Fig. 1

Computational strategy. a First, we used our computational kinetic model to simulate 120,000 distinct wound-healing scenarios. The output of each simulation comprised the time courses for the 28 model variables at 40 time points after wounding (each simulated time point represented the level of a variable on each of the 40 days post-wounding). In addition to the 120,000 simulations, we used the default parameter set to simulate a normal wound healing (i.e., normal healing) scenario. Second, we calculated fold changes (on day 40) of total collagen and fibroblast concentrations in each of the 120,000 simulations with respect to their corresponding values in the normal-healing simulation. Based on these fold changes, we classified the 120,000 simulations as “normal” (fold change ≤ 1), “mild pathological” (5 ≤ fold change ≤ 10), or “severe pathological” (fold change > 10) scarring simulations. Finally, we analysed the concentration distributions (or histograms) of 18 modeled wound proteins (excluding collagen) in the normal-healing and pathological-scarring simulations to determine the diagnostic and prognostic biomarkers of pathological scarring (see the “Methods” section for further details). b The pie chart shows the number of simulations that fell into each of the three categories of wound healing (i.e., normal, mild pathological, or severe pathological) after implementation of the classification criteria