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

Abstract

The inflammatory phenotype of genetically modified mice is complex, and the role of Gram-negative lipopolysaccharide (LPS) in acute inflammation induced by surgical cannulation trauma, alone or in combination with hemorrhage and resuscitation ("hemorrhagic shock"), is both complex and controversial. We sought to determine if a mathematical model of acute inflammation could elucidate both the phenotype of CD14-deficient (CD14 -/-) mice-following LPS, cannulation, or hemorrhagic shockand the role of LPS in trauma/hemorrhage-induced inflammation. A mathematical model of inflammation initially calibrated in wild-type (C57Bl/6) mice subjected to LPS, cannulation, and hemorrhagic shock was recalibrated in CD14 -/-mice subjected to the same insults, yielding an ensemble of models that suggested specific differences at the cellular and molecular levels (for example, 43-fold lower activation of leukocytes by LPS). The CD14-/--specific model ensemble could account for complex changes in inflammatory analytes in these mice following LPS treatment. Model prediction of similar organ damage in CD14 -/-and wild-type mice subjected to cannulation alone or with hemorrhagic shock was verified in vivo (similar ALT levels). These studies suggest that LPS-CD14 responses do not cause inflammation in surgical trauma/hemorrhagic shock and demonstrate a novel use of combined in silico and in vivo methods to elucidate the complex inflammatory phenotype of genetically modified animals.

Computational modelling of the inflammatory response in trauma, sepsis and wound healing: implications for modelling resilience

“…Initially formulated to deal with the clinical challenge of integrating acute inflammation and organ dysfunction in critical illness, this work expanded to include healing of acute and chronic wounds and infections in various diseases, and rational dynamic modulation of inflammation. Under the umbrella of translational systems biology of inflammation, we and others have created mechanistic computational models of acute inflammation in sepsis [43][44][45][46][47], endotoxemia [48][49][50][51][52][53][54][55][56][57][58][59][60][61][62] and trauma/ haemorrhage [48,50,63,64]. In large part, these models (both ODE and ABM) are based on the typical progression of the inflammatory pathway described in the preceding section.…”

“…In large part, these models (both ODE and ABM) are based on the typical progression of the inflammatory pathway described in the preceding section. Some of these models are purely theoretical [43][44][45][46][47]49], whereas others are based on data either at the protein [48,50,63,64] or mRNA [52,53,[58][59][60] level. Similar mechanistic models have focused on related diseases such as necrotizing enterocolitis [65][66][67][68].…”

“…A cohort of 10 000 virtual trauma patients was generated from the 33 patients' individual inflammatory and physiological trajectories. Each virtual patient was then subjected to three insults of trauma: low/intermediate Injury Severity Score (ISS) (5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20), intermediate/high ISS (20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35) and severe ISS (35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50). The in silico distributions of model variables equated with length of stay in the intensive care unit, degree of multiple organ dysfunction and interleukin (IL)-6 area under the curve were in concordance with...…”

“…We suggest that gleaning translationally relevant insights into the inflammatory response and its interconnected (patho)physiology will require integration of methods from across this spectrum [25,[46][47][48][49][50]63,64,74], in order to progress from data rsfs.royalsocietypublishing.org Interface Focus 4: 20140004 to models to actionable knowledge and prediction (ideally in an in vivo or clinical context) [6,75].…”

Modeling the influence of circadian rhythms on the acute inflammatory response

“…In recent years, a number of models have been developed by applying different modeling techniques (agent based modeling or equation based modeling), at different scales (molecular, cellular, systemic, or a combination), and focusing on different specific problems (acute inflammation, trauma, or the response to a specific disease) (An, 2008;Foteinou et al, 2009c;Jit et al, 2005;Kumar et al, 2008;Li et al, 2008;Lipniacki et al, 2006;Mi et al, 2007;Prince et al, 2006;Zuev et al, 2006). These models have been developed with the practical goals of impacting healthcare through translational systems biology (Foteinou et al, 2009d;Vodovotz et al, 2008) and rationalizing the design of experiments and clinical trials .…”