Whereas genomes can be rapidly sequenced, the functions of many genes are incompletely or erroneously annotated because of a lack of experimental evidence or prior functional knowledge in sequence databases. To address this weakness, we describe here a model-enabled gene search (MEGS) approach that (i) identifies metabolic functions either missing from an organism's genome annotation or incorrectly assigned to an ORF by using discrepancies between metabolic model predictions and experimental culturing data; (ii) designs functional selection experiments for these specific metabolic functions; and (iii) selects a candidate gene(s) responsible for these functions from a genomic library and directly interrogates this gene's function experimentally.
