An in silico hiPSC-Derived Cardiomyocyte Model Built With Genetic Algorithm.

Area of Science:

• Computational Biology
• Biophysics
• Cardiovascular Research

Background:

• Accurate in silico biophysical models are essential for understanding cellular electrophysiology.
• Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are increasingly used in cardiac safety pharmacology and disease modeling.
• Developing high-fidelity models for hiPSC-CMs requires robust optimization strategies.

Purpose of the Study:

• To demonstrate the effectiveness of heuristic techniques, specifically a novel genetic algorithm (GA) protocol, for formulating biophysical models.
• To develop a computational biophysical model of hiPSC-CMs using the proposed GA protocol.
• To validate the accuracy of the developed hiPSC-CM model by comparing its output with experimental data.

Main Methods:

• A novel genetic algorithm (GA) protocol was developed and applied to fit mathematical formulations to experimental data for five ionic currents in hiPSC-CMs.
• Maximum conductances of other ionic channels were scaled using literature recommendations.
• The GA protocol was used to optimize parameters for a hiPSC-CM biophysical model.

Main Results:

• Near-optimal parameter fitting was achieved for the GA-fitted ionic currents.
• The resulting hiPSC-CM model accurately recapitulated experimental action potential (AP) parameters, including AP durations (APD50, APD75, APD90), maximum diastolic potential, and automaticity frequency.
• The model demonstrated the potential of heuristic techniques in biophysical model formulation.

Conclusions:

• The study presents a novel GA protocol for formulating robust numerical biophysical models.
• The developed hiPSC-CM model validates the biophysics of these cells as substitutes for human cardiomyocytes.
• The findings have significant implications for cardiac safety pharmacology and the study of inherited cardiac disorders.