term weekly incidence, week of season onset, week of peak, and intensity of peak. Here, we describe our participation in a weekly prospective ILI forecasting challenge for the United States for the 2016-17 season and subsequent evaluation of our performance. We implemented a metapopulation model framework with 32 model variants. Variants differed from each other in their assumptions about: the force-of-infection (FOI), use of uninformative priors, the use of discounted historical data for not-yet-observed time points, and the treatment of regions as either independent or coupled. Individual model variants were chosen subjectively as the basis for our weekly forecasts, however, a subset of coupled models were only available part way through the season. Most frequently, during the 2016-17 season, we chose, FOI variants with both school vacations and humidity terms, uninformative priors, the inclusion of discounted historical data for not-yet-observed time points, and coupled regions (when available). Our near-term weekly forecasts substantially over-estimated incidence early in the season when coupled models were not available. However, our forecast accuracy improved in absolute terms and relative to other teams once coupled solutions were available. In retrospective analysis, we found that the 2016-17 season was not typical: on average, coupled models performed better when fit without historically augmented data. Also, we tested a simple ensemble model for the 2016-17 season and found that it underperformed our subjective choice for all forecast targets. In this study, we were able to improve accuracy during a prospective forecasting exercise by coupling dynamics between regions. Although reduction of forecast subjectivity should be a long-term goal, some degree of human intervention is likely to improve forecast accuracy in the medium-term in parallel with the systematic consideration of more sophisticated ensemble approaches.'),
(89706, 2019, 'plos', 'Establishing a computational biology flipped classroom', 'Compeau, Phillip', 'Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.', 'United States', 1, '-', 'Journal Article', '10.1371/journal.pcbi.1006764', '31120882', 'In a flipped classroom, students complete automated modules to replace a traditional lecture, allowing the time devoted for the lecture to be devoted to constructive tasks reinforcing student knowledge. Yet although the flipped classroom offers a compelling approach for fostering a constructivist, student-centric learning environment, research on the efficacy of flipped classes has been mixed. For that matter, is it possible to successfully flip a classroom in an advanced, heavily specialized course like a bioinformatics algorithms course? Over the past several years, the author has implemented a flipped version of such a course and will discuss some of the successes and pitfalls encountered.'),
(89707, 2019, 'plos', 'Leveraging genetic interactions for adverse drug-drug interaction prediction', 'Qian, Sheng|Liang, Siqi|Yu, Haiyuan', 'Department of Computational Biology, Cornell University, Ithaca, New York, United States of America.|Department of Computational Biology, Cornell University, Ithaca, New York, United States of America.|Department of Computational Biology, Cornell University, Ithaca, New York, United States of America.', 'United States|United States|United States', 1, '-', 'Journal Article', '10.1371/journal.pcbi.1007068', '31125330', 'In light of increased co-prescription of multiple drugs, the ability to discern and predict drug-drug interactions (DDI) has become crucial to guarantee the safety of patients undergoing treatment with multiple drugs. However, information on DDI profiles is incomplete and the experimental determination of DDIs is labor-intensive and time-consuming. Although previous studies have explored various feature spaces for in silico screening of interacting drug pairs, their use of conventional cross-validation prevents them from achieving generalizable performance on drug pairs where neither drug is seen during training. Here we demonstrate for the first time targets of adversely interacting drug pairs are significantly more likely to have synergistic genetic interactions than non-interacting drug pairs. Leveraging genetic interaction features and a novel training scheme, we construct a gradient boosting-based classifier that achieves robust DDI prediction even for drugs whose interaction profiles are completely unseen during training. We demonstrate that in addition to classification power-including the prediction of 432 novel DDIs-our genetic interaction approach offers interpretability by providing plausible mechanistic insights into the mode of action of DDIs.'),
(89708, 2019, 'plos', 'Modeling visual performance differences around the visual field: A computational observer approach', 'Kupers, Eline R|Carrasco, Marisa|Winawer, Jonathan', 'Department of Psychology, New York University, New York, New York, United States of America.|Department of Psychology, New York University, New York, New York, United States of America.|Department of Psychology, New York University, New York, New York, United States of America.', 'United States|United States|United States', 3, '-', 'Journal Article', '10.1371/journal.pcbi.1007063', '31125331', 'Visual performance depends on polar angle, even when eccentricity is held constant, on many psychophysical tasks observers perform best when stimuli are presented on the horizontal meridian, worst on the upper vertical, and intermediate on the lower vertical meridian. This variation in performance around the visual field can be as pronounced as that of doubling the stimulus eccentricity. The causes of these asymmetries in performance are largely unknown. Some factors in the eye, e.g. cone density, are positively correlated with the reported variations in visual performance with polar angle. However, the question remains whether these correlations can quantitatively explain the perceptual differences observed around the visual field. To investigate the extent to which the earliest stages of vision-optical quality and cone density-contribute to performance differences with polar angle, we created a computational observer model. The model uses the open-source software package ISETBIO to simulate an orientation discrimination task for which visual performance differs with polar angle. The model starts from the photons emitted by a display, which pass through simulated human optics with fixational eye movements, followed by cone isomerizations in the retina. Finally, we classify stimulus orientation using a support vector machine to learn a linear classifier on the photon absorptions. To account for the 30% increase in contrast thresholds for upper vertical compared to horizontal meridian, as observed psychophysically on the same task, our computational observer model would require either an increase of ~7 diopters of defocus or a reduction of 500% in cone density. These values far exceed the actual variations as a function of polar angle observed in human eyes. Therefore, we conclude that these factors in the eye only account for a small fraction of differences in visual performance with polar angle. Substantial additional asymmetries must arise in later retinal and/or cortical processing.'),
(89709, 2019, 'plos', 'Representational structure or task structure? Bias in neural representational similarity analysis and a Bayesian method for reducing bias', 'Cai, Ming Bo|Schuck, Nicolas W|Pillow, Jonathan W|Niv, Yael', 'Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States of America.|Max Planck Research Group Neurocode, Max Planck Institute for Human Development, Berlin, Germany.|Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States of America.|Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States of America.', 'Jersey|Germany|Jersey|Jersey', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1006299', '31125335', 'The activity of neural populations in the brains of humans and animals can exhibit vastly different spatial patterns when faced with different tasks or environmental stimuli. The degrees of similarity between these neural activity patterns in response to different events are used to characterize the representational structure of cognitive states in a neural population. The dominant methods of investigating this similarity structure first estimate neural activity patterns from noisy neural imaging data using linear regression, and then examine the similarity between the estimated patterns. Here, we show that this approach introduces spurious bias structure in the resulting similarity matrix, in particular when applied to fMRI data. This problem is especially severe when the signal-to-noise ratio is low and in cases where experimental conditions cannot be fully randomized in a task. We propose Bayesian Representational Similarity Analysis (BRSA), an alternative method for computing representational similarity, in which we treat the covariance structure of neural activity patterns as a hyper-parameter in a generative model of the neural data. By marginalizing over the unknown activity patterns, we can directly estimate this covariance structure from imaging data. This method offers significant reductions in bias and allows estimation of neural representational similarity with previously unattained levels of precision at low signal-to-noise ratio, without losing the possibility of deriving an interpretable distance measure from the estimated similarity. The method is closely related to Pattern Component Model (PCM), but instead of modeling the estimated neural patterns as in PCM, BRSA models the imaging data directly and is suited for analyzing data in which the order of task conditions is not fully counterbalanced. The probabilistic framework allows for jointly analyzing data from a group of participants. The method can also simultaneously estimate a signal-to-noise ratio map that shows where the learned representational structure is supported more strongly. Both this map and the learned covariance matrix can be used as a structured prior for maximum a posteriori estimation of neural activity patterns, which can be further used for fMRI decoding. Our method therefore paves the way towards a more unified and principled analysis of neural representations underlying fMRI signals. We make our tool freely available in Brain Imaging Analysis Kit (BrainIAK).'),
(89710, 2019, 'plos', 'Sparse discriminative latent characteristics for predicting cancer drug sensitivity from genomic features', 'Knowles, David A|Bouchard, Gina|Plevritis, Sylvia', 'Department of Radiology, Stanford University School of Medicine, Stanford, California, USA.|Department of Radiology, Stanford University School of Medicine, Stanford, California, USA.|Department of Radiology, Stanford University School of Medicine, Stanford, California, USA.', 'United States|United States|United States', 1, '-', 'Journal Article', '10.1371/journal.pcbi.1006743', '31136571', 'Drug screening studies typically involve assaying the sensitivity of a range of cancer cell lines across an array of anti-cancer therapeutics. Alongside these sensitivity measurements high dimensional molecular characterizations of the cell lines are typically available, including gene expression, copy number variation and genomic mutations. We propose a sparse multitask regression model which learns discriminative latent characteristics that predict drug sensitivity and are associated with specific molecular features. We use ideas from Bayesian nonparametrics to automatically infer the appropriate number of these latent characteristics. The resulting analysis couples high predictive performance with interpretability since each latent characteristic involves a typically small set of drugs, cell lines and genomic features. Our model uncovers a number of drug-gene sensitivity associations missed by single gene analyses. We functionally validate one such novel association: that increased expression of the cell-cycle regulator C/EBPxcexb4 decreases sensitivity to the histone deacetylase (HDAC) inhibitor panobinostat.'),
(89711, 2019, 'plos', 'Comprehensively benchmarking applications for detecting copy number variation', 'Zhang, Le|Bai, Wanyu|Yuan, Na|Du, Zhenglin', 'College of Computer Science, Sichuan University, Chengdu, China.|College of Computer Science, Sichuan University, Chengdu, China.|BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, PR China.|BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, PR China.', 'China|China|China|China', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1007069', '31136576', 'Recently, copy number variation (CNV) has gained considerable interest as a type of genomic variation that plays an important role in complex phenotypes and disease susceptibility. Since a number of CNV detection methods have recently been developed, it is necessary to help investigators choose suitable methods for CNV detection depending on their objectives. For this reason, this study compared ten commonly used CNV detection applications, including CNVnator, ReadDepth, RDXplorer, LUMPY and Control-FREEC, benchmarking the applications by sensitivity, specificity and computational demands. Taking the DGV gold standard variants as a standard dataset, we evaluated the ten applications with real sequencing data at sequencing depths from 5X to 50X. Among the ten methods benchmarked, LUMPY performs the best for both high sensitivity and specificity at each sequencing depth. For the purpose of high specificity, Canvas is also a good choice. If high sensitivity is preferred, CNVnator and RDXplorer are better choices. Additionally, CNVnator and GROM-RD perform well for low-depth sequencing data. Our results provide a comprehensive performance evaluation for these selected CNV detection methods and facilitate future development and improvement in CNV prediction methods.'),
(89712, 2019, 'plos', 'Correction: Accounting for non-stationarity in epidemiology by embedding time-varying parameters in stochastic models', 'PLOS Computational Biology Staff', 'BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, PR China.', 'China', 0, '-', 'Published Erratum', '10.1371/journal.pcbi.1007062', '31136579', '[This corrects the article DOI: 10.1371/journal.pcbi.1006211.].'),
(89713, 2019, 'plos', 'Modeling enamel matrix secretion in mammalian teeth', 'Häkkinen, Teemu J|Sova, S Susanna|Corfe, Ian J|Tjäderhane, Leo|Hannukainen, Antti|Jernvall, Jukka', 'Developmental Biology Program, Institute of Biotechnology, University of Helsinki, Helsinki, Finland.|Developmental Biology Program, Institute of Biotechnology, University of Helsinki, Helsinki, Finland.|Developmental Biology Program, Institute of Biotechnology, University of Helsinki, Helsinki, Finland.|Department of Oral and Maxillofacial Diseases, Helsinki University Hospital, University of Helsinki, Helsinki, Finland.|Department of Mathematics and Systems Analysis, Aalto University, Espoo, Finland.|Developmental Biology Program, Institute of Biotechnology, University of Helsinki, Helsinki, Finland.', 'Finland|Finland|Finland|Finland|Finland|Finland', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1007058', '31141513', 'The most mineralized tissue of the mammalian body is tooth enamel. Especially in species with thick enamel, three-dimensional (3D) tomography data has shown that the distribution of enamel varies across the occlusal surface of the tooth crown. Differences in enamel thickness among species and within the tooth crown have been used to examine taxonomic affiliations, life history, and functional properties of teeth. Before becoming fully mineralized, enamel matrix is secreted on the top of a dentine template, and it remains to be explored how matrix thickness is spatially regulated. To provide a predictive framework to examine enamel distribution, we introduce a computational model of enamel matrix secretion that maps the dentine topography to the enamel surface topography. Starting from empirical enamel-dentine junctions, enamel matrix deposition is modeled as a diffusion-limited free boundary problem. Using laboratory microCT and synchrotron tomographic data of pig molars that have markedly different dentine and enamel surface topographies, we show how diffusion-limited matrix deposition accounts for both the process of matrix secretion and the final enamel distribution. Simulations reveal how concave and convex dentine features have distinct effects on enamel surface, thereby explaining why the enamel surface is not a straightforward extrapolation of the dentine template. Human and orangutan molar simulations show that even subtle variation in dentine topography can be mapped to the enamel surface features. Mechanistic models of extracellular matrix deposition can be used to predict occlusal morphologies of teeth.'),
(89714, 2019, 'plos', 'Differential sustained and transient temporal processing across visual streams', 'Stigliani, Anthony|Jeska, Brianna|Grill-Spector, Kalanit', 'Psychology Department, Stanford University, Stanford, California, United States of America.|Psychology Department, Stanford University, Stanford, California, United States of America.|Psychology Department, Stanford University, Stanford, California, United States of America.', 'United States|United States|United States', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1007011', '31145723', 'How do high-level visual regions process the temporal aspects of our visual experience? While the temporal sensitivity of early visual cortex has been studied with fMRI in humans, temporal processing in high-level visual cortex is largely unknown. By modeling neural responses with millisecond precision in separate sustained and transient channels, and introducing a flexible encoding framework that captures differences in neural temporal integration time windows and response nonlinearities, we predict fMRI responses across visual cortex for stimuli ranging from 33 ms to 20 s. Using this innovative approach, we discovered that lateral category-selective regions respond to visual transients associated with stimulus onsets and offsets but not sustained visual information. Thus, lateral category-selective regions compute moment-to-moment visual transitions, but not stable features of the visual input. In contrast, ventral category-selective regions process both sustained and transient components of the visual input. Our model revealed that sustained channel responses to prolonged stimuli exhibit adaptation, whereas transient channel responses to stimulus offsets are surprisingly larger than for stimulus onsets. This large offset transient response may reflect a memory trace of the stimulus when it is no longer visible, whereas the onset transient response may reflect rapid processing of new items. Together, these findings reveal previously unconsidered, fundamental temporal mechanisms that distinguish visual streams in the human brain. Importantly, our results underscore the promise of modeling brain responses with millisecond precision to understand the underlying neural computations.'),
(89715, 2019, 'plos', 'Parameter estimation and identifiability in a neural population model for electro-cortical activity', 'Hartoyo, Agus|Cadusch, Peter J|Liley, David T J|Hicks, Damien G', 'Centre for Micro-Photonics, Swinburne University of Technology, Hawthorn, Victoria 3122, Australia.|Department of Physics and Astronomy, Swinburne University of Technology, Hawthorn, Victoria 3122, Australia.|Centre for Human Psychopharmacology, School of Health Sciences, Swinburne University of Technology, Hawthorn, Victoria 3122, Australia.|Centre for Micro-Photonics, Swinburne University of Technology, Hawthorn, Victoria 3122, Australia.', 'Australia|Australia|Australia|Australia', 3, '-', 'Journal Article', '10.1371/journal.pcbi.1006694', '31145724', 'Electroencephalography (EEG) provides a non-invasive measure of brain electrical activity. Neural population models, where large numbers of interacting neurons are considered collectively as a macroscopic system, have long been used to understand features in EEG signals. By tuning dozens of input parameters describing the excitatory and inhibitory neuron populations, these models can reproduce prominent features of the EEG such as the alpha-rhythm. However, the inverse problem, of directly estimating the parameters from fits to EEG data, remains unsolved. Solving this multi-parameter non-linear fitting problem will potentially provide a real-time method for characterizing average neuronal properties in human subjects. Here we perform unbiased fits of a 22-parameter neural population model to EEG data from 82 individuals, using both particle swarm optimization and Markov chain Monte Carlo sampling. We estimate how much is learned about individual parameters by computing Kullback-Leibler divergences between posterior and prior distributions for each parameter. Results indicate that only a single parameter, that determining the dynamics of inhibitory synaptic activity, is directly identifiable, while other parameters have large, though correlated, uncertainties. We show that the eigenvalues of the Fisher information matrix are roughly uniformly spaced over a log scale, indicating that the model is sloppy, like many of the regulatory network models in systems biology. These eigenvalues indicate that the system can be modeled with a low effective dimensionality, with inhibitory synaptic activity being prominent in driving system behavior.'),
(89716, 2019, 'plos', 'Close proximity interactions support transmission of ESBL-K pneumoniae but not ESBL-E coli in healthcare settings', 'Duval, Audrey|Obadia, Thomas|Boxc3xablle, Pierre-Yves|Fleury, Eric|Herrmann, Jean-Louis|Guillemot, Didier|Temime, Laura|Opatowski, Lulla|i-Bird Study group', 'Equipe PheMI, unitxc3xa9 B2PHI, Inserm, Universitxc3xa9 de Versailles Saint Quentin, Institut Pasteur,Paris, France.|Malaria: Parasites & Hosts Unit, Department of Parasites & Insect Vectors, Institut Pasteur,Paris, France.|Sorbonne Universitxc3xa9, INSERM, Institut Pierre Louis dEpidxc3xa9miologie et de Santxc3xa9 Publique, PARIS France.|Univ Lyon, Cnrs, ENS de Lyon, Inria, UCB Lyon 1, LIP UMR 5668, Lyon, FRANCE.|INSERM U1173, UFR Simone Veil, Versailles-Saint-Quentin University, Saint-Quentin en Yvelines, France AP-HP, Service de Microbiologie, Hxc3xb4pital Raymond Poincarxc3xa9, Garches, France.|Equipe PheMI, unitxc3xa9 B2PHI, Inserm, Universitxc3xa9 de Versailles Saint Quentin, Institut Pasteur,Paris, France.|Laboratoire MESuRS, Conservatoire national des Arts et Mxc3xa9tiers, Paris, France.|Equipe PheMI, unitxc3xa9 B2PHI, Inserm, Universitxc3xa9 de Versailles Saint Quentin, Institut Pasteur,Paris, France.|Equipe PheMI, unitxc3xa9 B2PHI, Inserm, Universitxc3xa9 de Versailles Saint Quentin, Institut Pasteur,Paris, France.', 'France|France|France|-|France|France|France|France|France', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1006496', '31145725', 'Antibiotic-resistance of hospital-acquired infections is a major public health issue. The worldwide emergence and diffusion of extended-spectrum xcexb2-lactamase (ESBL)-producing Enterobacteriaceae, including Escherichia coli (ESBL-EC) and Klebsiella pneumoniae (ESBL-KP), is of particular concern. Preventing their nosocomial spread requires understanding their transmission. Using Close Proximity Interactions (CPIs), measured by wearable sensors, and weekly ESBL-EC-and ESBL-KP-carriage data, we traced their possible transmission paths among 329 patients in a 200-bed long-term care facility over 4 months. Based on phenotypically defined resistance profiles to 12 antibiotics only, new bacterial acquisitions were tracked. Extending a previously proposed statistical method, the CPI networks ability to support observed incident-colonization episodes of ESBL-EC and ESBL-KP was tested. Finally, mathematical modeling based on our findings assessed the effect of several infection-control measures. A potential infector was identified in the CPI network for 80% (16/20) of ESBL-KP acquisition episodes. The lengths of CPI paths between ESBL-KP incident cases and their potential infectors were shorter than predicted by chance (P = 0.02), indicating that CPI-network relationships were consistent with dissemination. Potential ESBL-EC infectors were identified for 54% (19/35) of the acquisitions, with longer-than-expected lengths of CPI paths. These contrasting results yielded differing impacts of infection control scenarios, with contact reduction interventions proving less effective for ESBL-EC than for ESBL-KP. These results highlight the widely variable transmission patterns among ESBL-producing Enterobacteriaceae species. CPI networks supported ESBL-KP, but not ESBL-EC spread. These outcomes could help design more specific surveillance and control strategies to prevent in-hospital Enterobacteriaceae dissemination.'),
(89717, 2019, 'plos', 'Simulation of single-protein nanopore sensing shows feasibility for whole-proteome identification', 'Ohayon, Shilo|Girsault, Arik|Nasser, Maisa|Shen-Orr, Shai|Meller, Amit', 'Department of Biomedical Engineering, Technion-IIT, Haifa, Israel.|Department of Biomedical Engineering, Technion-IIT, Haifa, Israel.|Department of Biomedical Engineering, Technion-IIT, Haifa, Israel.|Rapport Faculty of Medicine, Technion-IIT, Haifa, Israel.|Department of Biomedical Engineering, Technion-IIT, Haifa, Israel.', 'Israel|Israel|Israel|Israel|Israel', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1007067', '31145734', 'Single-molecule techniques for protein sequencing are making headway towards single-cell proteomics and are projected to propel our understanding of cellular biology and disease. Yet, single cell proteomics presents a substantial unmet challenge due to the unavailability of protein amplification techniques, and the vast dynamic-range of protein expression in cells. Here, we describe and computationally investigate the feasibility of a novel approach for single-protein identification using tri-color fluorescence and plasmonic-nanopore devices. Comprehensive computer simulations of denatured protein translocation processes through the nanopores show that the tri-color fluorescence time-traces retain sufficient information to permit pattern-recognition algorithms to correctly identify the vast majority of proteins in the human proteome. Importantly, even when taking into account realistic experimental conditions, which restrict the spatial and temporal resolutions as well as the labeling efficiency, and add substantial noise, a deep-learning protein classifier achieves 97% whole-proteome accuracies. Applying our approach for protein datasets of clinical relevancy, such as the plasma proteome or cytokine panels, we obtain ~98% correct protein identification. This study suggests the feasibility of a method for accurate and high-throughput protein identification, which is highly versatile and applicable.'),
(89718, 2019, 'plos', 'Perturbing low dimensional activity manifolds in spiking neuronal networks', 'Wärnberg, Emil|Kumar, Arvind', 'Dept. of Computational Science and Technology, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden.|Dept. of Computational Science and Technology, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden.', 'Sweden|Sweden', 3, '-', 'Journal Article', '10.1371/journal.pcbi.1007074', '31150376', 'Several recent studies have shown that neural activity in vivo tends to be constrained to a low-dimensional manifold. Such activity does not arise in simulated neural networks with homogeneous connectivity and it has been suggested that it is indicative of some other connectivity pattern in neuronal networks. In particular, this connectivity pattern appears to be constraining learning so that only neural activity patterns falling within the intrinsic manifold can be learned and elicited. Here, we use three different models of spiking neural networks (echo-state networks, the Neural Engineering Framework and Efficient Coding) to demonstrate how the intrinsic manifold can be made a direct consequence of the circuit connectivity. Using this relationship between the circuit connectivity and the intrinsic manifold, we show that learning of patterns outside the intrinsic manifold corresponds to much larger changes in synaptic weights than learning of patterns within the intrinsic manifold. Assuming larger changes to synaptic weights requires extensive learning, this observation provides an explanation of why learning is easier when it does not require the neural activity to leave its intrinsic manifold.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(89719, 2019, 'plos', 'Efficient assembly and long-term stability of defensive microbiomes via private resources and community bistability', 'Boza, Gergely|Worsley, Sarah F|Yu, Douglas W|Scheuring, István', 'Evolutionary Systems Research Group, MTA Centre for Ecological Research, Hungarian Academy of Sciences, Tihany, Hungary.|School of Biological Sciences, University of East Anglia, Norwich Research Park, Norwich, United Kingdom.|School of Biological Sciences, University of East Anglia, Norwich Research Park, Norwich, United Kingdom.|Evolutionary Systems Research Group, MTA Centre for Ecological Research, Hungarian Academy of Sciences, Tihany, Hungary.', 'Hungary|United Kingdom|United Kingdom|Hungary', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1007109', '31150382', 'Understanding the mechanisms that promote the assembly and maintenance of host-beneficial microbiomes is an open problem. Empirical evidence supports the idea that animal and plant hosts can combine private resources with the ecological phenomenon known as community bistability to favour some microbial strains over others. We briefly review evidence showing that hosts can: (i) protect the growth of beneficial strains in an isolated habitat, (ii) use antibiotics to suppress non-beneficial, competitor strains, and (iii) provide resources that only beneficial strains are able to translate into an increased rate of growth, reproduction, or antibiotic production. We then demonstrate in a spatially explicit, individual-based model that these three mechanisms act similarly by selectively promoting the initial proliferation of preferred strains, that is, by acting as a private resource. The faster early growth of preferred strains, combined with the phenomenon of community bistability, allows those strains to continue to dominate the microbiome even after the private resource is withdrawn or made public. This is because after a beneficial colony reaches a sufficiently large size, it can resist invasion by parasites without further private support from the host. We further explicitly model localized microbial interactions and diffusion dynamics, and we show that an intermediate level of antibiotic diffusion is the most efficient mechanism in promoting preferred strains and that there is a wide range of parameters under which hosts can promote the assembly of a self-sustaining defensive microbiome. This in turn supports the idea that hosts readily evolve to promote host-beneficial defensive microbiomes.'),
(89720, 2019, 'plos', 'Properties of cardiac conduction in a cell-based computational model', 'Jæger, Karoline Horgmo|Edwards, Andrew G|McCulloch, Andrew|Tveito, Aslak', 'Simula Research Laboratory, Oslo, Norway.|Simula Research Laboratory, Oslo, Norway.|Department of Bioengineering, University of California, San Diego, California, United States of America.|Simula Research Laboratory, Oslo, Norway.', 'Norway|Norway|United States|Norway', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1007042', '31150383', 'The conduction of electrical signals through cardiac tissue is essential for maintaining the function of the heart, and conduction abnormalities are known to potentially lead to life-threatening arrhythmias. The properties of cardiac conduction have therefore been the topic of intense study for decades, but a number of questions related to the mechanisms of conduction still remain unresolved. In this paper, we demonstrate how the so-called EMI model may be used to study some of these open questions. In the EMI model, the extracellular space, the cell membrane, the intracellular space and the cell connections are all represented as separate parts of the computational domain, and the model therefore allows for study of local properties that are hard to represent in the classical homogenized bidomain or monodomain models commonly used to study cardiac conduction. We conclude that a non-uniform sodium channel distribution increases the conduction velocity and decreases the time delays over gap junctions of reduced coupling in the EMI model simulations. We also present a theoretical optimal cell length with respect to conduction velocity and consider the possibility of ephaptic coupling (i.e. cell-to-cell coupling through the extracellular potential) acting as an alternative or supporting mechanism to gap junction coupling. We conclude that for a non-uniform distribution of sodium channels and a sufficiently small intercellular distance, ephaptic coupling can influence the dynamics of the sodium channels and potentially provide cell-to-cell coupling when the gap junction connection is absent.'),
(89721, 2019, 'plos', 'Comment on A comprehensive overview and evaluation of circular RNA detection tools', 'Chen, Chia-Ying|Chuang, Trees-Juen', 'Genomics Research Center, Academia Sinica, Taipei, Taiwan.|Genomics Research Center, Academia Sinica, Taipei, Taiwan.', '-|-', 5, '-', 'Journal Article', '10.1371/journal.pcbi.1006158', '31150384', '-'),
(89722, 2019, 'plos', 'State-aware detection of sensory stimuli in the cortex of the awake mouse', 'Sederberg, Audrey J|Pala, Aurélie|Zheng, He J V|He, Biyu J|Stanley, Garrett B', 'Wallace H Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, United States of America.|Wallace H Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, United States of America.|Wallace H Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, United States of America.|Departments of Neurology, Neuroscience and Physiology, and Radiology, Neuroscience Institute, New York University Langone Medical Center, New York, New York, United States of America.|Wallace H Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, United States of America.', 'Georgia|Georgia|Georgia|United States|Georgia', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1006716', '31150385', 'Cortical responses to sensory inputs vary across repeated presentations of identical stimuli, but how this trial-to-trial variability impacts detection of sensory inputs is not fully understood. Using multi-channel local field potential (LFP) recordings in primary somatosensory cortex (S1) of the awake mouse, we optimized a data-driven cortical state classifier to predict single-trial sensory-evoked responses, based on features of the spontaneous, ongoing LFP recorded across cortical layers. Our findings show that, by utilizing an ongoing prediction of the sensory response generated by this state classifier, an ideal observer improves overall detection accuracy and generates robust detection of sensory inputs across various states of ongoing cortical activity in the awake brain, which could have implications for variability in the performance of detection tasks across brain states.'),
(89723, 2019, 'plos', 'Conformational coupling by trans-phosphorylation in calcium calmodulin dependent kinase II', 'Pandini, Alessandro|Schulman, Howard|Khan, Shahid', 'Department of Computer Science-Synthetic Biology Theme, Brunel University London, Uxbridge, United Kingdom.|Allosteros Therapeutics Inc., Sunnyvale, CA, United States of America.|Computational Cell and Molecular Biology, the Francis Crick Institute, London, United Kingdom.', 'United Kingdom|United States|United Kingdom', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1006796', '31150387', 'The calcium calmodulin-dependent protein kinase II (CaMKII) is a dodecameric holoenzyme important for encoding memory. Its activation, triggered by binding of calcium-calmodulin, persists autonomously after calmodulin dissociation. One (receiver) kinase captures and subsequently phosphorylates the regulatory domain peptide of a donor kinase forming a chained dimer as the first stage of autonomous activation. Protein dynamics simulations examined the conformational changes triggered by dimer formation and phosphorylation, aimed to provide a molecular rationale for human mutations that result in learning disabilities. Ensembles generated from X-ray crystal structures were characterized by network centrality and community analysis. Mutual information related collective motions to local fragment dynamics encoded with a structural alphabet. Implicit solvent tCONCOORD conformational ensembles revealed the dynamic architecture of inactive kinase domains was co-opted in the activated dimer but the network hub shifted from the nucleotide binding cleft to the captured peptide. Explicit solvent molecular dynamics (MD) showed nucleotide and substrate binding determinants formed coupled nodes in long-range signal relays between regulatory peptides in the dimer. Strain in the extended captured peptide was balanced by reduced flexibility of the receiver kinase C-lobe core. The relays were organized around a hydrophobic patch between the captured peptide and a key binding helix. The human mutations aligned along the relays. Thus, these mutations could disrupt the allosteric network alternatively, or in addition, to altered binding affinities. Non-binding protein sectors distant from the binding sites mediated the allosteric signalling, providing possible targets for inhibitor design. Phosphorylation of the peptide modulated the dielectric of its binding pocket to strengthen the patch, non-binding sectors, domain interface and temporal correlations between parallel relays. These results provide the molecular details underlying the reported positive kinase cooperativity to enrich the discussion on how autonomous activation by phosphorylation leads to long-term behavioural effects.'),
(89724, 2019, 'plos', 'Evolution of interface binding strengths in simplified model of protein quaternary structure', 'Leonard, Alexander S|Ahnert, Sebastian E', 'Theory of Condensed Matter, Cavendish Laboratory, University of Cambridge, Cambridge, United Kingdom.|Theory of Condensed Matter, Cavendish Laboratory, University of Cambridge, Cambridge, United Kingdom.', 'United Kingdom|United Kingdom', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1006886', '31158218', 'The self-assembly of proteins into protein quaternary structures is of fundamental importance to many biological processes, and protein misassembly is responsible for a wide range of proteopathic diseases. In recent years, abstract lattice models of protein self-assembly have been used to simulate the evolution and assembly of protein quaternary structure, and to provide a tractable way to study the genotype-phenotype map of such systems. Here we generalize these models by representing the interfaces as mutable binary strings. This simple change enables us to model the evolution of interface strengths, interface symmetry, and deterministic assembly pathways. Using the generalized model we are able to reproduce two important results established for real protein complexes: The first is that protein assembly pathways are under evolutionary selection to minimize misassembly. The second is that the assembly pathway of a complex mirrors its evolutionary history, and that both can be derived from the relative strengths of interfaces. These results demonstrate that the generalized lattice model offers a powerful new idealized framework to facilitate the study of protein self-assembly processes and their evolution.'),
(89725, 2019, 'plos', 'Exploring E-cadherin-peptidomimetics interaction using NMR and computational studies', 'Civera, Monica|Vasile, Francesca|Potenza, Donatella|Colombo, Cinzia|Parente, Sara|Vettraino, Chiara|Prosdocimi, Tommaso|Parisini, Emilio|Belvisi, Laura', 'Dipartimento di Chimica, Università degli Studi di Milano, Milan, Italy.|Dipartimento di Chimica, Università degli Studi di Milano, Milan, Italy.|Dipartimento di Chimica, Università degli Studi di Milano, Milan, Italy.|Dipartimento di Chimica, Università degli Studi di Milano, Milan, Italy.|Dipartimento di Scienza e Alta Tecnologia, Università degli Studi dellInsubria, Como, Italy.|Center for Nano Science and Technology @PoliMi, Istituto Italiano di Tecnologia, Milan, Italy.|Center for Nano Science and Technology @PoliMi, Istituto Italiano di Tecnologia, Milan, Italy.|Center for Nano Science and Technology @PoliMi, Istituto Italiano di Tecnologia, Milan, Italy.|Dipartimento di Chimica, Università degli Studi di Milano, Milan, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1007041', '31158220', 'Cadherins are homophilic cell-cell adhesion molecules whose aberrant expression has often been shown to correlate with different stages of tumor progression. In this work, we investigate the interaction of two peptidomimetic ligands with the extracellular portion of human E-cadherin using a combination of NMR and computational techniques. Both ligands have been previously developed as mimics of the tetrapeptide sequence Asp1-Trp2-Val3-Ile4 of the cadherin adhesion arm, and have been shown to inhibit E-cadherin-mediated adhesion in epithelial ovarian cancer cells with millimolar potency. To sample a set of possible interactions of these ligands with the E-cadherin extracellular portion, STD-NMR experiments in the presence of two slightly different constructs, the wild type E-cadherin-EC1-EC2 fragment and the truncated E-cadherin-(Val3)-EC1-EC2 fragment, were carried out at three temperatures. Depending on the protein construct, a different binding epitope of the ligand and also a different temperature effect on STD signals were observed, both suggesting an involvement of the Asp1-Trp2 protein sequence among all the possible binding events. To interpret the experimental results at the atomic level and to probe the role of the cadherin adhesion arm in the dynamic interaction with the peptidomimetic ligand, a computational protocol based on docking calculations and molecular dynamics simulations was applied. In agreement with NMR data, the simulations at different temperatures unveil high variability/dynamism in ligand-cadherin binding, thus explaining the differences in ligand binding epitopes. In particular, the modulation of the signals seems to be dependent on the protein flexibility, especially at the level of the adhesive arm, which appears to participate in the interaction with the ligand. Overall, these results will help the design of novel cadherin inhibitors that might prevent the swap dimer formation by targeting both the Trp2 binding pocket and the adhesive arm residues.'),
(89726, 2019, 'plos', 'Mathematical modelling reveals unexpected inheritance and variability patterns of cell cycle parameters in mammalian cells', 'Mura, Marzena|Feillet, Céline|Bertolusso, Roberto|Delaunay, Franck|Kimmel, Marek', 'System Engineering Group, Silesian University of Technology, Gliwice, Poland.|Université Côte dAzur, CNRS, INSERM, iBV, Nice, France.|Department of Statistics, Rice University, Houston, TX, United States of America.|Université Côte dAzur, CNRS, INSERM, iBV, Nice, France.|System Engineering Group, Silesian University of Technology, Gliwice, Poland.', 'Poland|France|United States|France|Poland', 2, '-', 'Journal Article', '10.1371/journal.pcbi.1007054', '31158226', 'The cell cycle is the fundamental process of cell populations, it is regulated by environmental cues and by intracellular checkpoints. Cell cycle variability in clonal cell population is caused by stochastic processes such as random partitioning of cellular components to progeny cells at division and random interactions among biomolecules in cells. One of the important biological questions is how the dynamics at the cell cycle scale, which is related to family dependencies between the cell and its descendants, affects cell population behavior in the long-run. We address this question using a mechanistic model, built based on observations of single cells over several cell generations, and then extrapolated in time. We used cell pedigree observations of NIH 3T3 cells including FUCCI markers, to determine patterns of inheritance of cell-cycle phase durations and single-cell protein dynamics. Based on that information we developed a hybrid mathematical model, involving bifurcating autoregression to describe stochasticity of partitioning and inheritance of cell-cycle-phase times, and an ordinary differential equation system to capture single-cell protein dynamics. Long-term simulations, concordant with in vitro experiments, demonstrated the model reproduced the main features of our data and had homeostatic properties. Moreover, heterogeneity of cell cycle may have important consequences during population development. We discovered an effect similar to genetic drift, amplified by family relationships among cells. In consequence, the progeny of a single cell with a short cell cycle time had a high probability of eventually dominating the population, due to the heritability of cell-cycle phases. Patterns of epigenetic heritability in proliferating cells are important for understanding long-term trends of cell populations which are either required to provide the influx of maturing cells (such as hematopoietic stem cells) or which started proliferating uncontrollably (such as cancer cells).'),
(89727, 2019, 'plos', 'Laboratory evolution reveals a two-dimensional rate-yield tradeoff in microbial metabolism', 'Cheng, Chuankai|OBrien, Edward J|McCloskey, Douglas|Utrilla, Jose|Olson, Connor|LaCroix, Ryan A|Sandberg, Troy E|Feist, Adam M|Palsson, Bernhard O|King, Zachary A', '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 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 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 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 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.', 'United States|United States|United States|United States|United States|United States|United States|Un', 4, '-', 'Journal Article', '10.1371/journal.pcbi.1007066', '31158228', 'Growth rate and yield are fundamental features of microbial growth. However, we lack a mechanistic and quantitative understanding of the rate-yield relationship. Studies pairing computational predictions with experiments have shown the importance of maintenance energy and proteome allocation in explaining rate-yield tradeoffs and overflow metabolism. Recently, adaptive evolution experiments of Escherichia coli reveal a phenotypic diversity beyond what has been explained using simple models of growth rate versus yield. Here, we identify a two-dimensional rate-yield tradeoff in adapted E. coli strains where the dimensions are (A) a tradeoff between growth rate and yield and (B) a tradeoff between substrate (glucose) uptake rate and growth yield. We employ a multi-scale modeling approach, combining a previously reported coarse-grained small-scale proteome allocation model with a fine-grained genome-scale model of metabolism and gene expression (ME-model), to develop a quantitative description of the full rate-yield relationship for E. coli K-12 MG1655. The multi-scale analysis resolves the complexity of ME-model which hindered its practical use in proteome complexity analysis, and provides a mechanistic explanation of the two-dimensional tradeoff. Further, the analysis identifies modifications to the P/O ratio and the flux allocation between glycolysis and pentose phosphate pathway (PPP) as potential mechanisms that enable the tradeoff between glucose uptake rate and growth yield. Thus, the rate-yield tradeoffs that govern microbial adaptation to new environments are more complex than previously reported, and they can be understood in mechanistic detail using a multi-scale modeling approach.'),
(89728, 2019, 'plos', 'Emergent decision-making behaviour and rhythm generation in a computational model of the ventromedial nucleus of the hypothalamus', 'MacGregor, Duncan J|Leng, Gareth', 'Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom.|Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom.', 'United Kingdom|United Kingdom', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1007092', '31158265', 'The ventromedial nucleus of the hypothalamus (VMN) has an important role in diverse behaviours. The common involvement in these of sex steroids, nutritionally-related signals, and emotional inputs from other brain areas, suggests that, at any given time, its output is in one of a discrete number of possible states corresponding to discrete motivational drives. Here we explored how networks of VMN neurons might generate such a decision-making architecture. We began with minimalist assumptions about the intrinsic properties of VMN neurons inferred from electrophysiological recordings of these neurons in rats in vivo, using an integrate-and-fire based model modified to simulate activity-dependent post-spike changes in neuronal excitability. We used a genetic algorithm based method to fit model parameters to the statistical features of spike patterning in each cell. The spike patterns in both recorded cells and model cells were assessed by analysis of interspike interval distributions and of the index of dispersion of firing rate over different binwidths. Simpler patterned cells could be closely matched by single neuron models incorporating a hyperpolarising afterpotential and either a slow afterhyperpolarisation or a depolarising afterpotential, but many others could not. We then constructed network models with the challenge of explaining the more complex patterns. We assumed that neurons of a given type (with heterogeneity introduced by independently random patterns of external input) were mutually interconnected at random by excitatory synaptic connections (with a variable delay and a random chance of failure). Simple network models of one or two cell types were able to explain the more complex patterns. We then explored the information processing features of such networks that might be relevant for a decision-making network. We concluded that rhythm generation (in the slow theta range) and bistability arise as emergent properties of networks of heterogeneous VMN neurons.'),
(89729, 2019, 'plos', 'Multiscale model of integrin adhesion assembly', 'Bidone, Tamara C|Skeeters, Austin V|Oakes, Patrick W|Voth, Gregory A', 'Department of Chemistry, Institute for Biophysical Dynamics, and James Franck Institute, The University of Chicago, Chicago, Illinois, United States of America.|Department of Physics & Astronomy, University of Rochester, Rochester, New York, United States of America.|Department of Physics & Astronomy, University of Rochester, Rochester, New York, United States of America.|Department of Chemistry, Institute for Biophysical Dynamics, and James Franck Institute, The University of Chicago, Chicago, Illinois, United States of America.', 'United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1007077', '31163027', 'The ability of adherent cells to form adhesions is critical to numerous phases of their physiology. The assembly of adhesions is mediated by several types of integrins. These integrins differ in physical properties, including rate of diffusion on the plasma membrane, rapidity of changing conformation from bent to extended, affinity for extracellular matrix ligands, and lifetimes of their ligand-bound states. However, the way in which nanoscale physical properties of integrins ensure proper adhesion assembly remains elusive. We observe experimentally that both xcexb2-1 and xcexb2-3 integrins localize in nascent adhesions at the cell leading edge. In order to understand how different nanoscale parameters of xcexb2-1 and xcexb2-3 integrins mediate proper adhesion assembly, we therefore develop a coarse-grained computational model. Results from the model demonstrate that morphology and distribution of nascent adhesions depend on ligand binding affinity and strength of pairwise interactions. Organization of nascent adhesions depends on the relative amounts of integrins with different bond kinetics. Moreover, the model shows that the architecture of an actin filament network does not perturb the total amount of integrin clustering and ligand binding, however, only bundled actin architectures favor adhesion stability and ultimately maturation. Together, our results support the view that cells can finely tune the expression of different integrin types to determine both structural and dynamic properties of adhesions.'),
(89730, 2019, 'plos', 'Erratum: Notification of Republication: Modeling individual time courses of thrombopoiesis during multi-cyclic chemotherapy', 'PLOS Computational Biology Staff', 'Department of Chemistry, Institute for Biophysical Dynamics, and James Franck Institute, The University of Chicago, Chicago, Illinois, United States of America.', 'United States', 0, '-', 'Published Erratum', '10.1371/journal.pcbi.1007110', '31163030', '[This corrects the article DOI: 10.1371/journal.pcbi.1006775.].'),
(89731, 2019, 'plos', 'Phenotypic heterogeneity and evolution of melanoma cells associated with targeted therapy resistance', 'Su, Yapeng|Bintz, Marcus|Yang, Yezi|Robert, Lidia|Ng, Alphonsus H C|Liu, Victoria|Ribas, Antoni|Heath, James R|Wei, Wei', 'Institute for Systems Biology, Seattle, Washington, United State of America.|Department of Molecular and Medical Pharmacology, University of California - Los Angeles, Los Angeles, California, United State of America.|Department of Molecular and Medical Pharmacology, University of California - Los Angeles, Los Angeles, California, United State of America.|Department of Medicine, University of California - Los Angeles, Los Angeles, California, United State of America.|Institute for Systems Biology, Seattle, Washington, United State of America.|Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California, United State of America.|Department of Molecular and Medical Pharmacology, University of California - Los Angeles, Los Angeles, California, United State of America.|Institute for Systems Biology, Seattle, Washington, United State of America.|Institute for Systems Biology, Seattle, Washington, United State of America.', '-|-|-|-|-|-|-|-|-', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1007034', '31166947', 'Phenotypic plasticity is associated with non-genetic drug tolerance in several cancers. Such plasticity can arise from chromatin remodeling, transcriptomic reprogramming, and/or protein signaling rewiring, and is characterized as a cell state transition in response to molecular or physical perturbations. This, in turn, can confound interpretations of drug responses and resistance development. Using BRAF-mutant melanoma cell lines as the prototype, we report on a joint theoretical and experimental investigation of the cell-state transition dynamics associated with BRAF inhibitor drug tolerance. Thermodynamically motivated surprisal analysis of transcriptome data was used to treat the cell population as an entropy maximizing system under the influence of time-dependent constraints. This permits the extraction of an epigenetic potential landscape for drug-induced phenotypic evolution. Single-cell flow cytometry data of the same system were modeled with a modified Fokker-Planck-type kinetic model. The two approaches yield a consistent picture that accounts for the phenotypic heterogeneity observed over the course of drug tolerance development. The results reveal that, in certain plastic cancers, the population heterogeneity and evolution of cell phenotypes may be understood by accounting for the competing interactions of the epigenetic potential landscape and state-dependent cell proliferation. Accounting for such competition permits accurate, experimentally verifiable predictions that can potentially guide the design of effective treatment strategies.'),
(89732, 2019, 'plos', 'Modern technologies and algorithms for scaffolding assembled genomes', 'Ghurye, Jay|Pop, Mihai', 'Department of Computer Science and Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, United States of America.|Department of Computer Science and Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, United States of America.', 'United States|United States', 6, '-', 'Journal Article', '10.1371/journal.pcbi.1006994', '31166948', 'The computational reconstruction of genome sequences from shotgun sequencing data has been greatly simplified by the advent of sequencing technologies that generate long reads. In the case of relatively small genomes (e.g., bacterial or viral), complete genome sequences can frequently be reconstructed computationally without the need for further experiments. However, large and complex genomes, such as those of most animals and plants, continue to pose significant challenges. In such genomes, assembly software produces incomplete and fragmented reconstructions that require additional experimentally derived information and manual intervention in order to reconstruct individual chromosome arms. Recent technologies originally designed to capture chromatin structure have been shown to effectively complement sequencing data, leading to much more contiguous reconstructions of genomes than previously possible. Here, we survey these technologies and the algorithms used to assemble and analyze large eukaryotic genomes, placed within the historical context of genome scaffolding technologies that have been in existence since the dawn of the genomic era.'),
(89733, 2019, 'plos', 'Mathematical model studies of the comprehensive generation of major and minor phyllotactic patterns in plants with a predominant focus on orixate phyllotaxis', 'Yonekura, Takaaki|Iwamoto, Akitoshi|Fujita, Hironori|Sugiyama, Munetaka', 'Botanical Gardens, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan.|Department of Biology, Tokyo Gakugei University, Koganei, Tokyo, Japan.|Astrobiology Center, National Institutes of Natural Sciences, Mitaka, Tokyo, Japan.|Botanical Gardens, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan.', 'Japan|Japan|Japan|Japan', 4, '-', 'Journal Article', '10.1371/journal.pcbi.1007044', '31170142', 'Plant leaves are arranged around the stem in a beautiful geometry that is called phyllotaxis. In the majority of plants, phyllotaxis exhibits a distichous, Fibonacci spiral, decussate, or tricussate pattern. To explain the regularity and limited variety of phyllotactic patterns, many theoretical models have been proposed, mostly based on the notion that a repulsive interaction between leaf primordia determines the position of primordium initiation. Among them, particularly notable are the two models of Douady and Couder (alternate-specific form, DC1, more generalized form, DC2), the key assumptions of which are that each leaf primordium emits a constant power that inhibits new primordium formation and that this inhibitory effect decreases with distance. It was previously demonstrated by computer simulations that any major type of phyllotaxis can occur as a self-organizing stable pattern in the framework of DC models. However, several phyllotactic types remain unaddressed. An interesting example is orixate phyllotaxis, which has a tetrastichous alternate pattern with periodic repetition of a sequence of different divergence angles: 180°, 90°, -180°, and -90°. Although the term orixate phyllotaxis was derived from Orixa japonica, this type is observed in several distant taxa, suggesting that it may reflect some aspects of a common mechanism of phyllotactic patterning. Here we examined DC models regarding the ability to produce orixate phyllotaxis and found that model expansion via the introduction of primordial age-dependent changes of the inhibitory power is absolutely necessary for the establishment of orixate phyllotaxis. The orixate patterns generated by the expanded version of DC2 (EDC2) were shown to share morphological details with real orixate phyllotaxis. Furthermore, the simulation results obtained using EDC2 fitted better the natural distribution of phyllotactic patterns than did those obtained using the previous models. Our findings imply that changing the inhibitory power is generally an important component of the phyllotactic patterning mechanism.'),
(89734, 2019, 'plos', 'The breadth of HIV-1 neutralizing antibodies depends on the conservation of key sites in their epitopes', 'Bai, Hongjun|Li, Yifan|Michael, Nelson L|Robb, Merlin L|Rolland, Morgane', 'U.S. Military HIV Research Program, Walter Reed Army Institute of Research, Silver Spring, MD, United States of America.|U.S. Military HIV Research Program, Walter Reed Army Institute of Research, Silver Spring, MD, United States of America.|U.S. Military HIV Research Program, Walter Reed Army Institute of Research, Silver Spring, MD, United States of America.|U.S. Military HIV Research Program, Walter Reed Army Institute of Research, Silver Spring, MD, United States of America.|U.S. Military HIV Research Program, Walter Reed Army Institute of Research, Silver Spring, MD, United States of America.', 'United States|United States|United States|United States|United States', 3, '-', 'Journal Article', '10.1371/journal.pcbi.1007056', '31170145', 'Developing HIV-1 vaccines that trigger broadly neutralizing antibodies (bnAbs) is a priority as bnAbs are considered key to elicitation of a protective immune response. To investigate whether the breadth of a neutralizing antibody (nAb) depended on the conservation of its epitope among circulating viruses, we examined Antibody:Envelope (Ab:Env) interactions and worldwide Env diversity. We found that sites corresponding to bnAb epitopes were as variable as other accessible, non-hypervariable Env sites (p = 0.50, Mann-Whitney U-test) with no significant relationship between epitope conservation and neutralization breadth (Spearmans xcfx81 = -0.44, adjusted p = 0.079). However, when accounting for key sites in the Ab:Env interaction, we showed that the broadest bnAbs targeted more conserved epitopes (Spearmans xcfx81 = -0.70, adjusted p = 5.0e-5). Neutralization breadth did not stem from the overall conservation of Ab epitopes but depended instead on the conservation of key sites of the Ab:Env interaction, revealing a mechanistic basis for neutralization breadth that could be exploited for vaccine design.'),
(89735, 2019, 'plos', 'Horizontal transfer between loose compartments stabilizes replication of fragmented ribozymes', 'Kamimura, Atsushi|Matsubara, Yoshiya J|Kaneko, Kunihiko|Takeuchi, Nobuto', '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.|Department of Basic Science, Graduate School of Arts and Sciences, The University of Tokyo, Komaba, Meguro-ku, Tokyo, Japan.|Research Center for Complex Systems Biology, Universal Biology Institute, The University of Tokyo, Komaba, Meguro-ku, Tokyo, Japan.', 'Japan|Japan|Japan|Japan', 1, '-', 'Journal Article', '10.1371/journal.pcbi.1007094', '31170146', 'The emergence of replicases that can replicate themselves is a central issue in the origin of life. Recent experiments suggest that such replicases can be realized if an RNA polymerase ribozyme is divided into fragments short enough to be replicable by the ribozyme and if these fragments self-assemble into a functional ribozyme. However, the continued self-replication of such replicases requires that the production of every essential fragment be balanced and sustained. Here, we use mathematical modeling to investigate whether and under what conditions fragmented replicases achieve continued self-replication. We first show that under a simple batch condition, the replicases fail to display continued self-replication owing to positive feedback inherent in these replicases. This positive feedback inevitably biases replication toward a subset of fragments, so that the replicases eventually fail to sustain the production of all essential fragments. We then show that this inherent instability can be resolved by small rates of random content exchange between loose compartments (i.e., horizontal transfer). In this case, the balanced production of all fragments is achieved through negative frequency-dependent selection operating in the population dynamics of compartments. The horizontal transfer also ensures the presence of all essential fragments in each compartment, sustaining self-replication. Taken together, our results underline compartmentalization and horizontal transfer in the origin of the first self-replicating replicases.'),
(89736, 2019, 'plos', 'Charting pathways to climate change mitigation in a coupled socio-climate model', 'Bury, Thomas M|Bauch, Chris T|Anand, Madhur', 'Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario, Canada.|Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario, Canada.|School of Environmental Sciences, University of Guelph, Guelph, Ontario, Canada.', 'Canada|Canada|Canada', 8, '-', 'Journal Article', '10.1371/journal.pcbi.1007000', '31170149', 'Geophysical models of climate change are becoming increasingly sophisticated, yet less effort is devoted to modelling the human systems causing climate change and how the two systems are coupled. Here, we develop a simple socio-climate model by coupling an Earth system model to a social dynamics model. We treat social processes endogenously-emerging from rules governing how individuals learn socially and how social norms develop-as well as being influenced by climate change and mitigation costs. Our goal is to gain qualitative insights into scenarios of potential socio-climate dynamics and to illustrate how such models can generate new research questions. We find that the social learning rate is strongly influential, to the point that variation of its value within empirically plausible ranges changes the peak global temperature anomaly by more than 1°C. Conversely, social norms reinforce majority behaviour and therefore may not provide help when we most need it because they suppress the early spread of mitigative behaviour. Finally, exploring the models parameter space for mitigation cost and social learning suggests optimal intervention pathways for climate change mitigation. We find that prioritising an increase in social learning as a first step, followed by a reduction in mitigation costs provides the most efficient route to a reduced peak temperature anomaly. We conclude that socio-climate models should be included in the ensemble of models used to project climate change.'),
(89737, 2019, 'plos', 'Dynamic properties of internal noise probed by modulating binocular rivalry', 'Baker, Daniel H|Richard, Bruno', 'Department of Psychology, University of York, Heslington, York, United Kingdom.|Department of Psychology, University of York, Heslington, York, United Kingdom.', 'United Kingdom|United Kingdom', 1, '-', 'Journal Article', '10.1371/journal.pcbi.1007071', '31170150', 'Neural systems are inherently noisy, and this noise can affect our perception from moment to moment. This is particularly apparent in binocular rivalry, where perception of competing stimuli shown to the left and right eyes alternates over time. We modulated rivalling stimuli using dynamic sequences of external noise of various rates and amplitudes. We repeated each external noise sequence twice, and assessed the consistency of percepts across repetitions. External noise modulations of sufficiently high contrast increased consistency scores above baseline, and were most effective at 1/8Hz. A computational model of rivalry in which internal noise has a 1/f (pink) temporal amplitude spectrum, and a standard deviation of 16% contrast, provided the best account of our data. Our novel technique provides detailed estimates of the dynamic properties of internal noise during binocular rivalry, and by extension the stochastic processes that drive our perception and other types of spontaneous brain activity.'),
(89738, 2019, 'plos', 'A neural field model for color perception unifying assimilation and contrast', 'Song, Anna|Faugeras, Olivier|Veltz, Romain', 'Student at Département de Mathématiques et Applications, École Normale Supérieure, 45 rue dUlm, 75005, Paris, France.|MathNeuro Team, Inria Sophia Antipolis Méditerranée, 2004 Route des Lucioles-BP 93, 06902, Sophia Antipolis, France.|MathNeuro Team, Inria Sophia Antipolis Méditerranée, 2004 Route des Lucioles-BP 93, 06902, Sophia Antipolis, France.', 'France|France|France', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1007050', '31173581', 'We address the question of color-space interactions in the brain, by proposing a neural field model of color perception with spatial context for the visual area V1 of the cortex. Our framework reconciles two opposing perceptual phenomena, known as simultaneous contrast and chromatic assimilation. They have been previously shown to act synergistically, so that at some point in an image, the color seems perceptually more similar to that of adjacent neighbors, while being more dissimilar from that of remote ones. Thus, their combined effects are enhanced in the presence of a spatial pattern, and can be measured as larger shifts in color matching experiments. Our model supposes a hypercolumnar structure coding for colors in V1, and relies on the notion of color opponency introduced by Hering. The connectivity kernel of the neural field exploits the balance between attraction and repulsion in color and physical spaces, so as to reproduce the sign reversal in the influence of neighboring points. The color sensation at a point, defined from a steady state of the neural activities, is then extracted as a nonlinear percept conveyed by an assembly of neurons. It connects the cortical and perceptual levels, because we describe the search for a color match in asymmetric matching experiments as a mathematical projection on color sensations. We validate our color neural field alongside this color matching framework, by performing a multi-parameter regression to data produced by psychophysicists and ourselves. All the results show that we are able to explain the nonlinear behavior of shifts observed along one or two dimensions in color space, which cannot be done using a simple linear model.'),
(89739, 2019, 'plos', 'How single neuron properties shape chaotic dynamics and signal transmission in random neural networks', 'Muscinelli, Samuel P|Gerstner, Wulfram|Schwalger, Tilo', 'School of Computer and Communication Sciences and School of Life Sciences, École polytechnique fédérale de Lausanne, Station 15, CH-1015 Lausanne EPFL, Switzerland.|School of Computer and Communication Sciences and School of Life Sciences, École polytechnique fédérale de Lausanne, Station 15, CH-1015 Lausanne EPFL, Switzerland.|Bernstein Center for Computational Neuroscience, 10115 Berlin, Germany.', 'Switzerland|Switzerland|Germany', 3, '-', 'Journal Article', '10.1371/journal.pcbi.1007122', '31181063', 'While most models of randomly connected neural networks assume single-neuron models with simple dynamics, neurons in the brain exhibit complex intrinsic dynamics over multiple timescales. We analyze how the dynamical properties of single neurons and recurrent connections interact to shape the effective dynamics in large randomly connected networks. A novel dynamical mean-field theory for strongly connected networks of multi-dimensional rate neurons shows that the power spectrum of the network activity in the chaotic phase emerges from a nonlinear sharpening of the frequency response function of single neurons. For the case of two-dimensional rate neurons with strong adaptation, we find that the network exhibits a state of resonant chaos, characterized by robust, narrow-band stochastic oscillations. The coherence of stochastic oscillations is maximal at the onset of chaos and their correlation time scales with the adaptation timescale of single units. Surprisingly, the resonance frequency can be predicted from the properties of isolated neurons, even in the presence of heterogeneity in the adaptation parameters. In the presence of these internally-generated chaotic fluctuations, the transmission of weak, low-frequency signals is strongly enhanced by adaptation, whereas signal transmission is not influenced by adaptation in the non-chaotic regime. Our theoretical framework can be applied to other mechanisms at the level of single neurons, such as synaptic filtering, refractoriness or spike synchronization. These results advance our understanding of the interaction between the dynamics of single units and recurrent connectivity, which is a fundamental step toward the description of biologically realistic neural networks.'),
(89740, 2019, 'plos', 'Predicting three-dimensional genome organization with chromatin states', 'Qi, Yifeng|Zhang, Bin', 'Departments of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.|Departments of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.', 'United States|United States', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1007024', '31181064', 'We introduce a computational model to simulate chromatin structure and dynamics. Starting from one-dimensional genomics and epigenomics data that are available for hundreds of cell types, this model enables de novo prediction of chromatin structures at five-kilo-base resolution. Simulated chromatin structures recapitulate known features of genome organization, including the formation of chromatin loops, topologically associating domains (TADs) and compartments, and are in quantitative agreement with chromosome conformation capture experiments and super-resolution microscopy measurements. Detailed characterization of the predicted structural ensemble reveals the dynamical flexibility of chromatin loops and the presence of cross-talk among neighboring TADs. Analysis of the models energy function uncovers distinct mechanisms for chromatin folding at various length scales and suggests a need to go beyond simple A/B compartment types to predict specific contacts between regulatory elements using polymer simulations.'),
(89741, 2019, 'plos', 'Models that learn how humans learn: The case of decision-making and its disorders', 'Dezfouli, Amir|Griffiths, Kristi|Ramos, Fabio|Dayan, Peter|Balleine, Bernard W', 'School of Psychology, UNSW, Sydney, Australia.|Westmead Institute for Medical Research, University of Sydney, Sydney, Australia.|University of Sydney, Sydney, Australia.|Gatsby Computational Neuroscience Unit, UCL, London, United Kingdom.|School of Psychology, UNSW, Sydney, Australia.', 'Australia|Australia|Australia|United Kingdom|Australia', 3, '-', 'Journal Article', '10.1371/journal.pcbi.1006903', '31185008', 'Popular computational models of decision-making make specific assumptions about learning processes that may cause them to underfit observed behaviours. Here we suggest an alternative method using recurrent neural networks (RNNs) to generate a flexible family of models that have sufficient capacity to represent the complex learning and decision- making strategies used by humans. In this approach, an RNN is trained to predict the next action that a subject will take in a decision-making task and, in this way, learns to imitate the processes underlying subjects choices and their learning abilities. We demonstrate the benefits of this approach using a new dataset drawn from patients with either unipolar (n = 34) or bipolar (n = 33) depression and matched healthy controls (n = 34) making decisions on a two-armed bandit task. The results indicate that this new approach is better than baseline reinforcement-learning methods in terms of overall performance and its capacity to predict subjects choices. We show that the model can be interpreted using off-policy simulations and thereby provides a novel clustering of subjects learning processes-something that often eludes traditional approaches to modelling and behavioural analysis.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(89742, 2019, 'plos', 'Metabolic reprogramming dynamics in tumor spheroids: Insights from a multicellular, multiscale model', 'Roy, Mahua|Finley, Stacey D', 'Department of Biomedical Engineering, University of Southern California, Los Angeles, California, United States of America.|Department of Biomedical Engineering, University of Southern California, Los Angeles, California, United States of America.', 'United States|United States', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1007053', '31185009', 'Mathematical modeling provides the predictive ability to understand the metabolic reprogramming and complex pathways that mediate cancer cells proliferation. We present a mathematical model using a multiscale, multicellular approach to simulate avascular tumor growth, applied to pancreatic cancer. The model spans three distinct spatial and temporal scales. At the extracellular level, reaction diffusion equations describe nutrient concentrations over a span of seconds. At the cellular level, a lattice-based energy driven stochastic approach describes cellular phenomena including adhesion, proliferation, viability and cell state transitions, occurring on the timescale of hours. At the sub-cellular level, we incorporate a detailed kinetic model of intracellular metabolite dynamics on the timescale of minutes, which enables the cells to uptake and excrete metabolites and use the metabolites to generate energy and building blocks for cell growth. This is a particularly novel aspect of the model. Certain defined criteria for the concentrations of intracellular metabolites lead to cancer cell growth, proliferation or death. Overall, we model the evolution of the tumor in both time and space. Starting with a cluster of tumor cells, the model produces an avascular tumor that quantitatively and qualitatively mimics experimental measurements of multicellular tumor spheroids. Through our model simulations, we can investigate the response of individual intracellular species under a metabolic perturbation and investigate how that response contributes to the response of the tumor as a whole. The predicted response of intracellular metabolites under various targeted strategies are difficult to resolve with experimental techniques. Thus, the model can give novel predictions as to the response of the tumor as a whole, identifies potential therapies to impede tumor growth, and predicts the effects of those therapeutic strategies. In particular, the model provides quantitative insight into the dynamic reprogramming of tumor cells at the intracellular level in response to specific metabolic perturbations. Overall, the model is a useful framework to study targeted metabolic strategies for inhibiting tumor growth.'),
(89743, 2019, 'plos', 'Correction: A dynamic genetic-hormonal regulatory network model explains multiple cellular behaviors of the root apical meristem of Arabidopsis thaliana', 'PLOS Computational Biology Staff', 'Department of Biomedical Engineering, University of Southern California, Los Angeles, California, United States of America.', 'United States', 0, '-', 'Published Erratum', '10.1371/journal.pcbi.1007140', '31188816', '[This corrects the article DOI: 10.1371/journal.pcbi.1005488.].'),
(89744, 2019, 'plos', 'Correction: Infectious reactivation of cytomegalovirus explaining age- and sex-specific patterns of seroprevalence', 'PLOS Computational Biology Staff', 'Department of Biomedical Engineering, University of Southern California, Los Angeles, California, United States of America.', 'United States', 0, '-', 'Published Erratum', '10.1371/journal.pcbi.1007146', '31188817', '[This corrects the article DOI: 10.1371/journal.pcbi.1005719.].'),
(89745, 2019, 'plos', 'Correction: Review: Precision medicine and driver mutations: Computational methods, functional assays and conformational principles for interpreting cancer drivers', 'Nussinov, Ruth|Jang, Hyunbum|Tsai, Chung-Jung|Cheng, Feixiong', 'Department of Biomedical Engineering, University of Southern California, Los Angeles, California, United States of America.|Department of Biomedical Engineering, University of Southern California, Los Angeles, California, United States of America.|Department of Biomedical Engineering, University of Southern California, Los Angeles, California, United States of America.|Department of Biomedical Engineering, University of Southern California, Los Angeles, California, United States of America.', 'United States|United States|United States|United States', 0, '-', 'Published Erratum', '10.1371/journal.pcbi.1007114', '31188819', '[This corrects the article DOI: 10.1371/journal.pcbi.1006658.].'),
(89746, 2019, 'plos', 'Correction: Atrial arrhythmogenicity of KCNJ2 mutations in short QT syndrome: Insights from virtual human atria', 'PLOS Computational Biology Staff', 'Department of Biomedical Engineering, University of Southern California, Los Angeles, California, United States of America.', 'United States', 0, '-', 'Published Erratum', '10.1371/journal.pcbi.1007145', '31194727', '[This corrects the article DOI: 10.1371/journal.pcbi.1005593.].'),
(89747, 2019, 'plos', 'Disentangling juxtacrine from paracrine signalling in dynamic tissue', 'Momiji, Hiroshi|Hassall, Kirsty L|Featherstone, Karen|McNamara, Anne V|Patist, Amanda L|Spiller, David G|Christian, Helen C|White, Michael R H|Davis, Julian R E|Finkenstädt, Bärbel F|Rand, David A', 'Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, University of Warwick, Coventry, United Kingdom, Mathematics Institute, University of Warwick, Coventry, United Kingdom.|Department of Statistics, University of Warwick, Coventry, United Kingdom.|Faculty of Biology, Medicine & Health, University of Manchester, Manchester, United Kingdom.|Systems Microscopy Centre, University of Manchester, Manchester, United Kingdom.|Faculty of Biology, Medicine & Health, University of Manchester, Manchester, United Kingdom.|Systems Microscopy Centre, University of Manchester, Manchester, United Kingdom.|Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom.|Systems Microscopy Centre, University of Manchester, Manchester, United Kingdom.|Faculty of Biology, Medicine & Health, University of Manchester, Manchester, United Kingdom.|Department of Statistics, University of Warwick, Coventry, United Kingdom.|Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, University of Warwick, Coventry, United Kingdom, Mathematics Institute, University of Warwick, Coventry, United Kingdom.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kin', 1, '-', 'Journal Article', '10.1371/journal.pcbi.1007030', '31194728', 'Prolactin is a major hormone product of the pituitary gland, the central endocrine regulator. Despite its physiological importance, the cell-level mechanisms of prolactin production are not well understood. Having significantly improved the resolution of real-time-single-cell-GFP-imaging, the authors recently revealed that prolactin gene transcription is highly dynamic and stochastic yet shows space-time coordination in an intact tissue slice. However, it still remains an open question as to what kind of cellular communication mediates the observed space-time organization. To determine the type of interaction between cells we developed a statistical model. The degree of similarity between two expression time series was studied in terms of two distance measures, Euclidean and geodesic, the latter being a network-theoretic distance defined to be the minimal number of edges between nodes, and this was used to discriminate between juxtacrine from paracrine signalling. The analysis presented here suggests that juxtacrine signalling dominates. To further determine whether the coupling is coordinating transcription or post-transcriptional activities we used stochastic switch modelling to infer the transcriptional profiles of cells and estimated their similarity measures to deduce that their spatial cellular coordination involves coupling of transcription via juxtacrine signalling. We developed a computational model that involves an inter-cell juxtacrine coupling, yielding simulation results that show space-time coordination in the transcription level that is in agreement with the above analysis. The developed model is expected to serve as the prototype for the further study of tissue-level organised gene expression for epigenetically regulated genes, such as prolactin.'),
(89748, 2019, 'plos', 'Development of a physiologically-based pharmacokinetic pediatric brain model for prediction of cerebrospinal fluid drug concentrations and the influence of meningitis', 'Verscheijden, Laurens F M|Koenderink, Jan B|de Wildt, Saskia N|Russel, Frans G M', 'Department of Pharmacology and Toxicology, Radboud University Medical Center, Radboud Institute for Molecular Life Sciences, Nijmegen, The Netherlands.|Department of Pharmacology and Toxicology, Radboud University Medical Center, Radboud Institute for Molecular Life Sciences, Nijmegen, The Netherlands.|Department of Pharmacology and Toxicology, Radboud University Medical Center, Radboud Institute for Molecular Life Sciences, Nijmegen, The Netherlands.|Department of Pharmacology and Toxicology, Radboud University Medical Center, Radboud Institute for Molecular Life Sciences, Nijmegen, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands', 4, '-', 'Journal Article', '10.1371/journal.pcbi.1007117', '31194730', 'Different pediatric physiologically-based pharmacokinetic (PBPK) models have been described incorporating developmental changes that influence plasma drug concentrations. Drug disposition into cerebrospinal fluid (CSF) is also subject to age-related variation and can be further influenced by brain diseases affecting blood-brain barrier integrity, like meningitis. Here, we developed a generic pediatric brain PBPK model to predict CSF concentrations of drugs that undergo passive transfer, including age-appropriate parameters. The model was validated for the analgesics paracetamol, ibuprofen, flurbiprofen and naproxen, and for a pediatric meningitis population by empirical optimization of the blood-brain barrier penetration of the antibiotic meropenem. Plasma and CSF drug concentrations derived from the literature were used to perform visual predictive checks and to calculate ratios between simulated and observed area under the concentration curves (AUCs) in order to evaluate model performance. Model-simulated concentrations were comparable to observed data over a broad age range (3 months-15 years postnatal age) for all drugs investigated. The ratios between observed and simulated AUCs (AUCo/AUCp) were within 2-fold difference both in plasma (range 0.92-1.09) and in CSF (range 0.64-1.23) indicating acceptable model performance. The model was also able to describe disease-mediated changes in neonates and young children (<3m postnatal age) related to meningitis and sepsis (range AUCo/AUCp plasma: 1.64-1.66, range AUCo/AUCp CSF: 1.43-1.73). Our model provides a new computational tool to predict CSF drug concentrations in children with and without meningitis and can be used as a template model for other compounds that passively enter the CNS.'),
(89749, 2019, 'plos', 'Integrative modeling of the HIV-1 ribonucleoprotein complex', 'Goodsell, David S|Jewett, Andrew|Olson, Arthur J|Forli, Stefano', 'Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California, United States of America.|Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California, United States of America.|Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California, United States of America.|Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California, United States of America.', 'United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1007150', '31194731', 'A coarse-grain computational method integrates biophysical and structural data to generate models of HIV-1 genomic RNA, nucleocapsid and integrase condensed into a mature ribonucleoprotein complex. Several hypotheses for the initial structure of the genomic RNA and oligomeric state of integrase are tested. In these models, integrase interaction captures features of the relative distribution of gRNA in the immature virion and increases the size of the RNP globule, and exclusion of nucleocapsid from regions with RNA secondary structure drives an asymmetric placement of the dimerized 5UTR at the surface of the RNP globule.'),
(89750, 2019, 'plos', 'Sequential exploration in the Iowa gambling task: Validation of a new computational model in a large dataset of young and old healthy participants', 'Ligneul, Romain', 'Donders Center for Cognitive Neuroimaging, Nijmegen, The Netherlands.', 'Netherlands', 5, '-', 'Journal Article', '10.1371/journal.pcbi.1006989', '31194733', 'The Iowa Gambling Task (IGT) is one of the most common paradigms used to assess decision-making and executive functioning in neurological and psychiatric disorders. Several reinforcement-learning (RL) models were recently proposed to refine the qualitative and quantitative inferences that can be made about these processes based on IGT data. Yet, these models do not account for the complex exploratory patterns which characterize participants behavior in the task. Using a dataset of more than 500 subjects, we demonstrate the existence of sequential exploration in the IGT and we describe a new computational architecture disentangling exploitation, random exploration and sequential exploration in this large population of participants. The new Value plus Sequential Exploration (VSE) architecture provided a better fit than previous models. Parameter recovery, model recovery and simulation analyses confirmed the superiority of the VSE scheme. Furthermore, using the VSE model, we confirmed the existence of a significant reduction in directed exploration across lifespan in the IGT, as previously reported with other paradigms. Finally, we provide a user-friendly toolbox enabling researchers to easily and flexibly fit computational models on the IGT data, hence promoting reanalysis of the numerous datasets acquired in various populations of patients and contributing to the development of computational psychiatry.'),
(89751, 2019, 'plos', 'Fostering bioinformatics education through skill development of professors: Big Genomic Data Skills Training for Professors', 'Zhan, Yingqian Ada|Wray, Charles Gregory|Namburi, Sandeep|Glantz, Spencer T|Laubenbacher, Reinhard|Chuang, Jeffrey H', 'The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, United States of America.|Genomic Education, The Jackson Laboratory, Bar Harbor, Maine, United States of America.|The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, United States of America.|The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, United States of America.|The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, United States of America.|The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, United States of America.', 'United States|United States|United States|United States|United States|United States', 2, '-', 'Journal Article', '10.1371/journal.pcbi.1007026', '31194735', 'Bioinformatics has become an indispensable part of life science over the past 2 decades. However, bioinformatics education is not well integrated at the undergraduate level, especially in liberal arts colleges and regional universities in the United States. One significant obstacle pointed out by the Network for Integrating Bioinformatics into Life Sciences Education is the lack of faculty in the bioinformatics area. Most current life science professors did not acquire bioinformatics analysis skills during their own training. Consequently, a great number of undergraduate and graduate students do not get the chance to learn bioinformatics or computational biology skills within a structured curriculum during their education. To address this gap, we developed a module-based, week-long short course to train small college and regional university professors with essential bioinformatics skills. The bioinformatics modules were built to be adapted by the professor-trainees afterward and used in their own classes. All the course materials can be accessed at https://github.com/TheJacksonLaboratory/JAXBD2K-ShortCourse.'),
(89752, 2019, 'plos', 'Pathogenicity and functional impact of non-frameshifting insertion/deletion variation in the human genome', 'Pagel, Kymberleigh A|Antaki, Danny|Lian, AoJie|Mort, Matthew|Cooper, David N|Sebat, Jonathan|Iakoucheva, Lilia M|Mooney, Sean D|Radivojac, Predrag', 'School of Informatics, Computing, and Engineering, Indiana University, Bloomington, Indiana, United States of America.|Department of Psychiatry, University of California San Diego, La Jolla, California, United States of America.|Department of Psychiatry, University of California San Diego, La Jolla, California, United States of America.|Institute of Medical Genetics, Cardiff University, Cardiff, United Kingdom.|Institute of Medical Genetics, Cardiff University, Cardiff, United Kingdom.|Department of Psychiatry, University of California San Diego, La Jolla, California, United States of America.|Department of Psychiatry, University of California San Diego, La Jolla, California, United States of America.|Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington, United States of America.|School of Informatics, Computing, and Engineering, Indiana University, Bloomington, Indiana, United States of America.', 'India|United States|United States|United Kingdom|United Kingdom|United States|United States|United S', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1007112', '31199787', 'Differentiation between phenotypically neutral and disease-causing genetic variation remains an open and relevant problem. Among different types of variation, non-frameshifting insertions and deletions (indels) represent an understudied group with widespread phenotypic consequences. To address this challenge, we present a machine learning method, MutPred-Indel, that predicts pathogenicity and identifies types of functional residues impacted by non-frameshifting insertion/deletion variation. The model shows good predictive performance as well as the ability to identify impacted structural and functional residues including secondary structure, intrinsic disorder, metal and macromolecular binding, post-translational modifications, allosteric sites, and catalytic residues. We identify structural and functional mechanisms impacted preferentially by germline variation from the Human Gene Mutation Database, recurrent somatic variation from COSMIC in the context of different cancers, as well as de novo variants from families with autism spectrum disorder. Further, the distributions of pathogenicity prediction scores generated by MutPred-Indel are shown to differentiate highly recurrent from non-recurrent somatic variation. Collectively, we present a framework to facilitate the interrogation of both pathogenicity and the functional effects of non-frameshifting insertion/deletion variants. The MutPred-Indel webserver is available at http://mutpred.mutdb.org/.'),
(89753, 2019, 'plos', 'Correction: Ten Simple Rules for Taking Advantage of Git and GitHub', 'PLOS Computational Biology Staff', 'School of Informatics, Computing, and Engineering, Indiana University, Bloomington, Indiana, United States of America.', 'India', 0, '-', 'Published Erratum', '10.1371/journal.pcbi.1007142', '31199788', '[This corrects the article DOI: 10.1371/journal.pcbi.1004947.].'),
(89754, 2019, 'plos', 'Correction: Deciphering Signaling Pathway Networks to Understand the Molecular Mechanisms of Metformin Action', 'PLOS Computational Biology Staff', 'School of Informatics, Computing, and Engineering, Indiana University, Bloomington, Indiana, United States of America.', 'India', 0, '-', 'Published Erratum', '10.1371/journal.pcbi.1007141', '31199793', '[This corrects the article DOI: 10.1371/journal.pcbi.1004202.].'),
(89755, 2019, 'plos', 'Correction: Effects of Darwinian Selection and Mutability on Rate of Broadly Neutralizing Antibody Evolution during HIV-1 Infection', 'PLOS Computational Biology Staff', 'School of Informatics, Computing, and Engineering, Indiana University, Bloomington, Indiana, United States of America.', 'India', 0, '-', 'Published Erratum', '10.1371/journal.pcbi.1007138', '31199795', '[This corrects the article DOI: 10.1371/journal.pcbi.1004940.].'),
(89756, 2019, 'plos', 'Correction: Geospatial characteristics of measles transmission in China during 2005-2014', 'Yang, Wan|Wen, Liang|Li, Shen-Long|Chen, Kai|Zhang, Wen-Yi|Shaman, Jeffrey', 'School of Informatics, Computing, and Engineering, Indiana University, Bloomington, Indiana, United States of America.|School of Informatics, Computing, and Engineering, Indiana University, Bloomington, Indiana, United States of America.|School of Informatics, Computing, and Engineering, Indiana University, Bloomington, Indiana, United States of America.|School of Informatics, Computing, and Engineering, Indiana University, Bloomington, Indiana, United States of America.|School of Informatics, Computing, and Engineering, Indiana University, Bloomington, Indiana, United States of America.|School of Informatics, Computing, and Engineering, Indiana University, Bloomington, Indiana, United States of America.', 'India|India|India|India|India|India', 0, '-', 'Published Erratum', '10.1371/journal.pcbi.1007144', '31199796', '[This corrects the article DOI: 10.1371/journal.pcbi.1005474.].'),
(89757, 2019, 'plos', 'DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences', 'Lee, Ingoo|Keum, Jongsoo|Nam, Hojung', 'School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Buk-ku, Gwangju, Republic of Korea.|School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Buk-ku, Gwangju, Republic of Korea.|School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Buk-ku, Gwangju, Republic of Korea.', '-|-|-', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1007129', '31199797', 'Identification of drug-target interactions (DTIs) plays a key role in drug discovery. The high cost and labor-intensive nature of in vitro and in vivo experiments have highlighted the importance of in silico-based DTI prediction approaches. In several computational models, conventional protein descriptors have been shown to not be sufficiently informative to predict accurate DTIs. Thus, in this study, we propose a deep learning based DTI prediction model capturing local residue patterns of proteins participating in DTIs. When we employ a convolutional neural network (CNN) on raw protein sequences, we perform convolution on various lengths of amino acids subsequences to capture local residue patterns of generalized protein classes. We train our model with large-scale DTI information and demonstrate the performance of the proposed model using an independent dataset that is not seen during the training phase. As a result, our model performs better than previous protein descriptor-based models. Also, our model performs better than the recently developed deep learning models for massive prediction of DTIs. By examining pooled convolution results, we confirmed that our model can detect binding sites of proteins for DTIs. In conclusion, our prediction model for detecting local residue patterns of target proteins successfully enriches the protein features of a raw protein sequence, yielding better prediction results than previous approaches. Our code is available at https://github.com/GIST-CSBL/DeepConv-DTI.'),
(89758, 2019, 'plos', 'Correction: Reconstructible Phylogenetic Networks: Do Not Distinguish the Indistinguishable', 'PLOS Computational Biology Staff', 'School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Buk-ku, Gwangju, Republic of Korea.', '-', 0, '-', 'Published Erratum', '10.1371/journal.pcbi.1007137', '31199798', '[This corrects the article DOI: 10.1371/journal.pcbi.1004135.].'),
(89759, 2019, 'plos', 'Correction: Most Undirected Random Graphs Are Amplifiers of Selection for Birth-Death Dynamics, but Suppressors of Selection for Death-Birth Dynamics', 'PLOS Computational Biology Staff', 'School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Buk-ku, Gwangju, Republic of Korea.', '-', 0, '-', 'Published Erratum', '10.1371/journal.pcbi.1007143', '31199801', '[This corrects the article DOI: 10.1371/journal.pcbi.1004437.].'),
(89760, 2019, 'plos', 'Correction: The Equivalence of Information-Theoretic and Likelihood-Based Methods for Neural Dimensionality Reduction', 'PLOS Computational Biology Staff', 'School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Buk-ku, Gwangju, Republic of Korea.', '-', 0, '-', 'Published Erratum', '10.1371/journal.pcbi.1007139', '31199805', '[This corrects the article DOI: 10.1371/journal.pcbi.1004141.].'),
(89761, 2019, 'plos', 'Rational discovery of dual-indication multi-target PDE/Kinase inhibitor for precision anti-cancer therapy using structural systems pharmacology', 'Lim, Hansaim|He, Di|Qiu, Yue|Krawczuk, Patrycja|Sun, Xiaoru|Xie, Lei', 'Ph.D. Program in Biochemistry, The Graduate Center, The City University of New York, New York, New York, United States of America.|Ph.D. Program in Computer Science, The Graduate Center, The City University of New York, New York, New York, United States of America.|Ph.D. Program in Biology, The Graduate Center, The City University of New York, New York, New York, United States of America.|Department of Computer Science, Hunter College, The City University of New York, New York, New York, United States of America.|Department of Computer Science, Hunter College, The City University of New York, New York, New York, United States of America.|Ph.D. Program in Biochemistry, The Graduate Center, The City University of New York, New York, New York, United States of America.', 'United States|United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1006619', '31206508', 'Many complex diseases such as cancer are associated with multiple pathological manifestations. Moreover, the therapeutics for their treatments often lead to serious side effects. Thus, it is needed to develop multi-indication therapeutics that can simultaneously target multiple clinical indications of interest and mitigate the side effects. However, conventional one-drug-one-gene drug discovery paradigm and emerging polypharmacology approach rarely tackle the challenge of multi-indication drug design. For the first time, we propose a one-drug-multi-target-multi-indication strategy. We develop a novel structural systems pharmacology platform 3D-REMAP that uses ligand binding site comparison and protein-ligand docking to augment sparse chemical genomics data for the machine learning model of genome-scale chemical-protein interaction prediction. Experimentally validated predictions systematically show that 3D-REMAP outperforms state-of-the-art ligand-based, receptor-based, and machine learning methods alone. As a proof-of-concept, we utilize the concept of drug repurposing that is enabled by 3D-REMAP to design dual-indication anti-cancer therapy. The repurposed drug can demonstrate anti-cancer activity for cancers that do not have effective treatment as well as reduce the risk of heart failure that is associated with all types of existing anti-cancer therapies. We predict that levosimendan, a PDE inhibitor for heart failure, inhibits serine/threonine-protein kinase RIOK1 and other kinases. Subsequent experiments and systems biology analyses confirm this prediction, and suggest that levosimendan is active against multiple cancers, notably lymphoma, through the direct inhibition of RIOK1 and RNA processing pathway. We further develop machine learning models to predict cancer cell-lines and a patients response to levosimendan. Our findings suggest that levosimendan can be a promising novel lead compound for the development of safe, effective, and precision multi-indication anti-cancer therapy. This study demonstrates the potential of structural systems pharmacology in designing polypharmacology for precision medicine. It may facilitate transforming the conventional one-drug-one-gene-one-disease drug discovery process and single-indication polypharmacology approach into a new one-drug-multi-target-multi-indication paradigm for complex diseases.'),
(89762, 2019, 'plos', 'Correction: Recentrifuge: Robust comparative analysis and contamination removal for metagenomics', 'Martí, Jose Manuel', 'Ph.D. Program in Biochemistry, The Graduate Center, The City University of New York, New York, New York, United States of America.', 'United States', 0, '-', 'Published Erratum', '10.1371/journal.pcbi.1007131', '31206511', '[This corrects the article DOI: 10.1371/journal.pcbi.1006967.].'),
(89763, 2019, 'plos', 'Correction: Path Similarity Analysis: A Method for Quantifying Macromolecular Pathways', 'Seyler, Sean L|Kumar, Avishek|Thorpe, M F|Beckstein, Oliver', 'Ph.D. Program in Biochemistry, The Graduate Center, The City University of New York, New York, New York, United States of America.|Ph.D. Program in Biochemistry, The Graduate Center, The City University of New York, New York, New York, United States of America.|Ph.D. Program in Biochemistry, The Graduate Center, The City University of New York, New York, New York, United States of America.|Ph.D. Program in Biochemistry, The Graduate Center, The City University of New York, New York, New York, United States of America.', 'United States|United States|United States|United States', 0, '-', 'Published Erratum', '10.1371/journal.pcbi.1007136', '31206513', '[This corrects the article DOI: 10.1371/journal.pcbi.1004568.].'),
(89764, 2019, 'plos', 'A multi-scale model of gas transport in the lung to study heterogeneous lung ventilation during the multiple-breath washout test', 'Hasler, David|Anagnostopoulou, Pinelopi|Nyilas, Sylvia|Latzin, Philipp|Schittny, Johannes|Obrist, Dominik', 'Pediatric Respiratory Medicine, Department of Pediatrics, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.|Pediatric Respiratory Medicine, Department of Pediatrics, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.|Pediatric Respiratory Medicine, Department of Pediatrics, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.|Pediatric Respiratory Medicine, Department of Pediatrics, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.|Institute of Anatomy, University of Bern, Bern, Switzerland.|ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 3, '-', 'Journal Article', '10.1371/journal.pcbi.1007079', '31206515', 'The multiple-breath washout (MBW) is a lung function test that measures the degree of ventilation inhomogeneity (VI). The test is used to identify small airway impairment in patients with lung diseases like cystic fibrosis. However, the physical and physiological factors that influence the test outcomes and differentiate health from disease are not well understood. Computational models have been used to better understand the interaction between anatomical structure and physiological properties of the lung, but none of them has dealt in depth with the tracer gas washout test in a whole. Thus, our aim was to create a lung model that simulates the entire MBW and investigate the role of lung morphology and tissue mechanics on the tracer gas washout procedure. To this end, we developed a multi-scale lung model to simulate the inert gas transport in airways of all size. We then applied systematically different modifications to geometrical and mechanical properties of the lung model (compliance, residual airway volume and flow resistance) which have been associated with VI. The modifications were applied to distinct parts of the model, and their effects on the gas distribution within the lung and on the gas concentration profile were assessed. We found that variability in compliance and residual volume of the airways, as well as the spatial distribution of this variability in the lung had a direct influence on gas distribution among airways and on the MBW pattern (washout duration, characteristic concentration profile during each expiration), while the effects of variable flow resistance were negligible. Based on these findings, it is possible to classify different types of inhomogeneities in the lung and relate them to specific features of the MBW pattern, which builds the basis for a more detailed association of lung function and structure.'),
(89765, 2019, 'plos', 'Hierarchical Bayesian inference for concurrent model fitting and comparison for group studies', 'Piray, Payam|Dezfouli, Amir|Heskes, Tom|Frank, Michael J|Daw, Nathaniel D', 'Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States of America.|Data61, CSIRO, Sydney, Australia.|Institute for Computing and Information Sciences, Radboud University, the Netherlands.|Department of Cognitive, Linguistics, and Psychological Sciences, Brown University, Providence, Rhode Island, United States of America.|Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States of America.', 'Jersey|Australia|Netherlands|United States|Jersey', 3, '-', 'Journal Article', '10.1371/journal.pcbi.1007043', '31211783', 'Computational modeling plays an important role in modern neuroscience research. Much previous research has relied on statistical methods, separately, to address two problems that are actually interdependent. First, given a particular computational model, Bayesian hierarchical techniques have been used to estimate individual variation in parameters over a population of subjects, leveraging their population-level distributions. Second, candidate models are themselves compared, and individual variation in the expressed model estimated, according to the fits of the models to each subject. The interdependence between these two problems arises because the relevant population for estimating parameters of a model depends on which other subjects express the model. Here, we propose a hierarchical Bayesian inference (HBI) framework for concurrent model comparison, parameter estimation and inference at the population level, combining previous approaches. We show that this framework has important advantages for both parameter estimation and model comparison theoretically and experimentally. The parameters estimated by the HBI show smaller errors compared to other methods. Model comparison by HBI is robust against outliers and is not biased towards overly simplistic models. Furthermore, the fully Bayesian approach of our theory enables researchers to make inference on group-level parameters by performing HBI t-test.'),
(89766, 2019, 'plos', 'Transcriptomic correlates of electrophysiological and morphological diversity within and across excitatory and inhibitory neuron classes', 'Bomkamp, Claire|Tripathy, Shreejoy J|Bengtsson Gonzales, Carolina|Hjerling-Leffler, Jens|Craig, Ann Marie|Pavlidis, Paul', 'Department of Psychiatry, University of British Columbia, Vancouver BC, Canada.|Department of Psychiatry, University of British Columbia, Vancouver BC, Canada.|Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden.|Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden.|Department of Psychiatry, University of British Columbia, Vancouver BC, Canada.|Department of Psychiatry, University of British Columbia, Vancouver BC, Canada.', 'Canada|Canada|Sweden|Sweden|Canada|Canada', 2, '-', 'Journal Article', '10.1371/journal.pcbi.1007113', '31211786', 'In order to further our understanding of how gene expression contributes to key functional properties of neurons, we combined publicly accessible gene expression, electrophysiology, and morphology measurements to identify cross-cell type correlations between these data modalities. Building on our previous work using a similar approach, we distinguished between correlations which were class-driven, meaning those that could be explained by differences between excitatory and inhibitory cell classes, and those that reflected graded phenotypic differences within classes. Taking cell class identity into account increased the degree to which our results replicated in an independent dataset as well as their correspondence with known modes of ion channel function based on the literature. We also found a smaller set of genes whose relationships to electrophysiological or morphological properties appear to be specific to either excitatory or inhibitory cell types. Next, using data from PatchSeq experiments, allowing simultaneous single-cell characterization of gene expression and electrophysiology, we found that some of the gene-property correlations observed across cell types were further predictive of within-cell type heterogeneity. In summary, we have identified a number of relationships between gene expression, electrophysiology, and morphology that provide testable hypotheses for future studies.'),
(89767, 2019, 'plos', 'Modeling metabolic networks of individual bacterial agents in heterogeneous and dynamic soil habitats (IndiMeSH)', 'Borer, Benedict|Ataman, Meriç|Hatzimanikatis, Vassily|Or, Dani', 'Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland.|Laboratory of Computational Systems Biotechnology, EPFL, Lausanne, Switzerland.|Laboratory of Computational Systems Biotechnology, EPFL, Lausanne, Switzerland.|Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland', 6, '-', 'Journal Article', '10.1371/journal.pcbi.1007127', '31216273', 'Natural soil is characterized as a complex habitat with patchy hydrated islands and spatially variable nutrients that is in a constant state of change due to wetting-drying dynamics. Soil microbial activity is often concentrated in sparsely distributed hotspots that contribute disproportionally to macroscopic biogeochemical nutrient cycling and greenhouse gas emissions. The mechanistic representation of such dynamic hotspots requires new modeling approaches capable of representing the interplay between dynamic local conditions and the versatile microbial metabolic adaptations. We have developed IndiMeSH (Individual-based Metabolic network model for Soil Habitats) as a spatially explicit model for the physical and chemical microenvironments of soil, combined with an individual-based representation of bacterial motility and growth using adaptive metabolic networks. The model uses angular pore networks and a physically based description of the aqueous phase as a backbone for nutrient diffusion and bacterial dispersal combined with dynamic flux balance analysis to calculate growth rates depending on local nutrient conditions. To maximize computational efficiency, reduced scale metabolic networks are used for the simulation scenarios and evaluated strategically to the genome scale model. IndiMeSH was compared to a well-established population-based spatiotemporal metabolic network model (COMETS) and to experimental data of bacterial spatial organization in pore networks mimicking soil aggregates. IndiMeSH was then used to strategically study dynamic response of a bacterial community to abrupt environmental perturbations and the influence of habitat geometry and hydration conditions. Results illustrate that IndiMeSH is capable of representing trophic interactions among bacterial species, predicting the spatial organization and segregation of bacterial populations due to oxygen and carbon gradients, and provides insights into dynamic community responses as a consequence of environmental changes. The modular design of IndiMeSH and its implementation are adaptable, allowing it to represent a wide variety of experimental and in silico microbial systems.'),
(89768, 2019, 'plos', 'Protein ensembles link genotype to phenotype', 'Nussinov, Ruth|Tsai, Chung-Jung|Jang, Hyunbum', 'Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute at Frederick, Frederick, Maryland, United States of America.|Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute at Frederick, Frederick, Maryland, United States of America.|Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute at Frederick, Frederick, Maryland, United States of America.', 'United States|United States|United States', 6, '-', 'Journal Article', '10.1371/journal.pcbi.1006648', '31220071', 'Classically, phenotype is what is observed, and genotype is the genetic makeup. Statistical studies aim to project phenotypic likelihoods of genotypic patterns. The traditional genotype-to-phenotype theory embraces the view that the encoded protein shape together with gene expression level largely determines the resulting phenotypic trait. Here, we point out that the molecular biology revolution at the turn of the century explained that the gene encodes not one but ensembles of conformations, which in turn spell all possible gene-associated phenotypes. The significance of a dynamic ensemble view is in understanding the linkage between genetic change and the gained observable physical or biochemical characteristics. Thus, despite the transformative shift in our understanding of the basis of protein structure and function, the literature still commonly relates to the classical genotype-phenotype paradigm. This is important because an ensemble view clarifies how even seemingly small genetic alterations can lead to pleiotropic traits in adaptive evolution and in disease, why cellular pathways can be modified in monogenic and polygenic traits, and how the environment may tweak protein function.'),
(89769, 2019, 'plos', 'Ten quick tips for effective dimensionality reduction', 'Nguyen, Lan Huong|Holmes, Susan', 'Institute for Mathematical and Computational Engineering, Stanford University, Stanford, California, United States of America.|Department of Statistics, Stanford University, Stanford, California, United States of America.', 'United States|United States', 23, '-', 'Journal Article', '10.1371/journal.pcbi.1006907', '31220072', '-'),
(89770, 2019, 'plos', 'Ten simple rules for successfully hosting an intern at a scientific software company', 'Briedis, Kristine|Chen, Yi-Shiou|Markel, Scott', 'Dassault Systèmes BIOVIA, San Diego, California, United States of America.|Dassault Systèmes BIOVIA, San Diego, California, United States of America.|Dassault Systèmes BIOVIA, San Diego, California, United States of America.', 'United States|United States|United States', 0, '-', 'Editorial', '10.1371/journal.pcbi.1007020', '31220073', '-'),
(89771, 2019, 'plos', 'Coupling water fluxes with cell wall mechanics in a multicellular model of plant development', 'Cheddadi, Ibrahim|Génard, Michel|Bertin, Nadia|Godin, Christophe', 'Univ. Grenoble Alpes, CNRS, Grenoble INP, TIMC-IMAG, 38000 Grenoble, France.|UR 1115 PSH, INRA, F-84914 Avignon Cedex 9, France.|UR 1115 PSH, INRA, F-84914 Avignon Cedex 9, France.|Virtual Plants, INRIA, Montpellier, France.', 'France|France|France|France', 3, '-', 'Journal Article', '10.1371/journal.pcbi.1007121', '31220080', 'The growth of plant organs is a complex process powered by osmosis that attracts water inside the cells, this influx induces simultaneously an elastic extension of the walls and pressure in the cells, called turgor pressure, above a threshold, the walls yield and the cells grow. Based on Lockharts seminal work, various models of plant morphogenesis have been proposed, either for single cells, or focusing on the wall mechanical properties. However, the synergistic coupling of fluxes and wall mechanics has not yet been fully addressed in a multicellular model. This work lays the foundations of such a model, by simplifying as much as possible each process and putting emphasis on the coupling itself. Its emergent properties are rich and can help to understand plant morphogenesis. In particular, we show that the model can display a new type of lateral inhibitory mechanism that amplifies growth heterogeneities due e.g to cell wall loosening.'),
(89772, 2019, 'plos', 'Resurgent Na+ Current Offers Noise Modulation in Bursting Neurons', 'Venugopal, Sharmila|Seki, Soju|Terman, David H|Pantazis, Antonios|Olcese, Riccardo|Wiedau-Pazos, Martina|Chandler, Scott H', 'Department of Integrative Biology and Physiology, University of California Los Angeles, Los Angeles, CA, United States of America.|Department of Integrative Biology and Physiology, University of California Los Angeles, Los Angeles, CA, United States of America.|Department of Mathematics, The Ohio State University, Columbus, OH, United States of America.|Department of Anesthesiology & Perioperative Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States of America.|Department of Anesthesiology & Perioperative Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States of America.|Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States of America.|Department of Integrative Biology and Physiology, University of California Los Angeles, Los Angeles, CA, United States of America.', 'United States|United States|United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1007154', '31226124', 'Neurons utilize bursts of action potentials as an efficient and reliable way to encode information. It is likely that the intrinsic membrane properties of neurons involved in burst generation may also participate in preserving its temporal features. Here we examined the contribution of the persistent and resurgent components of voltage-gated Na+ currents in modulating the burst discharge in sensory neurons. Using mathematical modeling, theory and dynamic-clamp electrophysiology, we show that, distinct from the persistent Na+ component which is important for membrane resonance and burst generation, the resurgent Na+ can help stabilize burst timing features including the duration and intervals. Moreover, such a physiological role for the resurgent Na+ offered noise tolerance and preserved the regularity of burst patterns. Model analysis further predicted a negative feedback loop between the persistent and resurgent gating variables which mediate such gain in burst stability. These results highlight a novel role for the voltage-gated resurgent Na+ component in moderating the entropy of burst-encoded neural information.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(89773, 2019, 'plos', 'Modelling the transport of fluid through heterogeneous, whole tumours in silico', 'Sweeney, Paul W|dEsposito, Angela|Walker-Samuel, Simon|Shipley, Rebecca J', 'Mechanical Engineering, University College London, London, United Kingdom.|Centre for Advanced Biomedical Engineering, University College London, London, United Kingdom.|Centre for Advanced Biomedical Engineering, University College London, London, United Kingdom.|Mechanical Engineering, University College London, London, United Kingdom.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 4, '-', 'Journal Article', '10.1371/journal.pcbi.1006751', '31226169', 'Cancers exhibit spatially heterogeneous, unique vascular architectures across individual samples, cell-lines and patients. This inherently disorganised collection of leaky blood vessels contribute significantly to suboptimal treatment efficacy. Preclinical tools are urgently required which incorporate the inherent variability and heterogeneity of tumours to optimise and engineer anti-cancer therapies. In this study, we present a novel computational framework which incorporates whole, realistic tumours extracted ex vivo to efficiently simulate vascular blood flow and interstitial fluid transport in silico for validation against in vivo biomedical imaging. Our model couples Poiseuille and Darcy descriptions of vascular and interstitial flow, respectively, and incorporates spatially heterogeneous blood vessel lumen and interstitial permeabilities to generate accurate predictions of tumour fluid dynamics. Our platform enables highly-controlled experiments to be performed which provide insight into how tumour vascular heterogeneity contributes to tumour fluid transport. We detail the application of our framework to an orthotopic murine glioma (GL261) and a human colorectal carcinoma (LS147T), and perform sensitivity analysis to gain an understanding of the key biological mechanisms which determine tumour fluid transport. Finally we mimic vascular normalization by modifying parameters, such as vascular and interstitial permeabilities, and show that incorporating realistic vasculatures is key to modelling the contrasting fluid dynamic response between tumour samples. Contrary to literature, we show that reducing tumour interstitial fluid pressure is not essential to increase interstitial perfusion and that therapies should seek to develop an interstitial fluid pressure gradient. We also hypothesise that stabilising vessel diameters and permeabilities are not key responses following vascular normalization and that therapy may alter interstitial hydraulic conductivity. Consequently, we suggest that normalizing the interstitial microenvironment may provide a more effective means to increase interstitial perfusion within tumours.'),
(89774, 2019, 'plos', 'Open collaborative writing with Manubot', 'Himmelstein, Daniel S|Rubinetti, Vincent|Slochower, David R|Hu, Dongbo|Malladi, Venkat S|Greene, Casey S|Gitter, Anthony', 'Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.|Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.|Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, California, United States of America.|Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.|Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America.|Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.|Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America.', 'United States|United States|United States|United States|United States|United States|United States', 1, '-', 'Journal Article', '10.1371/journal.pcbi.1007128', '31233491', 'Open, collaborative research is a powerful paradigm that can immensely strengthen the scientific process by integrating broad and diverse expertise. However, traditional research and multi-author writing processes break down at scale. We present new software named Manubot, available at https://manubot.org, to address the challenges of open scholarly writing. Manubot adopts the contribution workflow used by many large-scale open source software projects to enable collaborative authoring of scholarly manuscripts. With Manubot, manuscripts are written in Markdown and stored in a Git repository to precisely track changes over time. By hosting manuscript repositories publicly, such as on GitHub, multiple authors can simultaneously propose and review changes. A cloud service automatically evaluates proposed changes to catch errors. Publication with Manubot is continuous: When a manuscripts source changes, the rendered outputs are rebuilt and republished to a web page. Manubot automates bibliographic tasks by implementing citation by identifier, where users cite persistent identifiers (e.g. DOIs, PubMed IDs, ISBNs, URLs), whose metadata is then retrieved and converted to a user-specified style. Manubot modernizes publishing to align with the ideals of open science by making it transparent, reproducible, immediate, versioned, collaborative, and free of charge.'),
(89775, 2019, 'plos', 'A neural network model predicts community-level signaling states in a diverse microbial community', 'Silva, Kalinga Pavan T|Boedicker, James Q', 'Department of Physics and Astronomy, University of Southern California, Los Angeles, California, United States of America.|Department of Physics and Astronomy, University of Southern California, Los Angeles, California, United States of America.', 'United States|United States', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1007166', '31233492', 'Signal crosstalk within biological communication networks is common, and such crosstalk can have unexpected consequences for decision making in heterogeneous communities of cells. Here we examined crosstalk within a bacterial community composed of five strains of Bacillus subtilis, with each strain producing a variant of the quorum sensing peptide ComX. In isolation, each strain produced one variant of the ComX signal to induce expression of genes associated with bacterial competence. When strains were combined, a mixture of ComX variants was produced resulting in variable levels of gene expression. To examine gene regulation in mixed communities, we implemented a neural network model. Experimental quantification of asymmetric crosstalk between pairs of strains parametrized the model, enabling the accurate prediction of activity within the full five-strain network. Unlike the single strain system in which quorum sensing activated upon exceeding a threshold concentration of the signal, crosstalk within the five-strain community resulted in multiple community-level quorum sensing states, each with a unique combination of quorum sensing activation among the five strains. Quorum sensing activity of the strains within the community was influenced by the combination and ratio of strains as well as community dynamics. The community-level signaling state was altered through an external signal perturbation, and the output state depended on the timing of the perturbation. Given the ubiquity of signal crosstalk in diverse microbial communities, the application of such neural network models will increase accuracy of predicting activity within microbial consortia and enable new strategies for control and design of bacterial signaling networks.'),
(89776, 2019, 'plos', 'Bridging the gap between HIV epidemiology and antiretroviral resistance evolution: Modelling the spread of resistance in South Africa', 'Hauser, Anthony|Kusejko, Katharina|Johnson, Leigh F|Wandeler, Gilles|Riou, Julien|Goldstein, Fardo|Egger, Matthias|Kouyos, Roger D', 'Institute of Social and Preventive Medicine, University of Bern, Switzerland.|Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland.|Centre for Infectious Disease Epidemiology and Research, University of Cape Town, South Africa.|Institute of Social and Preventive Medicine, University of Bern, Switzerland.|Institute of Social and Preventive Medicine, University of Bern, Switzerland.|Institute of Social and Preventive Medicine, University of Bern, Switzerland.|Institute of Social and Preventive Medicine, University of Bern, Switzerland.|Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland.', 'Switzerland|Switzerland|South Africa|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1007083', '31233494', 'The scale-up of antiretroviral therapy (ART) in South Africa substantially reduced AIDS-related deaths and new HIV infections. However, its success is threatened by the emergence of resistance to non-nucleoside reverse-transcriptase inhibitors (NNRTI). The MARISA (Modelling Antiretroviral drug Resistance In South Africa) model presented here aims at investigating the time trends and factors driving NNRTI resistance in South Africa. MARISA is a compartmental model that includes the key aspects of the local HIV epidemic: continuum of care, disease progression, and gender. The dynamics of NNRTI resistance emergence and transmission are then added to this framework. Model parameters are informed using data from HIV cohorts participating in the International epidemiology Databases to Evaluate AIDS (IeDEA) and literature estimates, or fitted to UNAIDS estimates. Using this novel approach of triangulating clinical and resistance data from various sources, MARISA reproduces the time trends of HIV in South Africa in 2005-2016, with a decrease in new infections, undiagnosed individuals, and AIDS-related deaths. MARISA captures the dynamics of the spread of NNRTI resistance: high levels of acquired drug resistance (ADR, in 83% of first-line treatment failures in 2016), and increasing transmitted drug resistance (TDR, in 8.1% of ART initiators in 2016). Simulation of counter-factual scenarios reflecting alternative public health policies shows that increasing treatment coverage would have resulted in fewer new infections and deaths, at the cost of higher TDR (11.6% in 2016 for doubling the treatment rate). Conversely, improving switching to second-line treatment would have led to lower TDR (6.5% in 2016 for doubling the switching rate) and fewer new infections and deaths. Implementing drug resistance testing would have had little impact. The rapid ART scale-up and inadequate switching to second-line treatment were the key drivers of the spread of NNRTI resistance in South Africa. However, even though some interventions could have substantially reduced the level of NNRTI resistance, no policy including NNRTI-based first line regimens could have prevented this spread. Thus, by combining epidemiological data on HIV in South Africa with biological data on resistance evolution, our modelling approach identified key factors driving NNRTI resistance, highlighting the need of alternative first-line regimens.'),
(89777, 2019, 'plos', 'Correction: From Inverse Problems in Mathematical Physiology to Quantitative Differential Diagnoses', 'Zenker, Sven|Rubin, Jonathan|Clermont, Gilles', 'Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland.|Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland.|Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland.', 'Switzerland|Switzerland|Switzerland', 0, '-', 'Published Erratum', '10.1371/journal.pcbi.1007155', '31233499', '[This corrects the article DOI: 10.1371/journal.pcbi.0030204.].'),
(89778, 2019, 'plos', 'Learning to use past evidence in a sophisticated world model', 'Ahilan, Sanjeevan|Solomon, Rebecca B|Breton, Yannick-André|Conover, Kent|Niyogi, Ritwik K|Shizgal, Peter|Dayan, Peter', 'Gatsby Computational Neuroscience Unit, University College London, London, United Kingdom.|Center for Studies in Behavioral Neurobiology, Concordia University, Montreal, Canada.|Center for Studies in Behavioral Neurobiology, Concordia University, Montreal, Canada.|Center for Studies in Behavioral Neurobiology, Concordia University, Montreal, Canada.|Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom.|Center for Studies in Behavioral Neurobiology, Concordia University, Montreal, Canada.|Gatsby Computational Neuroscience Unit, University College London, London, United Kingdom.', 'United Kingdom|Canada|Canada|Canada|United Kingdom|Canada|United Kingdom', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1007093', '31233559', 'Humans and other animals are able to discover underlying statistical structure in their environments and exploit it to achieve efficient and effective performance. However, such structure is often difficult to learn and use because it is obscure, involving long-range temporal dependencies. Here, we analysed behavioural data from an extended experiment with rats, showing that the subjects learned the underlying statistical structure, albeit suffering at times from immediate inferential imperfections as to their current state within it. We accounted for their behaviour using a Hidden Markov Model, in which recent observations are integrated with evidence from the past. We found that over the course of training, subjects came to track their progress through the task more accurately, a change that our model largely attributed to improved integration of past evidence. This learning reflected the structure of the task, decreasing reliance on recent observations, which were potentially misleading.'),
(89779, 2019, 'plos', 'Model-inferred mechanisms of liver function from magnetic resonance imaging data: Validation and variation across a clinically relevant cohort', 'Forsgren, Mikael F|Karlsson, Markus|Dahlqvist Leinhard, Olof|Dahlström, Nils|Norén, Bengt|Romu, Thobias|Ignatova, Simone|Ekstedt, Mattias|Kechagias, Stergios|Lundberg, Peter|Cedersund, Gunnar', 'Wolfram MathCore AB and Department of Medical and Health Sciences, Linköping University, Linköping, Sweden.|Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.|Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.|Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.|Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.|Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.|Department of Clinical Pathology and Clinical Genetics, Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden.|Department of Gastroenterology and Hepatology, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden.|Department of Gastroenterology and Hepatology, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden.|Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.|Department of Biomedical Engineering, Linköping University, Linköping, Sweden.', 'Sweden|Sweden|Sweden|Sweden|Sweden|Sweden|Sweden|Sweden|Sweden|Sweden|Sweden', 1, '-', 'Journal Article', '10.1371/journal.pcbi.1007157', '31237870', 'Estimation of liver function is important to monitor progression of chronic liver disease (CLD). A promising method is magnetic resonance imaging (MRI) combined with gadoxetate, a liver-specific contrast agent. For this method, we have previously developed a model for an average healthy human. Herein, we extended this model, by combining it with a patient-specific non-linear mixed-effects modeling framework. We validated the model by recruiting 100 patients with CLD of varying severity and etiologies. The model explained all MRI data and adequately predicted both timepoints saved for validation and gadoxetate concentrations in both plasma and biopsies. The validated model provides a new and deeper look into how the mechanisms of liver function vary across a wide variety of liver diseases. The basic mechanisms remain the same, but increasing fibrosis reduces uptake and increases excretion of gadoxetate. These mechanisms are shared across many liver functions and can now be estimated from standard clinical images.'),
(89780, 2019, 'plos', 'A new framework for assessing subject-specific whole brain circulation and perfusion using MRI-based measurements and a multi-scale continuous flow model', 'Hodneland, Erlend|Hanson, Erik|Sævareid, Ove|Nævdal, Geir|Lundervold, Arvid|Šoltészová, Veronika|Munthe-Kaas, Antonella Z|Deistung, Andreas|Reichenbach, Jürgen R|Nordbotten, Jan M', 'Norwegian Research Centre, Bergen, Norway.|Department of Mathematics, University of Bergen, Bergen, Norway.|Norwegian Research Centre, Bergen, Norway.|Norwegian Research Centre, Bergen, Norway.|Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland Universitetssykehus, Bergen, Norway.|Norwegian Research Centre, Bergen, Norway.|Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland Universitetssykehus, Bergen, Norway.|Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital - Friedrich Schiller University Jena, Germany.|Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital - Friedrich Schiller University Jena, Germany.|Department of Mathematics, University of Bergen, Bergen, Norway.', 'Norway|Norway|Norway|Norway|Norway|Norway|Norway|Germany|Germany|Norway', 1, '-', 'Journal Article', '10.1371/journal.pcbi.1007073', '31237876', 'A large variety of severe medical conditions involve alterations in microvascular circulation. Hence, measurements or simulation of circulation and perfusion has considerable clinical value and can be used for diagnostics, evaluation of treatment efficacy, and for surgical planning. However, the accuracy of traditional tracer kinetic one-compartment models is limited due to scale dependency. As a remedy, we propose a scale invariant mathematical framework for simulating whole brain perfusion. The suggested framework is based on a segmentation of anatomical geometry down to imaging voxel resolution. Large vessels in the arterial and venous network are identified from time-of-flight (ToF) and quantitative susceptibility mapping (QSM). Macro-scale flow in the large-vessel-network is accurately modelled using the Hagen-Poiseuille equation, whereas capillary flow is treated as two-compartment porous media flow. Macro-scale flow is coupled with micro-scale flow by a spatially distributing support function in the terminal endings. Perfusion is defined as the transition of fluid from the arterial to the venous compartment. We demonstrate a whole brain simulation of tracer propagation on a realistic geometric model of the human brain, where the model comprises distinct areas of grey and white matter, as well as large vessels in the arterial and venous vascular network. Our proposed framework is an accurate and viable alternative to traditional compartment models, with high relevance for simulation of brain perfusion and also for restoration of field parameters in clinical brain perfusion applications.'),
(89781, 2019, 'plos', 'Energetic costs of cellular and therapeutic control of stochastic mitochondrial DNA populations', 'Hoitzing, Hanne|Gammage, Payam A|Haute, Lindsey Van|Minczuk, Michal|Johnston, Iain G|Jones, Nick S', 'Department of Mathematics, Imperial College London, London, SW7 2AZ, United Kingdom.|MRC Mitochondrial Biology Unit, University of Cambridge, Cambridge, CB2 0XY, United Kingdom.|MRC Mitochondrial Biology Unit, University of Cambridge, Cambridge, CB2 0XY, United Kingdom.|MRC Mitochondrial Biology Unit, University of Cambridge, Cambridge, CB2 0XY, United Kingdom.|Faculty of Mathematics and Natural Sciences, University of Bergen, Bergen, Norway.|Department of Mathematics, Imperial College London, London, SW7 2AZ, United Kingdom.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|Norway|United Kingdom', 6, '-', 'Journal Article', '10.1371/journal.pcbi.1007023', '31242175', 'The dynamics of the cellular proportion of mutant mtDNA molecules is crucial for mitochondrial diseases. Cellular populations of mitochondria are under homeostatic control, but the details of the control mechanisms involved remain elusive. Here, we use stochastic modelling to derive general results for the impact of cellular control on mtDNA populations, the cost to the cell of different mtDNA states, and the optimisation of therapeutic control of mtDNA populations. This formalism yields a wealth of biological results, including that an increasing mtDNA variance can increase the energetic cost of maintaining a tissue, that intermediate levels of heteroplasmy can be more detrimental than homoplasmy even for a dysfunctional mutant, that heteroplasmy distribution (not mean alone) is crucial for the success of gene therapies, and that long-term rather than short intense gene therapies are more likely to beneficially impact mtDNA populations.'),
(89782, 2019, 'plos', 'Predicting gastrointestinal drug effects using contextualized metabolic models', 'Ben Guebila, Marouen|Thiele, Ines', 'Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg.|Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg.', 'Luxembourg|Luxembourg', 2, '-', 'Journal Article', '10.1371/journal.pcbi.1007100', '31242176', 'Gastrointestinal side effects are among the most common classes of adverse reactions associated with orally absorbed drugs. These effects decrease patient compliance with the treatment and induce undesirable physiological effects. The prediction of drug action on the gut wall based on in vitro data solely can improve the safety of marketed drugs and first-in-human trials of new chemical entities. We used publicly available data of drug-induced gene expression changes to build drug-specific small intestine epithelial cell metabolic models. The combination of measured in vitro gene expression and in silico predicted metabolic rates in the gut wall was used as features for a multilabel support vector machine to predict the occurrence of side effects. We showed that combining local gut wall-specific metabolism with gene expression performs better than gene expression alone, which indicates the role of small intestine metabolism in the development of adverse reactions. Furthermore, we reclassified FDA-labeled drugs with respect to their genetic and metabolic profiles to show hidden similarities between seemingly different drugs. The linkage of xenobiotics to their transcriptomic and metabolic profiles could take pharmacology far beyond the usual indication-based classifications.'),
(89783, 2019, 'plos', 'Optimization of surgical intervention outside the epileptogenic zone in the Virtual Epileptic Patient (VEP)', 'An, Sora|Bartolomei, Fabrice|Guye, Maxime|Jirsa, Viktor', 'Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France.|Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France.|Aix Marseille Univ, CNRS, CRMBM UMR 7339, Marseille, France.|Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France.', 'France|France|France|France', 5, '-', 'Journal Article', '10.1371/journal.pcbi.1007051', '31242177', 'Studies to improve the efficacy of epilepsy surgery have focused on better refining the localization of the epileptogenic zone (EZ) with the aim of effectively resecting it. However, in a considerable number of patients, EZs are distributed across multiple brain regions and may involve eloquent areas that cannot be removed due to the risk of neurological complications. There is a clear need for developing alternative approaches to induce seizure relief, but minimal impact on normal brain functions. Here, we develop a personalized in-silico network approach, that suggests effective and safe surgical interventions for each patient. Based on the clinically identified EZ, we employ modularity analysis to identify target brain regions and fiber tracts involved in seizure propagation. We then construct and simulate a patient-specific brain network model comprising phenomenological neural mass models at the nodes, and patient-specific structural brain connectivity using the neuroinformatics platform The Virtual Brain (TVB), in order to evaluate effectiveness and safety of the target zones (TZs). In particular, we assess safety via electrical stimulation for pre- and post-surgical condition to quantify the impact on the signal transmission properties of the network. We demonstrate the existence of a large repertoire of efficient surgical interventions resulting in reduction of degree of seizure spread, but only a small subset of them proves safe. The identification of novel surgical interventions through modularity analysis and brain network simulations may provide exciting solutions to the treatment of inoperable epilepsies.'),
(89784, 2019, 'plos', 'Drosophila melanogaster grooming possesses syntax with distinct rules at different temporal scales', 'Mueller, Joshua M|Ravbar, Primoz|Simpson, Julie H|Carlson, Jean M', 'Interdepartmental Graduate Program in Dynamical Neuroscience, University of California, Santa Barbara, Santa Barbara, California, United States of America.|Department of Molecular, Cellular, and Developmental Biology, University of California, Santa Barbara, Santa Barbara, California, United States of America.|Interdepartmental Graduate Program in Dynamical Neuroscience, University of California, Santa Barbara, Santa Barbara, California, United States of America.|Interdepartmental Graduate Program in Dynamical Neuroscience, University of California, Santa Barbara, Santa Barbara, California, United States of America.', 'United States|United States|United States|United States', 3, '-', 'Journal Article', '10.1371/journal.pcbi.1007105', '31242178', 'Mathematical modeling of behavioral sequences yields insight into the rules and mechanisms underlying sequence generation. Grooming in Drosophila melanogaster is characterized by repeated execution of distinct, stereotyped actions in variable order. Experiments demonstrate that, following stimulation by an irritant, grooming progresses gradually from an early phase dominated by anterior cleaning to a later phase with increased walking and posterior cleaning. We also observe that, at an intermediate temporal scale, there is a strong relationship between the amount of time spent performing body-directed grooming actions and leg-directed actions. We then develop a series of data-driven Markov models that isolate and identify the behavioral features governing transitions between individual grooming bouts. We identify action order as the primary driver of probabilistic, but non-random, syntax structure, as has previously been identified. Subsequent models incorporate grooming bout duration, which also contributes significantly to sequence structure. Our results show that, surprisingly, the syntactic rules underlying probabilistic grooming transitions possess action duration-dependent structure, suggesting that sensory input-independent mechanisms guide grooming behavior at short time scales. Finally, the inclusion of a simple rule that modifies grooming transition probabilities over time yields a generative model that recapitulates the key features of observed grooming sequences at several time scales. These discoveries suggest that sensory input guides action selection by modulating internally generated dynamics. Additionally, the discovery of these principles governing grooming in D. melanogaster demonstrates the utility of incorporating temporal information when characterizing the syntax of behavioral sequences.'),
(89785, 2019, 'plos', 'Modeling the temporal dynamics of the gut microbial community in adults and infants', 'Shenhav, Liat|Furman, Ori|Briscoe, Leah|Thompson, Mike|Silverman, Justin D|Mizrahi, Itzhak|Halperin, Eran', 'Department of Computer Science, University of California Los Angeles, Los Angeles, California, United States of America.|Life Sciences, Ben Gurion University, Beer Sheva, Israel.|Department of Human Genetics, University of California Los Angeles, Los Angeles, California, United States of America.|Department of Human Genetics, University of California Los Angeles, Los Angeles, California, United States of America.|Center for Genomic and Computational Biology, Duke University, Durham, North Carolina, United States of America.|Life Sciences, Ben Gurion University, Beer Sheva, Israel.|Department of Computer Science, University of California Los Angeles, Los Angeles, California, United States of America.', 'United States|Israel|United States|United States|United States|Israel|United States', 7, '-', 'Journal Article', '10.1371/journal.pcbi.1006960', '31246943', 'Given the highly dynamic and complex nature of the human gut microbial community, the ability to identify and predict time-dependent compositional patterns of microbes is crucial to our understanding of the structure and functions of this ecosystem. One factor that could affect such time-dependent patterns is microbial interactions, wherein community composition at a given time point affects the microbial composition at a later time point. However, the field has not yet settled on the degree of this effect. Specifically, it has been recently suggested that only a minority of taxa depend on the microbial composition in earlier times. To address the issue of identifying and predicting temporal microbial patterns we developed a new model, MTV-LMM (Microbial Temporal Variability Linear Mixed Model), a linear mixed model for the prediction of microbial community temporal dynamics. MTV-LMM can identify time-dependent microbes (i.e., microbes whose abundance can be predicted based on the previous microbial composition) in longitudinal studies, which can then be used to analyze the trajectory of the microbiome over time. We evaluated the performance of MTV-LMM on real and synthetic time series datasets, and found that MTV-LMM outperforms commonly used methods for microbiome time series modeling. Particularly, we demonstrate that the effect of the microbial composition in previous time points on the abundance of taxa at later time points is underestimated by a factor of at least 10 when applying previous approaches. Using MTV-LMM, we demonstrate that a considerable portion of the human gut microbiome, both in infants and adults, has a significant time-dependent component that can be predicted based on microbiome composition in earlier time points. This suggests that microbiome composition at a given time point is a major factor in defining future microbiome composition and that this phenomenon is considerably more common than previously reported for the human gut microbiome.'),
(89786, 2019, 'plos', 'Problem-based learning in clinical bioinformatics education: Does it help to create communities of practice?', 'Davies, Angela C|Harris, Diane|Banks-Gatenby, Amanda|Brass, Andy', 'School of Health Sciences, Faculty of Biology Medicine and Health, The University of Manchester, Manchester, United Kingdom.|Manchester Institute of Education, University of Manchester, Manchester United Kingdom.|Manchester Institute of Education, University of Manchester, Manchester United Kingdom.|School of Health Sciences, Faculty of Biology Medicine and Health, The University of Manchester, Manchester, United Kingdom.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 1, '-', 'Journal Article', '10.1371/journal.pcbi.1006746', '31246944', 'We have now reached the genomics era within medicine, genomics is being used to personalise treatment, make diagnoses, prognoses, and predict adverse outcomes resulting from treatment with certain drugs. Genomic data is now abundant in healthcare, and the newly created profession of clinical bioinformaticians are responsible for its analysis. In the United Kingdom, clinical bioinformaticians are trained within a 3-year programme, integrating a work-based placement with a part-time Masters degree. As this profession is still developing, trainees can feel isolated from their peers whom are located in other hospitals and can find it difficult to gain the mentorship that they require to complete their training. Building strong networks or communities of practice (CoPs) and allowing sharing of knowledge and experiences is one solution to addressing this isolation. Within the Masters delivered at the University of Manchester, we have integrated group-centred problem-based learning (PBL) using real clinical case studies worked on during each course unit. This approach is combined with a flipped style of teaching providing access to online content in our Virtual Learning Environment before the course. The face-to-face teaching is used to focus on the application of the students knowledge to clinical case studies. In this study, we conducted semistructured interviews with 8 students, spanning 3 cohorts of students. We evaluated the effectiveness of this style of teaching and whether it had contributed to the formation of CoPs between our students. Our findings demonstrated that this style of teaching was preferred by our students to a more traditional lecture-based format and that the problem-based learning approach enabled the formation of CoPs within these cohorts. These CoPs are valuable in the development of this new profession and assist with the production of new guidelines and policies that are helping to professionalise this new group of healthcare scientists.'),
(89787, 2019, 'plos', 'Neural correlates of sparse coding and dimensionality reduction', 'Beyeler, Michael|Rounds, Emily L|Carlson, Kristofor D|Dutt, Nikil|Krichmar, Jeffrey L', 'Department of Psychology, University of Washington, Seattle, Washington, United States of America.|Department of Cognitive Sciences, University of California, Irvine, California, United States of America.|Department of Cognitive Sciences, University of California, Irvine, California, United States of America.|Department of Computer Science, University of California, Irvine, California, United States of America.|Department of Computer Science, University of California, Irvine, California, United States of America.', 'United States|United States|United States|United States|United States', 6, '-', 'Journal Article', '10.1371/journal.pcbi.1006908', '31246948', 'Supported by recent computational studies, there is increasing evidence that a wide range of neuronal responses can be understood as an emergent property of nonnegative sparse coding (NSC), an efficient population coding scheme based on dimensionality reduction and sparsity constraints. We review evidence that NSC might be employed by sensory areas to efficiently encode external stimulus spaces, by some associative areas to conjunctively represent multiple behaviorally relevant variables, and possibly by the basal ganglia to coordinate movement. In addition, NSC might provide a useful theoretical framework under which to understand the often complex and nonintuitive response properties of neurons in other brain areas. Although NSC might not apply to all brain areas (for example, motor or executive function areas) the success of NSC-based models, especially in sensory areas, warrants further investigation for neural correlates in other regions.'),
(89788, 2019, 'plos', 'From trainees to trainers to instructors: Sustainably building a national capacity in bioinformatics training', 'McGrath, Annette|Champ, Katherine|Shang, Catherine A|van Dam, Ellen|Brooksbank, Cath|Morgan, Sarah L', 'Data61, CSIRO, Brisbane, Queensland, Australia.|Bioplatforms Australia, Sydney, New South Wales, Australia.|Bioplatforms Australia, Sydney, New South Wales, Australia.|Bioplatforms Australia, Sydney, New South Wales, Australia.|EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom.|EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom.', 'Australia|Australia|Australia|Australia|United Kingdom|United Kingdom', 4, '-', 'Journal Article', '10.1371/journal.pcbi.1006923', '31246949', 'Demand for training life scientists in bioinformatics skills led to the development of a train-the-trainer collaboration between the European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI) and 2 Australian organisations, Bioplatforms Australia and Commonwealth Scientific and Industrial Research Organisation (CSIRO) in 2012. The goal of the collaboration was to establish a group of trained instructors who could develop and deliver short bioinformatics courses nationally. A train-the-trainer course introduces instructors to aspects of andragogy and evidence-based learning principles to help them better design, develop, and deliver high-quality training. Since then, both the number of trainers in the network and the course portfolio have grown. Best practises have been developed and shared between the Australian cohort and EMBL-EBI to address common challenges in bioinformatics training. The Australian trainer cohort undertook a train-the-trainer instructor course, again with EMBL-EBI, and subsequently successfully delivered train-the-trainer courses to interested bioinformatics trainers within Australia. We conclude that a train-the-trainer approach can help build national capacity and maintain a critical mass of trained instructors.'),
(89789, 2019, 'plos', 'Augmenting subnetwork inference with information extracted from the scientific literature', 'Kiblawi, Sid|Chasman, Deborah|Henning, Amanda|Park, Eunju|Poon, Hoifung|Gould, Michael|Ahlquist, Paul|Craven, Mark', 'Department of Computer Sciences, University of Wisconsin, Madison, WI, USA.|Wisconsin Institute for Discovery, University of Wisconsin, Madison, WI, USA.|Department of Oncology, University of Wisconsin, Madison, WI, USA.|Institute for Molecular Virology, University of Wisconsin, Madison, WI, USA.|Microsoft Research, Redmond, WA, USA.|Department of Oncology, University of Wisconsin, Madison, WI, USA.|Institute for Molecular Virology, University of Wisconsin, Madison, WI, USA.|Department of Computer Sciences, University of Wisconsin, Madison, WI, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1006758', '31246951', 'Many biological studies involve either (i) manipulating some aspect of a cell or its environment and then simultaneously measuring the effect on thousands of genes, or (ii) systematically manipulating each gene and then measuring the effect on some response of interest. A common challenge that arises in these studies is to explain how genes identified as relevant in the given experiment are organized into a subnetwork that accounts for the response of interest. The task of inferring a subnetwork is typically dependent on the information available in publicly available, structured databases, which suffer from incompleteness. However, a wealth of potentially relevant information resides in the scientific literature, such as information about genes associated with certain concepts of interest, as well as interactions that occur among various biological entities. We contend that by exploiting this information, we can improve the explanatory power and accuracy of subnetwork inference in multiple applications. Here we propose and investigate several ways in which information extracted from the scientific literature can be used to augment subnetwork inference. We show that we can use literature-extracted information to (i) augment the set of entities identified as being relevant in a subnetwork inference task, (ii) augment the set of interactions used in the process, and (iii) support targeted browsing of a large inferred subnetwork by identifying entities and interactions that are closely related to concepts of interest. We use this approach to uncover the pathways involved in interactions between a virus and a host cell, and the pathways that are regulated by a transcription factor associated with breast cancer. Our experimental results demonstrate that these approaches can provide more accurate and more interpretable subnetworks. Integer program code, background network data, and pathfinding code are available at https://github.com/Craven-Biostat-Lab/subnetwork_inference.'),
(89790, 2019, 'plos', 'Evidence accumulation is biased by motivation: A computational account', 'Gesiarz, Filip|Cahill, Donal|Sharot, Tali', 'Affective Brain Lab, Department of Experimental Psychology, University College London, London, United Kingdom.|Affective Brain Lab, Department of Experimental Psychology, University College London, London, United Kingdom.|Affective Brain Lab, Department of Experimental Psychology, University College London, London, United Kingdom.', 'United Kingdom|United Kingdom|United Kingdom', 1, '-', 'Journal Article', '10.1371/journal.pcbi.1007089', '31246955', 'To make good judgments people gather information. An important problem an agent needs to solve is when to continue sampling data and when to stop gathering evidence. We examine whether and how the desire to hold a certain belief influences the amount of information participants require to form that belief. Participants completed a sequential sampling task in which they were incentivized to accurately judge whether they were in a desirable state, which was associated with greater rewards than losses, or an undesirable state, which was associated with greater losses than rewards. While one state was better than the other, participants had no control over which they were in, and to maximize rewards they had to maximize accuracy. Results show that participants judgments were biased towards believing they were in the desirable state. They required a smaller proportion of supporting evidence to reach that conclusion and ceased gathering samples earlier when reaching the desirable conclusion. The findings were replicated in an additional sample of participants. To examine how this behavior was generated we modeled the data using a drift-diffusion model. This enabled us to assess two potential mechanisms which could be underlying the behavior: (i) a valence-dependent response bias and/or (ii) a valence-dependent process bias. We found that a valence-dependent model, with both a response bias and a process bias, fit the data better than a range of other alternatives, including valence-independent models and models with only a response or process bias. Moreover, the valence-dependent model provided better out-of-sample prediction accuracy than the valence-independent model. Our results provide an account for how the motivation to hold a certain belief decreases the need for supporting evidence. The findings also highlight the advantage of incorporating valence into evidence accumulation models to better explain and predict behavior.'),
(89791, 2019, 'plos', 'Telling ecological networks apart by their structure: A computational challenge', 'Michalska-Smith, Matthew J|Allesina, Stefano', 'Department of Ecology & Evolution, University of Chicago, Chicago, Illinois, United States of America.|Department of Ecology & Evolution, University of Chicago, Chicago, Illinois, United States of America.', 'United States|United States', 2, '-', 'Journal Article', '10.1371/journal.pcbi.1007076', '31246974', 'Ecologists have been compiling ecological networks for over a century, detailing the interactions between species in a variety of ecosystems. To this end, they have built networks for mutualistic (e.g., pollination, seed dispersal) as well as antagonistic (e.g., herbivory, parasitism) interactions. The type of interaction being represented is believed to be reflected in the structure of the network, which would differ substantially between mutualistic and antagonistic networks. Here, we put this notion to the test by attempting to determine the type of interaction represented in a network based solely on its structure. We find that, although it is easy to separate different kinds of nonecological networks, ecological networks display much structural variation, making it difficult to distinguish between mutualistic and antagonistic interactions. We therefore frame the problem as a challenge for the community of scientists interested in computational biology and machine learning. We discuss the features a good solution to this problem should possess and the obstacles that need to be overcome to achieve this goal.'),
(89792, 2019, 'plos', 'EternaBrain: Automated RNA design through move sets and strategies from an Internet-scale RNA videogame', 'Koodli, Rohan V|Keep, Benjamin|Coppess, Katherine R|Portela, Fernando|Eterna participants|Das, Rhiju', 'Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, United States of America.|Department of Education, Stanford University, Stanford, CA, United States of America.|Department of Physics, Stanford University, Stanford, CA, United States of America.|Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, United States of America.|Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, United States of America.|Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, United States of America.', 'United States|United States|United States|United States|United States|United States', 2, '-', 'Journal Article', '10.1371/journal.pcbi.1007059', '31247029', 'Emerging RNA-based approaches to disease detection and gene therapy require RNA sequences that fold into specific base-pairing patterns, but computational algorithms generally remain inadequate for these secondary structure design tasks. The Eterna project has crowdsourced RNA design to human video game players in the form of puzzles that reach extraordinary difficulty. Here, we demonstrate that Eterna participants moves and strategies can be leveraged to improve automated computational RNA design. We present an eternamoves-large repository consisting of 1.8 million of player moves on 12 of the most-played Eterna puzzles as well as an eternamoves-select repository of 30,477 moves from the top 72 players on a select set of more advanced puzzles. On eternamoves-select, we present a multilayer convolutional neural network (CNN) EternaBrain that achieves test accuracies of 51% and 34% in base prediction and location prediction, respectively, suggesting that top players moves are partially stereotyped. Pipelining this CNNs move predictions with single-action-playout (SAP) of six strategies compiled by human players solves 61 out of 100 independent puzzles in the Eterna100 benchmark. EternaBrain-SAP outperforms previously published RNA design algorithms and achieves similar or better performance than a newer generation of deep learning methods, while being largely orthogonal to these other methods. Our study provides useful lessons for future efforts to achieve human-competitive performance with automated RNA design algorithms.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(89793, 2019, 'plos', 'The invasion of de-differentiating cancer cells into hierarchical tissues', 'Zhou, Da|Luo, Yue|Dingli, David|Traulsen, Arne', 'School of Mathematical Sciences and Fujian Provincial Key Laboratory of Mathematical Modeling and High-Performance Scientific Computation, Xiamen University, Xiamen, Peoples Republic of China.|School of Mathematical Sciences and Fujian Provincial Key Laboratory of Mathematical Modeling and High-Performance Scientific Computation, Xiamen University, Xiamen, Peoples Republic of China.|Division of Hematology and Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota, United States of America.|Department of Evolutionary Theory, Max Planck Institute for Evolutionary Biology, Plön, Germany.', 'China|China|United States|Germany', 2, '-', 'Journal Article', '10.1371/journal.pcbi.1007167', '31260442', 'Many fast renewing tissues are characterized by a hierarchical cellular architecture, with tissue specific stem cells at the root of the cellular hierarchy, differentiating into a whole range of specialized cells. There is increasing evidence that tumors are structured in a very similar way, mirroring the hierarchical structure of the host tissue. In some tissues, differentiated cells can also revert to the stem cell phenotype, which increases the risk that mutant cells lead to long lasting clones in the tissue. However, it is unclear under which circumstances de-differentiating cells will invade a tissue. To address this, we developed mathematical models to investigate how de-differentiation is selected as an adaptive mechanism in the context of cellular hierarchies. We derive thresholds for which de-differentiation is expected to emerge, and it is shown that the selection of de-differentiation is a result of the combination of the properties of cellular hierarchy and de-differentiation patterns. Our results suggest that de-differentiation is most likely to be favored provided stem cells having the largest effective self-renewal rate. Moreover, jumpwise de-differentiation provides a wider range of favorable conditions than stepwise de-differentiation. Finally, the effect of de-differentiation on the redistribution of self-renewal and differentiation probabilities also greatly influences the selection for de-differentiation.'),
(89794, 2019, 'plos', 'DART-ID increases single-cell proteome coverage', 'Chen, Albert Tian|Franks, Alexander|Slavov, Nikolai', 'Department of Bioengineering, Northeastern University, Boston, Massachusetts, United States of America.|Department of Statistics and Applied Probability, University of California Santa Barbara, California, United States of America.|Department of Bioengineering, Northeastern University, Boston, Massachusetts, United States of America.', 'United States|United States|United States', 7, '-', 'Journal Article', '10.1371/journal.pcbi.1007082', '31260443', 'Analysis by liquid chromatography and tandem mass spectrometry (LC-MS/MS) can identify and quantify thousands of proteins in microgram-level samples, such as those comprised of thousands of cells. This process, however, remains challenging for smaller samples, such as the proteomes of single mammalian cells, because reduced protein levels reduce the number of confidently sequenced peptides. To alleviate this reduction, we developed Data-driven Alignment of Retention Times for IDentification (DART-ID). DART-ID implements principled Bayesian frameworks for global retention time (RT) alignment and for incorporating RT estimates towards improved confidence estimates of peptide-spectrum-matches. When applied to bulk or to single-cell samples, DART-ID increased the number of data points by 30-50% at 1% FDR, and thus decreased missing data. Benchmarks indicate excellent quantification of peptides upgraded by DART-ID and support their utility for quantitative analysis, such as identifying cell types and cell-type specific proteins. The additional datapoints provided by DART-ID boost the statistical power and double the number of proteins identified as differentially abundant in monocytes and T-cells. DART-ID can be applied to diverse experimental designs and is freely available at http://dart-id.slavovlab.net.'),
(89795, 2019, 'plos', 'Correction: From Dynamic Expression Patterns to Boundary Formation in the Presomitic Mesoderm', 'Tiedemann, Hendrik B|Schneltzer, Elida|Zeiser, Stefan|Hoesel, Bastian|Beckers, Johannes|Przemeck, Gerhard K H|de Angelis, Martin Hrabxc4x9b', 'Department of Bioengineering, Northeastern University, Boston, Massachusetts, United States of America.|Department of Bioengineering, Northeastern University, Boston, Massachusetts, United States of America.|Department of Bioengineering, Northeastern University, Boston, Massachusetts, United States of America.|Department of Bioengineering, Northeastern University, Boston, Massachusetts, United States of America.|Department of Bioengineering, Northeastern University, Boston, Massachusetts, United States of America.|Department of Bioengineering, Northeastern University, Boston, Massachusetts, United States of America.|Department of Bioengineering, Northeastern University, Boston, Massachusetts, United States of America.', 'United States|United States|United States|United States|United States|United States|United States', 0, '-', 'Published Erratum', '10.1371/journal.pcbi.1007191', '31265460', '[This corrects the article DOI: 10.1371/journal.pcbi.1002586.].'),
(89796, 2019, 'plos', 'Correction: Computational analysis of the oscillatory behavior at the translation level induced by mRNA levels oscillations due to finite intracellular resources', 'Zarai, Yoram|Tuller, Tamir', 'Department of Bioengineering, Northeastern University, Boston, Massachusetts, United States of America.|Department of Bioengineering, Northeastern University, Boston, Massachusetts, United States of America.', 'United States|United States', 0, '-', 'Published Erratum', '10.1371/journal.pcbi.1007192', '31265462', '[This corrects the article DOI: 10.1371/journal.pcbi.1006055.].'),
(89797, 2019, 'plos', 'Molecular noise of innate immunity shapes bacteria-phage ecologies', 'Ruess, Jakob|Plexc5xa1ka, Maroxc5xa1|Guet, Cxc7x8elin C|Tkaxc4x8dik, Gaxc5xa1per', 'Inria Saclay - Ile-de-France, 91120 Palaiseau, France.|Institute of Science and Technology Austria, A-3400 Klosterneuburg, Austria.|Institute of Science and Technology Austria, A-3400 Klosterneuburg, Austria.|Institute of Science and Technology Austria, A-3400 Klosterneuburg, Austria.', 'France|Austria|Austria|Austria', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1007168', '31265463', 'Mathematical models have been used successfully at diverse scales of biological organization, ranging from ecology and population dynamics to stochastic reaction events occurring between individual molecules in single cells. Generally, many biological processes unfold across multiple scales, with mutations being the best studied example of how stochasticity at the molecular scale can influence outcomes at the population scale. In many other contexts, however, an analogous link between micro- and macro-scale remains elusive, primarily due to the challenges involved in setting up and analyzing multi-scale models. Here, we employ such a model to investigate how stochasticity propagates from individual biochemical reaction events in the bacterial innate immune system to the ecology of bacteria and bacterial viruses. We show analytically how the dynamics of bacterial populations are shaped by the activities of immunity-conferring enzymes in single cells and how the ecological consequences imply optimal bacterial defense strategies against viruses. Our results suggest that bacterial populations in the presence of viruses can either optimize their initial growth rate or their population size, with the first strategy favoring simple immunity featuring a single restriction modification system and the second strategy favoring complex bacterial innate immunity featuring several simultaneously active restriction modification systems.'),
(89798, 2019, 'plos', 'Cross-species functional modules link proteostasis to human normal aging', 'Komljenovic, Andrea|Li, Hao|Sorrentino, Vincenzo|Kutalik, Zoltán|Auwerx, Johan|Robinson-Rechavi, Marc', 'Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland.|Laboratory of Integrative Systems Physiology, EPFL, Lausanne, Switzerland.|Laboratory of Integrative Systems Physiology, EPFL, Lausanne, Switzerland.|Swiss Institute of Bioinformatics, Lausanne, Switzerland.|Laboratory of Integrative Systems Physiology, EPFL, Lausanne, Switzerland.|Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 1, '-', 'Journal Article', '10.1371/journal.pcbi.1007162', '31269015', 'The evolutionarily conserved nature of the few well-known anti-aging interventions that affect lifespan, such as caloric restriction, suggests that aging-related research in model organisms is directly relevant to human aging. Since human lifespan is a complex trait, a systems-level approach will contribute to a more comprehensive understanding of the underlying aging landscape. Here, we integrate evolutionary and functional information of normal aging across human and model organisms at three levels: gene-level, process-level, and network-level. We identify evolutionarily conserved modules of normal aging across diverse taxa, and notably show proteostasis to be conserved in normal aging. Additionally, we find that mechanisms related to protein quality control network are enriched for genes harboring genetic variants associated with 22 age-related human traits and associated to caloric restriction. These results demonstrate that a systems-level approach, combined with evolutionary conservation, allows the detection of candidate aging genes and pathways relevant to human normal aging.'),
(89799, 2019, 'plos', 'Correction: Temporal Gillespie algorithm: Fast simulation of contagion processes on time-varying networks', 'Vestergaard, Christian L|Génois, Mathieu', 'Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland.|Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland.', 'Switzerland|Switzerland', 0, '-', 'Published Erratum', '10.1371/journal.pcbi.1007190', '31269017', '[This corrects the article DOI: 10.1371/journal.pcbi.1004579.].'),
(89800, 2019, 'plos', 'Systematic discovery of the functional impact of somatic genome alterations in individual tumors through tumor-specific causal inference', 'Cai, Chunhui|Cooper, Gregory F|Lu, Kevin N|Ma, Xiaojun|Xu, Shuping|Zhao, Zhenlong|Chen, Xueer|Xue, Yifan|Lee, Adrian V|Clark, Nathan|Chen, Vicky|Lu, Songjian|Chen, Lujia|Yu, Liyue|Hochheiser, Harry S|Jiang, Xia|Wang, Q Jane|Lu, Xinghua', 'Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States of America.|Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States of America.|Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States of America.|Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States of America.|Department of Pharmacology and Chemical Biology, University of Pittsburgh, Pittsburgh, PA, United States of America.|Department of Pharmacology and Chemical Biology, University of Pittsburgh, Pittsburgh, PA, United States of America.|Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States of America.|Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States of America.|Center for Causal Discovery, Pittsburgh, PA, United States of America.|Center for Causal Discovery, Pittsburgh, PA, United States of America.|Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States of America.|Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States of America.|Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States of America.|Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States of America.|Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States of America.|Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States of America.|Department of Pharmacology and Chemical Biology, University of Pittsburgh, Pittsburgh, PA, United States of America.|Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States of America.', 'United States|United States|United States|United States|United States|United States|United States|Un', 2, '-', 'Journal Article', '10.1371/journal.pcbi.1007088', '31276486', 'Cancer is mainly caused by somatic genome alterations (SGAs). Precision oncology involves identifying and targeting tumor-specific aberrations resulting from causative SGAs. We developed a novel tumor-specific computational framework that finds the likely causative SGAs in an individual tumor and estimates their impact on oncogenic processes, which suggests the disease mechanisms that are acting in that tumor. This information can be used to guide precision oncology. We report a tumor-specific causal inference (TCI) framework, which estimates causative SGAs by modeling causal relationships between SGAs and molecular phenotypes (e.g., transcriptomic, proteomic, or metabolomic changes) within an individual tumor. We applied the TCI algorithm to tumors from The Cancer Genome Atlas (TCGA) and estimated for each tumor the SGAs that causally regulate the differentially expressed genes (DEGs) in that tumor. Overall, TCI identified 634 SGAs that are predicted to cause cancer-related DEGs in a significant number of tumors, including most of the previously known drivers and many novel candidate cancer drivers. The inferred causal relationships are statistically robust and biologically sensible, and multiple lines of experimental evidence support the predicted functional impact of both the well-known and the novel candidate drivers that are predicted by TCI. TCI provides a unified framework that integrates multiple types of SGAs and molecular phenotypes to estimate which genome perturbations are causally influencing one or more molecular/cellular phenotypes in an individual tumor. By identifying major candidate drivers and revealing their functional impact in an individual tumor, TCI sheds light on the disease mechanisms of that tumor, which can serve to advance our basic knowledge of cancer biology and to support precision oncology that provides tailored treatment of individual tumors.'),
(89801, 2019, 'plos', 'With an eye on uncertainty: Modelling pupillary responses to environmental volatility', 'Vincent, Peter|Parr, Thomas|Benrimoh, David|Friston, Karl J', 'Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, United Kingdom.|Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, United Kingdom.|Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, United Kingdom.|Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, United Kingdom.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 6, '-', 'Journal Article', '10.1371/journal.pcbi.1007126', '31276488', 'Living creatures must accurately infer the nature of their environments. They do this despite being confronted by stochastic and context sensitive contingencies-and so must constantly update their beliefs regarding their uncertainty about what might come next. In this work, we examine how we deal with uncertainty that evolves over time. This prospective uncertainty (or imprecision) is referred to as volatility and has previously been linked to noradrenergic signals that originate in the locus coeruleus. Using pupillary dilatation as a measure of central noradrenergic signalling, we tested the hypothesis that changes in pupil diameter reflect inferences humans make about environmental volatility. To do so, we collected pupillometry data from participants presented with a stream of numbers. We generated these numbers from a process with varying degrees of volatility. By measuring pupillary dilatation in response to these stimuli-and simulating the inferences made by an ideal Bayesian observer of the same stimuli-we demonstrate that humans update their beliefs about environmental contingencies in a Bayes optimal way. We show this by comparing general linear (convolution) models that formalised competing hypotheses about the causes of pupillary changes. We found greater evidence for models that included Bayes optimal estimates of volatility than those without. We additionally explore the interaction between different causes of pupil dilation and suggest a quantitative approach to characterising a persons prior beliefs about volatility.'),
(89802, 2019, 'plos', 'Modelling the mechanics of exploration in larval Drosophila', 'Loveless, Jane|Lagogiannis, Konstantinos|Webb, Barbara', 'Institute for Perception, Action, and Behaviour, School of Informatics, University of Edinburgh, Edinburgh, Scotland, United Kingdom.|Institute for Perception, Action, and Behaviour, School of Informatics, University of Edinburgh, Edinburgh, Scotland, United Kingdom.|Institute for Perception, Action, and Behaviour, School of Informatics, University of Edinburgh, Edinburgh, Scotland, United Kingdom.', 'United Kingdom|United Kingdom|United Kingdom', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1006635', '31276489', 'The Drosophila larva executes a stereotypical exploratory routine that appears to consist of stochastic alternation between straight peristaltic crawling and reorientation events through lateral bending. We present a model of larval mechanics for axial and transverse motion over a planar substrate, and use it to develop a simple, reflexive neuromuscular model from physical principles. The mechanical model represents the midline of the larva as a set of point masses which interact with each other via damped translational and torsional springs, and with the environment via sliding friction forces. The neuromuscular model consists of: 1. segmentally localised reflexes that amplify axial compression in order to counteract frictive energy losses, and 2. long-range mutual inhibition between reflexes in distant segments, enabling overall motion of the model larva relative to its substrate. In the absence of damping and driving, the mechanical model produces axial travelling waves, lateral oscillations, and unpredictable, chaotic deformations. The neuromuscular model counteracts friction to recover these motion patterns, giving rise to forward and backward peristalsis in addition to turning. Our model produces spontaneous exploration, even though the nervous system has no intrinsic pattern generating or decision making ability, and neither senses nor drives bending motions. Ultimately, our model suggests a novel view of larval exploration as a deterministic superdiffusion process which is mechanistically grounded in the chaotic mechanics of the body. We discuss how this may provide new interpretations for existing observations at the level of tissue-scale activity patterns and neural circuitry, and provide some experimental predictions that would test the extent to which the mechanisms we present translate to the real larva.'),
(89803, 2019, 'plos', 'Disease gene prediction for molecularly uncharacterized diseases', 'Cáceres, Juan J|Paccanaro, Alberto', 'Centre for Systems and Synthetic Biology & Department of Computer Science, Royal Holloway, University of London, Egham, Surrey, United Kingdom.|Centre for Systems and Synthetic Biology & Department of Computer Science, Royal Holloway, University of London, Egham, Surrey, United Kingdom.', 'United Kingdom|United Kingdom', 5, '-', 'Journal Article', '10.1371/journal.pcbi.1007078', '31276496', 'Network medicine approaches have been largely successful at increasing our knowledge of molecularly characterized diseases. Given a set of disease genes associated with a disease, neighbourhood-based methods and random walkers exploit the interactome allowing the prediction of further genes for that disease. In general, however, diseases with no known molecular basis constitute a challenge. Here we present a novel network approach to prioritize gene-disease associations that is able to also predict genes for diseases with no known molecular basis. Our method, which we have called Cardigan (ChARting DIsease Gene AssociatioNs), uses semi-supervised learning and exploits a measure of similarity between disease phenotypes. We evaluated its performance at predicting genes for both molecularly characterized and uncharacterized diseases in OMIM, using both weighted and binary interactomes, and compared it with state-of-the-art methods. Our tests, which use datasets collected at different points in time to replicate the dynamics of the disease gene discovery process, prove that Cardigan is able to accurately predict disease genes for molecularly uncharacterized diseases. Additionally, standard leave-one-out cross validation tests show how our approach outperforms state-of-the-art methods at predicting genes for molecularly characterized diseases by 14%-65%. Cardigan can also be used for disease module prediction, where it outperforms state-of-the-art methods by 87%-299%.'),
(89804, 2019, 'plos', 'Human online adaptation to changes in prior probability', 'Norton, Elyse H|Acerbi, Luigi|Ma, Wei Ji|Landy, Michael S', 'Psychology Department, New York University, New York, New York, United States of America.|Psychology Department, New York University, New York, New York, United States of America.|Psychology Department, New York University, New York, New York, United States of America.|Psychology Department, New York University, New York, New York, United States of America.', 'United States|United States|United States|United States', 3, '-', 'Journal Article', '10.1371/journal.pcbi.1006681', '31283765', 'Optimal sensory decision-making requires the combination of uncertain sensory signals with prior expectations. The effect of prior probability is often described as a shift in the decision criterion. Can observers track sudden changes in probability? To answer this question, we used a change-point detection paradigm that is frequently used to examine behavior in changing environments. In a pair of orientation-categorization tasks, we investigated the effects of changing probabilities on decision-making. In both tasks, category probability was updated using a sample-and-hold procedure: probability was held constant for a period of time before jumping to another probability state that was randomly selected from a predetermined set of probability states. We developed an ideal Bayesian change-point detection model in which the observer marginalizes over both the current run length (i.e., time since last change) and the current category probability. We compared this model to various alternative models that correspond to different strategies-from approximately Bayesian to simple heuristics-that the observers may have adopted to update their beliefs about probabilities. While a number of models provided decent fits to the data, model comparison favored a model in which probability is estimated following an exponential averaging model with a bias towards equal priors, consistent with a conservative bias, and a flexible variant of the Bayesian change-point detection model with incorrect beliefs. We interpret the former as a simpler, more biologically plausible explanation suggesting that the mechanism underlying change of decision criterion is a combination of on-line estimation of prior probability and a stable, long-term equal-probability prior, thus operating at two very different timescales.'),
(89805, 2019, 'plos', 'Remarkable structural transformations of actin bundles are driven by their initial polarity, motor activity, crosslinking, and filament treadmilling', 'Chandrasekaran, Aravind|Upadhyaya, Arpita|Papoian, Garegin A', 'Department of Chemistry and Biochemistry, University of Maryland, College Park, Maryland, United States of America.|Institute for Physical Science and Technology, University of Maryland, College Park, Maryland, United States of America.|Department of Chemistry and Biochemistry, University of Maryland, College Park, Maryland, United States of America.', 'United States|United States|United States', 6, '-', 'Journal Article', '10.1371/journal.pcbi.1007156', '31287817', 'Bundled actin structures play a key role in maintaining cellular shape, in aiding force transmission to and from extracellular substrates, and in affecting cellular motility. Recent studies have also brought to light new details on stress generation, force transmission and contractility of actin bundles. In this work, we are primarily interested in the question of what determines the stability of actin bundles and what network geometries do unstable bundles eventually transition to. To address this problem, we used the MEDYAN mechano-chemical force field, modeling several micron-long actin bundles in 3D, while accounting for a comprehensive set of chemical, mechanical and transport processes. We developed a hierarchical clustering algorithm for classification of the different long time scale morphologies in our study. Our main finding is that initially unipolar bundles are significantly more stable compared with an apolar initial configuration. Filaments within the latter bundles slide easily with respect to each other due to myosin activity, producing a loose network that can be subsequently severely distorted. At high myosin concentrations, a morphological transition to aster-like geometries was observed. We also investigated how actin treadmilling rates influence bundle dynamics, and found that enhanced treadmilling leads to network fragmentation and disintegration, while this process is opposed by myosin and crosslinking activities. Interestingly, treadmilling bundles with an initial apolar geometry eventually evolve to a whole gamut of network morphologies based on relative positions of filament ends, such as sarcomere-like organization. We found that apolar bundles show a remarkable sensitivity to environmental conditions, which may be important in enabling rapid cytoskeletal structural reorganization and adaptation in response to intracellular and extracellular cues.'),
(89806, 2019, 'plos', 'ACAP1 assembles into an unusual protein lattice for membrane deformation through multiple stages', 'Chan, Chun|Pang, Xiaoyun|Zhang, Yan|Niu, Tongxin|Yang, Shengjiang|Zhao, Daohui|Li, Jian|Lu, Lanyuan|Hsu, Victor W|Zhou, Jian|Sun, Fei|Fan, Jun', 'Department of Materials Science and Engineering, City University of Hong Kong, Hong Kong, China.|National Laboratory of Biomacromolecules, CAS Center for excellence in biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.|National Laboratory of Biomacromolecules, CAS Center for excellence in biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.|National Laboratory of Biomacromolecules, CAS Center for excellence in biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.|School of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou, Guangdong, China.|School of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou, Guangdong, China.|Division of Rheumatology, Immunology and Allergy, Brigham and Womens Hospital, and Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States of America.|School of Biological Sciences, Nanyang Technological University, Singapore.|Division of Rheumatology, Immunology and Allergy, Brigham and Womens Hospital, and Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States of America.|School of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou, Guangdong, China.|National Laboratory of Biomacromolecules, CAS Center for excellence in biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.|Department of Materials Science and Engineering, City University of Hong Kong, Hong Kong, China.', 'China|China|China|China|China|China|United States|Singapore|United States|China|China|China', 1, '-', 'Journal Article', '10.1371/journal.pcbi.1007081', '31291238', 'Studies on the Bin-Amphiphysin-Rvs (BAR) domain have advanced a fundamental understanding of how proteins deform membrane. We previously showed that a BAR domain in tandem with a Pleckstrin Homology (PH domain) underlies the assembly of ACAP1 (Arfgap with Coil-coil, Ankryin repeat, and PH domain I) into an unusual lattice structure that also uncovers a new paradigm for how a BAR protein deforms membrane. Here, we initially pursued computation-based refinement of the ACAP1 lattice to identify its critical protein contacts. Simulation studies then revealed how ACAP1, which dimerizes into a symmetrical structure in solution, is recruited asymmetrically to the membrane through dynamic behavior. We also pursued electron microscopy (EM)-based structural studies, which shed further insight into the dynamic nature of the ACAP1 lattice assembly. As ACAP1 is an unconventional BAR protein, our findings broaden the understanding of the mechanistic spectrum by which proteins assemble into higher-ordered structures to achieve membrane deformation.'),
(89807, 2019, 'plos', 'Sensorimotor maps can be dynamically calibrated using an adaptive-filter model of the cerebellum', 'Wilson, Emma D|Anderson, Sean R|Dean, Paul|Porrill, John', 'School of Computing and Communications, Lancaster University, Lancaster, United Kingdom.|Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, United Kingdom.|Department of Psychology, University of Sheffield, Sheffield, United Kingdom.|Department of Psychology, University of Sheffield, Sheffield, United Kingdom.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1007187', '31295248', 'Substantial experimental evidence suggests the cerebellum is involved in calibrating sensorimotor maps. Consistent with this involvement is the well-known, but little understood, massive cerebellar projection to maps in the superior colliculus. Map calibration would be a significant new role for the cerebellum given the ubiquity of map representations in the brain, but how it could perform such a task is unclear. Here we investigated a dynamic method for map calibration, based on electrophysiological recordings from the superior colliculus, that used a standard adaptive-filter cerebellar model. The method proved effective for complex distortions of both unimodal and bimodal maps, and also for predictive map-based tracking of moving targets. These results provide the first computational evidence for a novel role for the cerebellum in dynamic sensorimotor map calibration, of potential importance for coordinate alignment during ongoing motor control, and for map calibration in future biomimetic systems. This computational evidence also provides testable experimental predictions concerning the role of the connections between cerebellum and superior colliculus in previously observed dynamic coordinate transformations.'),
(89808, 2019, 'plos', 'PrediTALE: A novel model learned from quantitative data allows for new perspectives on TALE targeting', 'Erkes, Annett|Mücke, Stefanie|Reschke, Maik|Boch, Jens|Grau, Jan', 'Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle, Germany.|Department of Plant Biotechnology, Leibniz Universität Hannover, Hannover, Germany.|Department of Plant Biotechnology, Leibniz Universität Hannover, Hannover, Germany.|Department of Plant Biotechnology, Leibniz Universität Hannover, Hannover, Germany.|Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle, Germany.', 'Germany|Germany|Germany|Germany|Germany', 2, '-', 'Journal Article', '10.1371/journal.pcbi.1007206', '31295249', 'Plant-pathogenic Xanthomonas bacteria secrete transcription activator-like effectors (TALEs) into host cells, where they act as transcriptional activators on plant target genes to support bacterial virulence. TALEs have a unique modular DNA-binding domain composed of tandem repeats. Two amino acids within each tandem repeat, termed repeat-variable diresidues, bind to contiguous nucleotides on the DNA sequence and determine target specificity. In this paper, we propose a novel approach for TALE target prediction to identify potential virulence targets. Our approach accounts for recent findings concerning TALE targeting, including frame-shift binding by repeats of aberrant lengths, and the flexible strand orientation of target boxes relative to the transcription start of the downstream target gene. The computational model can account for dependencies between adjacent RVD positions. Model parameters are learned from the wealth of quantitative data that have been generated over the last years. We benchmark the novel approach, termed PrediTALE, using RNA-seq data after Xanthomonas infection in rice, and find an overall improvement of prediction performance compared with previous approaches. Using PrediTALE, we are able to predict several novel putative virulence targets. However, we also observe that no target genes are predicted by any prediction tool for several TALEs, which we term orphan TALEs for this reason. We postulate that one explanation for orphan TALEs are incomplete gene annotations and, hence, propose to replace promoterome-wide by genome-wide scans for target boxes. We demonstrate that known targets from promoterome-wide scans may be recovered by genome-wide scans, whereas the latter, combined with RNA-seq data, are able to detect putative targets independent of existing gene annotations.'),
(89809, 2019, 'plos', 'Think: Theory for Africa', 'Currin, Christopher B|Khoza, Phumlani N|Antrobus, Alexander D|Latham, Peter E|Vogels, Tim P|Raimondo, Joseph V', 'Division of Cell Biology, Department of Human Biology, Neuroscience Institute and Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa.|School of Computer Science and Applied Mathematics, University of the Witwatersrand, Johannesburg, South Africa.|Gatsby Computational Neuroscience Unit, University College London, London, United Kingdom.|Gatsby Computational Neuroscience Unit, University College London, London, United Kingdom.|Centre for Neural Circuits and Behaviour, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom.|Division of Cell Biology, Department of Human Biology, Neuroscience Institute and Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa.', 'South Africa|South Africa|United Kingdom|United Kingdom|United Kingdom|South Africa', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1007049', '31295253', '-'),
(89810, 2019, 'plos', 'Identifying determinants of persistent MRSA bacteremia using mathematical modeling', 'Mikkaichi, Tsuyoshi|Yeaman, Michael R|Hoffmann, Alexander|MRSA Systems Immunobiology Group', 'Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, California, United States of America.|David Geffen School of Medicine at UCLA, Los Angeles, California, United States of America.|Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, California, United States of America.|Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, California, United States of America.', 'United States|United States|United States|United States', 1, '-', 'Journal Article', '10.1371/journal.pcbi.1007087', '31295255', 'Persistent bacteremia caused by Staphylococcus aureus (SA), especially methicillin-resistant SA (MRSA), is a significant cause of morbidity and mortality. Despite susceptibility phenotypes in vitro, persistent MRSA strains fail to clear with appropriate anti-MRSA therapy during bacteremia in vivo. Thus, identifying the factors that cause such MRSA persistence is of direct and urgent clinical relevance. To address the dynamics of MRSA persistence in the face of host immunity and typical antibiotic regimens, we developed a mathematical model based on the overarching assumption that phenotypic heterogeneity is a hallmark of MRSA persistence. First, we applied an ensemble modeling approach and obtained parameter sets that satisfied the condition of a minimum inoculum dose to establish infection. Second, by simulating with the selected parameter sets under vancomycin therapy which follows clinical practices, we distinguished the models resulting in resolving or persistent bacteremia, based on the total SA exceeding a detection limit after five days of treatment. Third, to find key determinants that discriminate resolving and persistent bacteremia, we applied a machine learning approach and found that the immune clearance rate of persister cells is a key feature. But, fourth, when relapsing bacteremia was considered, the growth rate of persister cells was also found to be a key feature. Finally, we explored pharmacological strategies for persistent and relapsing bacteremia and found that a persister killer, but not a persister formation inhibitor, could provide for an effective cure the persistent bacteremia. Thus, to develop better clinical solutions for MRSA persistence and relapse, our modeling results indicate that we need to better understand the pathogen-host interactions of persister MRSAs in vivo.'),
(89811, 2019, 'plos', 'Modeling the daily rhythm of human pain processing in the dorsal horn', 'Crodelle, Jennifer|Piltz, Sofia H|Hagenauer, Megan Hastings|Booth, Victoria', 'Courant Institute of Mathematical Sciences, New York University, New York, New York, United States of America.|Department of Mathematics, University of Michigan, Ann Arbor, Michigan, United States of America.|Molecular and Behavioral Neuroscience Institute, University of Michigan, Ann Arbor, Michigan, United States of America.|Department of Mathematics, University of Michigan, Ann Arbor, Michigan, United States of America.', 'United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1007106', '31295266', 'Experimental studies show that human pain sensitivity varies across the 24-hour day, with the lowest sensitivity usually occurring during the afternoon. Patients suffering from neuropathic pain, or nerve damage, experience an inversion in the daily modulation of pain sensitivity, with the highest sensitivity usually occurring during the early afternoon. Processing of painful stimulation occurs in the dorsal horn (DH), an area of the spinal cord that receives input from peripheral tissues via several types of primary afferent nerve fibers. The DH circuit is composed of different populations of neurons, including excitatory and inhibitory interneurons, and projection neurons, which constitute the majority of the output from the DH to the brain. In this work, we develop a mathematical model of the dorsal horn neural circuit to investigate mechanisms for the daily modulation of pain sensitivity. The model describes average firing rates of excitatory and inhibitory interneuron populations and projection neurons, whose activity is directly correlated with experienced pain. Response in afferent fibers to peripheral stimulation is simulated by a Poisson process generating nerve fiber spike trains at variable firing rates. Model parameters for fiber response to stimulation and the excitability properties of neuronal populations are constrained by experimental results found in the literature, leading to qualitative agreement between modeled responses to pain and experimental observations. We validate our model by reproducing the wind-up of pain response to repeated stimulation. We apply the model to investigate daily modulatory effects on pain inhibition, in which response to painful stimuli is reduced by subsequent non-painful stimuli. Finally, we use the model to propose a mechanism for the observed inversion of the daily rhythmicity of pain sensation under neuropathic pain conditions. Underlying mechanisms for the shift in rhythmicity have not been identified experimentally, but our model results predict that experimentally-observed dysregulation of inhibition within the DH neural circuit may be responsible. The model provides an accessible, biophysical framework that will be valuable for experimental and clinical investigations of diverse physiological processes modulating pain processing in humans.'),
(89812, 2019, 'plos', 'Machine and deep learning meet genome-scale metabolic modeling', 'Zampieri, Guido|Vijayakumar, Supreeta|Yaneske, Elisabeth|Angione, Claudio', 'Department of Computer Science and Information Systems, Teesside University, Middlesbrough, United Kingdom.|Department of Computer Science and Information Systems, Teesside University, Middlesbrough, United Kingdom.|Department of Computer Science and Information Systems, Teesside University, Middlesbrough, United Kingdom.|Department of Computer Science and Information Systems, Teesside University, Middlesbrough, United Kingdom.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 21, '-', 'Journal Article', '10.1371/journal.pcbi.1007084', '31295267', 'Omic data analysis is steadily growing as a driver of basic and applied molecular biology research. Core to the interpretation of complex and heterogeneous biological phenotypes are computational approaches in the fields of statistics and machine learning. In parallel, constraint-based metabolic modeling has established itself as the main tool to investigate large-scale relationships between genotype, phenotype, and environment. The development and application of these methodological frameworks have occurred independently for the most part, whereas the potential of their integration for biological, biomedical, and biotechnological research is less known. Here, we describe how machine learning and constraint-based modeling can be combined, reviewing recent works at the intersection of both domains and discussing the mathematical and practical aspects involved. We overlap systematic classifications from both frameworks, making them accessible to nonexperts. Finally, we delineate potential future scenarios, propose new joint theoretical frameworks, and suggest concrete points of investigation for this joint subfield. A multiview approach merging experimental and knowledge-driven omic data through machine learning methods can incorporate key mechanistic information in an otherwise biologically-agnostic learning process.'),
(89813, 2019, 'plos', 'Dimensionality in recurrent spiking networks: Global trends in activity and local origins in connectivity', 'Recanatesi, Stefano|Ocker, Gabriel Koch|Buice, Michael A|Shea-Brown, Eric', 'Center for Computational Neuroscience, University of Washington, Seattle, Washington, United States of America.|Allen Institute for Brain Science, Seattle, Washington, United States of America.|Center for Computational Neuroscience, University of Washington, Seattle, Washington, United States of America.|Center for Computational Neuroscience, University of Washington, Seattle, Washington, United States of America.', 'United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1371/journal.pcbi.1006446', '31299044', 'The dimensionality of a networks collective activity is of increasing interest in neuroscience. This is because dimensionality provides a compact measure of how coordinated network-wide activity is, in terms of the number of modes (or degrees of freedom) that it can independently explore. A low number of modes suggests a compressed low dimensional neural code and reveals interpretable dynamics [1], while findings of high dimension may suggest flexible computations [2, 3]. Here, we address the fundamental question of how dimensionality is related to connectivity, in both autonomous and stimulus-driven networks. Working with a simple spiking network model, we derive three main findings. First, the dimensionality of global activity patterns can be strongly, and systematically, regulated by local connectivity structures. Second, the dimensionality is a better indicator than average correlations in determining how constrained neural activity is. Third, stimulus evoked neural activity interacts systematically with neural connectivity patterns, leading to network responses of either greater or lesser dimensionality than the stimulus.'),
(89814, 2019, 'plos', 'Information-theoretic analysis of multivariate single-cell signaling responses', 'Jetka, Tomasz|Nienaxc5x82towski, Karol|Winarski, Tomasz|Bxc5x82oxc5x84ski, Sxc5x82awomir|Komorowski, Michaxc5x82', 'Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland.|Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland.|Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland.|Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland.|Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland.', 'Poland|Poland|Poland|Poland|Poland', 2, '-', 'Journal Article', '10.1371/journal.pcbi.1007132', '31299056', 'Mathematical methods of information theory appear to provide a useful language to describe how stimuli are encoded in activities of signaling effectors. Exploring the information-theoretic perspective, however, remains conceptually, experimentally and computationally challenging. Specifically, existing computational tools enable efficient analysis of relatively simple systems, usually with one input and output only. Moreover, their robust and readily applicable implementations are missing. Here, we propose a novel algorithm, SLEMI-statistical learning based estimation of mutual information, to analyze signaling systems with high-dimensional outputs and a large number of input values. Our approach is efficient in terms of computational time as well as sample size needed for accurate estimation. Analysis of the NF-xcexbaB single-cell signaling responses to TNF-xcexb1 reveals that NF-xcexbaB signaling dynamics improves discrimination of high concentrations of TNF-xcexb1 with a relatively modest impact on discrimination of low concentrations. Provided R-package allows the approach to be used by computational biologists with only elementary knowledge of information theory.');
