HeRO Seminar: Reza Avaz, PhD

Location: HSRB Auditorium

"Integrated Computational-Experimental Cardiac Modeling: Application to Pulmonary Arterial Hypertension and Pediatric Heart Diseases"

Reza Avaz, PhD,
Research Scientist, NIH K99/R00 Fellow
Willerson Center for Cardiovascular Modeling and Simulation
Institute for Computational Engineering and Sciences
Department of Biomedical Engineering
​The University of Texas at Austin


The development of integrated computational-experimental models of normal and impaired hearts offers novel ways to better understand the pathophysiology of heart remodeling in response to structural heart diseases and to design and personalize cardiac interventions. Pulmonary arterial hypertension (PAH) is a progressive structural heart disease that imposes a chronic pressure overload in the right ventricle (RV), leading to substantial remodeling events including hypertrophy of muscle cells and dilation of the RV. Many studies, including ours, suggest that the fate of a patient with PAH is not determined by the degree of pressure overload but rather by how the RV responds to it. However, the question of how to predict whether the RV remodeling in response to PAH stabilizes or rapidly transitions to RV failure remains largely unanswered.

In the first part of my talk, I will present our work on developing rat-specific computational heart models to study time-course cardiac remodeling in response to PAH. Our computational platform provides novel insights into how the remodeling events at multiple scales in the myocardium (including cellular, fiber, and tissue levels) collectively lead to the cardiac function impairment in the RV. From a therapeutic point of view, our platform offers a powerful toolset to predict the progression path of PAH, and accordingly guide patient-specific pharmaceutical and surgical interventions for PAH. In the second part of my talk, I will discuss the application of the integrated computational-experimental approach to congenital heart diseases (CHDs.) I will discuss, through specific project examples, how such an approach can significantly facilitate a paradigm shift in existing treatments of CHDs, and provide robust and predictive biomarkers to optimize medical devices and treatments for CHDs.

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