Empirical study of an adaptive multiscale model for simulating cardiac conduction

Area of Science:

• Computational biology
• Biophysics
• Cardiovascular research

Background:

• Cardiac action potential propagation is crucial for heart function.
• Accurate simulation requires resolving complex cellular and tissue-level dynamics.
• Existing multiscale models often necessitate precise alignment between cellular structures and computational grids.

Purpose of the Study:

• To modify and empirically study an adaptive multiscale model for simulating cardiac action potential propagation.
• To assess the impact of myocyte-grid alignment on model accuracy.
• To investigate the influence of gap-junctional coupling, ephaptic coupling, and grid spacing.

Main Methods:

• Developed a multiscale model combining microscale (cell-level) and macroscale (tissue-level) partial differential equations.
• Empirically studied the model's performance under varying coupling conditions and grid resolutions.
• Compared simulation results against fully microscale models.

Main Results:

• The modified multiscale model accurately reproduces cardiac action potential wavespeed and waveform.
• Perfect alignment between myocytes and the macroscale grid is not essential for accurate simulation.
• Optimal macroscale grid spacing can be related to the action potential's spatial width.
• Model accuracy is maintained across high, low, and zero gap-junctional coupling.

Conclusions:

• Adaptive multiscale models offer a viable and accurate approach for simulating cardiac electrophysiology.
• The developed model overcomes limitations of previous methods by not requiring precise myocyte-grid alignment.
• Findings provide guidance for efficient and accurate multiscale modeling of cardiac tissue.