Abstract:
:Genome-scale metabolic models promise important insights into cell function. However, the definition of pathways and functional network modules within these models, and in the biochemical literature in general, is often based on intuitive reasoning. Although mathematical methods have been proposed to identify modules, which are defined as groups of reactions with correlated fluxes, there is a need for experimental verification. We show here that multivariate statistical analysis of the NMR-derived intra- and extracellular metabolite profiles of single-gene deletion mutants in specific metabolic pathways in the yeast Saccharomyces cerevisiae identified outliers whose profiles were markedly different from those of the other mutants in their respective pathways. Application of flux coupling analysis to a metabolic model of this yeast showed that the deleted gene in an outlying mutant encoded an enzyme that was not part of the same functional network module as the other enzymes in the pathway. We suggest that metabolomic methods such as this, which do not require any knowledge of how a gene deletion might perturb the metabolic network, provide an empirical method for validating and ultimately refining the predicted network structure.
journal_name
Genome Resjournal_title
Genome researchauthors
Bundy JG,Papp B,Harmston R,Browne RA,Clayson EM,Burton N,Reece RJ,Oliver SG,Brindle KMdoi
10.1101/gr.5662207subject
Has Abstractpub_date
2007-04-01 00:00:00pages
510-9issue
4eissn
1088-9051issn
1549-5469pii
gr.5662207journal_volume
17pub_type
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