A Cellular Potts Model of skeletal muscle regeneration to reveal novel interventions that improve recovery from muscle injury

Megan Haase

University of Virginia

"A Cellular Potts Model of skeletal muscle regeneration to reveal novel interventions that improve recovery from muscle injury"

Muscle regeneration is a complex process due to dynamic and multiscale biochemical and cellular interactions, making it difficult to determine optimal treatments for muscle injury using experimental approaches alone. To understand the degree to which individual cellular behaviors impact endogenous mechanisms of muscle recovery, we developed an agent-based model (ABM) using the Cellular Potts framework to simulate the dynamic microenvironment of a cross-section of murine skeletal muscle tissue. We referenced more than 200 published studies to define over 100 parameters and rules that dictate the behavior of muscle fibers, satellite stem cells (SSC), fibroblasts, neutrophils, macrophages, microvessels, and lymphatic vessels, as well as their interactions with each other and the microenvironment. We utilized parameter density estimation to calibrate the model to temporal biological datasets describing cross-sectional area (CSA) recovery, SSC, and fibroblast cell counts at multiple time points following injury. The calibrated model was validated by comparison of other model outputs (macrophage, neutrophil, and capillaries counts) to experimental observations. Predictions for eight model perturbations that varied cell or cytokine input conditions were compared to published experimental studies to validate model predictive capabilities. Latin hypercube sampling and partial rank correlation coefficient were used to identify optimal therapeutic strategies which guided in-silico perturbations of cytokine diffusion coefficients and decay rates. This analysis suggests a new hypothesis that a combined alteration of specific cytokine decay and diffusion parameters results in greater fibroblast and SSC proliferation and increased fiber recovery at 28 days (97% vs 82%, p<0.001) as compared to the baseline condition. Future work will explore this new hypothesis through novel coupled in-vivo and in-silico experiments to understand treatment responses with various injury types and microenvironmental conditions.
Additional authors: Tien Comlekoglu University of Virginia; Alexa Petrucciani Purdue University; Shayn Peirce-Cottler University of Virginia, Silvia Blemker University of Virginia

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