Improving metabolic flux predictions using absolute gene expression data

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.

Inference and prediction of metabolic network fluxes

“…The authors then investigated the correspondence between the changes in flux and the changes in transcript levels for the corresponding reactions. They found very little correspondence between these changes, which is in stark contrast to the excellent agreement (coefficient of determination of 0.87 and 0.96 at 75% and 85% of optimal biomass) found in Saccharomyces cerevisiae (Lee et al, 2012). The reason for the low correspondence in the plant study may be due to the fact that the comparison was between fluxes determined under the steady-state assumption, and transcript levels, which, when used directly as proxies for fluxes, may violate the steady-state assumption.…”

Assessing the Metabolic Impact of Nitrogen Availability Using a Compartmentalized Maize Leaf Genome-Scale Model

“…Lee et al (2012) integrated gene expression data by minimizing the difference between the predicted flux levels and gene expression data over all reactions with corresponding expression levels. Using the Yeast 5 model (Heavner et al, 2012) for Saccharomyces cerevisiae, Lee et al (2012) compared the predicted fluxes with experimentally determined exometabolome fluxes using the coefficient of determination r 2 . The authors achieved r 2 values of 0.87 and 0.96 at 75% and 85% of the maximal biomass level, respectively.…”

Synthetic biology and regulatory networks: where metabolic systems biology meets control engineering

“…By minimizing such an objective function, the authors retrieved flux patterns where the reaction rates were more strongly correlated with their corresponding expression state. In Lee et al [66], absolute gene-expression data generated through RNA-Seq were used to provide a more precise indication of enzymatic activity than that generated through relative expression techniques such as in Shlomi et al [65], although lack of correspondence between mRNA and protein levels may compromise this approach if protein levels are not also taken into account [67]. Datadriven FBA may also use the exometabolome, i.e.…”