Computational modeling approaches are rarely applied to the right ventricle even though, like left ventricular failure (LVF), right ventricular failure (RVF) is multifactorial, multiscale and causes significant morbidity and mortality. In comparison to LVF, RVF is understudied with the important consequence that no RV-specific therapies exist. Computational multi-scale modeling offers a unique opportunity to integrate dysfunction manifest at multiple scales: at the organelle level, there are impairments of mitochondria, Ca2+- handling, and myofilament function; at the tissue level, there is myocyte necrosis, apoptosis, fibrosis and capillary rarefaction; at the organ level, hypertrophy and dilation; and at the organism level, exercise intolerance. Moreover, computational modeling is ideally suited to answering the question: what are the relative contributions from abnormalities at multiple scales to the overall phenotype of RVF? We propose to answer this question with a data-driven, multiscale, computational modeling approach. Beginning with an existing mitochondrial kinetic computational model fit to healthy and RVF mitochondrial function, we will predict the emergence of dysfunction at the tissue-level. Then, fitting a myocardial tissue computational model to healthy and RVF passive and active mechanics, we will predict emergence of dysfunction at the organ-level. Finally, by adapting an existing biventricular mechanics computational model to healthy and RVF pressure-volume dynamics, we will predict the emergence of dysfunction at the organism-level, i.e., exercise intolerance. Model assumptions and predictions will be driven-by and tested against experimental data collected using state-of-the-art techniques at the organelle-, tissue-, organ-, and organism-scales at multiple time points in an established rat model of RVF. Finally, we will use our data-driven computational modeling approach to confirm the human disease relevance of mechanisms of RVF found in rodent using our state-of-the- art experimental techniques on human failing and nonfailing myocardium.
Our specific aims are:
Aim 1 : Determine the drivers of systolic dysfunction in RVF. We hypothesize that the major driver of systolic dysfunction in RVF is impaired mitochondrial generation of ATP leading to impaired contraction of cardiac myofilaments. We will test this hypothesis with scale-specific models and multi-scale experimental data collected from rats with RVF.
Aim 2 : Determine the drivers of diastolic dysfunction in RVF. We hypothesize that diastolic dysfunction in RVF is driven by fibrosis and impaired myofilament relaxation. We will test this hypothesis with scale-specific models and multi-scale experimental data collected from rats with RVF.
Aim 3 : Determine the drivers of systolic and diastolic function in human RVF. Key predictions of organelle- and tissue-scale structural and functional drivers of RVF will be tested with multiscale modeling validated with state-of-the-art measurements at these scales in non-failing and failing human heart tissues.

Public Health Relevance

Right ventricular failure (RVF) is a prevalent cause of cardiovascular collapse and a major public health problem. Unlike left ventricular failure, RVF is not frequently investigated, which has contributed to the lack of effective therapies for RVF. Here we propose to use computational model predictions, based on state-of-the-art, multi- scale, comprehensive experimental datasets, to improve our understanding of RVF.

National Institute of Health (NIH)
National Heart, Lung, and Blood Institute (NHLBI)
Research Project (R01)
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Modeling and Analysis of Biological Systems Study Section (MABS)
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Shi, Yang
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University of Wisconsin Madison
Biomedical Engineering
Biomed Engr/Col Engr/Engr Sta
United States
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