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


CONCLUSIONS
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.
Becker
Scott A
SA
Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive 0412, La Jolla, CA 92093, USA. sabecker@ucsd.edu
Palsson
Bernhard O
BO
D000220:Adaptation, Biological; D018533:Biomass; D016000:Cluster Analysis; D020536:Enzyme Activators; D004791:Enzyme Inhibitors; D017353:Gene Deletion; D015964:Gene Expression Regulation, Bacterial; D053263:Gene Regulatory Networks / Q000502:physiology; D005798:Genes, Bacterial; D020043:Genes, Essential; D053858:Metabolic Networks and Pathways / Q000235:genetics*; D008954:Models, Biological; D011237:Predictive Value of Tests; D025941:Protein Interaction Mapping; D012107:Research Design; D015398:Signal Transduction / Q000235:genetics
D020536:Enzyme Activators; D004791:Enzyme Inhibitors