Duchenne muscular dystrophy (DMD) is a progressive muscle-wasting disease with no effective treatment. Multiple mechanisms are thought to contribute to muscle wasting, including increased susceptibility to contraction-induced damage, chronic inflammation, fibrosis, altered satellite stem cell (SSC) dynamics, and impaired regenerative capacity. The goals of this project were to 1) develop an agent-based model of skeletal muscle that predicts the dynamic regenerative response of muscle cells, fibroblasts, SSCs, and inflammatory cells as a result of contraction-induced injury, 2) calibrate and validate the model parameters based on comparisons with published experimental measurements, and 3) use the model to investigate how changing isolated and combined factors known to be associated with DMD (e.g., altered fibroblast or SSC behaviors) influence muscle regeneration. Our predictions revealed that the percent of injured muscle that recovered 28 days after injury was dependent on the peak SSC counts following injury. In simulations with near-full cross-sectional area recovery (healthy, 4-wk mdx, 3-mo mdx), the SSC counts correlated with the extent of initial injury; however, in simulations with impaired regeneration (9-mo mdx), the peak SSC counts were suppressed relative to initial injury. The differences in SSC counts between these groups were emergent predictions dependent on altered microenvironment factors known to be associated with DMD. Multiple cell types influenced the peak number of SSCs, but no individual parameter predicted the differences in SSC counts. This finding suggests that interventions to target the microenvironment rather than SSCs directly could be an effective method for improving regeneration in impaired muscle. NEW & NOTEWORTHY A computational model predicted that satellite stem cell (SSC) counts are correlated with muscle cross-sectional area (CSA) recovery following injury. In simulations with impaired CSA recovery, SSC counts are suppressed relative to healthy muscle. The suppressed SSC counts were an emergent model prediction, because all simulations had equal initial SSC counts. Fibroblast and anti-inflammatory macrophage counts influenced SSC counts, but no single factor was able to predict the pathological differences in SSC counts that lead to impaired regeneration.
Molecular dynamics (MD) method and agent-based model (ABM) in simulation of stem cell deposition on the surface with nanopattern: Current development stage of an in-house simulator
