Agent Based Modeling of Skeletal Muscle Adaptation

Martin, Kyle, Biomedical Engineering - School of Engineering and Applied Science, University of Virginia
Peirce-Cottler, Shayn, Department of Biomedical Engineering, University of Virginia
Blemker, Silvia, Department of Biomedical Engineering, University of Virginia

Skeletal muscle plasticity – the ability to adapt both structure and function in response to stimuli – is integral to physical activity and necessary for human health. Muscle adaptation enables both hypertrophy through exercise as well as recovery from injury. On the tissue level, adaptation is driven by cellular dynamics that dramatically alter the muscle composition. This is particularly true in the case of muscle regeneration, where damaged tissue must be removed before muscle fibers can regrow. The concurrent and interconnected collaboration of inflammatory cells (neutrophils and macrophages) and native muscle cells (fibroblasts, satellite stem cells, muscle fibers, and endothelial cells) during regeneration is vital for recovery of muscle health and function.

My dissertation investigates muscle adaptation through the development of novel agent-based models (ABMs). This computational modeling platform is well suited for simulating the stochastic behaviors of cells, such as proliferation, apoptosis, protein secretion, and migration. My first ABM focuses on disuse-induced muscle atrophy. I constructed and tuned the model using literature-derived experimental data, and simulated 4-week long atrophy across 49 different muscles. The ABM also predicted that fibroblast secretion of TNF-α can exacerbate disuse-induced atrophy. Next, I extended the ABM to simulate muscle injury and regeneration. After incorporating additional rules to describe the behaviors of inflammatory cells and satellite stem cells, I utilized genetic algorithms to calibrate the model to experimental data. In addition to recapitulating the effects of modulating inflammation (i.e. macrophage knockdown experiments), my ABM was capable of predicting the timing and efficacy of a pharmacological treatment aimed at accelerating muscle regeneration. I then used the ABM’s predictions to design an in vivo experiment in which muscle regeneration was manipulated using macrophage colony stimulating factor (M-CSF). As was seen in silico, M-CSF injections accelerated regeneration following muscle laceration, validating the ABM’s predictions.

In sum, these ABMs of muscle adaptation have provided insight into cellular interactions during muscle atrophy, explored the dynamics of inflammation in muscle regeneration, and generated a novel hypothesis that was confirmed through in vivo experiments. Future extensions of my ABMs could be integrated into finite-element computational models, allowing for multi-mechanism (biological and mechanical) predictions of functional and structural muscle adaptation. Furthermore, continuation of my ABMs could provide a platform for evaluating therapies to beneficially affect muscle adaptation during surgical recovery, aging, or disease.

PHD (Doctor of Philosophy)
Agent-based modeling, Skeletal muscle, regeneration, Muscle adaptation, Atrophy
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