Duchenne muscular dystrophy is a devastating muscle wasting disease affecting 1 in 3500 boys. It is caused by the lack of the dystrophin protein, which serves as a structural link to the muscle fiber membrane. Boys are typically diagnosed around age three to five as they exhibit changes in walking patterns, begin using a wheel-chair in their teens, and die due to respiratory or cardiac malfunction in their third decade of life. Despite extensive experimental research, there remains no cure for DMD. We hypothesize that one of the reasons DMD is so difficult to treat is that multiple mechanisms contribute to disease progression. Without the dystrophin protein the muscle is more susceptible to contraction-induced damage, resulting in chronic inflammation and fibrosis. Coupled with altered satellite stem cell (SSC) dynamics, these disease mechanisms lead to impaired muscle regeneration and progressive muscle wasting.

We believe this is an ideal opportunity to use computational models to help unravel the complex, multifaceted nature of DMD. My dissertation developed two computational models to investigate disease mechanisms in DMD. First, I developed a micromechanical finite element (FE) model that predicted that fibrosis would impair function by increasing the stiffness of the muscle, but protect the muscle from contraction-induced damage. This effect was dependent on whether the ECM was stiffer or more compliant than the skeletal muscle fibers. Then I developed an agent-based model (ABM) to study the cellular physiology driving disease progression. The model predicted muscle regeneration from injury, based on the autonomous actions of the different cell types in the model. The cell types included SSCs, fibroblasts, neutrophils, macrophages, ECM, and muscle cells. We simulated injury and regeneration in healthy and mdx mice (the most common animal model used in DMD). The simulations predicted that suppressed SSC counts at the later stages of disease impaired regeneration. However, no individual factor in the model was able to predict the decreased SSC counts. Finally, we used the model to design an experiment to test the effect of fibrosis on muscle regeneration in mdx mice. While our intervention increased the area fraction of collagen in the muscle, the stiffness of the muscle was decreased. Given this baseline condition, both our model and the experiment showed no effect on regeneration. However, our model predicted that if the fibrosis resulted in an increased ECM stiffness, then regeneration would be impaired.

Ultimately, the models developed in this dissertation were used to investigate the role of DMD disease mechanisms, both in isolation and combined in our model representations of dystrophic muscle. Both models predicted that the fibrotic microenvironment was a key regulator of function, damage susceptibility, and muscle regeneration in dystrophic muscle. Further, this work highlighted a key utility of this modeling framework for designing experiments, making predictions, and understanding the complex results of these experiments. Future development of the models in this dissertation could provide a platform for predicting chronic, long-term disease progression in DMD, and in silico therapeutic testing.