Biology subjects

Syunyaev, R. A.

Publications and source records attributed to Syunyaev, R. A..

3 recordsLinked to original sources

bioRxiv2023

The role of structural vs cellular remodeling in arrhythmogenesis: personalized computer models of atrial fibrillation.

Atrial fibrillation (AF) is a progressive disease involving both structural and functional remodeling. To investigate the contribution of cell-scale functional remodeling to AF pathogenesis, we combined personalized 3D anatomical models with pathology-specific ionic models. The latter were developed using recordings in myocytes isolated from patients in sinus rhythm, paroxysmal, postoperative, and persistent AF. To quantify AF dynamics, we developed a novel algorithm for locating RDs by backtracking the conduction velocity field from the wavebreak regions. We demonstrate that our novel algorithm is at least 700 times faster than the traditional phase singularity analysis. The inducibility of simulated AF was not pathology-dependent, but pathological models demonstrate a more extensive arrhythmogenic substrate compared to the sinus rhythm. AF driver locations depend on electrophysiological remodeling; differences between pathology-specific models are explained by differences in wavebreak patterns. Specifically, RDs tend to dwell in the regions with the highest wavebreak probability.

physiology↗

bioRxiv2020

Personalization of mathematical models of human atrial action potential

Atrial cardiomyocytes demonstrate a wide spectrum of patient-specific, tissue-specific, and pathology-specific action potential (AP) phenotypes due to differences in protein expression and posttranslational modifications. Accurate simulation of the AP excitation and propagation in healthy or diseased atria requires a mathematical model capable of reproducing all the differences by parameter rescaling. In the present study, we have benchmarked two widely used electrophysiological models of the human atrium: the Maleckar and the Grandi models. In particular, patch-clamp AP recordings from human atrial myocytes were fitted by the genetic algorithm (GA) to test the models' versatility. We have shown that the Maleckar model results in a more accurate fitting of heart rate dependence of action potential duration (APD) and resting potential (RP). On the other hand, both models demonstrate the poor fitting of the plateau phase and spike-and-dome morphologies. We propose that modifications to L-type calcium current-voltage relationships are required to improve atrial models' fidelity.

biophysics↗

bioRxiv2019

Genetic algorithm-based personalized models of human cardiac action potential

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 IKr, 7% for IK1 and INa and 13% for ICaL. 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.

biophysics↗