Genetic algorithm-based personalized models of human cardiac action potential

• DOI: 10.1371/journal.pone.0231695
• Publication Date: 2020-05-11T17:37:25Z
• Authors: Dmitrii Smirnov; Andrey Pikunov; Roman Syunyaev; Ruslan Deviatiiarov; Oleg Gusev; Kedar Aras; Anna Gams; Aaron Koppel; Igor R. Efimov
• Keywords: 0301 basic medicine; Patch-Clamp Techniques; Science; Heart Ventricles; Q; R; 610; Action Potentials; Gene Expression; Heart; Models, Biological; Tissue Donors; 03 medical and health sciences; Medicine; Heart Transplantation; Humans; Myocytes, Cardiac; RNA-Seq; Research Article

We present a novel modification of genetic algorithm (GA) which determines personalized parameters of cardiomyocyte electrophysiology model based on set of experimental human action potential (AP) recorded at different heart rates. In order to find the steady state solution, the optimized algorithm performs simultaneous search in the parametric and slow variables spaces. We demonstrate that several GA modifications are required for effective convergence. Firstly, we used Cauchy mutation along a random direction in the parametric space. Secondly, relatively large number of elite organisms (6-10% of the population passed on to new generation) was required for effective convergence. Test runs with synthetic AP as input data indicate that algorithm error is low for high amplitude ionic currents (1.6±1.6% for IKr, 3.2±3.5% for IK1, 3.9±3.5% for INa, 8.2±6.3% for ICaL). Experimental signal-to-noise ratio above 28 dB was required for high quality GA performance. GA was validated against optical mapping recordings of human ventricular AP and mRNA expression profile of donor hearts. In particular, GA output parameters were rescaled proportionally to mRNA levels ratio between patients. We have demonstrated that mRNA-based models predict the AP waveform dependence on heart rate with high precision. The latter also provides a novel technique of model personalization that makes it possible to map gene expression profile to cardiac function.