An approach to pathway reconstruction using whole genome metabolic models and sensitive sequence searching.
Metabolic models have the potential to impact on genome annotation and on the interpretation of gene expression and other high throughput genome data. The genome of Streptomyces coelicolor genome has been sequenced and some 30% of the open reading frames (ORFs) lack any functional annotation. A recently constructed metabolic network model for S. coelicolor highlights biochemical functions which should exist to make the metabolic model complete and consistent. These include 205 reactions for which no ORF is associated. Here we combine protein functional predictions for the unannotated open reading frames in the genome with \'missing but expected\' functions inferred from the metabolic model. The approach allows function predictions to be evaluated in the context of the biochemical pathway reconstruction, and feed back iteratively into the metabolic model. We describe the approach and discuss a few illustrative examples.
Barts and The London School of Medicine, Queen Mary, University of London, UK.
Mansoor
M
Saqi
Richard J B
RJ
Dobson
Preben
P
Kraben
David A
DA
Hodgson
David L
DL
Wild
D000595:Amino Acid Sequence; D001426:Bacterial Proteins / Q000235:genetics / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D016681:Genome, Fungal; D053858:Metabolic Networks and Pathways / Q000235:genetics*; D008969:Molecular Sequence Data; D016366:Open Reading Frames; D048372:Streptomyces coelicolor / Q000235:genetics* / Q000378:metabolism
D001426:Bacterial Proteins