“Translational Systems Biology of Inflammation: From Data Patterns to Knowledge”

Properly-regulated inflammation is central to homeostasis, but in adequate or overly-robust inflammation can lead to disease. Like many biological processes, inflammation and its various manifestations in disease are multi-dimensional. The advent of multiplexed platforms for gathering biological data, while providing an unprecedented level of detailed information about the dynamics of complex biological systems such as the inflammatory response, has paradoxically also flooded investigators with data they are often unable to use. Systems approaches, including data-driven and mechanistic computational modeling, have been used to decipher aspects of the inflammatory responses that characterize trauma/hemorrhage and sepsis. Data-driven and mechanistic models (including both differential equation- and agent-based models) of acute inflammation in mice, rats, swine, and humans were generated, suggesting a central role for the positive feedback loop of inflammation -> tissue damage/dysfunction -> inflammation. These systems-based insights have led to in silico models of individuals and populations in the settings of trauma and sepsis (1-10).

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