Bioinformatics 20(Suppl. 1) © Oxford University Press 2004; all rights reserved.
Filling gaps in a metabolic network using expression information
Department of Genetics, Harvard Medical School, 77 Louis Pasteur Avenue Boston, MA 02115, USA
Received on January 15, 2004; accepted on March 1, 2004
Motivation: The metabolic models of both newly sequenced and well-studied organisms contain reactions for which the enzymes have not been identified yet. We present a computational approach for identifying genes encoding such missing metabolic enzymes in a partially reconstructed metabolic network.
Results: The metabolic expression placement (MEP) method relies on the coexpression properties of the metabolic network and is complementary to the sequence homology and genome context methods that are currently being used to identify missing metabolic genes. The MEP algorithm predicts over 20% of all known Saccharomyces cerevisiae metabolic enzyme-encoding genes within the top 50 out of 5594 candidates for their enzymatic function, and 70% of metabolic genes whose expression level has been significantly perturbed across the conditions of the expression dataset used.
Availability: Freely available (in Supplementary information).
Supplementary information: Available at the following URL http://arep.med.harvard.edu/kharchenko/mep/supplements.html
Contact: g1m1c1{at}arep.med.harvard.edu
* To whom correspondence should be addressed.
The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors.