Physiology-augmented multivariate temporal learning for adaptive simulation of electrical excitation in cardiac myocytes

Cardiac electrophysiological modeling is an effective approach for investigating the mechanisms of arrhythmogenesis. So far, the mainstream of cardiac myocyte modeling depicts the electrophysiological properties of cardiac cells by using biophysically detailed differential equations to describe the activation and inactivation processes of ion channel dynamics. However, this approach is cumbersome and time-consuming to construct the mathematical representations for specific cases, impeding physiological adaptive modeling. Here, we introduce an innovative method called physiology-augmented multivariate temporal simulation (PAMTS), to rapidly and adaptively describe the cell-specific physiological behavior of cardiomyocytes. Based on the excitability of biological systems, PAMTS decouples cellular electric activity into external environmental influences and internal multivariate dynamic mechanisms. Then, the physiological information embedded in the mechanistic models is characterized as physiological templates that guide the complex dependencies across multi-physiological attributes, enabling stable and accurate prediction of future cellular physiological behavior. After systematically evaluating PAMTS in multiple electrophysiological modeling tasks, we demonstrate that PAMTS can be accurately and reliably applied to conduct adaptive modeling of the physiological behavior of various cell types. The study suggests that PAMTS has a promising potential to propel the development of personalized cardiac physiological models, which provide novel insights for rapid and adaptive modeling of complex physiological systems.