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Cell-Specific Cardiac Electrophysiology Models

The traditional cardiac model-building paradigm involves constructing a composite model using data collected from many cells. Equations are derived for each relevant cellular component (e.g., ion channel, exchanger) independently. After the equations for all components are combined to form the compo...

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Autores principales: Groenendaal, Willemijn, Ortega, Francis A., Kherlopian, Armen R., Zygmunt, Andrew C., Krogh-Madsen, Trine, Christini, David J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4415772/
https://www.ncbi.nlm.nih.gov/pubmed/25928268
http://dx.doi.org/10.1371/journal.pcbi.1004242
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author Groenendaal, Willemijn
Ortega, Francis A.
Kherlopian, Armen R.
Zygmunt, Andrew C.
Krogh-Madsen, Trine
Christini, David J.
author_facet Groenendaal, Willemijn
Ortega, Francis A.
Kherlopian, Armen R.
Zygmunt, Andrew C.
Krogh-Madsen, Trine
Christini, David J.
author_sort Groenendaal, Willemijn
collection PubMed
description The traditional cardiac model-building paradigm involves constructing a composite model using data collected from many cells. Equations are derived for each relevant cellular component (e.g., ion channel, exchanger) independently. After the equations for all components are combined to form the composite model, a subset of parameters is tuned, often arbitrarily and by hand, until the model output matches a target objective, such as an action potential. Unfortunately, such models often fail to accurately simulate behavior that is dynamically dissimilar (e.g., arrhythmia) to the simple target objective to which the model was fit. In this study, we develop a new approach in which data are collected via a series of complex electrophysiology protocols from single cardiac myocytes and then used to tune model parameters via a parallel fitting method known as a genetic algorithm (GA). The dynamical complexity of the electrophysiological data, which can only be fit by an automated method such as a GA, leads to more accurately parameterized models that can simulate rich cardiac dynamics. The feasibility of the method is first validated computationally, after which it is used to develop models of isolated guinea pig ventricular myocytes that simulate the electrophysiological dynamics significantly better than does a standard guinea pig model. In addition to improving model fidelity generally, this approach can be used to generate a cell-specific model. By so doing, the approach may be useful in applications ranging from studying the implications of cell-to-cell variability to the prediction of intersubject differences in response to pharmacological treatment.
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spelling pubmed-44157722015-05-07 Cell-Specific Cardiac Electrophysiology Models Groenendaal, Willemijn Ortega, Francis A. Kherlopian, Armen R. Zygmunt, Andrew C. Krogh-Madsen, Trine Christini, David J. PLoS Comput Biol Research Article The traditional cardiac model-building paradigm involves constructing a composite model using data collected from many cells. Equations are derived for each relevant cellular component (e.g., ion channel, exchanger) independently. After the equations for all components are combined to form the composite model, a subset of parameters is tuned, often arbitrarily and by hand, until the model output matches a target objective, such as an action potential. Unfortunately, such models often fail to accurately simulate behavior that is dynamically dissimilar (e.g., arrhythmia) to the simple target objective to which the model was fit. In this study, we develop a new approach in which data are collected via a series of complex electrophysiology protocols from single cardiac myocytes and then used to tune model parameters via a parallel fitting method known as a genetic algorithm (GA). The dynamical complexity of the electrophysiological data, which can only be fit by an automated method such as a GA, leads to more accurately parameterized models that can simulate rich cardiac dynamics. The feasibility of the method is first validated computationally, after which it is used to develop models of isolated guinea pig ventricular myocytes that simulate the electrophysiological dynamics significantly better than does a standard guinea pig model. In addition to improving model fidelity generally, this approach can be used to generate a cell-specific model. By so doing, the approach may be useful in applications ranging from studying the implications of cell-to-cell variability to the prediction of intersubject differences in response to pharmacological treatment. Public Library of Science 2015-04-30 /pmc/articles/PMC4415772/ /pubmed/25928268 http://dx.doi.org/10.1371/journal.pcbi.1004242 Text en © 2015 Groenendaal et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Groenendaal, Willemijn
Ortega, Francis A.
Kherlopian, Armen R.
Zygmunt, Andrew C.
Krogh-Madsen, Trine
Christini, David J.
Cell-Specific Cardiac Electrophysiology Models
title Cell-Specific Cardiac Electrophysiology Models
title_full Cell-Specific Cardiac Electrophysiology Models
title_fullStr Cell-Specific Cardiac Electrophysiology Models
title_full_unstemmed Cell-Specific Cardiac Electrophysiology Models
title_short Cell-Specific Cardiac Electrophysiology Models
title_sort cell-specific cardiac electrophysiology models
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4415772/
https://www.ncbi.nlm.nih.gov/pubmed/25928268
http://dx.doi.org/10.1371/journal.pcbi.1004242
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