A Heart for Diversity: Simulating Variability in Cardiac Arrhythmia Research

Abstract

In cardiac electrophysiology, there exist many sources of inter- and intra-personal variability. These include variability in conditions and environment, and genotypic and molecular diversity, including differences in expression and behavior of ion channels and transporters, which lead to phenotypic diversity (e.g., variable integrated responses at the cell, tissue, and organ levels). These variabilities play an important role in progression of heart disease and arrhythmia syndromes and outcomes of therapeutic interventions. Yet, the traditional in silico framework for investigating cardiac arrhythmias is built upon a parameter/property-averaging approach that typically overlooks the physiological diversity. Inspired by work done in genetics and neuroscience, new modeling frameworks of cardiac electrophysiology have been recently developed that take advantage of modern computational capabilities and approaches, and account for the variance in the biological data they are intended to illuminate. In this review, we outline the recent advances in statistical and computational techniques that take into account physiological variability, and move beyond the traditional cardiac model-building scheme that involves averaging over samples from many individuals in the construction of a highly tuned composite model. We discuss how these advanced methods have harnessed the power of big (simulated) data to study the mechanisms of cardiac arrhythmias, with a special emphasis on atrial fibrillation, and improve the assessment of proarrhythmic risk and drug response. The challenges of using in silico approaches with variability are also addressed and future directions are proposed.

In silico study of the effects of anti-arrhythmic drug treatment on sinoatrial node function for patients with atrial fibrillation

“…In the present study, IC 50 values are chosen based on experimental data from atrial cells (where data are available) and large ventricular cells (where atrial data are not available). Thirdly, the uncertainty analysis is important for a more precise evaluation of the safety of antiarrhythmic drugs 25,78 . Therefore, our models and methodology should be improved, and statistics and treatment of uncertainties should be considered [78][79][80] and further investigated.…”

“…Thirdly, the uncertainty analysis is important for a more precise evaluation of the safety of antiarrhythmic drugs 25,78 . Therefore, our models and methodology should be improved, and statistics and treatment of uncertainties should be considered [78][79][80] and further investigated. Fourthly, the block effects of amiodarone on beta-adrenergic receptors were modelled with the same percentage decrease in the effects of (2020) 10:305 | https://doi.org/10.1038/s41598-019-57246-5 www.nature.com/scientificreports www.nature.com/scientificreports/ beta-adrenergic receptor stimulation on targets.…”

Simulation of the Effects of Extracellular Calcium Changes Leads to a Novel Computational Model of Human Ventricular Action Potential With a Revised Calcium Handling

“…In the last few decades, computational models have been increasingly used to study biological systems, due to the productive synergy between the in silico approach and the collection of experimental data, as well as to the improvement in computational resources. One of the most productive fields of application is cardiac physiology: modeling has provided physiological insights through the prediction of phenomena and mechanisms, later confirmed or disproved experimentally (e.g., Denis and Yoram, 2001;Carusi et al, 2012;Severi et al, 2014;Krogh-Madsen et al, 2016;Ni et al, 2018;Niederer et al, 2019).…”

A Population Modelling Approach to Studying Age-Related Effects on Excitation-Contraction Coupling in Human Cardiomyocytes

“…They enable potential cross-links to be revealed in the regulation of the cellular function in norm and pathology and the role of cellular mechanisms in the response to interventions to be evaluated [32]. Along with the use of generic cellular models reproducing the average attributes of a population, a "population of models" approach has been actively developed recently [33]. It allows one to account not only for the natural variability in the cellular function even within one heart chamber but also for other sources of uncertainty inherent in mathematical models for biological systems.…”

“…It allows one to account not only for the natural variability in the cellular function even within one heart chamber but also for other sources of uncertainty inherent in mathematical models for biological systems. This approach has been successfully applied to different research tasks such as model parameter identification [37], prediction of drug-induced cardiotoxicity [36,35], and other challenges [33].…”