In the lung, alternatively activated macrophages (AAM) form the first line of defense against microbial infection. Due to the highly regulated nature of AAM, the lung can be considered as an immunosuppressive organ for respiratory pathogens. However, as infection progresses in the lung, another population of macrophages, known as classically activated macrophages (CAM) enters; these cells are typically activated by IFN-γ. CAM are far more effective than AAM in clearing the microbial load, producing proinflammatory cytokines and antimicrobial defense mechanisms necessary to mount an adequate immune response. Here, we are concerned with determining the first time when the population of CAM becomes more dominant than the population of AAM. This proposed “switching time” is explored in the context ofMycobacterium tuberculosis(MTb) infection. We have developed a mathematical model that describes the interactions among cells, bacteria, and cytokines involved in the activation of both AAM and CAM. The model, based on a system of differential equations, represents a useful tool to analyze strategies for reducing the switching time, and to generate hypotheses for experimental testing.

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.

Significance: Traumatic injury elicits a complex, dynamic, multi-dimensional inflammatory response that is intertwined with complications such as multiple organ dysfunction and nosocomial infection. The complex interplay between inflammation and physiology in critical illness remains a challenge for translational research, including the extrapolation to human disease from animal models. Recent Advances: Over the past decade, we and others have attempted to decipher the bio-complexity of inflammation in these settings of acute illness, using computational models to improve clinical translation. In silico modeling has been suggested as a computationally-based framework for integrating data derived from basic biology experiments as well as preclinical and clinical studies. Critical Issues: Extensive studies in cells, mice, and human blunt trauma patients have led us to suggest: 1) that while an adequate level of inflammation is required for healing post-trauma, inflammation can be harmful when it becomes self-sustaining via a Damage-Associated Molecular Pattern (DAMP) / Toll-Like Receptor (TLR)-driven, feed-forward circuit; 2) that chemokines play a central regulatory role in driving either self-resolving or self-maintaining inflammation that drives the early activation of both classical innate and more recently-recognized lymphoid pathways; and 3) the presence of multiple thresholds and feedback loops which could significantly affect the propagation of inflammation across multiple body compartments. Future Directions: These insights from data-driven models into the primary drivers and interconnected networks of inflammation have been used to generate mechanistic computational models. Together, these models may be used to gain basic insights as well as serving to help define novel biomarkers and therapeutic targets.

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 shock--and 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.