Filling gaps in a metabolic network using expression information.
BACKGROUND
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.


BACKGROUND
Freely available (in Supplementary information).
Kharchenko
Peter
P
Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.
Vitkup
Dennis
D
Church
George M
GM
D000465:Algorithms; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D008954:Models, Biological; D009097:Multienzyme Complexes / Q000378:metabolism*; D020543:Proteome / Q000378:metabolism*; D029701:Saccharomyces cerevisiae Proteins / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*
D009097:Multienzyme Complexes; D020543:Proteome; D029701:Saccharomyces cerevisiae Proteins