Mechanical load on the heart profoundly affects cardiac excitation-contraction (E-C) coupling that governs heart function. Recent experimental studies have revealed that mechanotransduction mechanisms link to multiple signaling pathways to modulate the activities of many ion channels, Ca2+ handling molecules, and contractile proteins, which work in concert to regulate contractile force to compensate for external load changes. Such autoregulation of contractility requires highly coordinated modulation of many molecules by mechanotransduction. PROBLEM: Current mathematical modeling of cardiomyocytes often uses one-at-a- time parameter changes in model simulations. To understand how multiple parameters and molecules change in a coordinated pattern, however, will require a new mathematical strategy. One-at-a-time parameter changes cannot address how multiple parameters change in a coordinated way. Studying the coordinated changes requires simultaneously changing many model parameters but even this does not, by itself, reveal how the changes are coordinated. INNOVATION: We will develop a new Functional Connectome approach by the following strategy. (a) Randomly change parameters of many subsystems. Because we make few a priori assumptions on what subsystems might be involved, this approach can reduce exclusion of some subsystems, which is important because cellular processes are highly interconnected. (b) From many simulated parameter combinations, we use experimental data to filter out a small number of subsets that fit all the data. Such a subset is called an Acceptable Parameter Set (APS). (c) To determine the coordinated changes of subsystems, we use the Singular Value Decomposition (SVD). SVD factorization of the parameter matrix shows that the APS often lies in a low-dimensional subspace of the entire high-dimension parameter space. The linear structure of this subspace gives both the map of connected subsystems and how the subsystems are modulated coordinately to produce the functional output. We call this connection map the Functional Connectome. Our interdisciplinary team will combine mathematical modeling with state-of-the-art experiments to achieve three specific aims: (1) Extend the cardiomyocyte mathematical model to include mechano-chemo-transduction feedback loop for studying autoregulation of Ca2+ and contractility in response to mechanical load changes. (2) Develop the Functional Connectome modeling platform to find patterns in myriad molecular changes. (3) Experimental test of the Functional Connectome predictions in mechanically loaded cardiomyocytes. SIGNIFICANCE: The outcome of this project will provide a new mathematical platform for studying coordinated changes in biological cells, which enables finding patterns in myriad molecular changes by various stimuli, and piece together many data to form a big picture. We will apply the Functional Connectome to study how mechanical load on cardiomyocyte causes coordinated molecular changes that give rise to the autoregulation of contractility in the heart. 1

Public Health Relevance

The questions we address in this proposal are both simple and fundamentally important: How are multiple cellular processes coordinated to produce the whole-cell response to an external stimulus? To answer this question, we will develop a new mathematical analyses platform called the Functional Connectome in this project. As a specific case study, we will use the Functional Connectome analyses to study how mechanical load caused coordinated changes in multiple Ca2+ handling molecules to autoregulate muscle contraction in the heart.

National Institute of Health (NIH)
National Heart, Lung, and Blood Institute (NHLBI)
Research Project (R01)
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Special Emphasis Panel (ZRG1)
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Balijepalli, Ravi C
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University of California Davis
Schools of Medicine
United States
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