Agent-based model illustrates the role of the microenvironment in regeneration in healthy and mdx skeletal muscle

Abstract

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: (i) 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, (ii) calibrate and validate the model parameters based on comparison with published experimental measurements, and (iii) 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 post injury was dependent on the peak SSC counts following injury. In simulations with near-full CSA recovery (healthy, 4 week mdx, 3 month mdx), the SSC counts correlated with the extent of initial injury; however, in simulations with impaired regeneration (9 month 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.

Multiscale Coupling of an Agent-Based Model of Tissue Fibrosis and a Logic-Based Model of Intracellular Signaling

“…An increasing number of hybrid models couple continuum with discrete approaches. These hybrid models typically couple ABMs, which use a discrete representation of 2D space or 3D volumes, with continuum based approaches that represent cytokine gradients and/or receptor-ligand kinetics (Warsinske et al, 2016(Warsinske et al, , 2017Virgilio et al, 2018). For example, Warsinske et al simulated granuloma-associated fibrosis by incorporating a system of ODEs and PDEs that describe molecular level diffusion of chemokines (TGFβ and prostaglandin) and receptor ligand signaling, coupled with discrete cellular agents whose behaviors were defined by a set of rules that related receptor activation levels to cell proliferation, differentiation, chemotaxis, and secretion of ECM proteins.…”

“…For example, previous computational models have demonstrated that simultaneous targeting of multiple cells types rather than fibroblasts alone can enhance the efficacy of therapies for pulmonary fibrosis (Warsinske et al, 2016). Inflammatory cells, including macrophages and neutrophils, will be incorporated into a model of post-MI wound healing as the primary source and modulators of inflammatory cytokines and TGFβ input, as has been demonstrated previously in simulations of skeletal muscle and lung fibrosis (Martin et al, 2016;Warsinske et al, 2016;Virgilio et al, 2018). This will add another layer of complexity to the spatial heterogeneity of the coupled model by representing cytokine production from individual cells, diffusion of soluble cytokines and growth factors, and migration that is driven by chemokine gradients.…”

“…Future applications will incorporate cell migration and proliferation rates that are driven by the dynamic network state of individual fibroblasts (Bailey et al, 2007(Bailey et al, , 2009. Additionally, this model focused specifically on the contributions of fibroblasts in the progression of fibrosis, through the coupling of a fibroblast signaling network, but future work will incorporate inflammatory cells that serve as local sources of many of the @ inflammatory cytokines that affect fibroblast signaling (Virgilio et al, 2018).…”

The FRiND Model: A Mathematical Model for Representing Macrophage Plasticity in Muscular Dystrophy Pathogenesis

“…(2015) and the model created by Virgilio et al. (2018). Normally, this is not an issue since myoblasts proliferate enough during the pro-inflammatory stage to give enough differentiated myocytes to repair muscle.…”

In SilicoTissue Engineering: A Coupled Agent-Based Finite Element Approach

“…Since then, various groups have tried to improve the mechanical strength of TEVGs by application of various loading regimens and/or manufacturing techniques. [16][17][18] Agent-based modeling (ABM), finite element analysis (FEA), and coupled AB-FEA have been widely used to study mechanobiological interactions in various diseased and healthy states, such as Duchenne muscular dystrophy, 19,20 volumetric muscle loss, 21 neovascularization in porous stents, 22 and vascular tissue engineering. 23 ABM is a computational technique for simulation of complex adaptive systems, 24 in which the overall response of the system is defined by the culmination of interactions and behaviors of individual agents (e.g., cells), and their response to external stimuli.…”