Genome-scale model for Clostridium acetobutylicum: Part I. Metabolic network resolution and analysis

Authors

  • Ryan S. Senger,

    Corresponding author
    1. Delaware Biotechnology Institute, University of Delaware, 15 Innovation Way Newark, Delaware 19711; telephone: 302-831-6168; fax: 302-831-4841
    2. Department of Chemical Engineering, Colburn Laboratory, University of Delaware, Newark, Delaware 19716
    3. Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois 60208-3120
    • Delaware Biotechnology Institute, University of Delaware, 15 Innovation Way Newark, Delaware 19711; telephone: 302-831-6168; fax: 302-831-4841.
    Search for more papers by this author
  • Eleftherios T. Papoutsakis

    1. Delaware Biotechnology Institute, University of Delaware, 15 Innovation Way Newark, Delaware 19711; telephone: 302-831-6168; fax: 302-831-4841
    2. Department of Chemical Engineering, Colburn Laboratory, University of Delaware, Newark, Delaware 19716
    3. Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois 60208-3120
    Search for more papers by this author

Abstract

A genome-scale metabolic network reconstruction for Clostridium acetobutylicum (ATCC 824) was carried out using a new semi-automated reverse engineering algorithm. The network consists of 422 intracellular metabolites involved in 552 reactions and includes 80 membrane transport reactions. The metabolic network illustrates the reliance of clostridia on the urea cycle, intracellular L-glutamate solute pools, and the acetylornithine transaminase for amino acid biosynthesis from the 2-oxoglutarate precursor. The semi-automated reverse engineering algorithm identified discrepancies in reaction network databases that are major obstacles for fully automated network-building algorithms. The proposed semi-automated approach allowed for the conservation of unique clostridial metabolic pathways, such as an incomplete TCA cycle. A thermodynamic analysis was used to determine the physiological conditions under which proposed pathways (e.g., reverse partial TCA cycle and reverse arginine biosynthesis pathway) are feasible. The reconstructed metabolic network was used to create a genome-scale model that correctly characterized the butyrate kinase knock-out and the asolventogenic M5 pSOL1 megaplasmid degenerate strains. Systematic gene knock-out simulations were performed to identify a set of genes encoding clostridial enzymes essential for growth in silico. Biotechnol. Bioeng. © 2008 Wiley Periodicals, Inc.

Ancillary

Advertisement