Improving metabolic flux predictions using absolute gene expression data

Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK. .
BMC Systems Biology (Impact Factor: 2.44). 06/2012; 6(1):73. DOI: 10.1186/1752-0509-6-73
Source: PubMed


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.
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.

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    • "The second, non-discretized approach utilizes the absolute gene expression levels to derive a flux description of a specific metabolic state (Lee et al, 2012), or for constraining reactions' maximal flux capacity for the purpose of building a specific model (Colijn et al, 2009; Fig 2). While these approaches maintain the basic structure of the network and are more sensitive to subtle differences in expression levels, their drawback is in their underlying implicit assumption that there is a strong monotonic positive association between gene expression levels and flux rates, an assumption that is known to hold only partially (Bordel et al, 2010). "
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    • "Constraint based techniques, including flux balance analysis (FBA) and flux variability analysis (FVA), provide quantitative predictions of cellular behavior such as metabolic flux patterns and cellular growth rates. These are computed by applying constraints , which can be assigned from experimentally measured nutrient uptake rates (Orth et al., 2010) and intracellular fluxes (Sauer, 2006), or inferred through interpretation of gene expression data (Lee et al., 2012). These predictions provide insights into the metabolic pathways active under different growth conditions (Liao et al., 2011), gene essentiality (Joyce and Palsson, 2008; Dobson et al., 2010; Heavner et al., 2012), and as a result, the fitness optimality of a given strain (Harcombe et al., 2013). "
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    • "This approach is similar to that adopted by Lee et al . ( 2012 ) , but by using QP , we find a single solution and avoid the iter - ative approach they describe . The calculations were done when the model was grown on heterotrophic media . After the minimal distance between experimental and model predicted fluxes was found via the QP problem as described above , we performed a Spearman correlation "
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