Reconstructing Ventricular Cardiomyocyte Dynamics and Parameter Estimation Using Data Assimilation

Cardiac ventricular myocyte action potential dynamics are regulated by intricate and nonlinear interactions between the cell transmembrane potential and ionic currents and concentrations. Present technology limits the ability to measure transmembrane potential and multiple ionic currents simultaneously, which narrows the scope of experiments to provide a complete snapshot of the cardiac myocyte state. This limitation presents an obstacle for understanding how perturbations can trigger arrhythmias and more broadly how the myocyte responds to different conditions, such as changes in pacing rate or responses to drug treatment. In this study, we demonstrate that a data-assimilation approach can successfully reconstruct and predict the dynamics of a heterogeneous virtual cardiac ventricular myocyte population in the presence of parameter uncertainty. A population of heterogeneous cardiac ventricular myocytes is generated by varying ionic current conductance parameters, and additional observational uncertainty is mimicked by the addition of Gaussian noise to the transmembrane potential. We demonstrate that the data-assimilation approach accurately reconstructs transmembrane potential, with error less than the magnitude of the noise. Further, the data-assimilation approach successfully estimates the conductances of ionic currents generally with high accuracy and requiring low computational time. As a proof of concept, we apply the data-assimilation approach to reconstruct action potential dynamics from optical mapping experiments in an ex vivo isolated guinea pig heart. Critically, we demonstrate that the ionic conductance parameters estimated from a recording at one pacing frequency can accurately predict action potential dynamics at different rates.