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Facilitating arrhythmia simulation: the method of quantitative cellular automata modeling and parallel running

BACKGROUND: Many arrhythmias are triggered by abnormal electrical activity at the ionic channel and cell level, and then evolve spatio-temporally within the heart. To understand arrhythmias better and to diagnose them more precisely by their ECG waveforms, a whole-heart model is required to explore...

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Detalles Bibliográficos
Autores principales: Zhu, Hao, Sun, Yan, Rajagopal, Gunaretnam, Mondry, Adrian, Dhar, Pawan
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2004
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC517726/
https://www.ncbi.nlm.nih.gov/pubmed/15339335
http://dx.doi.org/10.1186/1475-925X-3-29
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author Zhu, Hao
Sun, Yan
Rajagopal, Gunaretnam
Mondry, Adrian
Dhar, Pawan
author_facet Zhu, Hao
Sun, Yan
Rajagopal, Gunaretnam
Mondry, Adrian
Dhar, Pawan
author_sort Zhu, Hao
collection PubMed
description BACKGROUND: Many arrhythmias are triggered by abnormal electrical activity at the ionic channel and cell level, and then evolve spatio-temporally within the heart. To understand arrhythmias better and to diagnose them more precisely by their ECG waveforms, a whole-heart model is required to explore the association between the massively parallel activities at the channel/cell level and the integrative electrophysiological phenomena at organ level. METHODS: We have developed a method to build large-scale electrophysiological models by using extended cellular automata, and to run such models on a cluster of shared memory machines. We describe here the method, including the extension of a language-based cellular automaton to implement quantitative computing, the building of a whole-heart model with Visible Human Project data, the parallelization of the model on a cluster of shared memory computers with OpenMP and MPI hybrid programming, and a simulation algorithm that links cellular activity with the ECG. RESULTS: We demonstrate that electrical activities at channel, cell, and organ levels can be traced and captured conveniently in our extended cellular automaton system. Examples of some ECG waveforms simulated with a 2-D slice are given to support the ECG simulation algorithm. A performance evaluation of the 3-D model on a four-node cluster is also given. CONCLUSIONS: Quantitative multicellular modeling with extended cellular automata is a highly efficient and widely applicable method to weave experimental data at different levels into computational models. This process can be used to investigate complex and collective biological activities that can be described neither by their governing differentiation equations nor by discrete parallel computation. Transparent cluster computing is a convenient and effective method to make time-consuming simulation feasible. Arrhythmias, as a typical case, can be effectively simulated with the methods described.
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spelling pubmed-5177262004-09-19 Facilitating arrhythmia simulation: the method of quantitative cellular automata modeling and parallel running Zhu, Hao Sun, Yan Rajagopal, Gunaretnam Mondry, Adrian Dhar, Pawan Biomed Eng Online Research BACKGROUND: Many arrhythmias are triggered by abnormal electrical activity at the ionic channel and cell level, and then evolve spatio-temporally within the heart. To understand arrhythmias better and to diagnose them more precisely by their ECG waveforms, a whole-heart model is required to explore the association between the massively parallel activities at the channel/cell level and the integrative electrophysiological phenomena at organ level. METHODS: We have developed a method to build large-scale electrophysiological models by using extended cellular automata, and to run such models on a cluster of shared memory machines. We describe here the method, including the extension of a language-based cellular automaton to implement quantitative computing, the building of a whole-heart model with Visible Human Project data, the parallelization of the model on a cluster of shared memory computers with OpenMP and MPI hybrid programming, and a simulation algorithm that links cellular activity with the ECG. RESULTS: We demonstrate that electrical activities at channel, cell, and organ levels can be traced and captured conveniently in our extended cellular automaton system. Examples of some ECG waveforms simulated with a 2-D slice are given to support the ECG simulation algorithm. A performance evaluation of the 3-D model on a four-node cluster is also given. CONCLUSIONS: Quantitative multicellular modeling with extended cellular automata is a highly efficient and widely applicable method to weave experimental data at different levels into computational models. This process can be used to investigate complex and collective biological activities that can be described neither by their governing differentiation equations nor by discrete parallel computation. Transparent cluster computing is a convenient and effective method to make time-consuming simulation feasible. Arrhythmias, as a typical case, can be effectively simulated with the methods described. BioMed Central 2004-08-30 /pmc/articles/PMC517726/ /pubmed/15339335 http://dx.doi.org/10.1186/1475-925X-3-29 Text en Copyright © 2004 Zhu et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open-access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
Zhu, Hao
Sun, Yan
Rajagopal, Gunaretnam
Mondry, Adrian
Dhar, Pawan
Facilitating arrhythmia simulation: the method of quantitative cellular automata modeling and parallel running
title Facilitating arrhythmia simulation: the method of quantitative cellular automata modeling and parallel running
title_full Facilitating arrhythmia simulation: the method of quantitative cellular automata modeling and parallel running
title_fullStr Facilitating arrhythmia simulation: the method of quantitative cellular automata modeling and parallel running
title_full_unstemmed Facilitating arrhythmia simulation: the method of quantitative cellular automata modeling and parallel running
title_short Facilitating arrhythmia simulation: the method of quantitative cellular automata modeling and parallel running
title_sort facilitating arrhythmia simulation: the method of quantitative cellular automata modeling and parallel running
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC517726/
https://www.ncbi.nlm.nih.gov/pubmed/15339335
http://dx.doi.org/10.1186/1475-925X-3-29
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