The acute inflammatory response is a cascade of cellular and molecular events that takes place in the body after a traumatic injury or an infection. This response involves the immune, endocrine and neurological systems and aims to eliminate damaging agents and restore the body back to equilibrium. The clinical manifestation of this response is called the systemic inflammatory response syndrome (SIRS) or sepsis in the case of infection. There are approximately three-quarters of a million cases of SIRS severe enough to warrant hospitalization in the United States each year. Although much has been learned in the last several years on the molecular and cellular mechanisms of SIRS, this knowledge has not translated into improved outcome prediction or treatments. We hypothesize that a major reason effective treatments have not been developed is that a good understanding of the global dynamical behavior of the acute inflammatory response is lacking. We propose to address this shortcoming by developing biologically accurate mathematical models of the acute inflammatory response. These models will be tested and calibrated with carefully designed animal experiments in an iterative procedure that relies heavily on detailed statistical analysis. More specifically, we propose to 1) develop a hierarchy of mathematical models, each designed to address a specific set of questions; 2) refine and validate the mathematical models through an iterative process of experimentation, statistical analysis, and model development; and 3) analyze the various modes of behavior in the mathematical models and use these modes to make predictions of outcomes in different experimental scenarios. The long-term goal of this study is to provide a rational basis for the design of therapies to combat SIRS as well as to aid in patient management.
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