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PLoS Computational Biology

Nikhil Bhagwat, Joseph D Viviano, Aristotle N Voineskos, M Mallar Chakravarty
Computational models predicting symptomatic progression at the individual level can be highly beneficial for early intervention and treatment planning for Alzheimer's disease (AD). Individual prognosis is complicated by many factors including the definition of the prediction objective itself. In this work, we present a computational framework comprising machine-learning techniques for 1) modeling symptom trajectories and 2) prediction of symptom trajectories using multimodal and longitudinal data. We perform primary analyses on three cohorts from Alzheimer's Disease Neuroimaging Initiative (ADNI), and a replication analysis using subjects from Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL)...
September 14, 2018: PLoS Computational Biology
Jan Poleszczuk, Malgorzata Debowska, Wojciech Dabrowski, Alicja Wojcik-Zaluska, Wojciech Zaluska, Jacek Waniewski
Risk of cardiovascular associated death in dialysis patients is the highest among all other co-morbidities. Improving the identification of patients with the highest cardiovascular risk to design an adequate treatment is, therefore, of utmost importance. There are several non-invasive cardiovascular state biomarkers based on the pulse (pressure) wave propagation properties, but their major determinants are not fully understood. In the current study we aimed to provide a framework to precisely dissect the information available in non-invasively recorded pulse wave in hemodialysis patients...
September 14, 2018: PLoS Computational Biology
Edward M Hill, Thomas House, Madhur S Dhingra, Wantanee Kalpravidh, Subhash Morzaria, Muzaffar G Osmani, Eric Brum, Mat Yamage, Md A Kalam, Diann J Prosser, John Y Takekawa, Xiangming Xiao, Marius Gilbert, Michael J Tildesley
In Bangladesh, the poultry industry is an economically and socially important sector, but it is persistently threatened by the effects of H5N1 highly pathogenic avian influenza. Thus, identifying the optimal control policy in response to an emerging disease outbreak is a key challenge for policy-makers. To inform this aim, a common approach is to carry out simulation studies comparing plausible strategies, while accounting for known capacity restrictions. In this study we perform simulations of a previously developed H5N1 influenza transmission model framework, fitted to two separate historical outbreaks, to assess specific control objectives related to the burden or duration of H5N1 outbreaks among poultry farms in the Dhaka division of Bangladesh...
September 13, 2018: PLoS Computational Biology
Francisco J Esteban, Javier Galadí, José A Langa, José R Portillo, Fernando Soler-Toscano
Integrated Information Theory (IIT) has become nowadays the most sensible general theory of consciousness. In addition to very important statements, it opens the door for an abstract (mathematical) formulation of the theory. Given a mechanism in a particular state, IIT identifies a conscious experience with a conceptual structure, an informational object which exists, is composed of identified parts, is informative, integrated and maximally irreducible. This paper introduces a space-time continuous version of the concept of integrated information...
September 13, 2018: PLoS Computational Biology
Emelie Flood, Céline Boiteux, Toby W Allen
Bacterial and human voltage-gated sodium channels (Navs) exhibit similar cation selectivity, despite their distinct EEEE and DEKA selectivity filter signature sequences. Recent high-resolution structures for bacterial Navs have allowed us to learn about ion conduction mechanisms in these simpler homo-tetrameric channels, but our understanding of the function of their mammalian counterparts remains limited. To probe these conduction mechanisms, a model of the human Nav1.2 channel has been constructed by grafting residues of its selectivity filter and external vestibular region onto the bacterial NavRh channel with atomic-resolution structure...
September 12, 2018: PLoS Computational Biology
Sergi Elizalde, Ashley M Laughney, Samuel F Bakhoum
Cancer cells frequently undergo chromosome missegregation events during mitosis, whereby the copies of a given chromosome are not distributed evenly among the two daughter cells, thus creating cells with heterogeneous karyotypes. A stochastic model tracing cellular karyotypes derived from clonal populations over hundreds of generations was recently developed and experimentally validated, and it was capable of predicting favorable karyotypes frequently observed in cancer. Here, we construct and study a Markov chain that precisely describes karyotypic evolution during clonally expanding cancer cell populations...
September 11, 2018: PLoS Computational Biology
Fridolin Gross, Paolo Bonaiuti, Silke Hauf, Andrea Ciliberto
The mitotic checkpoint (also called spindle assembly checkpoint) is a signaling pathway that ensures faithful chromosome segregation. Mitotic checkpoint proteins inhibit the anaphase-promoting complex (APC/C) and its activator Cdc20 to prevent precocious anaphase. Checkpoint signaling leads to a complex of APC/C, Cdc20, and checkpoint proteins, in which the APC/C is inactive. In principle, this final product of the mitotic checkpoint can be obtained via different pathways, whose relevance still needs to be fully ascertained experimentally...
September 10, 2018: PLoS Computational Biology
Tatu Pantsar, Sami Rissanen, Daniel Dauch, Tuomo Laitinen, Ilpo Vattulainen, Antti Poso
A mutated KRAS protein is frequently observed in human cancers. Traditionally, the oncogenic properties of KRAS missense mutants at position 12 (G12X) have been considered as equal. Here, by assessing the probabilities of occurrence of all KRAS G12X mutations and KRAS dynamics we show that this assumption does not hold true. Instead, our findings revealed an outstanding mutational bias. We conducted a thorough mutational analysis of KRAS G12X mutations and assessed to what extent the observed mutation frequencies follow a random distribution...
September 10, 2018: PLoS Computational Biology
Lia Papadopoulos, Pablo Blinder, Henrik Ronellenfitsch, Florian Klimm, Eleni Katifori, David Kleinfeld, Danielle S Bassett
Distribution networks-from vasculature to urban transportation pathways-are spatially embedded networks that must route resources efficiently in the face of pressures induced by the costs of building and maintaining network infrastructure. Such requirements are thought to constrain the topological and spatial organization of these systems, but at the same time, different kinds of distribution networks may exhibit variable architectural features within those general constraints. In this study, we use methods from network science to compare and contrast two classes of biological transport networks: mycelial fungi and vasculature from the surface of rodent brains...
September 7, 2018: PLoS Computational Biology
Ahmed Abdul Quadeer, David Morales-Jimenez, Matthew R McKay
Mutational correlation patterns found in population-level sequence data for the Human Immunodeficiency Virus (HIV) and the Hepatitis C Virus (HCV) have been demonstrated to be informative of viral fitness. Such patterns can be seen as footprints of the intrinsic functional constraints placed on viral evolution under diverse selective pressures. Here, considering multiple HIV and HCV proteins, we demonstrate that these mutational correlations encode a modular co-evolutionary structure that is tightly linked to the structural and functional properties of the respective proteins...
September 7, 2018: PLoS Computational Biology
Helmut Schmidt, Daniele Avitabile, Ernest Montbrió, Alex Roxin
Oscillatory activity robustly correlates with task demands during many cognitive tasks. However, not only are the network mechanisms underlying the generation of these rhythms poorly understood, but it is also still unknown to what extent they may play a functional role, as opposed to being a mere epiphenomenon. Here we study the mechanisms underlying the influence of oscillatory drive on network dynamics related to cognitive processing in simple working memory (WM), and memory recall tasks. Specifically, we investigate how the frequency of oscillatory input interacts with the intrinsic dynamics in networks of recurrently coupled spiking neurons to cause changes of state: the neuronal correlates of the corresponding cognitive process...
September 6, 2018: PLoS Computational Biology
Jay S Coggan, Daniel Keller, Corrado Calì, Heikki Lehväslaiho, Henry Markram, Felix Schürmann, Pierre J Magistretti
The mechanism of rapid energy supply to the brain, especially to accommodate the heightened metabolic activity of excited states, is not well-understood. We explored the role of glycogen as a fuel source for neuromodulation using the noradrenergic stimulation of glia in a computational model of the neural-glial-vasculature ensemble (NGV). The detection of norepinephrine (NE) by the astrocyte and the coupled cAMP signal are rapid and largely insensitive to the distance of the locus coeruleus projection release sites from the glia, implying a diminished impact for volume transmission in high affinity receptor transduction systems...
August 30, 2018: PLoS Computational Biology
Assaf Amitai, Arup K Chakraborty, Mehran Kardar
The spikes on virus surfaces bind receptors on host cells to propagate infection. High spike densities (SDs) can promote infection, but spikes are also targets of antibody-mediated immune responses. Thus, diverse evolutionary pressures can influence virus SDs. HIV's SD is about two orders of magnitude lower than that of other viruses, a surprising feature of unknown origin. By modeling antibody evolution through affinity maturation, we find that an intermediate SD maximizes the affinity of generated antibodies...
August 30, 2018: PLoS Computational Biology
Ilya Patrushev, Christina James-Zorn, Aldo Ciau-Uitz, Roger Patient, Michael J Gilchrist
The precise anatomical location of gene expression is an essential component of the study of gene function. For most model organisms this task is usually undertaken via visual inspection of gene expression images by interested researchers. Computational analysis of gene expression has been developed in several model organisms, notably in Drosophila which exhibits a uniform shape and outline in the early stages of development. Here we address the challenge of computational analysis of gene expression in Xenopus, where the range of developmental stages of interest encompasses a wide range of embryo size and shape...
August 29, 2018: PLoS Computational Biology
Itamar Eliakim, Zahi Cohen, Gabor Kosa, Yossi Yovel
Echolocating bats rely on active sound emission (echolocation) for mapping novel environments and navigating through them. Many theoretical frameworks have been suggested to explain how they do so, but few attempts have been made to build an actual robot that mimics their abilities. Here, we present the 'Robat'-a fully autonomous bat-like terrestrial robot that relies on echolocation to move through a novel environment while mapping it solely based on sound. Using the echoes reflected from the environment, the Robat delineates the borders of objects it encounters, and classifies them using an artificial neural-network, thus creating a rich map of its environment...
September 2018: PLoS Computational Biology
Weizhong Chen, Yi Liu, Shanshan Zhu, Guoyu Chen, Jing-Dong J Han
Combinatorial effects of epigenetic modifications on transcription activity have been proposed as "histone codes". However, it is unclear whether there also exist inter-nucleosomal communications among epigenetic modifications at single nucleosome level, and if so, what functional roles they play. Meanwhile, how clear nucleosome patterns, such as nucleosome phasing and depletion, are formed at functional regions remains an intriguing enigma. To address these questions, we developed a Bayesian network model for interactions among different histone modifications across neighboring nucleosomes, based on the framework of dynamic Bayesian network (DBN)...
September 2018: PLoS Computational Biology
Zeynep Ertem, Dorrie Raymond, Lauren Ancel Meyers
Forecasting the emergence and spread of influenza viruses is an important public health challenge. Timely and accurate estimates of influenza prevalence, particularly of severe cases requiring hospitalization, can improve control measures to reduce transmission and mortality. Here, we extend a previously published machine learning method for influenza forecasting to integrate multiple diverse data sources, including traditional surveillance data, electronic health records, internet search traffic, and social media activity...
September 2018: PLoS Computational Biology
Alexander J Titus, Audrey Flower, Patrick Hagerty, Paul Gamble, Charlie Lewis, Todd Stavish, Kevin P O'Connell, Greg Shipley, Stephanie M Rogers
Genomic data are becoming increasingly valuable as we develop methods to utilize the information at scale and gain a greater understanding of how genetic information relates to biological function. Advances in synthetic biology and the decreased cost of sequencing are increasing the amount of privately held genomic data. As the quantity and value of private genomic data grows, so does the incentive to acquire and protect such data, which creates a need to store and process these data securely. We present an algorithm for the Secure Interrogation of Genomic DataBases (SIG-DB)...
September 2018: PLoS Computational Biology
Davide Risso, Liam Purvis, Russell B Fletcher, Diya Das, John Ngai, Sandrine Dudoit, Elizabeth Purdom
Clustering of genes and/or samples is a common task in gene expression analysis. The goals in clustering can vary, but an important scenario is that of finding biologically meaningful subtypes within the samples. This is an application that is particularly appropriate when there are large numbers of samples, as in many human disease studies. With the increasing popularity of single-cell transcriptome sequencing (RNA-Seq), many more controlled experiments on model organisms are similarly creating large gene expression datasets with the goal of detecting previously unknown heterogeneity within cells...
September 2018: PLoS Computational Biology
David G Mets, Michael S Brainard
Studies of learning mechanisms critically depend on the ability to accurately assess learning outcomes. This assessment can be impeded by the often complex, multidimensional nature of behavior. We present a novel, automated approach to evaluating imitative learning. Conceptually, our approach estimates how much of the content present in a reference behavior is absent from the learned behavior. We validate our approach through examination of songbird vocalizations, complex learned behaviors the study of which has provided many insights into sensory-motor learning in general and vocal learning in particular...
August 2018: PLoS Computational Biology
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