Abstract
BackgroundConstraint-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.ResultsAn 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.ConclusionDue 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.
Highlights
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
Traditional approaches to analysing such models have focused on constraint-based modelling, including widely used techniques such as flux balance analysis (FBA) [2]
Objective functions can cover a range of cellular objectives [3], such as maximisation / minimisation of ATP consumption, but frequently take the form
Summary
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. Assumption of growth rate maximisation in FBA studies has led to successful predictions of the actual growth rate in a number of organisms [4,9] Such an assumption, is likely to be invalid for individual cell types in multicellular organisms, where cellular objectives may differ greatly both between and within tissues. These issues have been demonstrated by the work of Gille et al [7], who modelled the metabolism of human hepatocyte, indicating their objective is to preserve homeostasis of blood compounds, a process that is modulated by the extracellular availability of oxygen and other nutrients
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