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Generation and Characterization of Novel Pathways to Degrade Xenobiotics

Stacey D. Finley1, Linda J. Broadbelt1, and Vassily Hatzimanikatis2. (1) Chemical and Biological Engineering, Northwestern University, 2145 Sheridan Road, E136, Evanston, IL 60208, (2) Laboratory of Computational Systems Biotechnology, EPFL, CH-1015, Lausanne, Switzerland

Anthropogenic compounds, called xenobiotics, are released into the environment in many different forms, including industrial pollutants and pesticides. Microorganisms are thought to be able to degrade these compounds; however, the reactions through which biodegradation occurs are largely unknown. We propose the use of the Biochemical Network Integrated Computational Explorer (BNICE), a computational framework for the discovery of biochemical reactions [1], in order to predict the biodegradation routes of xenobiotic compounds. The objective of this work was to demonstrate the applicability of this method and characterize the thermodynamic and cellular feasibility of the novel pathways generated by BNICE. A model of E. coli metabolism [2] was employed to determine how the novel pathways influence the existing metabolism of a reference organism. Using thermodynamic metabolic flux analysis (TMFA), thermodynamic constraints were combined with mass balance constraints and growth requirements of the cell to generate thermodynamically feasible flux profiles [3]. TMFA served as a tool to screen the thousands of novel pathways obtained from BNICE. As a result, we have obtained a set of pathways that are feasible under physiological conditions. We have compared the flux profiles for growth on glucose and growth on biphenyl in order to elucidate how growth using the xenobiotic as the sole carbon source impacts the central metabolic pathways. Additionally, we have explored how this analysis can be applied to P. putida, an organism more relevant to bioremediation.


1. Hatzimanikatis, V., et al., Exploring the diversity of complex metabolic networks. Bioinformatics, 2005. 21(8): p. 1603-1609.

2. Reed, J.L., et al., An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR). Genome Biology, 2003. 4: p. 54.1-54.12.

3. Henry, C.S., L.J. Broadbelt, and V. Hatzimanikatis, Thermodynamics-based metabolic flux analysis. Biophysical Journal, 2007. 92: p. 1792-1805.