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Gradient-based parameter optimization method to determine membrane ionic current composition in human induced

Hirohiko Kohjitani1, Shigeya Koda2, Yukiko Himeno2

  • 1Department of Cardiovascular Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan.

Scientific Reports
|November 9, 2022
PubMed
Summary
This summary is machine-generated.

This study validates a computational method to determine ionic channel function in human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs). The gradient-based optimization accurately recovers ionic conductance values, confirming its reliability for analyzing cardiac cell electrical activity.

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Area of Science:

  • Cardiology
  • Computational Biology
  • Stem Cell Biology

Background:

  • Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) exhibit heterogeneous action potentials (APs) due to varying membrane ionic current expression.
  • Accurate determination of ionic channel function is crucial for understanding and modeling cardiac electrophysiology in hiPSC-CMs.
  • Previous research suggests that identical AP configurations might arise from different combinations of ionic currents, posing challenges for inverse problem-solving.

Purpose of the Study:

  • To develop and computationally validate a method for determining whole-cell ionic conductance (Gx) patterns from individual spontaneous AP configurations in hiPSC-CMs.
  • To assess the feasibility and reliability of a gradient-based optimization method for estimating Gx values.
  • To investigate the potential for multiple solutions (local minima) in the inverse problem of Gx estimation.

Main Methods:

  • Generation of 'cell-specific models' by fitting a baseline model's AP output to experimental APs from hiPSC-CMs.
  • Randomization of Gx values for 4-6 major ionic currents within a ±5-15% range to create initial parameter sets.
  • Application of a gradient-based optimization algorithm to recover Gx values by minimizing the mean square error (MSE) between target and model APs.
  • Global search space analysis by randomizing Gx values over a 0.1-10 fold range to identify potential local minima in MSE.

Main Results:

  • The gradient-based optimization method demonstrated progressive convergence of randomized Gx populations towards the original cell-specific model values as MSE decreased.
  • Mapping the MSE across the global parameter space revealed a single global minimum, indicating the absence of other significant local minima.
  • The computational approach successfully recovered accurate ionic conductance values, confirming the uniqueness of the solution within the tested parameter space.

Conclusions:

  • The developed gradient-based optimization method is a feasible and reliable tool for determining ionic conductance patterns from hiPSC-CM action potentials.
  • The study confirms that for the tested models, the inverse problem of Gx estimation has a unique solution, mitigating concerns about non-identifiability.
  • This method provides a robust approach for characterizing ionic current expression in hiPSC-CMs, advancing their utility in disease modeling and drug screening.