Key result
A novel genetic algorithm protocol successfully developed an in silico hiPSC-CM biophysical model that accurately recapitulated experimental action potential parameters and ionic currents.
Why the study?
Formulating in silico biophysical models requires optimization strategies to reproduce experimental phenomena, and robust nonlinear regressive methods are crucial for high-fidelity electrophysiological modeling of hiPSC-CMs.
Population
Experimental data for five ionic currents recorded in hiPSC-CMs
Comparison
Novel genetic algorithm recipe to formulate an in silico biophysical model
Design
In silico biophysical modeling study
Authors
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Advances hiPSC-CM in silico modeling; leaves open validation for clinical translation.
A novel genetic algorithm protocol successfully developed an in silico hiPSC-CM biophysical model that accurately reproduces experimental action potential metrics, providing a tool for cardiac safety pharmacology and the study of inherited cardiac disorders.
Akwaboah et al. (2021) studied Computational modeling of human induced pluripotent stem cell-derived cardiomyocytes. Genetic Algorithm-based parameter fitting vs. Experimental patch-clamp data was evaluated on Goodness of fit (R2) and reproduction of action potential metrics. A novel genetic algorithm protocol successfully developed an in silico hiPSC-CM biophysical model that accurately recapitulated experimental action potential parameters and ionic currents.
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