Abstract
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.
Highlights
Over the past half century, mathematical models of cardiac electrophysiology came a long way in terms of complexity, precision, and the area of application
genetic algorithm (GA) signal-to-noise ratio (SNR) requirements are rather strict and hard to accomplish in a clinical setting, while mRNA expression profile is possible to measure from tissue biopsy
The immediate consequence of using steady state action potential (AP) waveform dependence on pacing cycle length (PCL) as GA input data is that output model AP should be steady state as well
Summary
Over the past half century, mathematical models of cardiac electrophysiology came a long way in terms of complexity, precision, and the area of application. Recent advances in computational cardiac electrophysiology make clinical application of computer models possible due to personalization of tissue geometry and fibers orientation [1]. While tissue-specific, person-specific and pathology-specific gene expression profiles affect AP waveform and propagation, these differences are usually not accounted for. The tissue-level simulations are usually based upon the same averaged single-cell elements of the model.
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