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Systematic Analysis of Conservation Relations in Escherichia coli Genome-Scale Metabolic Network Reveals Novel Growth Media

Marcin Imieliski ^*, Calin Belta , Harvey Rubin  and Ádam Halász 

^* Genomics and Computational Biology Graduate Group, Department of Medicine, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania; Department of Manufacturing Engineering, Boston University, Brookline, Massachusetts; and General Robotics, Automation, Sensing, and Perception Laboratory, University of Pennsylvania, Philadelphia, Pennsylvania

Correspondence: Address reprint requests to M. Imieliski, Tel.: 215-421-3980; E-mail: imielins{at}mail.med.upenn.edu.

A biochemical species is called producible in a constraints-based metabolic model if a feasible steady-state flux configuration exists that sustains its nonzero concentration during growth. Extreme semipositive conservation relations (ESCRs) are the simplest semipositive linear combinations of species concentrations that are invariant to all metabolic flux configurations. In this article, we outline a fundamental relationship between the ESCRs of a metabolic network and the producibility of a biochemical species under a nutrient media. We exploit this relationship in an algorithm that systematically enumerates all minimal nutrient sets that render an objective species weakly producible (i.e., producible in the absence of thermodynamic constraints) through a simple traversal of ESCRs. We apply our results to a recent genome scale model of Escherichia coli metabolism, in which we traverse the 51 anhydrous ESCRs of the metabolic network to determine all 928 minimal aqueous nutrient media that render biomass weakly producible. Applying irreversibility constraints, we find 287 of these 928 nutrient sets to be thermodynamically feasible. We also find that an additional 365 of these nutrient sets are thermodynamically feasible in the presence of oxygen. Since biomass producibility is commonly used as a surrogate for growth in genome scale metabolic models, our results represent testable hypotheses of alternate growth media derived from in silico analysis of the E. coli genome scale metabolic network.