Literature DB >> 28964122

A computational framework for testing arrhythmia marker sensitivities to model parameters in functionally calibrated populations of atrial cells.

Márcia R Vagos1, Hermenegild Arevalo1, Bernardo Lino de Oliveira1, Joakim Sundnes1, Mary M Maleckar1.   

Abstract

Models of cardiac cell electrophysiology are complex non-linear systems which can be used to gain insight into mechanisms of cardiac dynamics in both healthy and pathological conditions. However, the complexity of cardiac models can make mechanistic insight difficult. Moreover, these are typically fitted to averaged experimental data which do not incorporate the variability in observations. Recently, building populations of models to incorporate inter- and intra-subject variability in simulations has been combined with sensitivity analysis (SA) to uncover novel ionic mechanisms and potentially clarify arrhythmogenic behaviors. We used the Koivumäki human atrial cell model to create two populations, representing normal Sinus Rhythm (nSR) and chronic Atrial Fibrillation (cAF), by varying 22 key model parameters. In each population, 14 biomarkers related to the action potential and dynamic restitution were extracted. Populations were calibrated based on distributions of biomarkers to obtain reasonable physiological behavior, and subjected to SA to quantify correlations between model parameters and pro-arrhythmia markers. The two populations showed distinct behaviors under steady state and dynamic pacing. The nSR population revealed greater variability, and more unstable dynamic restitution, as compared to the cAF population, suggesting that simulated cAF remodeling rendered cells more stable to parameter variation and rate adaptation. SA revealed that the biomarkers depended mainly on five ionic currents, with noted differences in sensitivities to these between nSR and cAF. Also, parameters could be selected to produce a model variant with no alternans and unaltered action potential morphology, highlighting that unstable dynamical behavior may be driven by specific cell parameter settings. These results ultimately suggest that arrhythmia maintenance in cAF may not be due to instability in cell membrane excitability, but rather due to tissue-level effects which promote initiation and maintenance of reentrant arrhythmia.

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Year:  2017        PMID: 28964122     DOI: 10.1063/1.4999476

Source DB:  PubMed          Journal:  Chaos        ISSN: 1054-1500            Impact factor:   3.642


  7 in total

Review 1.  Computational Modeling of Electrophysiology and Pharmacotherapy of Atrial Fibrillation: Recent Advances and Future Challenges.

Authors:  Márcia Vagos; Ilsbeth G M van Herck; Joakim Sundnes; Hermenegild J Arevalo; Andrew G Edwards; Jussi T Koivumäki
Journal:  Front Physiol       Date:  2018-09-04       Impact factor: 4.566

2.  DENIS: Solving cardiac electrophysiological simulations with volunteer computing.

Authors:  Violeta Monasterio; Joel Castro-Mur; Jesús Carro
Journal:  PLoS One       Date:  2018-10-16       Impact factor: 3.240

Review 3.  Understanding AF Mechanisms Through Computational Modelling and Simulations.

Authors:  Konstantinos N Aronis; Rheeda L Ali; Jialiu A Liang; Shijie Zhou; Natalia A Trayanova
Journal:  Arrhythm Electrophysiol Rev       Date:  2019-07

4.  Multiscale Modeling of Dyadic Structure-Function Relation in Ventricular Cardiac Myocytes.

Authors:  Filippo G Cosi; Wolfgang Giese; Wilhelm Neubert; Stefan Luther; Nagaiah Chamakuri; Ulrich Parlitz; Martin Falcke
Journal:  Biophys J       Date:  2019-09-23       Impact factor: 4.033

5.  Mechanisms underlying pro-arrhythmic abnormalities arising from Pitx2-induced electrical remodelling: an in silico intersubject variability study.

Authors:  Yijie Zhu; Jieyun Bai; Andy Lo; Yaosheng Lu; Jichao Zhao
Journal:  Ann Transl Med       Date:  2021-01

Review 6.  How synergy between mechanistic and statistical models is impacting research in atrial fibrillation.

Authors:  Jieyun Bai; Yaosheng Lu; Huijin Wang; Jichao Zhao
Journal:  Front Physiol       Date:  2022-08-30       Impact factor: 4.755

Review 7.  A Heart for Diversity: Simulating Variability in Cardiac Arrhythmia Research.

Authors:  Haibo Ni; Stefano Morotti; Eleonora Grandi
Journal:  Front Physiol       Date:  2018-07-20       Impact factor: 4.566

  7 in total

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