Genetic Algorithm For Fitting Cardiac Cell Biophysical Model Formulations.

Modeling cardiac cell electrophysiology relies on fitting model equations to experimental data obtained under voltage/current clamping conditions. The fitting procedure for these often-nonlinear ionic current equations are mostly executed by trial-and-error by hand or by gradient-based optimization approaches. These methods, though sometimes sufficient at converging at optimal solutions is based on the premise that the characteristic objective function is convex, which often does not apply to cardiac model equations. Meta-heuristic methods, such as evolutionary algorithms and particle swarm algorithms, have proven resilient against early convergence to local optima and saddle-point parameter solutions. This work presents a genetic algorithm-based approach for fitting the adult cardiomyocyte biophysical model formulations to the experimental data obtained in human induced pluripotent stem cell-derived cardiomyocyte (hiPSC-CM). Specifically, whole-cell patch clamp ionic current data of rapid delayed rectifier potassium current, I, transient outward potassium current, I and hyperpolarization-activated current, I, was used for fitting. Using a two-point crossover scheme along with initial population and mutation constraints randomly selected from a uniformly distributed constrained parameter space, near-optimal fitting was achieved with R values (n = 5) of 0.9960±0.0007, 0.9995±0.0002, and 0.9974±0.0014 for I, I and I respectively.