Cordelia Ziraldo1, Qi Mi2, Gary An3, Yoram Vodovotz4. 1. Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh , Pittsburgh, Pennsylvania. ; Department of Computational and Systems Biology, University of Pittsburgh , Pittsburgh, Pennsylvania. 2. Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh , Pittsburgh, Pennsylvania. ; Department of Sports Medicine and Nutrition, University of Pittsburgh , Pittsburgh, Pennsylvania. 3. Department of Surgery, University of Chicago , Chicago, Illinois. 4. Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh , Pittsburgh, Pennsylvania. ; Department of Surgery, University of Pittsburgh , Pittsburgh, Pennsylvania.
Abstract
OBJECTIVE: Inflammation is both central to proper wound healing and a key driver of chronic tissue injury via a positive-feedback loop incited by incidental cell damage. We seek to derive actionable insights into the role of inflammation in wound healing in order to improve outcomes for individual patients. APPROACH: To date, dynamic computational models have been used to study the time evolution of inflammation in wound healing. Emerging clinical data on histo-pathological and macroscopic images of evolving wounds, as well as noninvasive measures of blood flow, suggested the need for tissue-realistic, agent-based, and hybrid mechanistic computational simulations of inflammation and wound healing. INNOVATION: We developed a computational modeling system, Simple Platform for Agent-based Representation of Knowledge, to facilitate the construction of tissue-realistic models. RESULTS: A hybrid equation-agent-based model (ABM) of pressure ulcer formation in both spinal cord-injured and -uninjured patients was used to identify control points that reduce stress caused by tissue ischemia/reperfusion. An ABM of arterial restenosis revealed new dynamics of cell migration during neointimal hyperplasia that match histological features, but contradict the currently prevailing mechanistic hypothesis. ABMs of vocal fold inflammation were used to predict inflammatory trajectories in individuals, possibly allowing for personalized treatment. CONCLUSIONS: The intertwined inflammatory and wound healing responses can be modeled computationally to make predictions in individuals, simulate therapies, and gain mechanistic insights.
OBJECTIVE:Inflammation is both central to proper wound healing and a key driver of chronic tissue injury via a positive-feedback loop incited by incidental cell damage. We seek to derive actionable insights into the role of inflammation in wound healing in order to improve outcomes for individual patients. APPROACH: To date, dynamic computational models have been used to study the time evolution of inflammation in wound healing. Emerging clinical data on histo-pathological and macroscopic images of evolving wounds, as well as noninvasive measures of blood flow, suggested the need for tissue-realistic, agent-based, and hybrid mechanistic computational simulations of inflammation and wound healing. INNOVATION: We developed a computational modeling system, Simple Platform for Agent-based Representation of Knowledge, to facilitate the construction of tissue-realistic models. RESULTS: A hybrid equation-agent-based model (ABM) of pressure ulcer formation in both spinal cord-injured and -uninjured patients was used to identify control points that reduce stress caused by tissue ischemia/reperfusion. An ABM of arterial restenosis revealed new dynamics of cell migration during neointimal hyperplasia that match histological features, but contradict the currently prevailing mechanistic hypothesis. ABMs of vocal fold inflammation were used to predict inflammatory trajectories in individuals, possibly allowing for personalized treatment. CONCLUSIONS: The intertwined inflammatory and wound healing responses can be modeled computationally to make predictions in individuals, simulate therapies, and gain mechanistic insights.
Authors: Samer Tohme; Hamza O Yazdani; Vikas Sud; Patricia Loughran; Hai Huang; Ruben Zamora; Richard L Simmons; Yoram Vodovotz; Allan Tsung Journal: J Immunol Date: 2018-11-30 Impact factor: 5.422
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Authors: S Michaela Rikard; Paul J Myers; Joachim Almquist; Peter Gennemark; Anthony C Bruce; Maria Wågberg; Regina Fritsche-Danielson; Kenny M Hansson; Matthew J Lazzara; Shayn M Peirce Journal: Cell Mol Bioeng Date: 2021-06-15 Impact factor: 2.321