ABMs represent the behaviors and interactions of autonomous agents, such as cells, which are governed by literature-derived rules^15,21,22. Agent-based modeling provides an excellent platform for studying complex cellular dynamics because they reveal how the interactions between individual cellular behaviors lead to emergent behaviors in the whole system.

We implemented the ABM in CompuCell3D (version 4.3.1), a Python-based modeling software²³. The ABM’s code is available for download (https://simtk.org/projects/muscle_regen). To build the model, we extended upon about 40 rules developed in previous agent based models of muscle regeneration^8,15,16 in combination with a deep literature search referencing over 100 published studies to define approximately 100 total rules that dictate the behavior of fiber cells, SSC, fibroblasts, neutrophils, and macrophages, as well as their interactions with the microenvironment, including microvasculature remodeling and cytokine diffusion and secretion (Fig. 1). For a rule to be incorporated into the model, there had to be an established understanding within the literature supporting the behavior (i.e. multiple studies reporting similar findings or supported by other reputable publications). When available, we used experimental data to define the parameters associated with the model rules. There were 52 parameters that could not be related to known physiological measurement; therefore, these parameters were calibrated using parameter density estimation which will be described below in Model Calibration. Following calibration of model parameters, separate model outputs were validated by comparison with experimental data, and various model perturbations were conducted and compared to literature results. This process allowed us to have confidence in the predictive capabilities of the model so that we could simulate and predict the sensitivity of muscle regeneration to changes in cytokines.

Prior work to construct computational models to represent muscle recovery have used ordinary differential equations²⁴ or agent-based modeling software, such as software such as Netlogo¹⁶ or Repast¹⁹. While these models have yielded great insights into skeletal muscle damage and recovery processes, they have limited capacity to represent the spatial diffusion of cytokines accurately and explicitly throughout the skeletal muscle. The Cellular-Potts model framework²³ (CPM, also known as the Glazier-Graner-Hogeweg model), proved an ideal choice because it represents diffusive species in a physically accurate manner while also allowing for logic-based representation of cellular behavior characteristic of agent-based modeling.

The ABM spatially represents a two-dimensional male murine skeletal muscle fascicle cross-section of approximately 50 muscle fibers (Fig. 2). The ABM depicts the microenvironment of the cross-section as well as the spatial migration of cells and diffusion of various cytokines. The ABM simulates the emergent phenomenon of muscle tissue from an acute injury over the course of 28 days. The spatial agents in the model include muscle fibers, necrotic muscle tissues, extracellular matrix (ECM), capillaries, lymphatic vessels, quiescent and activated fibroblasts, myofibroblasts, quiescent and activated SSCs, myoblasts, myocytes, immature myotubes, neutrophils, monocytes, resident macrophages, pro-inflammatory macrophages (M1), and anti-inflammatory macrophages (M2). In addition, the ABM includes seven diffusing factors, such as hepatocyte growth factor (HGF), monocyte chemoattractant protein-1 (MCP-1), matrix metalloproteinase-9 (MMP-9), transforming growth factor beta (TGF-β), tumor necrosis factor-alpha (TNF-α), vascular endothelial growth factor A (VEGF-A), and interleukin 10 (IL-10). A review of the literature led us to determine that these factors and cytokine isoforms were most critical for representing the behaviors of each cell during the regeneration cascade^25,26.

The muscle cross-section geometry was created by importing a histology image stained with laminin α2 into a custom MATLAB script that masked the histology image to distinguish between the fibers and ECM. The mask was imported into an initialization CC3D script that defined the muscle fibers, ECM, and microvasculature to specific cell types and generated a PIFF file that was imported into the ABM as the starting cross-section. The injury is simulated by stochastically selecting a region within the cross-section to replace the fiber elements with necrotic elements, where the percentage of CSA damage is an input parameter. When a threshold of fiber elements within a muscle fiber becomes damaged the entire muscle fiber turns necrotic and requires clearance. If the damage is below the threshold, only the region of necrosis must be removed and the SSCs can fuse to the remaining fiber. During model initialization, the injury criteria can be altered to simulate various degrees of myotoxin injury by changing the percent of necrotic tissue following injury.

Each Monte Carlo step (mcs) represents a 15-minute timestep, and the model simulations were run until 28 days post-injury. We used the CC3D feature Flip2DimRatio to control the number of times the Cellular-Potts algorithm runs per mcs. The cell velocity is limited by how many times the Cellular-Potts algorithm is run, so we increased the Flip2DimRatio until we could obtain cell speeds that were consistent with speeds derived from literature sources (Table 7). At each mcs, the agent behaviors are governed by rules that were derived from experimental data found in the literature. The behaviors of each agent are based on environmental conditions, such as nearby cells and cytokine gradients, as well as probability-based rules. As an example, a capillary located near a damaged fiber has a probability of becoming non-perfused and then senses the amount of VEGF-A and MMP-9 at its location to decide if the levels are adequate to induce angiogenesis (Table 6). Model outputs include CSA recovery (sum of total healthy fiber elements normalized by the initial CSA), capillary and collagen density, cell counts, relative cytokine abundance, and spatial coordinates of cells and cytokines.

Simulated behaviors (Fig. 2) of the neutrophils and macrophages include cytokine-dependent recruitment, chemotaxis, phagocytosis of damaged fibers (neutrophils, monocytes, and M1 macrophages), phagocytosis of apoptotic neutrophils (monocytes and M1 macrophages), secretion and uptake of cytokines, and apoptosis. The SSC and fibroblast agent behaviors also include cytokine-dependent recruitment, chemotaxis, secretion and uptake of cytokines, and apoptosis, in addition to quiescence, activation, division, and differentiation. The biological intricacy of some cell types, such as SSCs which have a more complex cell cycle and are regulated by dynamic interplay of intrinsic factors and an array of microenvironmental stimuli, led to the necessity for adding more rules that govern their behaviors²⁷. The SSCs have 33 parameters dictating the 17 agent rules (Table 3), fibroblasts have 27 parameters for 11 agent rules (Table 4), macrophages have 31 parameters for 15 agent rules (Table 2), neutrophils have 18 parameters for 7 agent rules (Table 1), fibers have 18 parameters for 4 agent rules (Table 5), and microvessels have 22 parameters for 6 agent rules (Table 6). At each mcs, cytokines are secreted by agents if certain conditions were met. For cell recruitment, the levels of recruiting cytokines for each agent are checked, and if the concentration is high enough to signal cell recruitment (Supplemental Table 1), a new agent is added to the field at the location of the highest concentration. The agents also undergo chemotaxis by sensing the surrounding cytokine gradients and move towards higher concentrations of cytokines, binding and removing that cytokine as they move along it to simulate physical binding of the cytokine to the receptor. Agents that are in a quiescent state require a certain threshold level of cytokines to become activated and cannot chemotax, secrete, divide, or differentiate until this threshold is reached. Our model assumes each unique cell type secretes the same concentration of cytokines per timestep for all relevant cytokines to drive model agent decisions. Each computational timestep represents 15 minutes of real-world time. We assume that this is of sufficient resolution to accurately reproduce immune cell agent behaviors during regeneration.

Neutrophils are recruited through capillaries to sites of necrotic tissue (Table 1). Neutrophils move to areas of necrotic tissue with high concentrations of HGF by chemotaxing along the HGF gradient to reach areas of necrosis^28,29. Neutrophils phagocytose necrotic tissue and facilitate remodeling into ECM with low-collagen density. During phagocytosis, neutrophils secrete MMP-9, MCP-1, and TNF-α^30,28,31,32. Individual neutrophil agents apoptose after phagocytosing two necrotic cells (based on calibration) or 12.5 hours after their recruitment³³.

Resident macrophages are distributed randomly throughout the tissue at a ratio of 1 macrophage per 5 myofibers at model initialization and secrete MCP-1³⁴ (Table 2). Resident macrophages chemotax along MCP-1 and HGF chemical gradients and secrete MMP-9, TNF-α, and MCP-1 during simulation^35–40. After tissue injury, monocytes are recruited through healthy capillary microvasculature and chemotax along MCP-1, VEGF-A, TGF-β^41,42–44. Monocytes infiltrate into the tissue if the MCP-1 concentration is above a specified threshold at a capillary site. Resident macrophages, monocytes, and the M1 macrophages differentiated from monocytes may phagocytose areas of necrotic tissue and apoptotic neutrophil agents^45–47. During phagocytosis, these agents secrete MMP-9, HGF, TGF-β, and IL-10^48–52.

Monocytes transition to M1 polarized macrophages when the monocyte agent experiences a large enough TNF-α concentration or if enough time has passed that a predefined transition time threshold is met. Each monocyte agent at creation has a defined transition time sampled from a gaussian distribution with mean and standard deviation (SD) set to reproduce literature-defined populations of M1 macrophages over time^51,53.

M1 macrophages may transition to M2 macrophages if the M1 macrophage agent experiences an IL-10 concentration that exceeds a threshold value or if the M1 macrophage has phagocytosed enough to meet a calibrated threshold value (as discussed in Model Calibration)^51,54,55. Following the transition to the anti-inflammatory phenotype, the M2 macrophages can proliferate, secrete TGF-β and IL-10, and chemotax along an MCP-1 gradient^38,56,57.

The model is initialized with 1 quiescent SSC per every 4 fibers and upon injury⁵⁸. Additional SSCs are recruited based on the amount of HGF, MMP-9, and TGF-β^59–62 (Table 3). For SSC activation there has to be enough HGF at the location of the quiescent SSC to induce activation^62–65. The SSCs also chemotax up the MMP-9 gradient, removing some of the MMP-9 as they move along it. Activated SSCs can also undergo symmetric or asymmetric division and differentiation given that the required cytokine signaling is met locally. Activated SSCs differentiated into myoblasts and myoblasts differentiate into myocytes^66–68. Myocytes can fuse to other myocytes to form new myotubes or fuse to fibers as long as the fiber is not fusion incompetent (i.e., fully necrotic)^27,69–71. Maturation of myotubes is required for fusion of additional myocytes to the new fiber^70,72,73. If the damage signal is not sustained, activated SSCs return to quiescence. If there is enough TGF-β to induce apoptosis and not enough VEGF-A or macrophages nearby to block it, the SSC undergoes cell death and leaves the simulation^42,74–76.

For model initialization, fibroblasts are randomly placed within the ECM at a population size that is proportional to the number of fibers⁷⁷ (Table 4). Fibroblasts are activated based on the concentration of TGF-β around the fibroblast^78,79. Fibroblasts include an additional expression in their effective energy function that directs their migration towards areas of low-density collagen ECM ⁸⁰. Specifically, fibroblasts can form spring-like links to drag them towards areas of low-density ECM which are implemented with the relation λ_ij (l_ij − L_ij)² where λ_ij denotes a hookean spring constant of a link between cells i and j, l represents the current distance between the centers of mass between the two cells, and L is the target length of the spring-like link. In addition to the cytokines secreted by fibroblasts (Table 4), collagen is secreted at low-density collagen ECM^81–86. Fibroblasts divide when they are near dividing SSCs and can differentiate into myofibroblasts with extended exposure to TGF-β^77,87,88. The myofibroblasts can secrete more collagen regardless of the ECM density⁸⁹. Fibroblasts can undergo apoptosis if there are adequate levels of TNF-α at the site of the cell but it can be blocked if there is sufficient TGF-β⁹⁰.

ECM elements surround the fiber elements and are assigned a collagen density parameter which varies based on the amount of necrotic tissue removed and the extent of fibroblast/myofibroblast collagen secretion. When necrotic elements are removed, the phagocytosing inflammatory cells secrete MMP-9s which degrade some of the collagen within that section of the ECM, thereby causing that element to have a lower collagen density²⁸. The collagen density of the ECM alters the diffusivity of the secreted factors, and fiber placement is dependent on the collagen density (discussed below). The fibroblasts help rebuild the ECM by secreting collagen on low collagen density ECM elements⁸¹. Myofibroblasts can secrete collagen on any ECM element and if prolonged results in high density collagen elements, representing a fibrotic state.

Upon model initialization, a portion of the muscle fiber agents are converted to necrotic fibers based on the user prescribed injury. Fibers that reach a damaged threshold became fully necrotic whereas those surrounding the area of necrosis were regenerating but not fully apoptotic cells. Healthy fiber elements secrete VEGF-A, and necrotic elements secrete HGF and TGF-β^64,91,92 (Table 5). Phagocytosing agents chemotax along those gradients to clear the necrosis, but before a new fiber can be deposited, the collagen has to be restored so that there is a scaffold to hold the fiber in place³⁴. Fully necrotic fibers are fusion incompetent and require myocyte-to-myocyte fusion to form a new myofiber and require maturation before additional myocyte fusion^70,72,73. Damaged fibers are regenerated by myocytes fusion to the healthy fiber edge⁹³.

The muscle fascicle environment includes approximately 4 capillaries per fiber and 1 lymphatic vessel^94,95 (Table 6). The model defines perfused capillaries as capillary agents that can transport neutrophils and monocytes into the system proportional to the concentration of recruiting cytokines^29,41. The lymphatic vessel agents act to drain the nearby cytokines and cells which removes them from the microenvironment⁹⁶. Capillaries that are neighboring areas of necrosis become non-perfused and therefore are unable to transport cells into the microenvironment until regenerated⁹⁷. Angiogenesis can occur as long as there is enough VEGF-A present at the non-perfused capillary⁹⁸. Similar to published studies, there is an increase in the capillary-to-myofiber ratio during muscle regeneration, which is due to the formation of new capillary sprouts modulated in part by local MMP-9 and VEGF-A levels^97,99,100.

For many of the agent behaviors described above there are associated binding events that play a role in regulation of the cytokine fields. Any cytokine dependent behavior is coupled with removal of a portion of that cytokine once the behavior is initiated. For example, upon SSC activation the amount of HGF required to activate is taken up by the SSC and removed from the cytokine field to simulate the binding event that led to SSC activation. Similar binding events were modeled for SSC and fibroblast division and differentiation, macrophage transitions, cell apoptosis, and chemotaxis along a cytokine gradient.

To estimate the diffusivity of the various cytokines within the ECM equation 5 was used to account for the combined effects of collagen and glycosaminoglycans (GAGs) which are known to hinder diffusivity (Table 7).¹⁰¹. The expression includes the radius of the cytokine (r_s), the radius of the fiber (r_f), the volume fraction (ϕ), D and D_∞ are the diffusivities of the cytokines in the polymer solution and in free solution, respectively. Throughout the model simulation, the diffusivity is recalculated with the updated collagen volume fraction, as the collagen density changes throughout the microenvironment. This allows the changes in collagen density within the ECM to be reflected in the diffusion rate of each of the cytokines in the model.

Known parameters were fixed to literature values, and uncertain parameters were calibrated by comparing simulation outcomes to published experimental data. Calibration data included published findings from injury models that have synchronous regeneration after tissue necrosis (i.e., cardiotoxin, notexin, and barium chloride)⁹⁹. Specifically, data that were used to calibrate the model included time-varying CSA¹⁰², SSC counts⁷⁷, and fibroblast counts⁷⁷. These metrics were used for calibration because of their key roles in the regeneration of muscle and the complex interplay between these outputs. Cell count data were normalized by the number of cells on the day of the experimental peak to allow for comparison between experiments and simulations. For CSA, experimental fold-change from pre-injury was compared with fold-change in model-simulated CSA. Model cell counts were normalized by the number of cells at the peak time point in the experimental data. SSC and fibroblast counts were normalized to day 5. Neutrophil counts were normalized to day 1. Total macrophage, M1, and M2 counts were normalized to day 3. The capillaries were normalized to fiber area, as done in the experimental data.

Initial ranges for the 52 unknown parameters were determined by literature review or by running the model to test possible upper and lower thresholds for parameters (Supplemental Table 1). To narrow the parameter ranges beyond those initial ranges, we used a recently published calibration protocol, CaliPro, which utilizes parameter density estimation to refine parameter space and calibrate to temporal biological datasets²⁰. CaliPro was selected as the calibration method because it is model-agnostic which allows it to handle the complexities of stochastic models such as ABMs, selects viable parameter ranges in the setting of a very high-dimensional parameter space, and circumvents the need for a cost function, a challenge when there are many objectives, as in our case. Briefly, Latin hypercube sampling (LHS) was used to generate 600 samples which were run in triplicate. These runs were then evaluated against a set of pass criteria, and the density functions of the passing runs and failing runs were calculated (Supplemental Table 2). Parameter ranges were narrowed by alternative density subtraction (ADS), where the new ranges were determined by the smallest and largest parameter values where the density of passing is higher than the density of failing. The sensitivity of the model outputs to the parameters was examined using LHS in combination with partial rank correlation coefficient (PRCC)¹⁰³. LHS/PRCC methods have been used for various differential equation models and ABMs¹⁰⁴. PRCC was computed using MATLAB to determine the correlation between ABM parameters (i.e. cytokine threshold for activation) and the ABM output (i.e. fibroblast cell count). Correlations with a p-value less than 0.05 were assumed to be statistically significant. This helped refine initial parameter bounds as well as make model adjustments based on the parameter dynamics elucidated from PRCC. This process of sampling parameter ranges, evaluating the model, and narrowing parameter ranges was repeated in an iterative fashion while updating pass criteria until a parameter set was identified that consistently met the strictest criteria (Supplemental Fig. 1). The final passing criteria were set to be within 1 SD for CSA recovery and 2.5 SD for SSC and fibroblast count. These criteria were selected so that the model followed experimental trends and accounted for both model stochasticity and experimental variability in datasets that had narrower SDs for certain timepoints. Following 8 iterations of narrowing the parameter space with CaliPro, we reached a set that had fewer passing runs than the previous iteration. We then returned to the runs from the prior iteration and set the bounds such that all 3 runs from the parameter set fell within the final passing criteria. The final parameter set was run 100 times to verify that the variation from the stochastic nature of the rules did not cause output that was inconsistent with experimental trends.

We compared model outputs (M1, M2, and total macrophage counts^99,105, neutrophil counts¹⁰⁶, and capillary counts¹⁰²) that were kept separate from the calibration criteria with published experimental data to verify that these outputs followed trends from the experimental data without requiring extra model tuning. In addition, we also altered various model input conditions (cell input conditions, cytokine dynamics, and microvessel dynamics) to simulate an array of model perturbations (Table 8) which allowed comparison of a set of model outputs with separate published experiments. For example, we simulated an IL-10 knockout condition by adjusting the diffusion and decay parameters so that the concentration of IL-10 throughout the simulation was zero. One hundred replicates of each model perturbation were performed, and perturbation outputs were compared with control simulation outputs via a two-sample t-test with a significance level of 0.05. We were then able to compare how the model outputs aligned with published experimental findings to determine if the model could capture the altered regeneration dynamics.

A sensitivity analysis was performed using LHS-PRCC to examine the impact of cytokine-related parameters on model outputs of interest. Diffusion coefficients and decay rates for the seven cytokines (HGF, TGF-β, MMP-9, TNF-α, VEGF-A, IL-10, MCP-1) were sampled across a range from 0.1 to 10 times the calibrated value while holding the other parameters constant. Three hundred samples were generated, and these parameter sets were simulated in triplicate. PRCCs were calculated with α=0.05 and a Bonferroni correction for the number of tests every 10 ticks/hours for CSA and cell counts for SSCs, fibroblasts, non-perfused capillaries, myoblasts, myocytes, neutrophils, M1 macrophages, and M2 macrophages.

To gain insight into the recovery response with altered angiogenesis, we simulated different levels of VEGF-A injections and conditions of hindered angiogenesis in which damaged capillaries were unable to reperfuse following injury (n = 100 for each simulation condition). Simulations were also conducted to examine correlations between cytokines and their impact on various cell behaviors and regeneration outcomes. Next, a sensitivity analysis was performed to understand how alterations in cytokines influence key metrics of regeneration. LHS-PRCC was used to quantify the impact of cytokine-related parameters (i.e. diffusion rates and decay coefficients) on outputs of interest (CSA, SSC, fibroblasts, non-perfused capillaries, myoblasts, myocytes, neutrophils, M1, and M2). A single time point for each output is summarized in Table 9, and these were chosen at the timepoint when PRCC values were peaking, with complete results available in Supplementary Figure 2.

This sensitivity analysis was then used to guide in silico experiments based on which cytokine parameters promoted favorable regeneration outcomes (i.e. improved recovery, fewer non-perfused capillaries, increased SSCs). Following individual cytokine parameter alterations, we combined the cytokine alterations based on beneficial outcomes from the initial in silico experiments to determine if the benefits would be cumulative.

Following parameter density-based calibration, the unknown parameters were narrowed into a final calibration parameter set (Supplemental table 1). The simulations captured SSC and fibroblast cellular behaviors, as well as CSA outcomes, that aligned with experimental studies (Fig. 3A-C). The model data were consistent with the experimental trends, and the 95% confidence interval was within the SD for all calibration data time points except for SSCs at day 3 (Fig. 3B). Macrophage (total, M1, and M2), neutrophil, and capillary counts, which were not used for model calibration, were found to also be consistent with experimental trends and allowed us to independently validate model outputs (Fig. 3D-H).

The model reproduced published findings for a variety of altered conditions that lead to both improved and diminished muscle regeneration (Fig. 4). For example, injections of VEGF-A led to faster CSA recovery, more damaged tissue clearance, and a concentration dependent dose response but with cell depletion there was an overall decrease in all markers of regeneration^74,107,108. When simulating hindered angiogenesis conditions, the model aligned with experimental studies showing detriments in CSA recovery, increased neutrophil and macrophage cells, and elevated ECM collagen density, indicating progression of fibrosis within the microenvironment¹⁰⁹.

The model allowed for new insights into the dynamics of muscle regeneration by providing additional timepoints and metrics to evaluate the response to exogenous delivery of VEGF-A and hindered angiogenesis. VEGF-A levels remained elevated compared to control simulations following the injection at day 5 post injury (Fig. 5A). CSA recovery had the highest increase at 28 days post injury with the high (10³) relative concentration delivered) VEGF-A injection followed by the extra high (2×10³) relative concentration delivered) injection (Fig. 5B). The medium (750 relative concentration delivered) and low (500 relative concentration delivered) VEGF-A injections had higher CSA recovery 15 days post injury but were not significantly different from the control at day 28. All VEGF-A injections had a higher capillary count and were proportional to the level of VEGF-A injection (Fig. 5C). The impact of VEGF-A injection on peak SSC and fibroblast counts was dependent on dosage amount, with the extra high VEGF-A injection resulting in the largest peaks (Fig. 5E&F). Cytokine concentration trends were similar for all injections, but most peak levels were dosage dependent (Fig. 5G-L). In contrast, HGF levels were elevated from day 5 to day 28 with hindered angiogenesis, as were TGF-β and IL-10 (Fig. 5I & L). MCP-1 concentration had a lower overall peak level with elevated levels from days 21 to 28 (Fig. 5H). Hindered angiogenesis had lower CSA recovery throughout the simulation and did not achieve unaltered regeneration levels (Fig. 5B).

Cytokine knockout perturbations revealed crosstalk and temporal interplay between cytokines (Fig. 6). For example, with MCP-1 KO there was an overall increase in cytokine levels for all other cytokines within the microenvironment except for VEGF-A at 12 hours post injury (Fig. 6A). By 7 days post injury TNF-α, TGF-β, IL-10, and MMP-9 had decreased from unaltered regeneration day 7 levels but VEGF-A and HGF were elevated. With TNF-α KO there was a decrease in TGF-β at early timepoints but a strong increase by day 28 (Fig. 6B). Following IL-10 KO there was an increase in TNF-α that peaked at 7 days post injury (Fig. 6C). HGF was slightly decreased throughout and TGF-β was strongly decreased by day 7. MMP-9 was decreased at 12 hours and 28 days post injury but heavily increased at day 7.

LHS-PRCC of cytokine decay and diffusion parameters elucidated temporal relationships between cytokine parameters and key regeneration metrics, such as positive correlations between CSA and TGF-β and MMP-9 decay (Table 9). Of all cytokine parameters, the model outputs were most sensitive to HGF decay, with all outputs expect M1 cell count being significantly impacted. PRCC plots showed that TGF-β and MMP decay were positively correlated and HGF decay was negatively correlated with CSA recovery, with higher significance at timepoints after 12 days (Supplemental Fig 2). Correlation plots for various cytokine concentrations and regeneration metrics showed trends in cytokine dependent cell behaviors such as the TNF-α concentration that led to heightened fibroblast cell counts and thresholds above that diminish fibroblast response (Supplemental Fig 3). These PRCC trends guided cytokine parameter perturbations to include lower HGF and VEGF-A decay, higher TGF-β, MMP-9, and MCP-1 decay, and higher MCP-1 diffusion because each of the cytokine modifications indicated some form of enhanced regeneration outcome metrics (Supplemental Table 3). All these perturbations except MCP-1 decay show increased CSA, increased healthy capillaries, and increased SSCs (Fig. 8). Finally, a combination of all changes except for MCP-1 decay was simulated. The combined cytokine alteration resulted in the highest CSA recovery (Fig. 8A), as well as increased M1 macrophage counts (Fig. 8B), decreased M2 macrophage counts (Fig. 8C), increased fibroblasts (Fig. 8D) and SSCs cell counts (Fig. 8E). Capillaries regenerated faster in the combined perturbation than at unaltered (Fig. 8F). The combined cytokine perturbation predicted a 13% improvement in CSA recovery compared to the unaltered regeneration amount at 28. The combined cytokine perturbation also had higher peaks in SSC and fibroblast counts than any of the singular cytokine perturbations, indicating the additive effects of altering the cytokine dynamics in combination.

The ABM offers valuable insights into the muscle regeneration dynamics under various altered conditions, elucidating the complex interplay of cytokines, angiogenesis, and cell behaviors. Systematic simulations reveal critical thresholds, nonlinear effects, and synergistic cytokine combinations impacting regeneration. Perturbations varying VEGF-A injection doses shows increased CSA recovery up to a threshold (high VEGF-A injection simulation), beyond which further improvements in CSA recovery cease. Relationships between cytokines and cellular outputs exhibit nonlinear effects, as seen with the limited impact of elevated HGF on CSA recovery beyond a threshold and the non-monotonic relationship between TNF-α and fibroblast counts (Supplemental Fig. 3). Further analysis revealed that specific combinations of cytokine perturbations could enhance regeneration beyond singular cytokine interventions. For example, a combined intervention of: 1) decreasing HGF and VEGF-A decay, 2) increasing TGF-β and MMP-9 decay, and 3) increasing MCP-1 diffusion enhanced muscle regeneration. Prior studies have shown that individually, HGF¹¹¹, VEGF-A⁷⁴, and MCP-1¹¹² stimulate muscle regeneration whereas reduced TGF-β¹¹³ and MMP-9¹¹⁴ activity improve recovery. The model suggests that combined alterations have a stronger regenerative effect than individual cytokine changes, enhancing muscle recovery through distinct mechanisms—increasing healthy capillaries, SSC counts, and reducing inflammatory cells.

Cytokine modifications intended to enhance muscle recovery can have clinical relevance and have been studied in various settings. For example, synthetic biomaterials coated with IL-4 have been implanted as a cytokine delivery vehicle and were successful in increasing M2 cells within the muscle¹¹⁵. Cytokine antagonist have been successful at promoting muscle regeneration, seen in prior work with anti-IL-6¹¹⁶. Studies have also shown that activation of plasmin is able to induce the release of ECM bound VEGF, increasing angiogenesis^14,117. Due to the complex network of cytokines, studies that deliver simple modulation of one or two cytokines typically have an insufficient response to generate appreciable improvements. This suggests that using a combination of biologic and synthetic biomaterials to modulate multiple cytokines is necessary, which aligns with our findings¹¹⁵. Multiple cytokines have been modulated through the use of PRP which contains VEGF-A and an array of other cytokines, but PRP has had mixed success in a clinical setting¹¹⁸. Our model could test and optimize combinations of cytokines, guiding future experiments and treatments.

Previous studies have employed computational models to investigate muscle regeneration across diverse contexts, such as Duchenne muscular dystrophy and volumetric muscle loss^8,15. Earlier muscle regeneration ABMs from our group have been used to test the effects of priming muscle with inflammatory cells prior to injury¹⁶. While these models laid the foundation for simulating muscle adaptations, they were constrained by limited diffusion capabilities and an absence of critical features related to microvessel growth and remodeling throughout the regeneration process. Similarly, other ABMs from our group have examined altered microenvironments, but their omission of spatial cytokine diffusion hindered comprehensive representation of cell behaviors pivotal to regeneration^8,15. Recently, new ABMs have been published that focus on cerebral palsy and the impact of injury type on eccentric contraction-induced damage^17,18. These models incorporate the effects of some cytokines but have simplified spatial behaviors that do not incorporate cytokine-specific diffusion and decay or dynamic changes in cytokine diffusion as a function of varied ECM properties. In addition, these models were not robustly calibrated or validated against experimental data and lack microvasculature components. Our model predictions are consistent with these prior models, though the added biological complexity our model led several new important insights. For example, in the hindered angiogenesis simulation there was a decrease in SSCs which was paired with poor CSA recovery similar to how lower SSC counts resulted in lower CSA recovery in perturbations in both healthy and DMD simulations¹⁵. Our model provides additional understanding about the corresponding spatial cytokine changes that ultimately result in modulation of SSC dynamics within the microenvironment. The additional model advancements incorporated address prior muscle regeneration modeling gaps in understanding of how angiogenesis alters recovery outcomes as well as the response of complex spatial cell and cytokine dynamics.

The computational model introduced in this paper provides three key advancements from previous models of muscle regenerations^8,15–18. First is the incorporation of microvasculature, second is the spatial interactions of cytokines, and third is the use of advanced calibration and sensitivity analysis techniques. The addition of microvessel growth and remodeling dynamics empowers investigations into how interventions impact angiogenesis during regeneration, thereby influencing muscle recovery outcomes. By considering the intricate relationship between microvessels and regeneration, our model opens avenues for evaluating the effects of interventions on the broader recovery process. Secondly, understanding how cytokines influence cell behaviors at different times during regeneration is crucial for determining optimal treatment targets and dosing. While cytokine dynamics can be altered experimentally, doing so is expensive and time consuming^12,14 so exploring many combinations of alterations would be practically infeasible. Our model incorporates decay and diffusion dynamics of a subset of cytokines to allow testing of far more alterations in cytokines than would be reasonable to conduct experimentally. Lastly, we leveraged the CaliPro technique for parameter density estimation-based calibration and LHS-PRCC to gain biological insight by analyzing how altered microenvironmental parameters could benefit regeneration outcomes. This approach of implementing parameter identification to guide model perturbations demonstrates the capabilities of the model as a novel tool for generating new hypotheses and identifying mechanisms to target for enhanced regeneration outcomes.

There are some important limitations of this study that should be discussed. First, the model does not include all cytokines that are known to influence muscle regeneration, such as IGF, FGF, PDGF, and additional isoforms of IL. These cytokines likely have redundant functions, given the model effectively captures relevant cell behaviors using the included cytokines. Second, while this model does not currently consider hypertrophy during regeneration, which restricts CSA recovery from surpassing 100%, the cell dynamics it portrays remain consistent with those observed in studies that lead to hypertrophy following injury. By integrating datasets from diverse sources, we capture the intricate dynamics of muscle regeneration, while acknowledging inherent limitations arising from variations in sample sizes and experimental techniques across sources. Lastly, the current model was calibrated to male mice data despite known sex difference in skeletal muscle, regeneration mechanisms, and the timeline of recovery^119–121. Data on female muscle regeneration is fairly limited because most muscle injury studies only use male mice or do not distinguish between sexes, making it difficult to incorporate sex differences into the model¹²².

This paper describes a significant advancement in modeling the complex process of muscle regeneration. Future efforts will extend the use of parameter density estimation to optimize the selection, doses, and timing of injections of exogenously delivered cytokines. Additionally, we aim to explore diverse muscle injury types and their varying recovery responses, addressing challenges in comparing different acute injury techniques found in the literature. This study underscores the significance of cellular and cytokine spatial dynamics in muscle regeneration. Further inclusion of additional factors and hormones would provide a more holistic understanding of the system and how treatments may be altered based on microenvironmental conditions, providing a unique framework for the study of personalized muscle injury treatment.