Wound fibrosis (i.e., excessive scar formation) is a medical problem of increasing prevalence, with poorly understood mechanistic triggers and limited therapeutic options. In this study, we employed an integrated approach that combines computational predictions with new experimental studies in mice to identify plausible mechanistic triggers of pathological scarring in skin wounds. We developed a computational model that predicts the time courses for six essential cell types, 18 essential molecular mediators, and collagen, which are involved in inflammation and proliferation during wound healing. By performing global sensitivity analyses using thousands of model-simulated wound-healing scenarios, we identified five key processes (among the 90 modeled processes) whose dysregulation may lead to pathological scarring in wounds. By modulating a subset of these key processes, we simulated fibrosis in wounds. Moreover, among the 18 modeled molecular mediators, we identified TGF-β and the matrix metalloproteinases as therapeutic targets whose modulation may reduce fibrosis. The model predicted that simultaneous modulation of TGF-β and matrix metalloproteinases would be more effective in treating excessive scarring than modulation of either therapeutic target alone. Our model was validated with previously published and newly generated experimental data, and suggested new in vivo experiments.
This work was supported by the Clinical and Rehabilitative Medicine Research Program of the U.S. Army Medical Research and Materiel Command, Fort Detrick, MD.
The opinions and assertions contained in this study are the private views of the authors and are not to be construed as official or as reflecting the views of the U.S. Army or of the U.S. Department of Defense.
The online version of this article contains supplemental material.
Abbreviations used in this article:
- extracellular matrix
- extended Fourier amplitude sensitivity testing
- global sensitivity analysis
- matrix metalloproteinase
- partial rank correlation coefficient
- root mean squared error
- tolerance interval.
- Received July 22, 2016.
- Accepted November 15, 2016.
- Copyright © 2017 by The American Association of Immunologists, Inc.