Creating cell-specific computational models of stem cell-derived cardiomyocytes using optical experiments

Area of Science:

• Cardiology
• Computational Biology
• Stem Cell Research

Background:

• Human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) are valuable for cardiac research but exhibit immature electrophysiology and phenotypic variability.
• Existing mathematical models of iPSC-CMs do not fully capture this observed variability.

Approach:

• Developed a computational pipeline using a genetic algorithm (GA) to calibrate iPSC-CM electrophysiological parameters.
• Optimized experimental protocols by simulating datasets from diverse *in silico* cell populations.
• Tested calibration using voltage and calcium transient data under varied conditions.

Key Points:

• Calibrating models with varied experimental data (pacing, ion channel blockade, buffer changes) improved parameter accuracy.
• Normalized fluorescence recordings, accessible and high-throughput, sufficiently informed conductance parameters.
• The pipeline can determine cell line-specific ion channel properties.

Conclusions:

• This computational approach addresses iPSC-CM electrophysiological immaturity and variability.
• Enables better understanding of mechanisms underlying perturbation responses in different iPSC-CM preparations.
• Facilitates more accurate cardiac modeling for disease and therapeutic research.