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class="control is-expanded"> <label for="query" class="hidden-label">Search term or terms</label> <input class="input is-medium" id="query" name="query" placeholder="Search term..." type="text" value="Houston"> </div> <div class="select control is-medium"> <label class="is-hidden" for="searchtype">Field</label> <select class="is-medium" id="searchtype" name="searchtype"><option value="all">All fields</option><option value="title">Title</option><option selected value="author">Author(s)</option><option value="abstract">Abstract</option><option value="comments">Comments</option><option value="journal_ref">Journal reference</option><option value="acm_class">ACM classification</option><option value="msc_class">MSC classification</option><option value="report_num">Report number</option><option value="paper_id">arXiv identifier</option><option value="doi">DOI</option><option value="orcid">ORCID</option><option value="license">License (URI)</option><option value="author_id">arXiv author 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name="searchtype"><option value="all">All fields</option><option value="title">Title</option><option selected value="author">Author(s)</option><option value="abstract">Abstract</option><option value="comments">Comments</option><option value="journal_ref">Journal reference</option><option value="acm_class">ACM classification</option><option value="msc_class">MSC classification</option><option value="report_num">Report number</option><option value="paper_id">arXiv identifier</option><option value="doi">DOI</option><option value="orcid">ORCID</option><option value="license">License (URI)</option><option value="author_id">arXiv author ID</option><option value="help">Help pages</option><option value="full_text">Full text</option></select> <input id="query" name="query" type="text" value="Houston"> <ul id="abstracts"><li><input checked id="abstracts-0" name="abstracts" type="radio" value="show"> <label for="abstracts-0">Show abstracts</label></li><li><input id="abstracts-1" name="abstracts" type="radio" value="hide"> <label for="abstracts-1">Hide abstracts</label></li></ul> </div> <div class="box field is-grouped is-grouped-multiline level-item"> <div class="control"> <span class="select is-small"> <select id="size" name="size"><option value="25">25</option><option selected value="50">50</option><option value="100">100</option><option value="200">200</option></select> </span> <label for="size">results per page</label>. </div> <div class="control"> <label for="order">Sort results by</label> <span class="select is-small"> <select id="order" name="order"><option selected value="-announced_date_first">Announcement date (newest first)</option><option value="announced_date_first">Announcement date (oldest first)</option><option value="-submitted_date">Submission date (newest first)</option><option value="submitted_date">Submission date (oldest first)</option><option value="">Relevance</option></select> </span> </div> <div class="control"> <button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2503.18810">arXiv:2503.18810</a> <span> [<a href="https://arxiv.org/pdf/2503.18810">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Genomics">q-bio.GN</span> </div> </div> <p class="title is-5 mathjax"> Combining multiplexed functional data to improve variant classification </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Alliance%2C+A+o+V+E">Atlas of Variant Effects Alliance</a>, <a href="/search/q-bio?searchtype=author&query=%3A"> :</a>, <a href="/search/q-bio?searchtype=author&query=Calhoun%2C+J+D">Jeffrey D. Calhoun</a>, <a href="/search/q-bio?searchtype=author&query=Dawood%2C+M">Moez Dawood</a>, <a href="/search/q-bio?searchtype=author&query=Rowlands%2C+C+F">Charlie F. Rowlands</a>, <a href="/search/q-bio?searchtype=author&query=Fayer%2C+S">Shawn Fayer</a>, <a href="/search/q-bio?searchtype=author&query=Radford%2C+E+J">Elizabeth J. Radford</a>, <a href="/search/q-bio?searchtype=author&query=McEwen%2C+A+E">Abbye E. McEwen</a>, <a href="/search/q-bio?searchtype=author&query=Turnbull%2C+C">Clare Turnbull</a>, <a href="/search/q-bio?searchtype=author&query=Spurdle%2C+A+B">Amanda B. Spurdle</a>, <a href="/search/q-bio?searchtype=author&query=Starita%2C+L+M">Lea M. Starita</a>, <a href="/search/q-bio?searchtype=author&query=Jagannathan%2C+S">Sujatha Jagannathan</a>, <a href="/search/q-bio?searchtype=author&query=Ken"> Ken</a>, <a href="/search/q-bio?searchtype=author&query=Neurology%2C+R+D+D+o">Ruth Davee Department of Neurology</a>, <a href="/search/q-bio?searchtype=author&query=Medicine%2C+N+F+S+o">Northwestern Feinberg School of Medicine</a>, <a href="/search/q-bio?searchtype=author&query=Chicago"> Chicago</a>, <a href="/search/q-bio?searchtype=author&query=Illinois"> Illinois</a>, <a href="/search/q-bio?searchtype=author&query=Center%2C+H+G+S">Human Genome Sequencing Center</a>, <a href="/search/q-bio?searchtype=author&query=Medicine%2C+B+C+o">Baylor College of Medicine</a>, <a href="/search/q-bio?searchtype=author&query=Houston"> Houston</a>, <a href="/search/q-bio?searchtype=author&query=TX"> TX</a>, <a href="/search/q-bio?searchtype=author&query=USA"> USA</a>, <a href="/search/q-bio?searchtype=author&query=Molecular%2C+D+o">Department of Molecular</a>, <a href="/search/q-bio?searchtype=author&query=Genetics%2C+H">Human Genetics</a>, <a href="/search/q-bio?searchtype=author&query=Medicine%2C+B+C+o">Baylor College of Medicine</a> , et al. (66 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2503.18810v1-abstract-short" style="display: inline;"> With the surge in the number of variants of uncertain significance (VUS) reported in ClinVar in recent years, there is an imperative to resolve VUS at scale. Multiplexed assays of variant effect (MAVEs), which allow the functional consequence of 100s to 1000s of genetic variants to be measured in a single experiment, are emerging as a source of evidence which can be used for clinical gene variant… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.18810v1-abstract-full').style.display = 'inline'; document.getElementById('2503.18810v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2503.18810v1-abstract-full" style="display: none;"> With the surge in the number of variants of uncertain significance (VUS) reported in ClinVar in recent years, there is an imperative to resolve VUS at scale. Multiplexed assays of variant effect (MAVEs), which allow the functional consequence of 100s to 1000s of genetic variants to be measured in a single experiment, are emerging as a source of evidence which can be used for clinical gene variant classification. Increasingly, there are multiple published MAVEs for the same gene, sometimes measuring different aspects of variant impact. Where multiple functional consequences may need to be considered to get a more complete understanding of variant effects for a given gene, combining data from multiple MAVEs may lead to the assignment of increased evidence strength which could impact variant classifications. Here, we provide guidance for combining such multiplexed functional data, incorporating a stepwise process from data curation and collection to model generation and validation. We illustrate the potential of this approach by showing the integration of multiplexed functional data from four MAVEs for the gene TP53. By following these steps, researchers can maximize the value of MAVEs, strengthen the functional evidence for clinical variant classification, reclassify more VUS, and potentially uncover novel mechanisms of pathogenicity for clinically relevant genes. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.18810v1-abstract-full').style.display = 'none'; document.getElementById('2503.18810v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 24 March, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2001.10954">arXiv:2001.10954</a> <span> [<a href="https://arxiv.org/pdf/2001.10954">pdf</a>, <a href="https://arxiv.org/format/2001.10954">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Applications">stat.AP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computation">stat.CO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1098/rsta.2019.0339">10.1098/rsta.2019.0339 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Reducing complexity and unidentifiability when modelling human atrial cells </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Houston%2C+C">C. Houston</a>, <a href="/search/q-bio?searchtype=author&query=Marchand%2C+B">B. Marchand</a>, <a href="/search/q-bio?searchtype=author&query=Engelbert%2C+L">L. Engelbert</a>, <a href="/search/q-bio?searchtype=author&query=Cantwell%2C+C+D">C. D. Cantwell</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2001.10954v1-abstract-short" style="display: inline;"> Mathematical models of a cellular action potential in cardiac modelling have become increasingly complex, particularly in gating kinetics which control the opening and closing of individual ion channel currents. As cardiac models advance towards use in personalised medicine to inform clinical decision-making, it is critical to understand the uncertainty hidden in parameter estimates from their cal… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2001.10954v1-abstract-full').style.display = 'inline'; document.getElementById('2001.10954v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2001.10954v1-abstract-full" style="display: none;"> Mathematical models of a cellular action potential in cardiac modelling have become increasingly complex, particularly in gating kinetics which control the opening and closing of individual ion channel currents. As cardiac models advance towards use in personalised medicine to inform clinical decision-making, it is critical to understand the uncertainty hidden in parameter estimates from their calibration to experimental data. This study applies approximate Bayesian computation to re-calibrate the gating kinetics of four ion channels in two existing human atrial cell models to their original datasets, providing a measure of uncertainty and indication of potential issues with selecting a single unique value given the available experimental data. Two approaches are investigated to reduce the uncertainty present: re-calibrating the models to a more complete dataset and using a less complex formulation with fewer parameters to constrain. The re-calibrated models are inserted back into the full cell model to study the overall effect on the action potential. The use of more complete datasets does not eliminate uncertainty present in parameter estimates. The less complex model, particularly for the fast sodium current, gave a better fit to experimental data alongside lower parameter uncertainty and improved computational speed. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2001.10954v1-abstract-full').style.display = 'none'; document.getElementById('2001.10954v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 29 January, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">15 pages, 6 figures, submitted to Philosophical Transactions A</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2001.04230">arXiv:2001.04230</a> <span> [<a href="https://arxiv.org/pdf/2001.04230">pdf</a>, <a href="https://arxiv.org/format/2001.04230">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation">stat.CO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Applications">stat.AP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1098/rsta.2019.0349">10.1098/rsta.2019.0349 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Considering discrepancy when calibrating a mechanistic electrophysiology model </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Lei%2C+C+L">Chon Lok Lei</a>, <a href="/search/q-bio?searchtype=author&query=Ghosh%2C+S">Sanmitra Ghosh</a>, <a href="/search/q-bio?searchtype=author&query=Whittaker%2C+D+G">Dominic G. Whittaker</a>, <a href="/search/q-bio?searchtype=author&query=Aboelkassem%2C+Y">Yasser Aboelkassem</a>, <a href="/search/q-bio?searchtype=author&query=Beattie%2C+K+A">Kylie A. Beattie</a>, <a href="/search/q-bio?searchtype=author&query=Cantwell%2C+C+D">Chris D. Cantwell</a>, <a href="/search/q-bio?searchtype=author&query=Delhaas%2C+T">Tammo Delhaas</a>, <a href="/search/q-bio?searchtype=author&query=Houston%2C+C">Charles Houston</a>, <a href="/search/q-bio?searchtype=author&query=Novaes%2C+G+M">Gustavo Montes Novaes</a>, <a href="/search/q-bio?searchtype=author&query=Panfilov%2C+A+V">Alexander V. Panfilov</a>, <a href="/search/q-bio?searchtype=author&query=Pathmanathan%2C+P">Pras Pathmanathan</a>, <a href="/search/q-bio?searchtype=author&query=Riabiz%2C+M">Marina Riabiz</a>, <a href="/search/q-bio?searchtype=author&query=Santos%2C+R+W+d">Rodrigo Weber dos Santos</a>, <a href="/search/q-bio?searchtype=author&query=Walmsley%2C+J">John Walmsley</a>, <a href="/search/q-bio?searchtype=author&query=Worden%2C+K">Keith Worden</a>, <a href="/search/q-bio?searchtype=author&query=Mirams%2C+G+R">Gary R. Mirams</a>, <a href="/search/q-bio?searchtype=author&query=Wilkinson%2C+R+D">Richard D. Wilkinson</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2001.04230v2-abstract-short" style="display: inline;"> Uncertainty quantification (UQ) is a vital step in using mathematical models and simulations to take decisions. The field of cardiac simulation has begun to explore and adopt UQ methods to characterise uncertainty in model inputs and how that propagates through to outputs or predictions. In this perspective piece we draw attention to an important and under-addressed source of uncertainty in our pr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2001.04230v2-abstract-full').style.display = 'inline'; document.getElementById('2001.04230v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2001.04230v2-abstract-full" style="display: none;"> Uncertainty quantification (UQ) is a vital step in using mathematical models and simulations to take decisions. The field of cardiac simulation has begun to explore and adopt UQ methods to characterise uncertainty in model inputs and how that propagates through to outputs or predictions. In this perspective piece we draw attention to an important and under-addressed source of uncertainty in our predictions -- that of uncertainty in the model structure or the equations themselves. The difference between imperfect models and reality is termed model discrepancy, and we are often uncertain as to the size and consequences of this discrepancy. Here we provide two examples of the consequences of discrepancy when calibrating models at the ion channel and action potential scales. Furthermore, we attempt to account for this discrepancy when calibrating and validating an ion channel model using different methods, based on modelling the discrepancy using Gaussian processes (GPs) and autoregressive-moving-average (ARMA) models, then highlight the advantages and shortcomings of each approach. Finally, suggestions and lines of enquiry for future work are provided. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2001.04230v2-abstract-full').style.display = 'none'; document.getElementById('2001.04230v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 April, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 13 January, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">This version is published in Philosophical Transactions of the Royal Society A; Updated in response to reviewer comments, including: added details to the introduction, fixed mathematical notations for clarity, and moved the original Table 3 to the supplement to avoid confusion</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Phil. Trans. R. Soc. A. 378 (2020): 20190349 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1810.04227">arXiv:1810.04227</a> <span> [<a href="https://arxiv.org/pdf/1810.04227">pdf</a>, <a href="https://arxiv.org/format/1810.04227">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Dynamical Systems">math.DS</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Tissues and Organs">q-bio.TO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1016/j.compbiomed.2018.10.015">10.1016/j.compbiomed.2018.10.015 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Rethinking multiscale cardiac electrophysiology with machine learning and predictive modelling </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Cantwell%2C+C+D">Chris D. Cantwell</a>, <a href="/search/q-bio?searchtype=author&query=Mohamied%2C+Y">Yumnah Mohamied</a>, <a href="/search/q-bio?searchtype=author&query=Tzortzis%2C+K+N">Konstantinos N. Tzortzis</a>, <a href="/search/q-bio?searchtype=author&query=Garasto%2C+S">Stef Garasto</a>, <a href="/search/q-bio?searchtype=author&query=Houston%2C+C">Charles Houston</a>, <a href="/search/q-bio?searchtype=author&query=Chowdhury%2C+R+A">Rasheda A. Chowdhury</a>, <a href="/search/q-bio?searchtype=author&query=Ng%2C+F+S">Fu Siong Ng</a>, <a href="/search/q-bio?searchtype=author&query=Bharath%2C+A+A">Anil A. Bharath</a>, <a href="/search/q-bio?searchtype=author&query=Peters%2C+N+S">Nicholas S. Peters</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1810.04227v1-abstract-short" style="display: inline;"> We review some of the latest approaches to analysing cardiac electrophysiology data using machine learning and predictive modelling. Cardiac arrhythmias, particularly atrial fibrillation, are a major global healthcare challenge. Treatment is often through catheter ablation, which involves the targeted localized destruction of regions of the myocardium responsible for initiating or perpetuating the… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1810.04227v1-abstract-full').style.display = 'inline'; document.getElementById('1810.04227v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1810.04227v1-abstract-full" style="display: none;"> We review some of the latest approaches to analysing cardiac electrophysiology data using machine learning and predictive modelling. Cardiac arrhythmias, particularly atrial fibrillation, are a major global healthcare challenge. Treatment is often through catheter ablation, which involves the targeted localized destruction of regions of the myocardium responsible for initiating or perpetuating the arrhythmia. Ablation targets are either anatomically defined, or identified based on their functional properties as determined through the analysis of contact intracardiac electrograms acquired with increasing spatial density by modern electroanatomic mapping systems. While numerous quantitative approaches have been investigated over the past decades for identifying these critical curative sites, few have provided a reliable and reproducible advance in success rates. Machine learning techniques, including recent deep-learning approaches, offer a potential route to gaining new insight from this wealth of highly complex spatio-temporal information that existing methods struggle to analyse. Coupled with predictive modelling, these techniques offer exciting opportunities to advance the field and produce more accurate diagnoses and robust personalised treatment. We outline some of these methods and illustrate their use in making predictions from the contact electrogram and augmenting predictive modelling tools, both by more rapidly predicting future states of the system and by inferring the parameters of these models from experimental observations. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1810.04227v1-abstract-full').style.display = 'none'; document.getElementById('1810.04227v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 9 October, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2018. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1610.03521">arXiv:1610.03521</a> <span> [<a href="https://arxiv.org/pdf/1610.03521">pdf</a>, <a href="https://arxiv.org/format/1610.03521">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Tissues and Organs">q-bio.TO</span> </div> </div> <p class="title is-5 mathjax"> A Review of Mathematical Models for Muscular Dystrophy: A Systems Biology Approach </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Cameron%2C+A+N">Amanda N. Cameron</a>, <a href="/search/q-bio?searchtype=author&query=Houston%2C+M+T">Matthew T. Houston</a>, <a href="/search/q-bio?searchtype=author&query=Gutierrez%2C+J+B">Juan B. Gutierrez</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1610.03521v2-abstract-short" style="display: inline;"> Muscular dystrophy (MD) describes generalized progressive muscular weakness due to the wasting of muscle fibers. The progression of the disease is affected by known immunological and mechanical factors, and possibly other unknown mechanisms. These dynamics have begun to be elucidated in the last two decades. This article reviews mathematical models of MD that characterize molecular and cellular co… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1610.03521v2-abstract-full').style.display = 'inline'; document.getElementById('1610.03521v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1610.03521v2-abstract-full" style="display: none;"> Muscular dystrophy (MD) describes generalized progressive muscular weakness due to the wasting of muscle fibers. The progression of the disease is affected by known immunological and mechanical factors, and possibly other unknown mechanisms. These dynamics have begun to be elucidated in the last two decades. This article reviews mathematical models of MD that characterize molecular and cellular components implicated in MD progression. A biological background for these processes is also presented. Molecular effectors that contribute to MD include mitochondrial bioenergetics and genetic factors; both drive cellular metabolism, communication and signaling. These molecular events leave cells vulnerable to mechanical stress which can activate an immunological cascade that weakens cells and surrounding tissues. This review article lays the foundation for a systems biology approach to study MD progression. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1610.03521v2-abstract-full').style.display = 'none'; document.getElementById('1610.03521v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 October, 2016; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 11 October, 2016; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2016. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">23 pages, 2 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 92C42 </p> </li> </ol> <div class="is-hidden-tablet">  <span 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