467602 Deductive Determination of Dynamic Cellular Objectives from Biological Data

Thursday, November 17, 2016: 5:03 PM
Continental 8 (Hilton San Francisco Union Square)
Frank T. DeVilbiss1, Mano R. Maurya2,3, Shakti Gupta2,3, Shankar Subramaniam4 and Doraiswami Ramkrishna1, (1)School of Chemical Engineering, Purdue University, West Lafayette, IN, (2)Department of Bioengineering, University of California, San Diego, La Jolla, CA, (3)San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA, (4)San Diego Supercomputer Center, Department of Bioengineering, Department of Chemistry & Biochemistry, University of California San Diego, La Jolla, CA

Cybernetic models describe metabolic changes by means of simplifying intricate cellular regulation mechanisms with the concept of “dynamic metabolic goals.” In prior work, these goals have taken the form of inductive assumptions about the nature of metabolism and are explicitly stated in forms like “maximization of growth rate” or “maximization of the rate of carbon uptake.” Also, often cellular phenotypes resulting from specific stimulus can serve as context-specific objective functions. For instance, in the case of macrophage metabolism in response to stimulation by LPS, the inflammation phenotype represented by cytokine readout profiles can serve as an objective function. These inductively determined metabolic goals have yielded numerous predictions of metabolic phenomena, but a procedure to determine the most descriptive cybernetic objective function for a given system has yet to be proposed. To address this, we develop a method to deductively resolve metabolic objectives from a combination of metabolomic and transcriptomic data.

This analysis simultaneously relates dynamic metabolic changes with the regulation of enzymes that influence those metabolic changes. It provides objective functions that take the form of optimal weightings which represent the return on investment for various metabolic pathways that are competing for same pool of resources. To give biological context to these returns on investment, a method to mine gene expression data and explain the objective function using pathway enrichment analysis is proposed. We use this approach to analyze three systems: two diauxic growth scenarios and prostaglandin metabolism in a mammalian cell line. Pathway enrichment analysis of the genes mined using this procedure provides objective functions that show agreement with already established experimental knowledge related to the behavior of these systems in their respective conditions. The ability to determine objective functions deductively from the data allows for the formulation of robust cybernetic descriptions of systems where objective functions are difficult to determine inductively. This approach has applications in specifying objective functions for more complicated metabolic systems in multi-cellular organisms.


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