title: In silico models of acute inflammation in animals.
abstract: Trauma and hemorrhagic shock elicit an acute inflammatory response, predisposing patients to sepsis, organ dysfunction, and death. Few approved therapies exist for these acute inflammatory states, mainly due to the complex interplay of interacting inflammatory and physiological elements working at multiple levels. Various animal models have been used to simulate these phenomena, but these models often do not replicate the clinical setting of multiple overlapping insults. Mathematical modeling of complex systems is an approach for understanding the interplay among biological interactions. We constructed a mathematical model using ordinary differential equations that encompass the dynamics of cells and cytokines of the acute inflammatory response, as well as global tissue dysfunction. The model was calibrated in C57Bl/6 mice subjected to (1) various doses of lipopolysaccharide (LPS) alone, (2) surgical trauma, and (3) surgery + hemorrhagic shock. We tested the model's predictive ability in scenarios on which it had not been trained, namely, (1) surgery +/- hemorrhagic shock + LPS given at times after the beginning of surgical instrumentation, and (2) surgery + hemorrhagic shock + bilateral femoral fracture. Software was created that facilitated fitting of the mathematical model to experimental data, as well as for simulation of experiments with various inflammatory challenges and associated variations (gene knockouts, inhibition of specific cytokines, etc.). Using this software, the C57Bl/6-specific model was recalibrated for inflammatory analyte data in CD14-/- mice and was used to elucidate altered features of inflammation in these animals. In other experiments, rats were subjected to surgical trauma +/- LPS or to bacterial infection via fibrin clots impregnated with various inocula of Escherichia coli. Mathematical modeling may provide insights into the complex dynamics of acute inflammation in a manner that can be tested in vivo using many fewer animals than has been possible previously.
authors.affiliation: Department of Surgery, University of Pittsburgh, Pittsburgh, PA 15213, USA. vodovotzy@upmc.edu
authors.forename: Yoram
authors.initials: Y
authors.lastname: Vodovotz
authors.forename: Carson C
authors.initials: CC
authors.lastname: Chow
authors.forename: John
authors.initials: J
authors.lastname: Bartels
authors.forename: Claudio
authors.initials: C
authors.lastname: Lagoa
authors.forename: Jose M
authors.initials: JM
authors.lastname: Prince
authors.forename: Ryan M
authors.initials: RM
authors.lastname: Levy
authors.forename: Rukmini
authors.initials: R
authors.lastname: Kumar
authors.forename: Judy
authors.initials: J
authors.lastname: Day
authors.forename: Jonathan
authors.initials: J
authors.lastname: Rubin
authors.forename: Greg
authors.initials: G
authors.lastname: Constantine
authors.forename: Timothy R
authors.initials: TR
authors.lastname: Billiar
authors.forename: Mitchell P
authors.initials: MP
authors.lastname: Fink
authors.forename: Gilles
authors.initials: G
authors.lastname: Clermont
mesh_terms: D000818:Animals; D003198:Computer Simulation; D004195:Disease Models, Animal; D006801:Humans; D007249:Inflammation / Q000276:immunology / Q000378:metabolism / Q000503:physiopathology*; D051379:Mice; D018345:Mice, Knockout; D008954:Models, Biological; D051381:Rats