Multiscale Modeling of Heart Disease and Musculoskeletal Performance

Abstract

Computational modeling in physiology can be integrative in several different ways. In this seminar I will use examples from our research at UC San Diego on heart disease and musculoskeletal performance to illustrate these approaches and how they can be used together. Biophysically based multiscale models integrate structurally across scales of biological organization from molecular to organism. I will show how we have combined atomistic molecular modeling, subcellular scale Markov state models, whole cell systems models with organ and system scale continuum and lumped parameter models to predict therapeutic mechanisms of 2-deoxyATP as a myosin activator in heart failure. Systems modeling of biological networks integrate the functions of biochemical modules into the emergent properties of signaling pathways that regulate genome-scale transcription. I will show how we have applied this approach to elucidate the signaling interactions that control mechanoregulated gene expression in cardiac myocytes. And statistical models and machine learning enabling the development of clinical data integration tools for better diagnosis and management of congenital heart disease and arrhythmias.While our research on the heart has focused on disease mechanisms and pathogenesis, studying trained athletes is giving us new insights into the biological principles underlying elite human performance especially in the musculoskeletal system. I will end with an overview of how we are now applying the modeling and experimental strategies we have used for studying heart disease to understand the effects of exercise training especially on skeletal muscle biology and human movement competency.