肌肉再生是一个复杂的过程，由于动态和多尺度的生化和细胞相互作用，使用实验方法单独确定肌肉损伤的最佳治疗方法变得困难。为了了解个体细胞行为对内源性肌肉恢复机制的影响程度，我们开发了一个基于个体的模型（ABM），使用细胞波茨框架模拟小鼠骨骼肌组织横截面的动态微环境。我们参考了100多项已发表的研究，定义了100多个参数和规则，规定了肌纤维、卫星干细胞（SSC）、成纤维细胞、中性粒细胞、巨噬细胞、微血管和淋巴管的行为，以及它们彼此之间和与微环境的相互作用。我们利用参数密度估计，将模型校准到描述损伤后多个时间点的横截面积（CSA）恢复、SSC和成纤维细胞细胞计数的时间生物数据集。通过将其他模型输出（巨噬细胞、中性粒细胞和毛细血管计数）与实验观察结果进行比较，验证了校准模型。将变量或细胞因子输入条件有所变化的八个模型扰动与已发表的实验研究进行比较，以验证模型的预测能力。我们使用拉丁超立方抽样和局部秩相关系数来确定体外扰动的细胞因子扩散系数和衰减速率，以增强CSA的恢复。这种分析表明，特定细胞因子衰减和扩散参数的联合改变使得成纤维细胞和SSC增殖更多，相比于单个扰动，CSA恢复率增加了13%，与未改变的再生相比，28天时的恢复率提高。这些结果为引导发展治疗策略提供了指导，这些策略在肌肉再生过程中同样改变肌肉生理（例如，将ECM结合的细胞因子转化为游离扩散的形式，如癌症治疗或外源性细胞因子的输送），以增强肌肉损伤后的恢复能力。

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 100 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 combined alterations of specific cytokine decay and diffusion parameters result in greater fibroblast and SSC proliferation compared to individual perturbations with a 13% increase in CSA recovery compared to unaltered regeneration at 28 days. These results enable guided development of therapeutic strategies that similarly alter muscle physiology (i.e. converting ECM-bound cytokines into freely diffusible forms as studied in cancer therapeutics or delivery of exogenous cytokines) during regeneration to enhance muscle recovery after injury.