Title | Computational and experimental analysis of redundancy in the central metabolism of Geobacter sulfurreducens. |
Publication Type | Journal Article |
Year of Publication | 2008 |
Authors | Segura D, Mahadevan R, Juárez K, Lovley DR |
Journal | PLoS Comput Biol |
Volume | 4 |
Issue | 2 |
Pagination | e36 |
Date Published | 2008 Feb |
ISSN | 1553-7358 |
Keywords | Bacterial Proteins, Base Sequence, Computer Simulation, Gene Expression Regulation, Bacterial, Geobacter, Models, Biological, Molecular Sequence Data, Multienzyme Complexes, Signal Transduction |
Abstract | Previous model-based analysis of the metabolic network of Geobacter sulfurreducens suggested the existence of several redundant pathways. Here, we identified eight sets of redundant pathways that included redundancy for the assimilation of acetate, and for the conversion of pyruvate into acetyl-CoA. These equivalent pathways and two other sub-optimal pathways were studied using 5 single-gene deletion mutants in those pathways for the evaluation of the predictive capacity of the model. The growth phenotypes of these mutants were studied under 12 different conditions of electron donor and acceptor availability. The comparison of the model predictions with the resulting experimental phenotypes indicated that pyruvate ferredoxin oxidoreductase is the only activity able to convert pyruvate into acetyl-CoA. However, the results and the modeling showed that the two acetate activation pathways present are not only active, but needed due to the additional role of the acetyl-CoA transferase in the TCA cycle, probably reflecting the adaptation of these bacteria to acetate utilization. In other cases, the data reconciliation suggested additional capacity constraints that were confirmed with biochemical assays. The results demonstrate the need to experimentally verify the activity of key enzymes when developing in silico models of microbial physiology based on sequence-based reconstruction of metabolic networks. |
DOI | 10.1371/journal.pcbi.0040036 |
Alternate Journal | PLoS Comput. Biol. |
PubMed ID | 18266464 |
Department of Microbiology