Key result
Genetic algorithm pipeline improves iPSC-CM parameter estimates and channel block predictions via voltage and calcium calibration.
Why the study?
Existing mathematical models of iPSC-CM electrophysiology individually do not capture the phenotypic variability observed in iPSC-CMs resulting from maturation protocols and donor genetic backgrounds.
Population
In silico cells and human induced pluripotent stem cell-derived cardiomyocytes
Comparison
Calibrating models to voltage and calcium transient data under 3 varied experimental conditions vs baseline
Design
Computational model development and simulation study
Authors
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May advance personalized iPSC-CM modeling for arrhythmia studies; leaves open clinical translation.
A novel computational pipeline using genetic algorithms and fluorescence recordings under varied conditions can accurately calibrate cell-specific electrophysiological models of iPSC-CMs to predict arrhythmia susceptibility.
Yang et al. (2024) studied this question. Computational pipeline using genetic algorithm was evaluated on Model parameter estimates and predictions of unseen channel block responses. A computational pipeline using a genetic algorithm improved model parameter estimates and predictions of unseen channel block responses by calibrating to voltage and calcium transient data.
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