DescriptionDysregulation of the inflammatory response is a critical component of many clinically challenging disorders such as sepsis. Inflammation is a biological process designed to lead to healing and recovery, ultimately restoring homeostasis; however, the failure to fully achieve those beneficial results can leave a patient in a dangerous persistent inflammatory state. One of the primary challenges in developing novel therapies in this area is that inflammation is comprised of a complex network of interacting pathways. Here, we discuss our approaches towards addressing this problem through computational systems biology, with a particular focus on how the presence of biological rhythms and importantly circadian (~24hr) and the disruption of these rhythms may be applied in a translational context. By leveraging the information content embedded in physiologic variability, and its loss under acute inflammatory response we aim to gain insight into the underlying physiology. Heart rate variability (HRV) has been studied as a potential prognostic marker in inflammation-linked diseases. We modeled the interactions between human endotoxemia mediators and the autonomic nervous system in order to understand the loss of HRV in presence of stress, allowing for the rationalization of experimental observations in the framework of a quantitative model. Furthermore, by modeling the flow of circadian information from the environmental light/dark cycles to the systemic cortisol level and ultimately to the single immune cell level, we identified critical dynamics that confer robust synchronization and rhythmicity both of which are characteristics associated with well-being. Lastly, by considering the disparate role of cortisol as an immunopermissive and immunosuppressive agent, we elucidated the dynamics leading to a time of day dependence of body’s inflammatory response. These results denote the critical importance of physiological rhythms in homeostasis and stress, and elucidate the potential to derive critical information by the analysis of variability and its source both at the systemic and at the single cell level.