Key result
A novel genetic algorithm-based approach accurately determined cardiomyocyte model parameters from human action potential recordings, with algorithm error below 5% for IKr and AP duration error <16 ms.
A novel genetic algorithm-based approach accurately determines cardiomyocyte model parameters from experimental human action potential recordings, enabling personalized cardiac electrophysiology modeling.
Hypothesis-generating for personalized cardiomyocyte modeling; should not change practice without validation.
We present a novel genetic algorithm-based solution to determine the set of cardiomyocyte model parameters based on experimental human action potential (AP) recordings. The novel approach is based on AP waveform dependence on the heart rate. In order to find the steady-state solution, optimized parameters include conductivities of ionic channels and exchangers augmented by slow variables, intracellular sodium concentration and sarcoplasmic reticulum calcium load. The algorithm is enhanced by a novel mutation operator, based on Cauchy distribution along a random direction in parameter space. We also demonstrate that increasing the number of elite organisms up to 7% results in faster convergence. Test runs indicate that algorithm error is below 5% for I Kr , 7% for I K1 and I Na and 13% for I CaL . Experimental signal-to-noise ratio above 28 dB was sufficient for high quality algorithm performance. The algorithm validation using optical mapping recordings of human ventricular AP demonstrated low error that was below 6 mV for AP waveform and less than 16 ms for AP duration. Further validation of the personalized models was done using mRNA expression profile of two donor hearts. The mRNA-based model reproduced AP waveform dependence on cycle length with 13 mV accuracy and AP duration with 20 ms accuracy.
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Смирнов et al. (2019) studied Human cardiac action potential modeling (n=2). Genetic algorithm-based personalized models was evaluated on Algorithm error for ionic channels and AP waveform/duration. A novel genetic algorithm-based approach accurately determined cardiomyocyte model parameters from human action potential recordings, with algorithm error below 5% for IKr and AP duration error <16 ms.
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