Journal
BMC SYSTEMS BIOLOGY
Volume 6, Issue -, Pages -Publisher
BMC
DOI: 10.1186/1752-0509-6-73
Keywords
Flux balance analysis; Metabolic flux; Metabolic networks; Transcriptomics; RNA-Seq; Exometabolomics
Categories
Funding
- EU FP7 through the UNICELLSYS project [201142]
- BBSRC
- EPSRC through the Manchester Centre for Integrative Systems Biology [BB/C008219/1]
- Doctoral Training Centre ISBML [EP/F500009/1]
- Biotechnology and Biological Sciences Research Council [BB/C008219/1, 972501] Funding Source: researchfish
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Background: 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. Results: 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. Conclusion: 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.
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