Title
Improving metabolic flux predictions using absolute gene expression data.
Abstract
Constraint-based analysis of genome-scale metabolic models typically relies upon maximisation of a cellular objective function such as the rate or efficiency of biomass production. Whilst this assumption may be valid in the case of microorganisms growing under certain conditions, it is likely invalid in general, and especially for multicellular organisms, where cellular objectives differ greatly both between and within cell types. Moreover, for the purposes of biotechnological applications, it is normally the flux to a specific metabolite or product that is of interest rather than the rate of production of biomass per se.An alternative objective function is presented, that is based upon maximising the correlation between experimentally measured absolute gene expression data and predicted internal reaction fluxes. Using quantitative transcriptomics data acquired from Saccharomyces cerevisiae cultures under two growth conditions, the method outperforms traditional approaches for predicting experimentally measured exometabolic flux that are reliant upon maximisation of the rate of biomass production.Due to its improved prediction of experimentally measured metabolic fluxes, and of its lack of a requirement for knowledge of the biomass composition of the organism under the conditions of interest, the approach is likely to be of rather general utility. The method has been shown to predict fluxes reliably in single cellular systems. Subsequent work will investigate the method's ability to generate condition- and tissue-specific flux predictions in multicellular organisms.
Year
DOI
Venue
2012
10.1186/1752-0509-6-73
BMC systems biology
Keywords
Field
DocType
systems biology,bioinformatics,algorithms
Biomass,Multicellular organism,Biology,Gene expression,Systems biology,Cell type,Flux,Bioinformatics,Flux balance analysis
Journal
Volume
Issue
ISSN
6
1
1752-0509
Citations 
PageRank 
References 
11
0.62
7
Authors
8
Name
Order
Citations
PageRank
Dave Lee1120.97
Kieran Smallbone21429.16
Warwick Dunn3745.38
Ettore Murabito4110.95
Catherine Winder5281.36
Douglas B Kell6100494.11
Pedro Mendes734953.14
Neil Swainston827414.07