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Machine learning meets Monte Carlo methods for models of muscle’s molecular machinery to classify mutations
The timing and magnitude of force generation by a muscle depend on complex interactions in a compliant, contractile filament lattice. Perturbations in these interactions can result in cardiac muscle diseases. In this study, we address the fundamental challenge of connecting the temporal features of...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Rockefeller University Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10067704/ https://www.ncbi.nlm.nih.gov/pubmed/37000171 http://dx.doi.org/10.1085/jgp.202213291 |
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author | Asencio, Anthony Malingen, Sage Kooiker, Kristina B. Powers, Joseph D. Davis, Jennifer Daniel, Thomas Moussavi-Harami, Farid |
author_facet | Asencio, Anthony Malingen, Sage Kooiker, Kristina B. Powers, Joseph D. Davis, Jennifer Daniel, Thomas Moussavi-Harami, Farid |
author_sort | Asencio, Anthony |
collection | PubMed |
description | The timing and magnitude of force generation by a muscle depend on complex interactions in a compliant, contractile filament lattice. Perturbations in these interactions can result in cardiac muscle diseases. In this study, we address the fundamental challenge of connecting the temporal features of cardiac twitches to underlying rate constants and their perturbations associated with genetic cardiomyopathies. Current state-of-the-art metrics for characterizing the mechanical consequence of cardiac muscle disease do not utilize information embedded in the complete time course of twitch force. We pair dimension reduction techniques and machine learning methods to classify underlying perturbations that shape the timing of twitch force. To do this, we created a large twitch dataset using a spatially explicit Monte Carlo model of muscle contraction. Uniquely, we modified the rate constants of this model in line with mouse models of cardiac muscle disease and varied mutation penetrance. Ultimately, the results of this study show that machine learning models combined with biologically informed dimension reduction techniques can yield excellent classification accuracy of underlying muscle perturbations. |
format | Online Article Text |
id | pubmed-10067704 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Rockefeller University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-100677042023-09-30 Machine learning meets Monte Carlo methods for models of muscle’s molecular machinery to classify mutations Asencio, Anthony Malingen, Sage Kooiker, Kristina B. Powers, Joseph D. Davis, Jennifer Daniel, Thomas Moussavi-Harami, Farid J Gen Physiol Article The timing and magnitude of force generation by a muscle depend on complex interactions in a compliant, contractile filament lattice. Perturbations in these interactions can result in cardiac muscle diseases. In this study, we address the fundamental challenge of connecting the temporal features of cardiac twitches to underlying rate constants and their perturbations associated with genetic cardiomyopathies. Current state-of-the-art metrics for characterizing the mechanical consequence of cardiac muscle disease do not utilize information embedded in the complete time course of twitch force. We pair dimension reduction techniques and machine learning methods to classify underlying perturbations that shape the timing of twitch force. To do this, we created a large twitch dataset using a spatially explicit Monte Carlo model of muscle contraction. Uniquely, we modified the rate constants of this model in line with mouse models of cardiac muscle disease and varied mutation penetrance. Ultimately, the results of this study show that machine learning models combined with biologically informed dimension reduction techniques can yield excellent classification accuracy of underlying muscle perturbations. Rockefeller University Press 2023-03-31 /pmc/articles/PMC10067704/ /pubmed/37000171 http://dx.doi.org/10.1085/jgp.202213291 Text en © 2023 Asencio et al. https://creativecommons.org/licenses/by-nc-sa/4.0/http://www.rupress.org/terms/This article is distributed under the terms of an Attribution–Noncommercial–Share Alike–No Mirror Sites license for the first six months after the publication date (see http://www.rupress.org/terms/). After six months it is available under a Creative Commons License (Attribution–Noncommercial–Share Alike 4.0 International license, as described at https://creativecommons.org/licenses/by-nc-sa/4.0/). |
spellingShingle | Article Asencio, Anthony Malingen, Sage Kooiker, Kristina B. Powers, Joseph D. Davis, Jennifer Daniel, Thomas Moussavi-Harami, Farid Machine learning meets Monte Carlo methods for models of muscle’s molecular machinery to classify mutations |
title | Machine learning meets Monte Carlo methods for models of muscle’s molecular machinery to classify mutations |
title_full | Machine learning meets Monte Carlo methods for models of muscle’s molecular machinery to classify mutations |
title_fullStr | Machine learning meets Monte Carlo methods for models of muscle’s molecular machinery to classify mutations |
title_full_unstemmed | Machine learning meets Monte Carlo methods for models of muscle’s molecular machinery to classify mutations |
title_short | Machine learning meets Monte Carlo methods for models of muscle’s molecular machinery to classify mutations |
title_sort | machine learning meets monte carlo methods for models of muscle’s molecular machinery to classify mutations |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10067704/ https://www.ncbi.nlm.nih.gov/pubmed/37000171 http://dx.doi.org/10.1085/jgp.202213291 |
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