Experimental and Computational Studies in Genetic Cardiomyopathies PI: Farid Moussavi-Harami Abstract Cardiomyopathies, including hypertrophic cardiomyopathy (HCM) and dilated cardiomyopathy (DCM), are an ideal venue for implementing precision medicine strategies. This is due to more routine use of genetic testing and the vast amount of knowledge regarding underlying biophysical mechanisms of sarcomeric variants, which contribute to both DCM and HCM. While the mechanisms of how sarcomeric variants cause cardiomyopathies is an active area of investigation, it is clear that they disrupt the finely tuned force-generation properties of cardiomyocytes. Many investigators have used a variety of biophysical and biochemical assays to study mechanism of sarcomeric variants and then scale these studies up to cells, tissues and animal models. These approaches are informative, but incremental and unable to asses many variants at once. Success in this area requires robust high-throughput assays with the ability for analysis of thousands of divergent variants at once. Our proposal will directly overcome limitations in the field by applying data analytics to biophysical simulations and experimental cardiac twitches. The fundamental hypothesis is that the principal features of cardiac twitches summarize the complex intra and inter-filament interactions of sarcomeric variants. Moreover, we can utilize these features for variant classification, predicting therapeutic response and identification of new therapeutics targets. Testing these hypotheses requires 1) large datasets of variants, 2) models that account for variant location and abundance in sarcomeres and 3) development and validation of data analytic methods. Biophysical simulations of sarcomeric variants can provide such datasets, but require validation in experimental systems. We will use a spatially explicit computational model of the sarcomere that can simulate how perturbations in sarcomere mechanochemistry change myocyte force generation. Simulated twitches will be generated, validated and used for predicting targeted therapeutics.