for Journals by Title or ISSN
for Articles by Keywords
help
Journal Cover PLoS Computational Biology
  [SJR: 3.405]   [H-I: 112]   [143 followers]  Follow
    
  This is an Open Access Journal Open Access journal
   ISSN (Print) 1553-734X - ISSN (Online) 1553-7358
   Published by PLoS Homepage  [13 journals]
  • MAGPIE: Simplifying access and execution of computational models in the
           life sciences

    • Authors: Christoph Baldow Sebastian Salentin Michael Schroeder Ingo Roeder Ingmar Glauche
      Abstract: by Christoph Baldow, Sebastian Salentin, Michael Schroeder, Ingo Roeder, Ingmar GlaucheOver the past decades, quantitative methods linking theory and observation became increasingly important in many areas of life science. Subsequently, a large number of mathematical and computational models has been developed. The BioModels database alone lists more than 140,000 Systems Biology Markup Language (SBML) models. However, while the exchange within specific models classes has been supported by standardisation and database efforts, the generic application and especially the re-use of models is still limited by practical issues such as easy and straight forward model execution. MAGPIE, a Modeling and Analysis Generic Platform with Integrated Evaluation, closes this gap by providing a software platform for both, publishing and executing computational models without restrictions on the programming language, thereby combining a maximum on flexibility for programmers with easy handling for non-technical users. MAGPIE goes beyond classical SBML platforms by including all models, independent of the underlying programming language, ranging from simple script models to complex data integration and computations. We demonstrate the versatility of MAGPIE using four prototypic example cases. We also outline the potential of MAGPIE to improve transparency and reproducibility of computational models in life sciences. A demo server is available at magpie.imb.medizin.tu-dresden.de.
      PubDate: 2017-12-15T22:00:00Z
      DOI: 10.1371/journal.pcbi.1005898
       
  • Optimal occlusion uniformly partitions red blood cells fluxes within a
           microvascular network

    • Authors: Shyr-Shea Chang Shenyinying Tu Kyung In Baek Andrew Pietersen Yu-Hsiu Liu Van M. Savage Sheng-Ping L. Hwang Tzung K. Hsiai Marcus Roper
      Abstract: by Shyr-Shea Chang, Shenyinying Tu, Kyung In Baek, Andrew Pietersen, Yu-Hsiu Liu, Van M. Savage, Sheng-Ping L. Hwang, Tzung K. Hsiai, Marcus RoperIn animals, gas exchange between blood and tissues occurs in narrow vessels, whose diameter is comparable to that of a red blood cell. Red blood cells must deform to squeeze through these narrow vessels, transiently blocking or occluding the vessels they pass through. Although the dynamics of vessel occlusion have been studied extensively, it remains an open question why microvessels need to be so narrow. We study occlusive dynamics within a model microvascular network: the embryonic zebrafish trunk. We show that pressure feedbacks created when red blood cells enter the finest vessels of the trunk act together to uniformly partition red blood cells through the microvasculature. Using mathematical models as well as direct observation, we show that these occlusive feedbacks are tuned throughout the trunk network to prevent the vessels closest to the heart from short-circuiting the network. Thus occlusion is linked with another open question of microvascular function: how are red blood cells delivered at the same rate to each micro-vessel' Our analysis shows that tuning of occlusive feedbacks increase the total dissipation within the network by a factor of 11, showing that uniformity of flows rather than minimization of transport costs may be prioritized by the microvascular network.
      PubDate: 2017-12-15T22:00:00Z
      DOI: 10.1371/journal.pcbi.1005892
       
  • Costs of task allocation with local feedback: Effects of colony size and
           extra workers in social insects and other multi-agent systems

    • Authors: Tsvetomira Radeva Anna Dornhaus Nancy Lynch Radhika Nagpal Hsin-Hao Su
      Abstract: by Tsvetomira Radeva, Anna Dornhaus, Nancy Lynch, Radhika Nagpal, Hsin-Hao SuAdaptive collective systems are common in biology and beyond. Typically, such systems require a task allocation algorithm: a mechanism or rule-set by which individuals select particular roles. Here we study the performance of such task allocation mechanisms measured in terms of the time for individuals to allocate to tasks. We ask: (1) Is task allocation fundamentally difficult, and thus costly' (2) Does the performance of task allocation mechanisms depend on the number of individuals' And (3) what other parameters may affect their efficiency' We use techniques from distributed computing theory to develop a model of a social insect colony, where workers have to be allocated to a set of tasks; however, our model is generalizable to other systems. We show, first, that the ability of workers to quickly assess demand for work in tasks they are not currently engaged in crucially affects whether task allocation is quickly achieved or not. This indicates that in social insect tasks such as thermoregulation, where temperature may provide a global and near instantaneous stimulus to measure the need for cooling, for example, it should be easy to match the number of workers to the need for work. In other tasks, such as nest repair, it may be impossible for workers not directly at the work site to know that this task needs more workers. We argue that this affects whether task allocation mechanisms are under strong selection. Second, we show that colony size does not affect task allocation performance under our assumptions. This implies that when effects of colony size are found, they are not inherent in the process of task allocation itself, but due to processes not modeled here, such as higher variation in task demand for smaller colonies, benefits of specialized workers, or constant overhead costs. Third, we show that the ratio of the number of available workers to the workload crucially affects performance. Thus, workers in excess of those needed to complete all tasks improve task allocation performance. This provides a potential explanation for the phenomenon that social insect colonies commonly contain inactive workers: these may be a ‘surplus’ set of workers that improves colony function by speeding up optimal allocation of workers to tasks. Overall our study shows how limitations at the individual level can affect group level outcomes, and suggests new hypotheses that can be explored empirically.
      PubDate: 2017-12-14T22:00:00Z
      DOI: 10.1371/journal.pcbi.1005904
       
  • A master equation approach to actin polymerization applied to endocytosis
           in yeast

    • Authors: Xinxin Wang Anders E. Carlsson
      Abstract: by Xinxin Wang, Anders E. CarlssonWe present a Master Equation approach to calculating polymerization dynamics and force generation by branched actin networks at membranes. The method treats the time evolution of the F-actin distribution in three dimensions, with branching included as a directional spreading term. It is validated by comparison with stochastic simulations of force generation by actin polymerization at obstacles coated with actin “nucleation promoting factors” (NPFs). The method is then used to treat the dynamics of actin polymerization and force generation during endocytosis in yeast, using a model in which NPFs form a ring around the endocytic site, centered by a spot of molecules attaching the actin network strongly to the membrane. We find that a spontaneous actin filament nucleation mechanism is required for adequate forces to drive the process, that partial inhibition of branching and polymerization lead to different characteristic responses, and that a limited range of polymerization-rate values provide effective invagination and obtain correct predictions for the effects of mutations in the active regions of the NPFs.
      PubDate: 2017-12-14T22:00:00Z
      DOI: 10.1371/journal.pcbi.1005901
       
  • Specific excitatory connectivity for feature integration in mouse primary
           visual cortex

    • Authors: Dylan R. Muir Patricia Molina-Luna Morgane M. Roth Fritjof Helmchen Björn M. Kampa
      Abstract: by Dylan R. Muir, Patricia Molina-Luna, Morgane M. Roth, Fritjof Helmchen, Björn M. KampaLocal excitatory connections in mouse primary visual cortex (V1) are stronger and more prevalent between neurons that share similar functional response features. However, the details of how functional rules for local connectivity shape neuronal responses in V1 remain unknown. We hypothesised that complex responses to visual stimuli may arise as a consequence of rules for selective excitatory connectivity within the local network in the superficial layers of mouse V1. In mouse V1 many neurons respond to overlapping grating stimuli (plaid stimuli) with highly selective and facilitatory responses, which are not simply predicted by responses to single gratings presented alone. This complexity is surprising, since excitatory neurons in V1 are considered to be mainly tuned to single preferred orientations. Here we examined the consequences for visual processing of two alternative connectivity schemes: in the first case, local connections are aligned with visual properties inherited from feedforward input (a ‘like-to-like’ scheme specifically connecting neurons that share similar preferred orientations); in the second case, local connections group neurons into excitatory subnetworks that combine and amplify multiple feedforward visual properties (a ‘feature binding’ scheme). By comparing predictions from large scale computational models with in vivo recordings of visual representations in mouse V1, we found that responses to plaid stimuli were best explained by assuming ‘feature binding’ connectivity. Unlike under the ‘like-to-like’ scheme, selective amplification within feature-binding excitatory subnetworks replicated experimentally observed facilitatory responses to plaid stimuli; explained selective plaid responses not predicted by grating selectivity; and was consistent with broad anatomical selectivity observed in mouse V1. Our results show that visual feature binding can occur through local recurrent mechanisms without requiring feedforward convergence, and that such a mechanism is consistent with visual responses and cortical anatomy in mouse V1.
      PubDate: 2017-12-14T22:00:00Z
      DOI: 10.1371/journal.pcbi.1005888
       
  • Novel linear motif filtering protocol reveals the role of the LC8 dynein
           light chain in the Hippo pathway

    • Authors: Gábor Erdős Tamás Szaniszló Mátyás Pajkos Borbála Hajdu-Soltész Bence Kiss Gábor Pál László Nyitray Zsuzsanna Dosztányi
      Abstract: by Gábor Erdős, Tamás Szaniszló, Mátyás Pajkos, Borbála Hajdu-Soltész, Bence Kiss, Gábor Pál, László Nyitray, Zsuzsanna DosztányiProtein-protein interactions (PPIs) formed between short linear motifs and globular domains play important roles in many regulatory and signaling processes but are highly underrepresented in current protein-protein interaction databases. These types of interactions are usually characterized by a specific binding motif that captures the key amino acids shared among the interaction partners. However, the computational proteome-level identification of interaction partners based on the known motif is hindered by the huge number of randomly occurring matches from which biologically relevant motif hits need to be extracted. In this work, we established a novel bioinformatic filtering protocol to efficiently explore interaction network of a hub protein. We introduced a novel measure that enabled the optimization of the elements and parameter settings of the pipeline which was built from multiple sequence-based prediction methods. In addition, data collected from PPI databases and evolutionary analyses were also incorporated to further increase the biological relevance of the identified motif hits. The approach was applied to the dynein light chain LC8, a ubiquitous eukaryotic hub protein that has been suggested to be involved in motor-related functions as well as promoting the dimerization of various proteins by recognizing linear motifs in its partners. From the list of putative binding motifs collected by our protocol, several novel peptides were experimentally verified to bind LC8. Altogether 71 potential new motif instances were identified. The expanded list of LC8 binding partners revealed the evolutionary plasticity of binding partners despite the highly conserved binding interface. In addition, it also highlighted a novel, conserved function of LC8 in the upstream regulation of the Hippo signaling pathway. Beyond the LC8 system, our work also provides general guidelines that can be applied to explore the interaction network of other linear motif binding proteins or protein domains.
      PubDate: 2017-12-14T22:00:00Z
      DOI: 10.1371/journal.pcbi.1005885
       
  • Feature reliability determines specificity and transfer of perceptual
           learning in orientation search

    • Authors: Amit Yashar Rachel N. Denison
      Abstract: by Amit Yashar, Rachel N. DenisonTraining can modify the visual system to produce a substantial improvement on perceptual tasks and therefore has applications for treating visual deficits. Visual perceptual learning (VPL) is often specific to the trained feature, which gives insight into processes underlying brain plasticity, but limits VPL’s effectiveness in rehabilitation. Under what circumstances VPL transfers to untrained stimuli is poorly understood. Here we report a qualitatively new phenomenon: intrinsic variation in the representation of features determines the transfer of VPL. Orientations around cardinal are represented more reliably than orientations around oblique in V1, which has been linked to behavioral consequences such as visual search asymmetries. We studied VPL for visual search of near-cardinal or oblique targets among distractors of the other orientation while controlling for other display and task attributes, including task precision, task difficulty, and stimulus exposure. Learning was the same in all training conditions; however, transfer depended on the orientation of the target, with full transfer of learning from near-cardinal to oblique targets but not the reverse. To evaluate the idea that representational reliability was the key difference between the orientations in determining VPL transfer, we created a model that combined orientation-dependent reliability, improvement of reliability with learning, and an optimal search strategy. Modeling suggested that not only search asymmetries but also the asymmetric transfer of VPL depended on preexisting differences between the reliability of near-cardinal and oblique representations. Transfer asymmetries in model behavior also depended on having different learning rates for targets and distractors, such that greater learning for low-reliability distractors facilitated transfer. These findings suggest that training on sensory features with intrinsically low reliability may maximize the generalizability of learning in complex visual environments.
      PubDate: 2017-12-14T22:00:00Z
      DOI: 10.1371/journal.pcbi.1005882
       
  • Ten simple rules for writing a career development award proposal

    • Authors: Crystal M. Botham Joshua A. Arribere Sky W. Brubaker Kevin T. Beier
      Abstract: by Crystal M. Botham, Joshua A. Arribere, Sky W. Brubaker, Kevin T. Beier
      PubDate: 2017-12-14T22:00:00Z
      DOI: 10.1371/journal.pcbi.1005863
       
  • Correction: Sequential inference as a mode of cognition and its correlates
           in fronto-parietal and hippocampal brain regions

    • Authors: The PLOS Computational Biology Staff
      Abstract: by The PLOS Computational Biology Staff
      PubDate: 2017-12-13T22:00:00Z
      DOI: 10.1371/journal.pcbi.1005908
       
  • Correction: Identifying influential neighbors in animal flocking

    • Authors: The PLOS Computational Biology Staff
      Abstract: by The PLOS Computational Biology Staff
      PubDate: 2017-12-12T22:00:00Z
      DOI: 10.1371/journal.pcbi.1005902
       
  • Stabilizing patterns in time: Neural network approach

    • Authors: Nadav Ben-Shushan Misha Tsodyks
      Abstract: by Nadav Ben-Shushan, Misha TsodyksRecurrent and feedback networks are capable of holding dynamic memories. Nonetheless, training a network for that task is challenging. In order to do so, one should face non-linear propagation of errors in the system. Small deviations from the desired dynamics due to error or inherent noise might have a dramatic effect in the future. A method to cope with these difficulties is thus needed. In this work we focus on recurrent networks with linear activation functions and binary output unit. We characterize its ability to reproduce a temporal sequence of actions over its output unit. We suggest casting the temporal learning problem to a perceptron problem. In the discrete case a finite margin appears, providing the network, to some extent, robustness to noise, for which it performs perfectly (i.e. producing a desired sequence for an arbitrary number of cycles flawlessly). In the continuous case the margin approaches zero when the output unit changes its state, hence the network is only able to reproduce the sequence with slight jitters. Numerical simulation suggest that in the discrete time case, the longest sequence that can be learned scales, at best, as square root of the network size. A dramatic effect occurs when learning several short sequences in parallel, that is, their total length substantially exceeds the length of the longest single sequence the network can learn. This model easily generalizes to an arbitrary number of output units, which boost its performance. This effect is demonstrated by considering two practical examples for sequence learning. This work suggests a way to overcome stability problems for training recurrent networks and further quantifies the performance of a network under the specific learning scheme.
      PubDate: 2017-12-12T22:00:00Z
      DOI: 10.1371/journal.pcbi.1005861
       
  • Non-linear auto-regressive models for cross-frequency coupling in neural
           time series

    • Authors: Tom Dupré la Tour Lucille Tallot Laetitia Grabot Valérie Doyère Virginie van Wassenhove Yves Grenier Alexandre Gramfort
      Abstract: by Tom Dupré la Tour, Lucille Tallot, Laetitia Grabot, Valérie Doyère, Virginie van Wassenhove, Yves Grenier, Alexandre GramfortWe address the issue of reliably detecting and quantifying cross-frequency coupling (CFC) in neural time series. Based on non-linear auto-regressive models, the proposed method provides a generative and parametric model of the time-varying spectral content of the signals. As this method models the entire spectrum simultaneously, it avoids the pitfalls related to incorrect filtering or the use of the Hilbert transform on wide-band signals. As the model is probabilistic, it also provides a score of the model “goodness of fit” via the likelihood, enabling easy and legitimate model selection and parameter comparison; this data-driven feature is unique to our model-based approach. Using three datasets obtained with invasive neurophysiological recordings in humans and rodents, we demonstrate that these models are able to replicate previous results obtained with other metrics, but also reveal new insights such as the influence of the amplitude of the slow oscillation. Using simulations, we demonstrate that our parametric method can reveal neural couplings with shorter signals than non-parametric methods. We also show how the likelihood can be used to find optimal filtering parameters, suggesting new properties on the spectrum of the driving signal, but also to estimate the optimal delay between the coupled signals, enabling a directionality estimation in the coupling.
      PubDate: 2017-12-11T22:00:00Z
      DOI: 10.1371/journal.pcbi.1005893
       
  • Thalamocortical control of propofol phase-amplitude coupling

    • Authors: Austin E. Soplata Michelle M. McCarthy Jason Sherfey Shane Lee Patrick L. Purdon Emery N. Brown Nancy Kopell
      Abstract: by Austin E. Soplata, Michelle M. McCarthy, Jason Sherfey, Shane Lee, Patrick L. Purdon, Emery N. Brown, Nancy KopellThe anesthetic propofol elicits many different spectral properties on the EEG, including alpha oscillations (8–12 Hz), Slow Wave Oscillations (SWO, 0.1–1.5 Hz), and dose-dependent phase-amplitude coupling (PAC) between alpha and SWO. Propofol is known to increase GABAA inhibition and decrease H-current strength, but how it generates these rhythms and their interactions is still unknown. To investigate both generation of the alpha rhythm and its PAC to SWO, we simulate a Hodgkin-Huxley network model of a hyperpolarized thalamus and corticothalamic inputs. We find, for the first time, that the model thalamic network is capable of independently generating the sustained alpha seen in propofol, which may then be relayed to cortex and expressed on the EEG. This dose-dependent sustained alpha critically relies on propofol GABAA potentiation to alter the intrinsic spindling mechanisms of the thalamus. Furthermore, the H-current conductance and background excitation of these thalamic cells must be within specific ranges to exhibit any intrinsic oscillations, including sustained alpha. We also find that, under corticothalamic SWO UP and DOWN states, thalamocortical output can exhibit maximum alpha power at either the peak or trough of this SWO; this implies the thalamus may be the source of propofol-induced PAC. Hyperpolarization level is the main determinant of whether the thalamus exhibits trough-max PAC, which is associated with lower propofol dose, or peak-max PAC, associated with higher dose. These findings suggest: the thalamus generates a novel rhythm under GABAA potentiation such as under propofol, its hyperpolarization may determine whether a patient experiences trough-max or peak-max PAC, and the thalamus is a critical component of propofol-induced cortical spectral phenomena. Changes to the thalamus may be a critical part of how propofol accomplishes its effects, including unconsciousness.
      PubDate: 2017-12-11T22:00:00Z
      DOI: 10.1371/journal.pcbi.1005879
       
  • Predicting the pathogenicity of novel variants in mitochondrial tRNA with
           MitoTIP

    • Authors: Sanjay Sonney Jeremy Leipzig Marie T. Lott Shiping Zhang Vincent Procaccio Douglas C. Wallace Neal Sondheimer
      Abstract: by Sanjay Sonney, Jeremy Leipzig, Marie T. Lott, Shiping Zhang, Vincent Procaccio, Douglas C. Wallace, Neal SondheimerNovel or rare variants in mitochondrial tRNA sequences may be observed after mitochondrial DNA analysis. Determining whether these variants are pathogenic is critical, but confirmation of the effect of a variant on mitochondrial function can be challenging. We have used available databases of benign and pathogenic variants, alignment between diverse tRNAs, structural information and comparative genomics to predict the impact of all possible single-base variants and deletions. The Mitochondrial tRNA Informatics Predictor (MitoTIP) is available through MITOMAP at www.mitomap.org. The source code for MitoTIP is available at www.github.com/sonneysa/MitoTIP.
      PubDate: 2017-12-11T22:00:00Z
      DOI: 10.1371/journal.pcbi.1005867
       
  • A cyber-linked undergraduate research experience in computational
           biomolecular structure prediction and design

    • Authors: Rebecca F. Alford Andrew Leaver-Fay Lynda Gonzales Erin L. Dolan Jeffrey J. Gray
      Abstract: by Rebecca F. Alford, Andrew Leaver-Fay, Lynda Gonzales, Erin L. Dolan, Jeffrey J. GrayComputational biology is an interdisciplinary field, and many computational biology research projects involve distributed teams of scientists. To accomplish their work, these teams must overcome both disciplinary and geographic barriers. Introducing new training paradigms is one way to facilitate research progress in computational biology. Here, we describe a new undergraduate program in biomolecular structure prediction and design in which students conduct research at labs located at geographically-distributed institutions while remaining connected through an online community. This 10-week summer program begins with one week of training on computational biology methods development, transitions to eight weeks of research, and culminates in one week at the Rosetta annual conference. To date, two cohorts of students have participated, tackling research topics including vaccine design, enzyme design, protein-based materials, glycoprotein modeling, crowd-sourced science, RNA processing, hydrogen bond networks, and amyloid formation. Students in the program report outcomes comparable to students who participate in similar in-person programs. These outcomes include the development of a sense of community and increases in their scientific self-efficacy, scientific identity, and science values, all predictors of continuing in a science research career. Furthermore, the program attracted students from diverse backgrounds, which demonstrates the potential of this approach to broaden the participation of young scientists from backgrounds traditionally underrepresented in computational biology.
      PubDate: 2017-12-07T22:00:00Z
      DOI: 10.1371/journal.pcbi.1005837
       
  • Ten simple rules for international short-term research stays

    • Authors: Diego A. Forero Sandra Lopez-Leon George P. Patrinos
      Abstract: by Diego A. Forero, Sandra Lopez-Leon, George P. Patrinos
      PubDate: 2017-12-07T22:00:00Z
      DOI: 10.1371/journal.pcbi.1005832
       
  • Automated classification of dolphin echolocation click types from the Gulf
           of Mexico

    • Authors: Kaitlin E. Frasier Marie A. Roch Melissa S. Soldevilla Sean M. Wiggins Lance P. Garrison John A. Hildebrand
      Abstract: by Kaitlin E. Frasier, Marie A. Roch, Melissa S. Soldevilla, Sean M. Wiggins, Lance P. Garrison, John A. HildebrandDelphinids produce large numbers of short duration, broadband echolocation clicks which may be useful for species classification in passive acoustic monitoring efforts. A challenge in echolocation click classification is to overcome the many sources of variability to recognize underlying patterns across many detections. An automated unsupervised network-based classification method was developed to simulate the approach a human analyst uses when categorizing click types: Clusters of similar clicks were identified by incorporating multiple click characteristics (spectral shape and inter-click interval distributions) to distinguish within-type from between-type variation, and identify distinct, persistent click types. Once click types were established, an algorithm for classifying novel detections using existing clusters was tested. The automated classification method was applied to a dataset of 52 million clicks detected across five monitoring sites over two years in the Gulf of Mexico (GOM). Seven distinct click types were identified, one of which is known to be associated with an acoustically identifiable delphinid (Risso’s dolphin) and six of which are not yet identified. All types occurred at multiple monitoring locations, but the relative occurrence of types varied, particularly between continental shelf and slope locations. Automatically-identified click types from autonomous seafloor recorders without verifiable species identification were compared with clicks detected on sea-surface towed hydrophone arrays in the presence of visually identified delphinid species. These comparisons suggest potential species identities for the animals producing some echolocation click types. The network-based classification method presented here is effective for rapid, unsupervised delphinid click classification across large datasets in which the click types may not be known a priori.
      PubDate: 2017-12-07T22:00:00Z
      DOI: 10.1371/journal.pcbi.1005823
       
  • Correction: Signatures of criticality arise from random subsampling in
           simple population models

    • Authors: The PLOS Computational Biology Staff
      Abstract: by The PLOS Computational Biology Staff
      PubDate: 2017-12-06T22:00:00Z
      DOI: 10.1371/journal.pcbi.1005886
       
  • pSSAlib: The partial-propensity stochastic chemical network simulator

    • Authors: Oleksandr Ostrenko Pietro Incardona Rajesh Ramaswamy Lutz Brusch Ivo F. Sbalzarini
      Abstract: by Oleksandr Ostrenko, Pietro Incardona, Rajesh Ramaswamy, Lutz Brusch, Ivo F. SbalzariniChemical reaction networks are ubiquitous in biology, and their dynamics is fundamentally stochastic. Here, we present the software library pSSAlib, which provides a complete and concise implementation of the most efficient partial-propensity methods for simulating exact stochastic chemical kinetics. pSSAlib can import models encoded in Systems Biology Markup Language, supports time delays in chemical reactions, and stochastic spatiotemporal reaction-diffusion systems. It also provides tools for statistical analysis of simulation results and supports multiple output formats. It has previously been used for studies of biochemical reaction pathways and to benchmark other stochastic simulation methods. Here, we describe pSSAlib in detail and apply it to a new model of the endocytic pathway in eukaryotic cells, leading to the discovery of a stochastic counterpart of the cut-out switch motif underlying early-to-late endosome conversion. pSSAlib is provided as a stand-alone command-line tool and as a developer API. We also provide a plug-in for the SBMLToolbox. The open-source code and pre-packaged installers are freely available from http://mosaic.mpi-cbg.de.
      PubDate: 2017-12-04T22:00:00Z
      DOI: 10.1371/journal.pcbi.1005865
       
  • Efficient encoding of motion is mediated by gap junctions in the fly
           visual system

    • Authors: Siwei Wang Alexander Borst Noga Zaslavsky Naftali Tishby Idan Segev
      Abstract: by Siwei Wang, Alexander Borst, Noga Zaslavsky, Naftali Tishby, Idan SegevUnderstanding the computational implications of specific synaptic connectivity patterns is a fundamental goal in neuroscience. In particular, the computational role of ubiquitous electrical synapses operating via gap junctions remains elusive. In the fly visual system, the cells in the vertical-system network, which play a key role in visual processing, primarily connect to each other via axonal gap junctions. This network therefore provides a unique opportunity to explore the functional role of gap junctions in sensory information processing. Our information theoretical analysis of a realistic VS network model shows that within 10 ms following the onset of the visual input, the presence of axonal gap junctions enables the VS system to efficiently encode the axis of rotation, θ, of the fly’s ego motion. This encoding efficiency, measured in bits, is near-optimal with respect to the physical limits of performance determined by the statistical structure of the visual input itself. The VS network is known to be connected to downstream pathways via a subset of triplets of the vertical system cells; we found that because of the axonal gap junctions, the efficiency of this subpopulation in encoding θ is superior to that of the whole vertical system network and is robust to a wide range of signal to noise ratios. We further demonstrate that this efficient encoding of motion by this subpopulation is necessary for the fly's visually guided behavior, such as banked turns in evasive maneuvers. Because gap junctions are formed among the axons of the vertical system cells, they only impact the system’s readout, while maintaining the dendritic input intact, suggesting that the computational principles implemented by neural circuitries may be much richer than previously appreciated based on point neuron models. Our study provides new insights as to how specific network connectivity leads to efficient encoding of sensory stimuli.
      PubDate: 2017-12-04T22:00:00Z
      DOI: 10.1371/journal.pcbi.1005846
       
  • A model of the onset of the senescence associated secretory phenotype
           after DNA damage induced senescence

    • Authors: Patrick Meyer Pallab Maity Andre Burkovski Julian Schwab Christoph Müssel Karmveer Singh Filipa F. Ferreira Linda Krug Harald J. Maier Meinhard Wlaschek Thomas Wirth Hans A. Kestler Karin Scharffetter-Kochanek
      Abstract: by Patrick Meyer, Pallab Maity, Andre Burkovski, Julian Schwab, Christoph Müssel, Karmveer Singh, Filipa F. Ferreira, Linda Krug, Harald J. Maier, Meinhard Wlaschek, Thomas Wirth, Hans A. Kestler, Karin Scharffetter-KochanekCells and tissues are exposed to stress from numerous sources. Senescence is a protective mechanism that prevents malignant tissue changes and constitutes a fundamental mechanism of aging. It can be accompanied by a senescence associated secretory phenotype (SASP) that causes chronic inflammation. We present a Boolean network model-based gene regulatory network of the SASP, incorporating published gene interaction data. The simulation results describe current biological knowledge. The model predicts different in-silico knockouts that prevent key SASP-mediators, IL-6 and IL-8, from getting activated upon DNA damage. The NF-κB Essential Modulator (NEMO) was the most promising in-silico knockout candidate and we were able to show its importance in the inhibition of IL-6 and IL-8 following DNA-damage in murine dermal fibroblasts in-vitro. We strengthen the speculated regulator function of the NF-κB signaling pathway in the onset and maintenance of the SASP using in-silico and in-vitro approaches. We were able to mechanistically show, that DNA damage mediated SASP triggering of IL-6 and IL-8 is mainly relayed through NF-κB, giving access to possible therapy targets for SASP-accompanied diseases.
      PubDate: 2017-12-04T22:00:00Z
      DOI: 10.1371/journal.pcbi.1005741
       
  • Sequence-dependent nucleosome sliding in rotation-coupled and uncoupled
           modes revealed by molecular simulations

    • Authors: Toru Niina Giovanni B. Brandani Cheng Tan Shoji Takada
      Abstract: by Toru Niina, Giovanni B. Brandani, Cheng Tan, Shoji TakadaWhile nucleosome positioning on eukaryotic genome play important roles for genetic regulation, molecular mechanisms of nucleosome positioning and sliding along DNA are not well understood. Here we investigated thermally-activated spontaneous nucleosome sliding mechanisms developing and applying a coarse-grained molecular simulation method that incorporates both long-range electrostatic and short-range hydrogen-bond interactions between histone octamer and DNA. The simulations revealed two distinct sliding modes depending on the nucleosomal DNA sequence. A uniform DNA sequence showed frequent sliding with one base pair step in a rotation-coupled manner, akin to screw-like motions. On the contrary, a strong positioning sequence, the so-called 601 sequence, exhibits rare, abrupt transitions of five and ten base pair steps without rotation. Moreover, we evaluated the importance of hydrogen bond interactions on the sliding mode, finding that strong and weak bonds favor respectively the rotation-coupled and -uncoupled sliding movements.
      PubDate: 2017-12-01T22:00:00Z
      DOI: 10.1371/journal.pcbi.1005880
       
  • Olfactory coding in the turbulent realm

    • Authors: Vincent Jacob Christelle Monsempès Jean-Pierre Rospars Jean-Baptiste Masson Philippe Lucas
      Abstract: by Vincent Jacob, Christelle Monsempès, Jean-Pierre Rospars, Jean-Baptiste Masson, Philippe LucasLong-distance olfactory search behaviors depend on odor detection dynamics. Due to turbulence, olfactory signals travel as bursts of variable concentration and spacing and are characterized by long-tail distributions of odor/no-odor events, challenging the computing capacities of olfactory systems. How animals encode complex olfactory scenes to track the plume far from the source remains unclear. Here we focus on the coding of the plume temporal dynamics in moths. We compare responses of olfactory receptor neurons (ORNs) and antennal lobe projection neurons (PNs) to sequences of pheromone stimuli either with white-noise patterns or with realistic turbulent temporal structures simulating a large range of distances (8 to 64 m) from the odor source. For the first time, we analyze what information is extracted by the olfactory system at large distances from the source. Neuronal responses are analyzed using linear–nonlinear models fitted with white-noise stimuli and used for predicting responses to turbulent stimuli. We found that neuronal firing rate is less correlated with the dynamic odor time course when distance to the source increases because of improper coding during long odor and no-odor events that characterize large distances. Rapid adaptation during long puffs does not preclude however the detection of puff transitions in PNs. Individual PNs but not individual ORNs encode the onset and offset of odor puffs for any temporal structure of stimuli. A higher spontaneous firing rate coupled to an inhibition phase at the end of PN responses contributes to this coding property. This allows PNs to decode the temporal structure of the odor plume at any distance to the source, an essential piece of information moths can use in their tracking behavior.
      PubDate: 2017-12-01T22:00:00Z
      DOI: 10.1371/journal.pcbi.1005870
       
  • Genetic drift and selection in many-allele range expansions

    • Authors: Bryan T. Weinstein Maxim O. Lavrentovich Wolfram Möbius Andrew W. Murray David R. Nelson
      Abstract: by Bryan T. Weinstein, Maxim O. Lavrentovich, Wolfram Möbius, Andrew W. Murray, David R. NelsonWe experimentally and numerically investigate the evolutionary dynamics of four competing strains of E. coli with differing expansion velocities in radially expanding colonies. We compare experimental measurements of the average fraction, correlation functions between strains, and the relative rates of genetic domain wall annihilations and coalescences to simulations modeling the population as a one-dimensional ring of annihilating and coalescing random walkers with deterministic biases due to selection. The simulations reveal that the evolutionary dynamics can be collapsed onto master curves governed by three essential parameters: (1) an expansion length beyond which selection dominates over genetic drift; (2) a characteristic angular correlation describing the size of genetic domains; and (3) a dimensionless constant quantifying the interplay between a colony’s curvature at the frontier and its selection length scale. We measure these parameters with a new technique that precisely measures small selective differences between spatially competing strains and show that our simulations accurately predict the dynamics without additional fitting. Our results suggest that the random walk model can act as a useful predictive tool for describing the evolutionary dynamics of range expansions composed of an arbitrary number of genotypes with different fitnesses.
      PubDate: 2017-12-01T22:00:00Z
      DOI: 10.1371/journal.pcbi.1005866
       
  • Metabomatching: Using genetic association to identify metabolites in
           proton NMR spectroscopy

    • Authors: Rico Rueedi Roger Mallol Johannes Raffler David Lamparter Nele Friedrich Peter Vollenweider Gérard Waeber Gabi Kastenmüller Zoltán Kutalik Sven Bergmann
      Abstract: by Rico Rueedi, Roger Mallol, Johannes Raffler, David Lamparter, Nele Friedrich, Peter Vollenweider, Gérard Waeber, Gabi Kastenmüller, Zoltán Kutalik, Sven BergmannA metabolome-wide genome-wide association study (mGWAS) aims to discover the effects of genetic variants on metabolome phenotypes. Most mGWASes use as phenotypes concentrations of limited sets of metabolites that can be identified and quantified from spectral information. In contrast, in an untargeted mGWAS both identification and quantification are forgone and, instead, all measured metabolome features are tested for association with genetic variants. While the untargeted approach does not discard data that may have eluded identification, the interpretation of associated features remains a challenge. To address this issue, we developed metabomatching to identify the metabolites underlying significant associations observed in untargeted mGWASes on proton NMR metabolome data. Metabomatching capitalizes on genetic spiking, the concept that because metabolome features associated with a genetic variant tend to correspond to the peaks of the NMR spectrum of the underlying metabolite, genetic association can allow for identification. Applied to the untargeted mGWASes in the SHIP and CoLaus cohorts and using 180 reference NMR spectra of the urine metabolome database, metabomatching successfully identified the underlying metabolite in 14 of 19, and 8 of 9 associations, respectively. The accuracy and efficiency of our method make it a strong contender for facilitating or complementing metabolomics analyses in large cohorts, where the availability of genetic, or other data, enables our approach, but targeted quantification is limited.
      PubDate: 2017-12-01T22:00:00Z
      DOI: 10.1371/journal.pcbi.1005839
       
  • A network diffusion approach to inferring sample-specific function reveals
           functional changes associated with breast cancer

    • Authors: Sushant Patkar Assaf Magen Roded Sharan Sridhar Hannenhalli
      Abstract: by Sushant Patkar, Assaf Magen, Roded Sharan, Sridhar HannenhalliGuilt-by-association codifies the empirical observation that a gene’s function is informed by its neighborhood in a biological network. This would imply that when a gene’s network context is altered, for instance in disease condition, so could be the gene’s function. Although context-specific changes in biological networks have been explored, the potential changes they may induce on the functional roles of genes are yet to be characterized. Here we analyze, for the first time, the network-induced potential functional changes in breast cancer. Using transcriptomic samples for 1047 breast tumors and 110 healthy breast tissues from TCGA, we derive sample-specific protein interaction networks and assign sample-specific functions to genes via a diffusion strategy. Testing for significant changes in the inferred functions between normal and cancer samples, we find several functions to have significantly gained or lost genes in cancer, not due to differential expression of genes known to perform the function, but rather due to changes in the network topology. Our predicted functional changes are supported by mutational and copy number profiles in breast cancers. Our diffusion-based functional assignment provides a novel characterization of a tumor that is complementary to the standard approach based on functional annotation alone. Importantly, this characterization is effective in predicting patient survival, as well as in predicting several known histopathological subtypes of breast cancer.
      PubDate: 2017-11-30T22:00:00Z
      DOI: 10.1371/journal.pcbi.1005793
       
  • Dynamical compensation and structural identifiability of biological
           models: Analysis, implications, and reconciliation

    • Authors: Alejandro F. Villaverde Julio R. Banga
      Abstract: by Alejandro F. Villaverde, Julio R. BangaThe concept of dynamical compensation has been recently introduced to describe the ability of a biological system to keep its output dynamics unchanged in the face of varying parameters. However, the original definition of dynamical compensation amounts to lack of structural identifiability. This is relevant if model parameters need to be estimated, as is often the case in biological modelling. Care should we taken when using an unidentifiable model to extract biological insight: the estimated values of structurally unidentifiable parameters are meaningless, and model predictions about unmeasured state variables can be wrong. Taking this into account, we explore alternative definitions of dynamical compensation that do not necessarily imply structural unidentifiability. Accordingly, we show different ways in which a model can be made identifiable while exhibiting dynamical compensation. Our analyses enable the use of the new concept of dynamical compensation in the context of parameter identification, and reconcile it with the desirable property of structural identifiability.
      PubDate: 2017-11-29T22:00:00Z
      DOI: 10.1371/journal.pcbi.1005878
       
  • The effect of spatial randomness on the average fixation time of mutants

    • Authors: Suzan Farhang-Sardroodi Amirhossein H. Darooneh Moladad Nikbakht Natalia L. Komarova Mohammad Kohandel
      Abstract: by Suzan Farhang-Sardroodi, Amirhossein H. Darooneh, Moladad Nikbakht, Natalia L. Komarova, Mohammad KohandelThe mean conditional fixation time of a mutant is an important measure of stochastic population dynamics, widely studied in ecology and evolution. Here, we investigate the effect of spatial randomness on the mean conditional fixation time of mutants in a constant population of cells, N. Specifically, we assume that fitness values of wild type cells and mutants at different locations come from given probability distributions and do not change in time. We study spatial arrangements of cells on regular graphs with different degrees, from the circle to the complete graph, and vary assumptions on the fitness probability distributions. Some examples include: identical probability distributions for wild types and mutants; cases when only one of the cell types has random fitness values while the other has deterministic fitness; and cases where the mutants are advantaged or disadvantaged. Using analytical calculations and stochastic numerical simulations, we find that randomness has a strong impact on fixation time. In the case of complete graphs, randomness accelerates mutant fixation for all population sizes, and in the case of circular graphs, randomness delays mutant fixation for N larger than a threshold value (for small values of N, different behaviors are observed depending on the fitness distribution functions). These results emphasize fundamental differences in population dynamics under different assumptions on cell connectedness. They are explained by the existence of randomly occurring “dead zones” that can significantly delay fixation on networks with low connectivity; and by the existence of randomly occurring “lucky zones” that can facilitate fixation on networks of high connectivity. Results for death-birth and birth-death formulations of the Moran process, as well as for the (haploid) Wright Fisher model are presented.
      PubDate: 2017-11-27T22:00:00Z
      DOI: 10.1371/journal.pcbi.1005864
       
  • Strawberry: Fast and accurate genome-guided transcript reconstruction and
           quantification from RNA-Seq

    • Authors: Ruolin Liu Julie Dickerson
      Abstract: by Ruolin Liu, Julie DickersonWe propose a novel method and software tool, Strawberry, for transcript reconstruction and quantification from RNA-Seq data under the guidance of genome alignment and independent of gene annotation. Strawberry consists of two modules: assembly and quantification. The novelty of Strawberry is that the two modules use different optimization frameworks but utilize the same data graph structure, which allows a highly efficient, expandable and accurate algorithm for dealing large data. The assembly module parses aligned reads into splicing graphs, and uses network flow algorithms to select the most likely transcripts. The quantification module uses a latent class model to assign read counts from the nodes of splicing graphs to transcripts. Strawberry simultaneously estimates the transcript abundances and corrects for sequencing bias through an EM algorithm. Based on simulations, Strawberry outperforms Cufflinks and StringTie in terms of both assembly and quantification accuracies. Under the evaluation of a real data set, the estimated transcript expression by Strawberry has the highest correlation with Nanostring probe counts, an independent experiment measure for transcript expression. Availability: Strawberry is written in C++14, and is available as open source software at https://github.com/ruolin/strawberry under the MIT license.
      PubDate: 2017-11-27T22:00:00Z
      DOI: 10.1371/journal.pcbi.1005851
       
  • Initial-state-dependent, robust, transient neural dynamics encode
           conscious visual perception

    • Authors: Alexis T. Baria Brian Maniscalco Biyu J. He
      Abstract: by Alexis T. Baria, Brian Maniscalco, Biyu J. HeRecent research has identified late-latency, long-lasting neural activity as a robust correlate of conscious perception. Yet, the dynamical nature of this activity is poorly understood, and the mechanisms governing its presence or absence and the associated conscious perception remain elusive. We applied dynamic-pattern analysis to whole-brain slow (< 5 Hz) cortical dynamics recorded by magnetoencephalography (MEG) in human subjects performing a threshold-level visual perception task. Up to 1 second before stimulus onset, brain activity pattern across widespread cortices significantly predicted whether a threshold-level visual stimulus was later consciously perceived. This initial state of brain activity interacts nonlinearly with stimulus input to shape the evolving cortical activity trajectory, with seen and unseen trials following well separated trajectories. We observed that cortical activity trajectories during conscious perception are fast evolving and robust to small variations in the initial state. In addition, spontaneous brain activity pattern prior to stimulus onset also influences unconscious perceptual making in unseen trials. Together, these results suggest that brain dynamics underlying conscious visual perception belongs to the class of initial-state-dependent, robust, transient neural dynamics.
      PubDate: 2017-11-27T22:00:00Z
      DOI: 10.1371/journal.pcbi.1005806
       
 
 
JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
Fax: +00 44 (0)131 4513327
 
Home (Search)
Subjects A-Z
Publishers A-Z
Customise
APIs
Your IP address: 54.196.182.102
 
About JournalTOCs
API
Help
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-2016