The goal of this research is to develop a predictive multiscale model that will improve understanding of familial cardiomyopathies and that can be used to help screen potential new therapies for cardiac disease. Familial cardiomyopathies are the most frequently inherited heart defect and affect about 700,000 Americans. Most of the genetic mutations affect myosin or regulatory proteins that modulate myosin function. The majority of these mutations also induce abnormal cardiac growth termed hypertrophy. This project will develop, calibrate, and validate an innovative multiscale model that uses data quantifying myosin-level function to predict how hearts hypertrophy over time. This is a critical step on the path to developing patient-specific computer models that can be used to optimize treatments for heart failure and to predict the effects of different types of pharmaceutical intervention. In the future, one could envision clinicians testing drug treatments in silico and selecting the intervention that produces the greatest long-term benefit for their patient. The research team consists of two physiologists/biophysicists (Campbell & Yengo) and two engineers (Wenk & Lee) who share a common interest in cardiac biology. Together, their research skills span from structure-function analysis of myosin molecules to computer simulations of hearts that grow and remodel over time. The research plan integrates state-of-the-art hierarchically-coupled mathematical models with validation experiments that range from stopped-flow molecular kinetic assays to magnetic resonance imaging of myocardial strain patterns. The model will be tested using molecular to organ-level experimental data obtained from wild-type mice and from transgenic animals that develop cardiac hypertrophy because of a K104E mutation in myosin regulatory light chain. Additional tests will be performed using drugs that enhance (omecamtiv mecarbil) and inhibit (MYK-461) myosin-level contractile function. There are three specific aims.
Aim 1 : Integrate a multistate kinetic model of myosin into an organ-level finite framework to predict the effects of genetic and/or pharmaceutical modulation of myosin function.
Aim 2 : Develop growth and remodeling algorithms to predict chronic changes in ventricular structure and function resulting from genetic and/or pharmaceutical modulation of myosin function.
Aim 3 : Calibrate and validate the model using experimental data quantifying different spatial and temporal scales.
This project develops computer software and mathematical techniques that can be used to predict how hearts grow and adapt in response to genetic mutations and/or drugs. In the future, scientists and clinicians will be able to use the software to help optimize personalized treatment plans for patients who have different types of cardiovascular disease.
