A computationally efficient dynamic model of human epicardial tissue

We present a new phenomenological model of human ventricular epicardial cells and we test its reentry dynamics. The model is derived from the Rogers-McCulloch formulation of the FitzHugh-Nagumo equations and represents the total ionic current divided into three contributions corresponding to the exc...

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Autores principales: Niccoló Biasi, Alessandro Tognetti
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Lenguaje:EN
Publicado: Public Library of Science (PLoS) 2021
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Acceso en línea:https://doaj.org/article/531e65dbf17d44a3bc350c2af6f1079f
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spelling oai:doaj.org-article:531e65dbf17d44a3bc350c2af6f1079f2021-11-04T06:19:41ZA computationally efficient dynamic model of human epicardial tissue1932-6203https://doaj.org/article/531e65dbf17d44a3bc350c2af6f1079f2021-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8547700/?tool=EBIhttps://doaj.org/toc/1932-6203We present a new phenomenological model of human ventricular epicardial cells and we test its reentry dynamics. The model is derived from the Rogers-McCulloch formulation of the FitzHugh-Nagumo equations and represents the total ionic current divided into three contributions corresponding to the excitatory, recovery and transient outward currents. Our model reproduces the main characteristics of human epicardial tissue, including action potential amplitude and morphology, upstroke velocity, and action potential duration and conduction velocity restitution curves. The reentry dynamics is stable, and the dominant period is about 270 ms, which is comparable to clinical values. The proposed model is the first phenomenological model able to accurately resemble human experimental data by using only 3 state variables and 17 parameters. Indeed, it is more computationally efficient than existing models (i.e., almost two times faster than the minimal ventricular model). Beyond the computational efficiency, the low number of parameters facilitates the process of fitting the model to the experimental data.Niccoló BiasiAlessandro TognettiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 10 (2021)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle Medicine
R
Science
Q
Niccoló Biasi
Alessandro Tognetti
A computationally efficient dynamic model of human epicardial tissue
description We present a new phenomenological model of human ventricular epicardial cells and we test its reentry dynamics. The model is derived from the Rogers-McCulloch formulation of the FitzHugh-Nagumo equations and represents the total ionic current divided into three contributions corresponding to the excitatory, recovery and transient outward currents. Our model reproduces the main characteristics of human epicardial tissue, including action potential amplitude and morphology, upstroke velocity, and action potential duration and conduction velocity restitution curves. The reentry dynamics is stable, and the dominant period is about 270 ms, which is comparable to clinical values. The proposed model is the first phenomenological model able to accurately resemble human experimental data by using only 3 state variables and 17 parameters. Indeed, it is more computationally efficient than existing models (i.e., almost two times faster than the minimal ventricular model). Beyond the computational efficiency, the low number of parameters facilitates the process of fitting the model to the experimental data.
format article
author Niccoló Biasi
Alessandro Tognetti
author_facet Niccoló Biasi
Alessandro Tognetti
author_sort Niccoló Biasi
title A computationally efficient dynamic model of human epicardial tissue
title_short A computationally efficient dynamic model of human epicardial tissue
title_full A computationally efficient dynamic model of human epicardial tissue
title_fullStr A computationally efficient dynamic model of human epicardial tissue
title_full_unstemmed A computationally efficient dynamic model of human epicardial tissue
title_sort computationally efficient dynamic model of human epicardial tissue
publisher Public Library of Science (PLoS)
publishDate 2021
url https://doaj.org/article/531e65dbf17d44a3bc350c2af6f1079f
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