Machine learning and mechanistic computational modeling of inflammation as tools for designing immunomodulatory biomaterials

Abstract The inflammatory response is a dynamic, complex process that involves multiple interacting cell types and molecular mediators. Proinflammatory, feed-forward processes allow for proper ramp-up of inflammation but can also drive a maladaptive, overly exuberant response that harms tissues. Antiinflammatory, negative feedback processes keep these feed-forward aspects of inflammation in check and often stimulate healing, but when produced inappropriately can either impair healing or, at the other extreme, drive excessive scaring and fibrosis. Biomaterials are used to repair injured tissues but may be suboptimal or even harmful if they exacerbate the inflammatory response in those tissues. In this chapter, we describe how computational approaches to modeling inflammation in silico—including some studies involving biomaterials—have helped us decipher some of the complexity of inflammation, suggesting applications of computational modeling to optimize the preclinical and clinical use of biomaterials.