Since their inception nearly three decades ago, cybernetic models have been used to analyze the dynamic control of metabolism based off of goals relevant to the organism's functioning. In the case of single cellular organisms, goals like the maximization of carbon uptake rate or growth rate have shown much success in predicting phenomena like dynamic intracellular fluxes, gene knockout behavior and hysteresis effects in chemostat cultures. The goal of this work is to take this existing framework for modeling single-celled organisms and modify it appropriately to describe more complex mammalian metabolic networks. A key step in this process is finding an appropriate description of the metabolic objectives of these cells or cellular subsystems.

The focus of study in this work is the response of eicosanoid metabolism in RAW 264.7 macrophages to a chemical marker of bacterial infection, lipopolysaccharide (LPS). Eicosanoids are a diverse group of molecules derived from the oxidation of fatty acids that have a wide range of signaling functions including the generation of inflammation. For this system, a cybernetic model was developed to describe the change in eicosanoid levels as a response to LPS and a control scenario with no LPS. The objective function used for this system is the maximization of the rate of TNF? production which is a key player in the inflammatory process. Fitting this model to metabolite data and comparing cybernetic variables with dynamic gene expression data at the major branch point in this network shows a significant level of agreement. This result serves as an important validation of cybernetic variables using real data from cells. Moreover, it shows that cybernetic variables can be used to infer trends in gene expression data only using information taken from the metabolite level and a description of the metabolic network\u2019s organizing principle.