Reconstructing action potential and ionic current dynamics of a mammalian ventricular cell using data assimilation.

Abstract

The dynamics of the cardiac ventricular myocyte action potential (AP) is regulated by intricate and nonlinear interactions between the cell transmembrane potential, and ionic currents and concentrations. Present technology limits the ability to simultaneously measuring multiple ionic currents and concentration during an AP in in vitro single cell experiments, which limits the scope of experimental data to provide a complete snapshot of the cardiac myocyte system state. This limitation presents an obstacle for understanding how cellular perturbations can trigger arrhythmogenic conditions. In this study, we applied a data assimilation approach, which combines limited noisy observations with predictions from a computational model, paired with a parameter estimation to minimize the effects of parameter uncertainty and model error. We performed a series of in silico experiments, in which a forecasting system was tasked with reconstructing AP dynamics of a virtual ventricular myocyte in the presence varying degrees of parameter and model error. With appropriate protocol design, we find that a data assimilation approach can successfully reconstruct and predict the dynamics of not only the directly observed state variables (such as the transmembrane potential and a selected ionic current), but also unobserved state variables, such as unmeasured ionic currents and concentrations. As an extension of the results of these series of in silico experiments, we generated simulations to assess which ionic currents, when measured, offer greater predictive ability using the data assimilation algorithm. This can offer valuable information for the design of single cardiac in vitro experiments, as it is not feasible to measure all ionic currents and concentrations across time within the same cell during an AP. Future work will investigate the longer-term accuracy of this approach predicting potentially pro-arrhythmic perturbations for arrhythmia risk assessment.