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Updated: Feb 22, 2026

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

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Published on: January 8, 2013

Efficient parameterization of cardiac action potential models using a genetic algorithm.

Darby I Cairns¹, Flavio H Fenton², E M Cherry¹

¹School of Mathematical Sciences, Rochester Institute of Technology, Rochester, New York 14623, USA.

Chaos (Woodbury, N.Y.)

|October 2, 2017

Summary

This summary is machine-generated.

Optimizing mathematical models of cardiac cells is feasible using genetic algorithms. This method efficiently parameterizes complex models, but parameter variability highlights identifiability challenges.

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Area of Science:

Computational Biology
Biophysics
Mathematical Modeling

Background:

Mathematical models of cardiac cells are crucial for understanding electrophysiology.
Parameter estimation for these complex models is a significant computational challenge.

Purpose of the Study:

To demonstrate an efficient method for parameterizing complex cardiac cell models.
To assess the performance and limitations of a genetic algorithm for model fitting.

Main Methods:

Simultaneous fitting of up to 27 parameters using a genetic algorithm.
Application to two flexible phenomenological models of cardiac action potentials.
Validation through 'model recovery' and experimental data fitting.

Main Results:

Efficient parameterization achieved in 30-40 seconds.
Good agreement between model predictions and underlying system dynamics.
Significant variability in parameter values, indicating identifiability and sensitivity issues.

Conclusions:

Genetic algorithms offer an efficient approach to parameterize cardiac cell models.
Model parameter identifiability and sensitivity require careful consideration.
Different model structures impact the ease of accurate parameterization.