Model-driven optimal experimental design for calibrating cardiac electrophysiology models

Abstract

Models of the cardiomyocyte action potential (AP) have contributed immensely to the understanding of heart function, pathophysiology, and the origin of heart rhythm disturbances. However, AP models are nonlinear, complex, and can contain more than a hundred differential equations, making them difficult to parameterise. Therefore, cellular cardiac models have been limited to describing average cell dynamics, when cell-specific models would be ideal to uncover inter-cell variability but are too experimentally challenging to be achieved. Here, we focus on automatically designing experimental protocols that allow us to better identify cell-specific maximum conductance values for each major current type--optimal experimental designs--for both voltage-clamp and current-clamp experiments. We show that optimal designs are able to perform better than many of the existing experiment designs in the literature in terms of identifying model parameters and hence model predictive power. For cardiac cellular electrophysiology, this approach will allow researchers to define their hypothesis of the dynamics of the system and automatically design experimental protocols that will result in theoretically optimal designs.