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.

The Acute Inflammatory Response in Trauma /Hemorrhage and Traumatic Brain Injury: Current State and Emerging Prospects

“…A more recent concept has been that of "Translational Systems Biology" [11,72,82,108], which includes computational simulations of clinical trials [83][84][85][86], potential clinical diagnostics in the form of patientspecific models [144], streamlined usage of experimental animals [87], and rational device design [89]. Using these approaches, we have shed basic insights into the basic www.ljm.org.ly interactions of trauma with hemorrhage in mice [81], and have already begun to create patient-specific, predictive simulations in human T/HS [145] and TBI [146].…”

In Silico and In Vivo Approach to Elucidate the Inflammatory Complexity of CD14-deficient Mice

“…We have previously demonstrated that our model could account for the features of sepsis patients in a virtual clinical trial of anti-TNF antibodies in sepsis (8) and suggest inflammatory biomarkers for patients undergoing cardiopulmonary bypass (6). We have used the model to streamline animal use in experiments involving bone fracture along with hemorrhagic shock (56). The present study shows for the first time that we can use such a baseline mathematical model of inflammation calibrated with a robust dataset in mice, combined with a relatively limited amount of data in a genetically altered mouse, to yield an ensemble of models that yield essentially the same prediction regarding a given outcome (overall organ damage/dysfunction).…”

Translational Systems Biology: Introduction of an Engineering Approach to the Pathophysiology of the Burn Patient

“…The primary methods of dynamic mathematical modeling that have been thus far used in the TSB community are equation-based modeling (EBM) [27][28][29][30][31][32][46][47][48] and agent-based modeling (ABM) 24,25,49 The two forms of dynamic mathematical models have their relative strengths and weaknesses, but they are not mutually exclusive, 50 and the utilization of both methods in the TSB community demonstrates a pragmatic, goal-directed approach that is not tied to a particular modeling platform. 26 For a more detailed description of these methods see "Appendix.…”

“…47 Next, the phenomenon of endotoxin priming and tolerance was examined. 48 In order to develop calibration and validation methodologies, a series of EBMs were matched to in vivo models of endotoxin administration in mice, 29 where lethal doses of endotoxin were prospectively predicted using the model, 28 and models of septic shock and adult respiratory distress syndrome in swine, 61,62 where the model was used to predict circulating cytokine levels at various time points and severity of illness. Moreover, these models were coupled to novel data-fitting algorithms and circulating inflammation markers obtained from gene knockout mice in order to discern features underlying the altered inflammation in these animals.…”

“…23 In the interest of developing an engineering approach to the burn patient, we review TSB applications that have been directed at the acute inflammatory response (AIR), specifically related to sepsis, trauma, hemorrhagic shock, and wound healing. [24][25][26][27][28][29][30][31][32] Many of the primary groups that have applied TSB to acute inflammation, shock, and critical care have coalesced into an organization named the Society of Complexity in Acute Illness (SCAI, Website at http://www.scai-med.org), which has provided a framework for the integration of TSB into the general research community.…”