Abstract

Mathematical models of cardiac cells have been established to broaden understanding of cardiac function. In the process of developing electrophysiological models for cardiac myocytes, precise parameter tuning is a crucial step. The membrane resistance (Rm) is an essential feature obtained from cardiac myocytes. This feature reflects intercellular coupling and affects important phenomena, such as conduction velocity, and early after-depolarizations, but it is often overlooked during the phase of parameter fitting. Thus, the traditional parameter fitting that only includes action potential (AP) waveform may yield incorrect values for Rm. In this paper, a novel multi-objective parameter fitting formulation is proposed and tested that includes different regions of the Rm profile as additional objective functions for optimization. As Rm depends on the transmembrane voltage (Vm) and exhibits singularities for some specific values of Vm, analyses are conducted to carefully select the regions of interest for the proper characterization of Rm. Non-dominated sorting genetic algorithm II is utilized to solve the proposed multi-objective optimization problem. To verify the efficacy of the proposed problem formulation, case studies and comparisons are carried out using multiple models of human cardiac ventricular cells. Results demonstrate Rm is correctly reproduced by the tuned cell models after considering the curve of Rm obtained from the late phase of repolarization and Rm value calculated in the rest phase as additional objectives. However, relative deterioration of the AP fit is observed, demonstrating trade-off among the objectives. This framework can be useful for a wide range of applications, including the parameters fitting phase of the cardiac cell model development and investigation of normal and pathological scenarios in which reproducing both cellular and intercellular properties are of great importance.

Highlights

  • Mathematical models of cardiac electrophysiology are of the utmost importance in solving a wide range of biomedical and pharmacological problems [1]

  • In accordance with the literature review, one can notice that the optimization approaches developed for tuning cardiac cell models are mainly limited to the single objective problems of fitting action potential (AP) waveforms, ionic channel currents, or a combination thereof, i.e., properties of single cardiac cells

  • To reach the steady-state condition, each model is allowed to run for 20 beats at a 1 s interval (1 Hz), and the 20th AP is selected for analysis

Read more

Summary

Introduction

Mathematical models of cardiac electrophysiology are of the utmost importance in solving a wide range of biomedical and pharmacological problems [1]. Computational models of cardiac myocytes facilitate the understanding of important properties and phenomena at both cellular and tissue levels [2]. In accordance with the literature review, one can notice that the optimization approaches developed for tuning cardiac cell models are mainly limited to the single objective problems of fitting action potential (AP) waveforms, ionic channel currents, or a combination thereof, i.e., properties of single cardiac cells. These optimization protocols can be inaccurate in reproducing interconnected cell properties, i.e., tissue behavior [3]. Intercellular electrical coupling properties are critical in the context of large-scale modeling [9]

Methods
Results
Discussion
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call