384407 Predicting Gene Expression Data from Changes in Eicosanoid Metabolite Levels in Raw 264.7 Macrophages Using Cybernetic Variables

Tuesday, November 18, 2014: 2:18 PM
214 (Hilton Atlanta)
Frank T. DeVilbiss1, Devaraj Jayachandran1, Mano R. Maurya2, Shakti Gupta3, Shankar Subramaniam4 and Doraiswami Ramkrishna1, (1)School of Chemical Engineering, Purdue University, West Lafayette, IN, (2)San Diego Supercomputer Center, University of California San Diego, La Jolla, CA, (3)Department of Bioengineering, University of California, San Diego, CA, (4)San Diego Supercomputer Center, Department of Bioengineering, Department of Chemistry & Biochemistry, University of California San Diego, La Jolla, CA

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 an 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 lipopolysaccharide (LPS), a biochemical marker of bacterial infection. Eicosanoids are a diverse group of molecules derived from the oxidation of fatty acids that play a role in wide range of functions including signaling, inflammation and immunity. For this system, a cybernetic model was developed to describe the change in eicosanoid levels as a response to LPS and also in a control scenario with no LPS. The objective function used for this system is the maximization of the rate of TNF-alpha production which is a key player in inflammation. After fitting this model to metabolite data, the model's cybernetic variables were compared with dynamic gene expression data at the major branch points in this network. The comparison 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’s organizing principle.


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