BACKGROUND: Existing large-scale metabolic models of sequenced organisms 
commonly include enzymatic functions which can not be attributed to any gene in 
that organism. Existing computational strategies for identifying such missing 
genes rely primarily on sequence homology to known enzyme-encoding genes.
RESULTS: We present a novel method for identifying genes encoding for a specific 
metabolic function based on a local structure of metabolic network and multiple 
types of functional association evidence, including clustering of genes on the 
chromosome, similarity of phylogenetic profiles, gene expression, protein fusion 
events and others. Using E. coli and S. cerevisiae metabolic networks, we 
illustrate predictive ability of each individual type of association evidence 
and show that significantly better predictions can be obtained based on the 
combination of all data. In this way our method is able to predict 60% of 
enzyme-encoding genes of E. coli metabolism within the top 10 (out of 3551) 
candidates for their enzymatic function, and as a top candidate within 43% of 
the cases.
CONCLUSION: We illustrate that a combination of genome context and other 
functional association evidence is effective in predicting genes encoding 
metabolic enzymes. Our approach does not rely on direct sequence homology to 
known enzyme-encoding genes, and can be used in conjunction with traditional 
homology-based metabolic reconstruction methods. The method can also be used to 
target orphan metabolic activities.