The overall goal of this U01 application is to develop novel approaches for multiscale modeling in cardiac electrophysiology and arrhythmia research. To accomplish this goal, we will use innovative combinations of experimental and computational studies at multiple spatial scales and across multiple conceptual scales. Because cardiac cells are complex systems involving dozens of interacting molecular entities, mathematical modeling has long been a valuable technique for uncovering arrhythmia mechanisms. However, established methods for combining modeling with experiments have important limitations, including: (1) most studies test only a limited number of model predictions; (2) models usually predict the response of a sample considered representative of a population, thereby ignoring differences between individuals; and (3) tissue-level simulations may incorporate physiological differences between regions but do not account for the fact that each cell in the tissue is different. We will address these limitations using innovative and synergistic computational and experimental methodologies developed by the PIs. These methods allow for rigorous parameter estimation, systematic and quantitative predictions, and testing multiple perturbations in each experimental sample, and quantitative mappings between different cell types. To achieve our overall goals, we propose to: 1. improve heart cell models through rigorous experimental testing and the development of mathematical models specific to each cell studied. 2. calibrate models of heterogeneous cell populations and experimentally test predictions regarding ionic current variation and co-variation across populations 3. develop models to predict the effects of perturbations in one species based on recordings made in a different species 4. predict how variability between individual cells influences arrhythmia risk at the tissue level. The research is likely to demonstrate improved, broadly applicable methods for rigorous and systematic coupling between experiments and simulations at multiple spatial scales. By so doing, the combined studies will provide important insight into the consequences of variability at both the cellular and tissue levels.
Cardiac arrhythmias kill hundreds of thousands of people each year, but the heart's inherent complexity prevents experiments from illuminating all aspects of arrhythmias. While computational modeling fills many of the experimental voids, important limitations restrict the utility of these models. The overall goal of this project is to develop novel approaches for multiscale cardiac electrophysiology modeling, including protocols for more predictive models, rigorous representations of variability between samples, quantitative mappings between species, and the effects of heterogeneity at the tissue level.
