Improving metabolic flux predictions using absolute gene expression data

ArticleinBMC Systems Biology 6(1):73 · June 2012with37 Reads
Impact Factor: 2.44 · 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.



Available from: Ettore Murabito
    • "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. restrict the exchange flux pattern to what is observed experimentally . "
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    • "A systems approach that utilizes metabolic networks may offer a potential solution. Network reconstruction is one such means of creating a scaffold for synthesizing multiple data types (Feist and Palsson, 2008; Lee et al., 2012; Reed et al., 2006; Töpfer et al., 2015). Metabolic models are composed of a collection of individual chemical reactions that are governed by the fundamental laws of mass conservation and thermodynamics . "
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    • "The input expression data is used to identify, which reactions are not required for the objective and can function therefore be removed from the model due to low expression values (Table 3). iMAT (Zur et al., 2010; Lee et al., 2012) and RegrEx (Robaina Estévez and Nikoloski, 2015) maximize the consistency between the flux and the expression discarding reactions that have high expression values if necessary, which might be problematic if reactions have to be included in the model like i.e., the biomass function. INIT (Agren et al., 2012) uses weighted activity indicators as objective, with those having stronger evidence being weighted higher. "
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