When a pathogen enters the human body, an acute inflammatory response is activated to eliminate the intruder. However, in some patients an extreme response of the immune system may occur which can lead to tissue damage, organ failure, and eventually death. This overwhelming reaction of the immune system is called sepsis.
We present a computational model consisting of ordinary differential equations to explore the dynamics of the acute inflammatory response against infections caused when a pathogen makes its way into a host. This model incorporates energy production along with the energy requirements that arise when fighting such an infection. In particular, we investigate the role of energetics during infection and explore the relation between overproduction of Nitric Oxide (NO), Lactate, altered Adenosine Triphosphate (ATP) levels, and sepsis.
Finally, we have followed a data-driven approach to extend our model to try to better understand the role of energy in sepsis. This extended model is calibrated by fitting animal data from a study done in thirty-two baboons that were induced into sepsis after infusing E. coli intravenously. Using Bayesian analysis, we quantify uncertainty in model parameters to investigate differences across survivors and non survivors.
704 Thackeray Hall