Three factors underlying incorrect in silico predictions of essential metabolic genes.

TitleThree factors underlying incorrect in silico predictions of essential metabolic genes.
Publication TypeJournal Article
Year of Publication2008
AuthorsBecker SA, Palsson BØ
JournalBMC systems biology
Volume2
Pagination14
PubMed Date2008
ISSN1752-0509
KeywordsAdaptation, Biological, Biomass, Cluster Analysis, Enzyme Activators, Enzyme Inhibitors, Gene Deletion, Gene Expression Regulation, Bacterial, Gene Regulatory Networks, Genes, Bacterial, Genes, Essential, Metabolic Networks and Pathways, Models, Biological, Predictive Value of Tests, Protein Interaction Mapping, Research Design, Signal Transduction
Abstract

BACKGROUND: The indispensability of certain genes in an organism is important for studies of microorganism physiology, antibiotic targeting, and the engineering of minimal genomes. Time and resource intensive genome-wide experimental screens can be conducted to determine which genes are likely essential. For metabolic genes, a reconstructed metabolic network can be used to predict which genes are likely essential. The success rate of these predictions is less than desirable, especially with regard to comprehensively locating essential genes.

RESULTS: We show that genes that are falsely predicted to be non-essential (for growth) share three characteristics across multiple organisms and growth media. First, these genes are on average connected to fewer reactions in the network than correctly predicted essential genes, suggesting incomplete knowledge of the functions of these genes. Second, they are more likely to be blocked (their associated reactions are prohibited from carrying flux in the given condition) than other genes, implying incomplete knowledge of metabolism surrounding these genes. Third, they are connected to less overcoupled metabolites.

CONCLUSION: The results presented herein indicate genes that cannot be correctly predicted as essential have commonalities in different organisms. These elucidated failure modes can be used to better understand the biology of individual organisms and to improve future predictions.

Alternate JournalBMC Syst Biol
PubMed ID18248675

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