A Computational Model of Human Ventricular Action Potentials Incorporating Experimental Variability Arising from CACNA1C mutations

Jieyun Bai1, Yaosheng Lu1, Kuanquan Wang2, Henggui Zhang3
1Department of Electronic Engineering, College of Information Science and Technology, Jinan University, 2School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China, 3University of Manchester


Mutations in the CACNA1C gene, encoding an alpha-1 subunit of a voltage-dependent calcium channel (ICaL), are associated with ventricular tachycardia. Variability in ICaL is one of the most daunting aspects of forecasting arrhythmia vulnerability in response to these inherited diseases (including TS: timothy syndrome, LQT: long QT, SQT: short QT, CHD: congenital heart defect, ERS: early repolarization syndrome, HCM: hypertrophic cardiomyopathy, SCD: sudden cardiac death, SSS: sick sinus syndrome, SUDY: sudden unexplained death of the young, and Brugada syndrome). Here, we aimed to utilize a computational framework to build CACNA1C mutations-induced cell populations that can be employed to capture genotype-phenotype relationships and pinpoint key sensitive parameters. Based on ICaL in the Ten Tusscher-Panfilov model, CACNA1C mutations-induced cell populations were generated by considering the wide variability of ICaL parameters on the maximum conductance, the equilibrium potential, half-voltage and slope factor of steady-state activation, the time constant of activation, half-voltage and slope factor of steady-state inactivation, the minimum value of inactivation, and time constants of slow and fast inactivation. Populations of AP models yielding I-V relationships of ICaL in range with experimental recordings in human ventricular myocytes under wild-type and mutant conditions were selected. The resulting population of AP models predicts robust inter-subject variability in both wild-type and mutant cells. This approach links molecular mechanisms to known cellular-level phenotypes by comparing wild-type and mutant populations of models to analyze the contributing factors underlying each phenotype.