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- PMID: 22713172
- UKPMCID: 22713172
- DOI: 10.1186/1752-0509-6-73
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Improving metabolic flux predictions using absolute gene expression data.
Lee, Dave; Smallbone, Kieran; Dunn, Warwick B; Murabito, Ettore; Winder, Catherine L; Kell, Douglas B; Mendes, Pedro; Swainston, Neil
BMC systems biology. 2012;6(1):73.
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Full-text held externally
- PMID: 22713172
- UKPMCID: 22713172
- DOI: 10.1186/1752-0509-6-73
Abstract
ABSTRACT: BACKGROUND: Constraint-based analysis of genome-scale metabolic models typically relies uponmaximisation of a cellular objective function such as the rate or efficiency of biomassproduction. Whilst this assumption may be valid in the case of microorganisms growingunder certain conditions, it is likely invalid in general, and especially for multicellularorganisms, 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 biomassper se. RESULTS: An alternative objective function is presented, that is based upon maximising the correlationbetween experimentally measured absolute gene expression data and predicted internalreaction fluxes. Using quantitative transcriptomics data acquired from Saccharomycescerevisiae cultures under two growth conditions, the method outperforms traditionalapproaches for predicting experimentally measured exometabolic flux that are reliant uponmaximisation of the rate of biomass production. CONCLUSION: Due to its improved prediction of experimentally measured metabolic fluxes, and of its lackof a requirement for knowledge of the biomass composition of the organism under theconditions of interest, the approach is likely to be of rather general utility. The method hasbeen shown to predict fluxes reliably in single cellular systems. Subsequent work willinvestigate the method's ability to generate condition- and tissue-specific flux predictions inmulticellular organisms.