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Journal Cover PLoS Computational Biology
  [SJR: 3.405]   [H-I: 112]   [140 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]
  • A quadratically regularized functional canonical correlation analysis for
           identifying the global structure of pleiotropy with NGS data

    • Authors: Nan Lin Yun Zhu Ruzong Fan Momiao Xiong
      Abstract: by Nan Lin, Yun Zhu, Ruzong Fan, Momiao XiongInvestigating the pleiotropic effects of genetic variants can increase statistical power, provide important information to achieve deep understanding of the complex genetic structures of disease, and offer powerful tools for designing effective treatments with fewer side effects. However, the current multiple phenotype association analysis paradigm lacks breadth (number of phenotypes and genetic variants jointly analyzed at the same time) and depth (hierarchical structure of phenotype and genotypes). A key issue for high dimensional pleiotropic analysis is to effectively extract informative internal representation and features from high dimensional genotype and phenotype data. To explore correlation information of genetic variants, effectively reduce data dimensions, and overcome critical barriers in advancing the development of novel statistical methods and computational algorithms for genetic pleiotropic analysis, we proposed a new statistic method referred to as a quadratically regularized functional CCA (QRFCCA) for association analysis which combines three approaches: (1) quadratically regularized matrix factorization, (2) functional data analysis and (3) canonical correlation analysis (CCA). Large-scale simulations show that the QRFCCA has a much higher power than that of the ten competing statistics while retaining the appropriate type 1 errors. To further evaluate performance, the QRFCCA and ten other statistics are applied to the whole genome sequencing dataset from the TwinsUK study. We identify a total of 79 genes with rare variants and 67 genes with common variants significantly associated with the 46 traits using QRFCCA. The results show that the QRFCCA substantially outperforms the ten other statistics.
      PubDate: 2017-10-17T21:00:00Z
      DOI: 10.1371/journal.pcbi.1005788
       
  • A machine learning approach for predicting CRISPR-Cas9 cleavage
           efficiencies and patterns underlying its mechanism of action

    • Authors: Shiran Abadi Winston X. Yan David Amar Itay Mayrose
      Abstract: by Shiran Abadi, Winston X. Yan, David Amar, Itay MayroseThe adaptation of the CRISPR-Cas9 system as a genome editing technique has generated much excitement in recent years owing to its ability to manipulate targeted genes and genomic regions that are complementary to a programmed single guide RNA (sgRNA). However, the efficacy of a specific sgRNA is not uniquely defined by exact sequence homology to the target site, thus unintended off-targets might additionally be cleaved. Current methods for sgRNA design are mainly concerned with predicting off-targets for a given sgRNA using basic sequence features and employ elementary rules for ranking possible sgRNAs. Here, we introduce CRISTA (CRISPR Target Assessment), a novel algorithm within the machine learning framework that determines the propensity of a genomic site to be cleaved by a given sgRNA. We show that the predictions made with CRISTA are more accurate than other available methodologies. We further demonstrate that the occurrence of bulges is not a rare phenomenon and should be accounted for in the prediction process. Beyond predicting cleavage efficiencies, the learning process provides inferences regarding patterns that underlie the mechanism of action of the CRISPR-Cas9 system. We discover that attributes that describe the spatial structure and rigidity of the entire genomic site as well as those surrounding the PAM region are a major component of the prediction capabilities.
      PubDate: 2017-10-16T21:00:00Z
      DOI: 10.1371/journal.pcbi.1005807
       
  • Clusternomics: Integrative context-dependent clustering for heterogeneous
           datasets

    • Authors: Evelina Gabasova John Reid Lorenz Wernisch
      Abstract: by Evelina Gabasova, John Reid, Lorenz WernischIntegrative clustering is used to identify groups of samples by jointly analysing multiple datasets describing the same set of biological samples, such as gene expression, copy number, methylation etc. Most existing algorithms for integrative clustering assume that there is a shared consistent set of clusters across all datasets, and most of the data samples follow this structure. However in practice, the structure across heterogeneous datasets can be more varied, with clusters being joined in some datasets and separated in others. In this paper, we present a probabilistic clustering method to identify groups across datasets that do not share the same cluster structure. The proposed algorithm, Clusternomics, identifies groups of samples that share their global behaviour across heterogeneous datasets. The algorithm models clusters on the level of individual datasets, while also extracting global structure that arises from the local cluster assignments. Clusters on both the local and the global level are modelled using a hierarchical Dirichlet mixture model to identify structure on both levels. We evaluated the model both on simulated and on real-world datasets. The simulated data exemplifies datasets with varying degrees of common structure. In such a setting Clusternomics outperforms existing algorithms for integrative and consensus clustering. In a real-world application, we used the algorithm for cancer subtyping, identifying subtypes of cancer from heterogeneous datasets. We applied the algorithm to TCGA breast cancer dataset, integrating gene expression, miRNA expression, DNA methylation and proteomics. The algorithm extracted clinically meaningful clusters with significantly different survival probabilities. We also evaluated the algorithm on lung and kidney cancer TCGA datasets with high dimensionality, again showing clinically significant results and scalability of the algorithm.
      PubDate: 2017-10-16T21:00:00Z
      DOI: 10.1371/journal.pcbi.1005781
       
  • The role of glutamate in neuronal ion homeostasis: A case study of
           spreading depolarization

    • Authors: Niklas Hübel Mahshid S. Hosseini-Zare Jokūbas Žiburkus Ghanim Ullah
      Abstract: by Niklas Hübel, Mahshid S. Hosseini-Zare, Jokūbas Žiburkus, Ghanim UllahSimultaneous changes in ion concentrations, glutamate, and cell volume together with exchange of matter between cell network and vasculature are ubiquitous in numerous brain pathologies. A complete understanding of pathological conditions as well as normal brain function, therefore, hinges on elucidating the molecular and cellular pathways involved in these mostly interdependent variations. In this paper, we develop the first computational framework that combines the Hodgkin–Huxley type spiking dynamics, dynamic ion concentrations and glutamate homeostasis, neuronal and astroglial volume changes, and ion exchange with vasculature into a comprehensive model to elucidate the role of glutamate uptake in the dynamics of spreading depolarization (SD)—the electrophysiological event underlying numerous pathologies including migraine, ischemic stroke, aneurysmal subarachnoid hemorrhage, intracerebral hematoma, and trauma. We are particularly interested in investigating the role of glutamate in the duration and termination of SD caused by K+ perfusion and oxygen-glucose deprivation. Our results demonstrate that glutamate signaling plays a key role in the dynamics of SD, and that impaired glutamate uptake leads to recovery failure of neurons from SD. We confirm predictions from our model experimentally by showing that inhibiting astrocytic glutamate uptake using TFB-TBOA nearly quadruples the duration of SD in layers 2-3 of visual cortical slices from juvenile rats. The model equations are either derived purely from first physical principles of electroneutrality, osmosis, and conservation of particles or a combination of these principles and known physiological facts. Accordingly, we claim that our approach can be used as a future guide to investigate the role of glutamate, ion concentrations, and dynamics cell volume in other brain pathologies and normal brain function.
      PubDate: 2017-10-12T21:00:00Z
      DOI: 10.1371/journal.pcbi.1005804
       
  • Network approaches and applications in biology

    • Authors: Trey Ideker Ruth Nussinov
      Abstract: by Trey Ideker, Ruth Nussinov
      PubDate: 2017-10-12T21:00:00Z
      DOI: 10.1371/journal.pcbi.1005771
       
  • Ten simple rules for writing a response to reviewers

    • Authors: William Stafford Noble
      Abstract: by William Stafford Noble
      PubDate: 2017-10-12T21:00:00Z
      DOI: 10.1371/journal.pcbi.1005730
       
  • BeWith: A Between-Within method to discover relationships between cancer
           modules via integrated analysis of mutual exclusivity, co-occurrence and
           functional interactions

    • Authors: Phuong Dao Yoo-Ah Kim Damian Wojtowicz Sanna Madan Roded Sharan Teresa M. Przytycka
      Abstract: by Phuong Dao, Yoo-Ah Kim, Damian Wojtowicz, Sanna Madan, Roded Sharan, Teresa M. PrzytyckaThe analysis of the mutational landscape of cancer, including mutual exclusivity and co-occurrence of mutations, has been instrumental in studying the disease. We hypothesized that exploring the interplay between co-occurrence, mutual exclusivity, and functional interactions between genes will further improve our understanding of the disease and help to uncover new relations between cancer driving genes and pathways. To this end, we designed a general framework, BeWith, for identifying modules with different combinations of mutation and interaction patterns. We focused on three different settings of the BeWith schema: (i) BeME-WithFun, in which the relations between modules are enriched with mutual exclusivity, while genes within each module are functionally related; (ii) BeME-WithCo, which combines mutual exclusivity between modules with co-occurrence within modules; and (iii) BeCo-WithMEFun, which ensures co-occurrence between modules, while the within module relations combine mutual exclusivity and functional interactions. We formulated the BeWith framework using Integer Linear Programming (ILP), enabling us to find optimally scoring sets of modules. Our results demonstrate the utility of BeWith in providing novel information about mutational patterns, driver genes, and pathways. In particular, BeME-WithFun helped identify functionally coherent modules that might be relevant for cancer progression. In addition to finding previously well-known drivers, the identified modules pointed to other novel findings such as the interaction between NCOR2 and NCOA3 in breast cancer. Additionally, an application of the BeME-WithCo setting revealed that gene groups differ with respect to their vulnerability to different mutagenic processes, and helped us to uncover pairs of genes with potentially synergistic effects, including a potential synergy between mutations in TP53 and the metastasis related DCC gene. Overall, BeWith not only helped us uncover relations between potential driver genes and pathways, but also provided additional insights on patterns of the mutational landscape, going beyond cancer driving mutations. Implementation is available at https://www.ncbi.nlm.nih.gov/CBBresearch/Przytycka/software/bewith.html
      PubDate: 2017-10-12T21:00:00Z
      DOI: 10.1371/journal.pcbi.1005695
       
  • Identifying direct contacts between protein complex subunits from their
           conditional dependence in proteomics datasets

    • Authors: Kevin Drew Christian L. Müller Richard Bonneau Edward M. Marcotte
      Abstract: by Kevin Drew, Christian L. Müller, Richard Bonneau, Edward M. MarcotteDetermining the three dimensional arrangement of proteins in a complex is highly beneficial for uncovering mechanistic function and interpreting genetic variation in coding genes comprising protein complexes. There are several methods for determining co-complex interactions between proteins, among them co-fractionation / mass spectrometry (CF-MS), but it remains difficult to identify directly contacting subunits within a multi-protein complex. Correlation analysis of CF-MS profiles shows promise in detecting protein complexes as a whole but is limited in its ability to infer direct physical contacts among proteins in sub-complexes. To identify direct protein-protein contacts within human protein complexes we learn a sparse conditional dependency graph from approximately 3,000 CF-MS experiments on human cell lines. We show substantial performance gains in estimating direct interactions compared to correlation analysis on a benchmark of large protein complexes with solved three-dimensional structures. We demonstrate the method’s value in determining the three dimensional arrangement of proteins by making predictions for complexes without known structure (the exocyst and tRNA multi-synthetase complex) and by establishing evidence for the structural position of a recently discovered component of the core human EKC/KEOPS complex, GON7/C14ORF142, providing a more complete 3D model of the complex. Direct contact prediction provides easily calculable additional structural information for large-scale protein complex mapping studies and should be broadly applicable across organisms as more CF-MS datasets become available.
      PubDate: 2017-10-12T21:00:00Z
      DOI: 10.1371/journal.pcbi.1005625
       
  • Vicus: Exploiting local structures to improve network-based analysis of
           biological data

    • Authors: Bo Wang Lin Huang Yuke Zhu Anshul Kundaje Serafim Batzoglou Anna Goldenberg
      Abstract: by Bo Wang, Lin Huang, Yuke Zhu, Anshul Kundaje, Serafim Batzoglou, Anna GoldenbergBiological networks entail important topological features and patterns critical to understanding interactions within complicated biological systems. Despite a great progress in understanding their structure, much more can be done to improve our inference and network analysis. Spectral methods play a key role in many network-based applications. Fundamental to spectral methods is the Laplacian, a matrix that captures the global structure of the network. Unfortunately, the Laplacian does not take into account intricacies of the network’s local structure and is sensitive to noise in the network. These two properties are fundamental to biological networks and cannot be ignored. We propose an alternative matrix Vicus. The Vicus matrix captures the local neighborhood structure of the network and thus is more effective at modeling biological interactions. We demonstrate the advantages of Vicus in the context of spectral methods by extensive empirical benchmarking on tasks such as single cell dimensionality reduction, protein module discovery and ranking genes for cancer subtyping. Our experiments show that using Vicus, spectral methods result in more accurate and robust performance in all of these tasks.
      PubDate: 2017-10-12T21:00:00Z
      DOI: 10.1371/journal.pcbi.1005621
       
  • Systematic, network-based characterization of therapeutic target
           inhibitors

    • Authors: Yao Shen Mariano J. Alvarez Brygida Bisikirska Alexander Lachmann Ronald Realubit Sergey Pampou Jorida Coku Charles Karan Andrea Califano
      Abstract: by Yao Shen, Mariano J. Alvarez, Brygida Bisikirska, Alexander Lachmann, Ronald Realubit, Sergey Pampou, Jorida Coku, Charles Karan, Andrea CalifanoA large fraction of the proteins that are being identified as key tumor dependencies represent poor pharmacological targets or lack clinically-relevant small-molecule inhibitors. Availability of fully generalizable approaches for the systematic and efficient prioritization of tumor-context specific protein activity inhibitors would thus have significant translational value. Unfortunately, inhibitor effects on protein activity cannot be directly measured in systematic and proteome-wide fashion by conventional biochemical assays. We introduce OncoLead, a novel network based approach for the systematic prioritization of candidate inhibitors for arbitrary targets of therapeutic interest. In vitro and in vivo validation confirmed that OncoLead analysis can recapitulate known inhibitors as well as prioritize novel, context-specific inhibitors of difficult targets, such as MYC and STAT3. We used OncoLead to generate the first unbiased drug/regulator interaction map, representing compounds modulating the activity of cancer-relevant transcription factors, with potential in precision medicine.
      PubDate: 2017-10-12T21:00:00Z
      DOI: 10.1371/journal.pcbi.1005599
       
  • Network propagation in the cytoscape cyberinfrastructure

    • Authors: Daniel E. Carlin Barry Demchak Dexter Pratt Eric Sage Trey Ideker
      Abstract: by Daniel E. Carlin, Barry Demchak, Dexter Pratt, Eric Sage, Trey IdekerNetwork propagation is an important and widely used algorithm in systems biology, with applications in protein function prediction, disease gene prioritization, and patient stratification. However, up to this point it has required significant expertise to run. Here we extend the popular network analysis program Cytoscape to perform network propagation as an integrated function. Such integration greatly increases the access to network propagation by putting it in the hands of biologists and linking it to the many other types of network analysis and visualization available through Cytoscape. We demonstrate the power and utility of the algorithm by identifying mutations conferring resistance to Vemurafenib.
      PubDate: 2017-10-12T21:00:00Z
      DOI: 10.1371/journal.pcbi.1005598
       
  • Incorporating networks in a probabilistic graphical model to find drivers
           for complex human diseases

    • Authors: Aziz M. Mezlini Anna Goldenberg
      Abstract: by Aziz M. Mezlini, Anna GoldenbergDiscovering genetic mechanisms driving complex diseases is a hard problem. Existing methods often lack power to identify the set of responsible genes. Protein-protein interaction networks have been shown to boost power when detecting gene-disease associations. We introduce a Bayesian framework, Conflux, to find disease associated genes from exome sequencing data using networks as a prior. There are two main advantages to using networks within a probabilistic graphical model. First, networks are noisy and incomplete, a substantial impediment to gene discovery. Incorporating networks into the structure of a probabilistic models for gene inference has less impact on the solution than relying on the noisy network structure directly. Second, using a Bayesian framework we can keep track of the uncertainty of each gene being associated with the phenotype rather than returning a fixed list of genes. We first show that using networks clearly improves gene detection compared to individual gene testing. We then show consistently improved performance of Conflux compared to the state-of-the-art diffusion network-based method Hotnet2 and a variety of other network and variant aggregation methods, using randomly generated and literature-reported gene sets. We test Hotnet2 and Conflux on several network configurations to reveal biases and patterns of false positives and false negatives in each case. Our experiments show that our novel Bayesian framework Conflux incorporates many of the advantages of the current state-of-the-art methods, while offering more flexibility and improved power in many gene-disease association scenarios.
      PubDate: 2017-10-12T21:00:00Z
      DOI: 10.1371/journal.pcbi.1005580
       
  • Structural host-microbiota interaction networks

    • Authors: Emine Guven-Maiorov Chung-Jung Tsai Ruth Nussinov
      Abstract: by Emine Guven-Maiorov, Chung-Jung Tsai, Ruth NussinovHundreds of different species colonize multicellular organisms making them “metaorganisms”. A growing body of data supports the role of microbiota in health and in disease. Grasping the principles of host-microbiota interactions (HMIs) at the molecular level is important since it may provide insights into the mechanisms of infections. The crosstalk between the host and the microbiota may help resolve puzzling questions such as how a microorganism can contribute to both health and disease. Integrated superorganism networks that consider host and microbiota as a whole–may uncover their code, clarifying perhaps the most fundamental question: how they modulate immune surveillance. Within this framework, structural HMI networks can uniquely identify potential microbial effectors that target distinct host nodes or interfere with endogenous host interactions, as well as how mutations on either host or microbial proteins affect the interaction. Furthermore, structural HMIs can help identify master host cell regulator nodes and modules whose tweaking by the microbes promote aberrant activity. Collectively, these data can delineate pathogenic mechanisms and thereby help maximize beneficial therapeutics. To date, challenges in experimental techniques limit large-scale characterization of HMIs. Here we highlight an area in its infancy which we believe will increasingly engage the computational community: predicting interactions across kingdoms, and mapping these on the host cellular networks to figure out how commensal and pathogenic microbiota modulate the host signaling and broadly cross-species consequences.
      PubDate: 2017-10-12T21:00:00Z
      DOI: 10.1371/journal.pcbi.1005579
       
  • Women are underrepresented in computational biology: An analysis of the
           scholarly literature in biology, computer science and computational
           biology

    • Authors: Kevin S. Bonham Melanie I. Stefan
      Abstract: by Kevin S. Bonham, Melanie I. StefanWhile women are generally underrepresented in STEM fields, there are noticeable differences between fields. For instance, the gender ratio in biology is more balanced than in computer science. We were interested in how this difference is reflected in the interdisciplinary field of computational/quantitative biology. To this end, we examined the proportion of female authors in publications from the PubMed and arXiv databases. There are fewer female authors on research papers in computational biology, as compared to biology in general. This is true across authorship position, year, and journal impact factor. A comparison with arXiv shows that quantitative biology papers have a higher ratio of female authors than computer science papers, placing computational biology in between its two parent fields in terms of gender representation. Both in biology and in computational biology, a female last author increases the probability of other authors on the paper being female, pointing to a potential role of female PIs in influencing the gender balance.
      PubDate: 2017-10-12T21:00:00Z
      DOI: 10.1371/journal.pcbi.1005134
       
  • The structured ‘low temperature’ phase of the retinal
           population code

    • Authors: Mark L. Ioffe Michael J. Berry II
      Abstract: by Mark L. Ioffe, Michael J. Berry IIRecent advances in experimental techniques have allowed the simultaneous recordings of populations of hundreds of neurons, fostering a debate about the nature of the collective structure of population neural activity. Much of this debate has focused on the empirical findings of a phase transition in the parameter space of maximum entropy models describing the measured neural probability distributions, interpreting this phase transition to indicate a critical tuning of the neural code. Here, we instead focus on the possibility that this is a first-order phase transition which provides evidence that the real neural population is in a ‘structured’, collective state. We show that this collective state is robust to changes in stimulus ensemble and adaptive state. We find that the pattern of pairwise correlations between neurons has a strength that is well within the strongly correlated regime and does not require fine tuning, suggesting that this state is generic for populations of 100+ neurons. We find a clear correspondence between the emergence of a phase transition, and the emergence of attractor-like structure in the inferred energy landscape. A collective state in the neural population, in which neural activity patterns naturally form clusters, provides a consistent interpretation for our results.
      PubDate: 2017-10-11T21:00:00Z
      DOI: 10.1371/journal.pcbi.1005792
       
  • General principles of binding between cell surface receptors and
           multi-specific ligands: A computational study

    • Authors: Jiawen Chen Steven C. Almo Yinghao Wu
      Abstract: by Jiawen Chen, Steven C. Almo, Yinghao WuThe interactions between membrane receptors and extracellular ligands control cell-cell and cell-substrate adhesion, and environmental responsiveness by representing the initial steps of cell signaling pathways. These interactions can be spatial-temporally regulated when different extracellular ligands are tethered. The detailed mechanisms of this spatial-temporal regulation, including the competition between distinct ligands with overlapping binding sites and the conformational flexibility in multi-specific ligand assemblies have not been quantitatively evaluated. We present a new coarse-grained model to realistically simulate the binding process between multi-specific ligands and membrane receptors on cell surfaces. The model simplifies each receptor and each binding site in a multi-specific ligand as a rigid body. Different numbers or types of ligands are spatially organized together in the simulation. These designs were used to test the relation between the overall binding of a multi-specific ligand and the affinity of its cognate binding site. When a variety of ligands are exposed to cells expressing different densities of surface receptors, we demonstrated that ligands with reduced affinities have higher specificity to distinguish cells based on the relative concentrations of their receptors. Finally, modification of intramolecular flexibility was shown to play a role in optimizing the binding between receptors and ligands. In summary, our studies bring new insights to the general principles of ligand-receptor interactions. Future applications of our method will pave the way for new strategies to generate next-generation biologics.
      PubDate: 2017-10-10T21:00:00Z
      DOI: 10.1371/journal.pcbi.1005805
       
  • Neural coding in the visual system of Drosophila melanogaster: How do
           small neural populations support visually guided behaviours'

    • Authors: Alex D. M. Dewar Antoine Wystrach Andrew Philippides Paul Graham
      Abstract: by Alex D. M. Dewar, Antoine Wystrach, Andrew Philippides, Paul GrahamAll organisms wishing to survive and reproduce must be able to respond adaptively to a complex, changing world. Yet the computational power available is constrained by biology and evolution, favouring mechanisms that are parsimonious yet robust. Here we investigate the information carried in small populations of visually responsive neurons in Drosophila melanogaster. These so-called ‘ring neurons’, projecting to the ellipsoid body of the central complex, are reported to be necessary for complex visual tasks such as pattern recognition and visual navigation. Recently the receptive fields of these neurons have been mapped, allowing us to investigate how well they can support such behaviours. For instance, in a simulation of classic pattern discrimination experiments, we show that the pattern of output from the ring neurons matches observed fly behaviour. However, performance of the neurons (as with flies) is not perfect and can be easily improved with the addition of extra neurons, suggesting the neurons’ receptive fields are not optimised for recognising abstract shapes, a conclusion which casts doubt on cognitive explanations of fly behaviour in pattern recognition assays. Using artificial neural networks, we then assess how easy it is to decode more general information about stimulus shape from the ring neuron population codes. We show that these neurons are well suited for encoding information about size, position and orientation, which are more relevant behavioural parameters for a fly than abstract pattern properties. This leads us to suggest that in order to understand the properties of neural systems, one must consider how perceptual circuits put information at the service of behaviour.
      PubDate: 2017-10-10T21:00:00Z
      DOI: 10.1371/journal.pcbi.1005735
       
  • Clonal dominance and transplantation dynamics in hematopoietic stem cell
           compartments

    • Authors: Peter Ashcroft Markus G. Manz Sebastian Bonhoeffer
      Abstract: by Peter Ashcroft, Markus G. Manz, Sebastian BonhoefferHematopoietic stem cells in mammals are known to reside mostly in the bone marrow, but also transitively passage in small numbers in the blood. Experimental findings have suggested that they exist in a dynamic equilibrium, continuously migrating between these two compartments. Here we construct an individual-based mathematical model of this process, which is parametrised using existing empirical findings from mice. This approach allows us to quantify the amount of migration between the bone marrow niches and the peripheral blood. We use this model to investigate clonal hematopoiesis, which is a significant risk factor for hematologic cancers. We also analyse the engraftment of donor stem cells into non-conditioned and conditioned hosts, quantifying the impact of different treatment scenarios. The simplicity of the model permits a thorough mathematical analysis, providing deeper insights into the dynamics of both the model and of the real-world system. We predict the time taken for mutant clones to expand within a host, as well as chimerism levels that can be expected following transplantation therapy, and the probability that a preconditioned host is reconstituted by donor cells.
      PubDate: 2017-10-09T21:00:00Z
      DOI: 10.1371/journal.pcbi.1005803
       
  • Image-based model of the spectrin cytoskeleton for red blood cell
           simulation

    • Authors: Thomas G. Fai Alejandra Leo-Macias David L. Stokes Charles S. Peskin
      Abstract: by Thomas G. Fai, Alejandra Leo-Macias, David L. Stokes, Charles S. PeskinWe simulate deformable red blood cells in the microcirculation using the immersed boundary method with a cytoskeletal model that incorporates structural details revealed by tomographic images. The elasticity of red blood cells is known to be supplied by both their lipid bilayer membranes, which resist bending and local changes in area, and their cytoskeletons, which resist in-plane shear. The cytoskeleton consists of spectrin tetramers that are tethered to the lipid bilayer by ankyrin and by actin-based junctional complexes. We model the cytoskeleton as a random geometric graph, with nodes corresponding to junctional complexes and with edges corresponding to spectrin tetramers such that the edge lengths are given by the end-to-end distances between nodes. The statistical properties of this graph are based on distributions gathered from three-dimensional tomographic images of the cytoskeleton by a segmentation algorithm. We show that the elastic response of our model cytoskeleton, in which the spectrin polymers are treated as entropic springs, is in good agreement with the experimentally measured shear modulus. By simulating red blood cells in flow with the immersed boundary method, we compare this discrete cytoskeletal model to an existing continuum model and predict the extent to which dynamic spectrin network connectivity can protect against failure in the case of a red cell subjected to an applied strain. The methods presented here could form the basis of disease- and patient-specific computational studies of hereditary diseases affecting the red cell cytoskeleton.
      PubDate: 2017-10-09T21:00:00Z
      DOI: 10.1371/journal.pcbi.1005790
       
  • Object detection through search with a foveated visual system

    • Authors: Emre Akbas Miguel P. Eckstein
      Abstract: by Emre Akbas, Miguel P. EcksteinHumans and many other species sense visual information with varying spatial resolution across the visual field (foveated vision) and deploy eye movements to actively sample regions of interests in scenes. The advantage of such varying resolution architecture is a reduced computational, hence metabolic cost. But what are the performance costs of such processing strategy relative to a scheme that processes the visual field at high spatial resolution' Here we first focus on visual search and combine object detectors from computer vision with a recent model of peripheral pooling regions found at the V1 layer of the human visual system. We develop a foveated object detector that processes the entire scene with varying resolution, uses retino-specific object detection classifiers to guide eye movements, aligns its fovea with regions of interest in the input image and integrates observations across multiple fixations. We compared the foveated object detector against a non-foveated version of the same object detector which processes the entire image at with homogeneous high spatial resolution. We evaluated the accuracy of the foveated and non-foveated object detectors identifying 20 different objects classes in scenes from a standard computer vision data set (the PASCAL VOC 2007 dataset). We show that the foveated object detector can approximate the performance of the object detector with homogeneous high spatial resolution processing while bringing significant computational cost savings. Additionally, we assessed the impact of foveation on the computation of bottom-up saliency. An implementation of a simple foveated bottom-up saliency model with eye movements showed agreement in the selection of top salient regions of scenes with those selected by a non-foveated high resolution saliency model. Together, our results might help explain the evolution of foveated visual systems with eye movements as a solution that preserves perceptual performance in visual search while resulting in computational and metabolic savings to the brain.
      PubDate: 2017-10-09T21:00:00Z
      DOI: 10.1371/journal.pcbi.1005743
       
  • A Bayesian approach to modelling heterogeneous calcium responses in cell
           populations

    • Authors: Agne Tilūnaitė Wayne Croft Noah Russell Tomas C. Bellamy Rüdiger Thul
      Abstract: by Agne Tilūnaitė, Wayne Croft, Noah Russell, Tomas C. Bellamy, Rüdiger ThulCalcium responses have been observed as spikes of the whole-cell calcium concentration in numerous cell types and are essential for translating extracellular stimuli into cellular responses. While there are several suggestions for how this encoding is achieved, we still lack a comprehensive theory. To achieve this goal it is necessary to reliably predict the temporal evolution of calcium spike sequences for a given stimulus. Here, we propose a modelling framework that allows us to quantitatively describe the timing of calcium spikes. Using a Bayesian approach, we show that Gaussian processes model calcium spike rates with high fidelity and perform better than standard tools such as peri-stimulus time histograms and kernel smoothing. We employ our modelling concept to analyse calcium spike sequences from dynamically-stimulated HEK293T cells. Under these conditions, different cells often experience diverse stimuli time courses, which is a situation likely to occur in vivo. This single cell variability and the concomitant small number of calcium spikes per cell pose a significant modelling challenge, but we demonstrate that Gaussian processes can successfully describe calcium spike rates in these circumstances. Our results therefore pave the way towards a statistical description of heterogeneous calcium oscillations in a dynamic environment.
      PubDate: 2017-10-06T21:00:00Z
      DOI: 10.1371/journal.pcbi.1005794
       
  • The ins and outs of vanillyl alcohol oxidase: Identification of ligand
           migration paths

    • Authors: Gudrun Gygli Maria Fátima Lucas Victor Guallar Willem J. H. van Berkel
      Abstract: by Gudrun Gygli, Maria Fátima Lucas, Victor Guallar, Willem J. H. van BerkelVanillyl alcohol oxidase (VAO) is a homooctameric flavoenzyme belonging to the VAO/PCMH family. Each VAO subunit consists of two domains, the FAD-binding and the cap domain. VAO catalyses, among other reactions, the two-step conversion of p-creosol (2-methoxy-4-methylphenol) to vanillin (4-hydroxy-3-methoxybenzaldehyde). To elucidate how different ligands enter and exit the secluded active site, Monte Carlo based simulations have been performed. One entry/exit path via the subunit interface and two additional exit paths have been identified for phenolic ligands, all leading to the si side of FAD. We argue that the entry/exit path is the most probable route for these ligands. A fourth path leading to the re side of FAD has been found for the co-ligands dioxygen and hydrogen peroxide. Based on binding energies and on the behaviour of ligands in these four paths, we propose a sequence of events for ligand and co-ligand migration during catalysis. We have also identified two residues, His466 and Tyr503, which could act as concierges of the active site for phenolic ligands, as well as two other residues, Tyr51 and Tyr408, which could act as a gateway to the re side of FAD for dioxygen. Most of the residues in the four paths are also present in VAO’s closest relatives, eugenol oxidase and p-cresol methylhydroxylase. Key path residues show movements in our simulations that correspond well to conformations observed in crystal structures of these enzymes. Preservation of other path residues can be linked to the electron acceptor specificity and oligomerisation state of the three enzymes. This study is the first comprehensive overview of ligand and co-ligand migration in a member of the VAO/PCMH family, and provides a proof of concept for the use of an unbiased method to sample this process.
      PubDate: 2017-10-06T21:00:00Z
      DOI: 10.1371/journal.pcbi.1005787
       
  • Inferring oscillatory modulation in neural spike trains

    • Authors: Kensuke Arai Robert E. Kass
      Abstract: by Kensuke Arai, Robert E. KassOscillations are observed at various frequency bands in continuous-valued neural recordings like the electroencephalogram (EEG) and local field potential (LFP) in bulk brain matter, and analysis of spike-field coherence reveals that spiking of single neurons often occurs at certain phases of the global oscillation. Oscillatory modulation has been examined in relation to continuous-valued oscillatory signals, and independently from the spike train alone, but behavior or stimulus triggered firing-rate modulation, spiking sparseness, presence of slow modulation not locked to stimuli and irregular oscillations with large variability in oscillatory periods, present challenges to searching for temporal structures present in the spike train. In order to study oscillatory modulation in real data collected under a variety of experimental conditions, we describe a flexible point-process framework we call the Latent Oscillatory Spike Train (LOST) model to decompose the instantaneous firing rate in biologically and behaviorally relevant factors: spiking refractoriness, event-locked firing rate non-stationarity, and trial-to-trial variability accounted for by baseline offset and a stochastic oscillatory modulation. We also extend the LOST model to accommodate changes in the modulatory structure over the duration of the experiment, and thereby discover trial-to-trial variability in the spike-field coherence of a rat primary motor cortical neuron to the LFP theta rhythm. Because LOST incorporates a latent stochastic auto-regressive term, LOST is able to detect oscillations when the firing rate is low, the modulation is weak, and when the modulating oscillation has a broad spectral peak.
      PubDate: 2017-10-06T21:00:00Z
      DOI: 10.1371/journal.pcbi.1005596
       
  • A unifying Bayesian account of contextual effects in value-based choice

    • Authors: Francesco Rigoli Christoph Mathys Karl J. Friston Raymond J. Dolan
      Abstract: by Francesco Rigoli, Christoph Mathys, Karl J. Friston, Raymond J. DolanEmpirical evidence suggests the incentive value of an option is affected by other options available during choice and by options presented in the past. These contextual effects are hard to reconcile with classical theories and have inspired accounts where contextual influences play a crucial role. However, each account only addresses one or the other of the empirical findings and a unifying perspective has been elusive. Here, we offer a unifying theory of context effects on incentive value attribution and choice based on normative Bayesian principles. This formulation assumes that incentive value corresponds to a precision-weighted prediction error, where predictions are based upon expectations about reward. We show that this scheme explains a wide range of contextual effects, such as those elicited by other options available during choice (or within-choice context effects). These include both conditions in which choice requires an integration of multiple attributes and conditions where a multi-attribute integration is not necessary. Moreover, the same scheme explains context effects elicited by options presented in the past or between-choice context effects. Our formulation encompasses a wide range of contextual influences (comprising both within- and between-choice effects) by calling on Bayesian principles, without invoking ad-hoc assumptions. This helps clarify the contextual nature of incentive value and choice behaviour and may offer insights into psychopathologies characterized by dysfunctional decision-making, such as addiction and pathological gambling.
      PubDate: 2017-10-05T21:00:00Z
      DOI: 10.1371/journal.pcbi.1005769
       
  • Differential temperature sensitivity of synaptic and firing processes in a
           neural mass model of epileptic discharges explains heterogeneous response
           of experimental epilepsy to focal brain cooling

    • Authors: Jaymar Soriano Takatomi Kubo Takao Inoue Hiroyuki Kida Toshitaka Yamakawa Michiyasu Suzuki Kazushi Ikeda
      Abstract: by Jaymar Soriano, Takatomi Kubo, Takao Inoue, Hiroyuki Kida, Toshitaka Yamakawa, Michiyasu Suzuki, Kazushi IkedaExperiments with drug-induced epilepsy in rat brains and epileptic human brain region reveal that focal cooling can suppress epileptic discharges without affecting the brain’s normal neurological function. Findings suggest a viable treatment for intractable epilepsy cases via an implantable cooling device. However, precise mechanisms by which cooling suppresses epileptic discharges are still not clearly understood. Cooling experiments in vitro presented evidence of reduction in neurotransmitter release from presynaptic terminals and loss of dendritic spines at post-synaptic terminals offering a possible synaptic mechanism. We show that termination of epileptic discharges is possible by introducing a homogeneous temperature factor in a neural mass model which attenuates the post-synaptic impulse responses of the neuronal populations. This result however may be expected since such attenuation leads to reduced post-synaptic potential and when the effect on inhibitory interneurons is less than on excitatory interneurons, frequency of firing of pyramidal cells is consequently reduced. While this is observed in cooling experiments in vitro, experiments in vivo exhibit persistent discharges during cooling but suppressed in magnitude. This leads us to conjecture that reduction in the frequency of discharges may be compensated through intrinsic excitability mechanisms. Such compensatory mechanism is modelled using a reciprocal temperature factor in the firing response function in the neural mass model. We demonstrate that the complete model can reproduce attenuation of both magnitude and frequency of epileptic discharges during cooling. The compensatory mechanism suggests that cooling lowers the average and the variance of the distribution of threshold potential of firing across the population. Bifurcation study with respect to the temperature parameters of the model reveals how heterogeneous response of epileptic discharges to cooling (termination or suppression only) is exhibited. Possibility of differential temperature effects on post-synaptic potential generation of different populations is also explored.
      PubDate: 2017-10-05T21:00:00Z
      DOI: 10.1371/journal.pcbi.1005736
       
  • Ten simple rules in considering a career in academia versus government

    • Authors: Philip E. Bourne
      Abstract: by Philip E. Bourne
      PubDate: 2017-10-05T21:00:00Z
      DOI: 10.1371/journal.pcbi.1005729
       
  • Designing a course model for distance-based online bioinformatics training
           in Africa: The H3ABioNet experience

    • Authors: Kim T. Gurwitz Shaun Aron Sumir Panji Suresh Maslamoney Pedro L. Fernandes David P. Judge Amel Ghouila Jean-Baka Domelevo Entfellner Fatma Z. Guerfali Colleen Saunders Ahmed Mansour Alzohairy Samson P. Salifu Rehab Ahmed Ruben Cloete Jonathan Kayondo Deogratius Ssemwanga Nicola Mulder H3ABioNet Consortium's Education Training; Working Group as members of the H3Africa Consortium
      Abstract: by Kim T. Gurwitz, Shaun Aron, Sumir Panji, Suresh Maslamoney, Pedro L. Fernandes, David P. Judge, Amel Ghouila, Jean-Baka Domelevo Entfellner, Fatma Z. Guerfali, Colleen Saunders, Ahmed Mansour Alzohairy, Samson P. Salifu, Rehab Ahmed, Ruben Cloete, Jonathan Kayondo, Deogratius Ssemwanga, Nicola Mulder, H3ABioNet Consortium's Education Training and Working Group as members of the H3Africa Consortium Africa is not unique in its need for basic bioinformatics training for individuals from a diverse range of academic backgrounds. However, particular logistical challenges in Africa, most notably access to bioinformatics expertise and internet stability, must be addressed in order to meet this need on the continent. H3ABioNet (www.h3abionet.org), the Pan African Bioinformatics Network for H3Africa, has therefore developed an innovative, free-of-charge “Introduction to Bioinformatics” course, taking these challenges into account as part of its educational efforts to provide on-site training and develop local expertise inside its network. A multiple-delivery–mode learning model was selected for this 3-month course in order to increase access to (mostly) African, expert bioinformatics trainers. The content of the course was developed to include a range of fundamental bioinformatics topics at the introductory level. For the first iteration of the course (2016), classrooms with a total of 364 enrolled participants were hosted at 20 institutions across 10 African countries. To ensure that classroom success did not depend on stable internet, trainers pre-recorded their lectures, and classrooms downloaded and watched these locally during biweekly contact sessions. The trainers were available via video conferencing to take questions during contact sessions, as well as via online “question and discussion” forums outside of contact session time. This learning model, developed for a resource-limited setting, could easily be adapted to other settings.
      PubDate: 2017-10-05T21:00:00Z
      DOI: 10.1371/journal.pcbi.1005715
       
  • The evolutionary origins of Lévy walk foraging

    • Authors: Marina E. Wosniack Marcos C. Santos Ernesto P. Raposo Gandhi M. Viswanathan Marcos G. E. da Luz
      Abstract: by Marina E. Wosniack, Marcos C. Santos, Ernesto P. Raposo, Gandhi M. Viswanathan, Marcos G. E. da LuzWe study through a reaction-diffusion algorithm the influence of landscape diversity on the efficiency of search dynamics. Remarkably, the identical optimal search strategy arises in a wide variety of environments, provided the target density is sparse and the searcher’s information is restricted to its close vicinity. Our results strongly impact the current debate on the emergentist vs. evolutionary origins of animal foraging. The inherent character of the optimal solution (i.e., independent on the landscape for the broad scenarios assumed here) suggests an interpretation favoring the evolutionary view, as originally implied by the Lévy flight foraging hypothesis. The latter states that, under conditions of scarcity of information and sparse resources, some organisms must have evolved to exploit optimal strategies characterized by heavy-tailed truncated power-law distributions of move lengths. These results strongly suggest that Lévy strategies—and hence the selection pressure for the relevant adaptations—are robust with respect to large changes in habitat. In contrast, the usual emergentist explanation seems not able to explain how very similar Lévy walks can emerge from all the distinct non-Lévy foraging strategies that are needed for the observed large variety of specific environments. We also report that deviations from Lévy can take place in plentiful ecosystems, where locomotion truncation is very frequent due to high encounter rates. So, in this case normal diffusion strategies—performing as effectively as the optimal one—can naturally emerge from Lévy. Our results constitute the strongest theoretical evidence to date supporting the evolutionary origins of experimentally observed Lévy walks.
      PubDate: 2017-10-03T21:00:00Z
      DOI: 10.1371/journal.pcbi.1005774
       
  • Identifying parameter regions for multistationarity

    • Authors: Carsten Conradi Elisenda Feliu Maya Mincheva Carsten Wiuf
      Abstract: by Carsten Conradi, Elisenda Feliu, Maya Mincheva, Carsten WiufMathematical modelling has become an established tool for studying the dynamics of biological systems. Current applications range from building models that reproduce quantitative data to identifying systems with predefined qualitative features, such as switching behaviour, bistability or oscillations. Mathematically, the latter question amounts to identifying parameter values associated with a given qualitative feature. We introduce a procedure to partition the parameter space of a parameterized system of ordinary differential equations into regions for which the system has a unique or multiple equilibria. The procedure is based on the computation of the Brouwer degree, and it creates a multivariate polynomial with parameter depending coefficients. The signs of the coefficients determine parameter regions with and without multistationarity. A particular strength of the procedure is the avoidance of numerical analysis and parameter sampling. The procedure consists of a number of steps. Each of these steps might be addressed algorithmically using various computer programs and available software, or manually. We demonstrate our procedure on several models of gene transcription and cell signalling, and show that in many cases we obtain a complete partitioning of the parameter space with respect to multistationarity.
      PubDate: 2017-10-03T21:00:00Z
      DOI: 10.1371/journal.pcbi.1005751
       
  • Signatures of criticality arise from random subsampling in simple
           population models

    • Authors: Marcel Nonnenmacher Christian Behrens Philipp Berens Matthias Bethge Jakob H. Macke
      Abstract: by Marcel Nonnenmacher, Christian Behrens, Philipp Berens, Matthias Bethge, Jakob H. MackeThe rise of large-scale recordings of neuronal activity has fueled the hope to gain new insights into the collective activity of neural ensembles. How can one link the statistics of neural population activity to underlying principles and theories' One attempt to interpret such data builds upon analogies to the behaviour of collective systems in statistical physics. Divergence of the specific heat—a measure of population statistics derived from thermodynamics—has been used to suggest that neural populations are optimized to operate at a “critical point”. However, these findings have been challenged by theoretical studies which have shown that common inputs can lead to diverging specific heat. Here, we connect “signatures of criticality”, and in particular the divergence of specific heat, back to statistics of neural population activity commonly studied in neural coding: firing rates and pairwise correlations. We show that the specific heat diverges whenever the average correlation strength does not depend on population size. This is necessarily true when data with correlations is randomly subsampled during the analysis process, irrespective of the detailed structure or origin of correlations. We also show how the characteristic shape of specific heat capacity curves depends on firing rates and correlations, using both analytically tractable models and numerical simulations of a canonical feed-forward population model. To analyze these simulations, we develop efficient methods for characterizing large-scale neural population activity with maximum entropy models. We find that, consistent with experimental findings, increases in firing rates and correlation directly lead to more pronounced signatures. Thus, previous reports of thermodynamical criticality in neural populations based on the analysis of specific heat can be explained by average firing rates and correlations, and are not indicative of an optimized coding strategy. We conclude that a reliable interpretation of statistical tests for theories of neural coding is possible only in reference to relevant ground-truth models.
      PubDate: 2017-10-03T21:00:00Z
      DOI: 10.1371/journal.pcbi.1005718
       
 
 
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