%0 Journal Article
%T Improving metabolic flux predictions using absolute gene expression data
%A Dave Lee
%A Kieran Smallbone
%A Warwick B Dunn
%A Ettore Murabito
%A Catherine L Winder
%A Douglas B Kell
%A Pedro Mendes
%A Neil Swainston
%J BMC Systems Biology
%D 2012
%I BioMed Central
%R 10.1186/1752-0509-6-73
%X 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.The applications of genome-scale metabolic modelling have increased over recent years, as have the number of metabolic models available and the diversity of organisms that such reconstructions cover [1]. Traditional approaches to analysing such models have focused on constraint-based modelling, including widely used techniques such as flux balance analysis (FBA) [2]. FBA relies upon specification of an objective function that the cell is assumed to optimise. Objective functions can cover a range of cellular objectives [3], such as maximisation / minimisation of ATP consumption, but frequently (and particularly in the case of microorganisms) take the form of an assumed ¡°biomass¡± function; a hypothetical reaction that mimics cell growth rate [4]. Such a biomass function is used to account for the flow of materials that are necessary for building new cells, and is commonly required in constraint-based models even when maximising variables other than growth rate.Maximisation of biomass yield is not generally considered a vali
%K Flux balance analysis
%K Metabolic flux
%K Metabolic networks
%K Transcriptomics
%K RNA-Seq
%K Exometabolomics
%U http://www.biomedcentral.com/1752-0509/6/73