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Computational Modelling of the Inflammatory Response in Trauma, Sepsis and Wound Healing: Implications for Modelling Resilience

Overview
Journal Interface Focus
Specialty Biology
Date 2014 Oct 7
PMID 25285195
Citations 10
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Abstract

Resilience refers to the ability to recover from illness or adversity. At the cell, tissue, organ and whole-organism levels, the response to perturbations such as infections and injury involves the acute inflammatory response, which in turn is connected to and controlled by changes in physiology across all organ systems. When coordinated properly, inflammation can lead to the clearance of infection and healing of damaged tissues. However, when either overly or insufficiently robust, inflammation can drive further cell stress, tissue damage, organ dysfunction and death through a feed-forward process of inflammation → damage → inflammation. To address this complexity, we have obtained extensive datasets regarding the dynamics of inflammation in cells, animals and patients, and created data-driven and mechanistic computational simulations of inflammation and its recursive effects on tissue, organ and whole-organism (patho)physiology. Through this approach, we have discerned key regulatory mechanisms, recapitulated in silico key features of clinical trials for acute inflammation and captured diverse, patient-specific outcomes. These insights may allow for the determination of individual-specific tolerances to illness and adversity, thereby defining the role of inflammation in resilience.

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References
1.
Foteinou P, Calvano S, Lowry S, Androulakis I . A physiological model for autonomic heart rate regulation in human endotoxemia. Shock. 2010; 35(3):229-39. PMC: 3045969. DOI: 10.1097/SHK.0b013e318200032b. View

2.
Arciero J, Ermentrout G, Upperman J, Vodovotz Y, Rubin J . Using a mathematical model to analyze the role of probiotics and inflammation in necrotizing enterocolitis. PLoS One. 2010; 5(4):e10066. PMC: 2856678. DOI: 10.1371/journal.pone.0010066. View

3.
Nathan C . Points of control in inflammation. Nature. 2002; 420(6917):846-52. DOI: 10.1038/nature01320. View

4.
Reynolds A, Rubin J, Clermont G, Day J, Vodovotz Y, Ermentrout G . A reduced mathematical model of the acute inflammatory response: I. Derivation of model and analysis of anti-inflammation. J Theor Biol. 2006; 242(1):220-36. DOI: 10.1016/j.jtbi.2006.02.016. View

5.
An G . A model of TLR4 signaling and tolerance using a qualitative, particle-event-based method: introduction of spatially configured stochastic reaction chambers (SCSRC). Math Biosci. 2008; 217(1):43-52. DOI: 10.1016/j.mbs.2008.10.001. View