Muscle Regeneration Agent-Based Model Predicts Enhanced Regeneration Outcomes with Altered Cytokine Dynamics

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. We used Latin hypercube sampling and partial rank correlation coefficient to identify in-silico perturbations of cytokine diffusion coefficients and decay rates to enhance CSA recovery. This analysis suggests 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 as compared to the baseline condition. These results may guide development of therapeutic strategies that similarly alter muscle physiology during regeneration to enhance muscle recovery after injury. For more information see: https://elifesciences.org/articles/91924 *Contents* 00:00 - Introduction 05:27 - Muscle Regeneration Agent-Based Model Predicts Enhanced Regeneration Outcomes with Altered Cytokine Dynamics 32:11 - Questions & Discussion Moderator: James A. Glazier, PhD, Indiana University, Bloomington For slides for this video visit: https://docs.google.com/presentation/d/1Hm6jw7m7rrqQonbOpIvmtohdn7GwrWKa/ If you found this video useful, please check out our other videos on computational modeling, infection and immunology: https://youtube.com/playlist?list=PLiEtieOeWbMKh9VcQoinSwODcSZKMTGat Please consider joining our IMAG/MSM WG on Multiscale Modeling and Viral Pandemics: https://www.imagwiki.nibib.nih.gov/content/msm-viral-pandemics-meetings Please also consider joining the Global Alliance for Immune Prediction and Intervention: http://glimprint.org/