Inferring Metabolic States in Uncharacterized Environments Using Gene-Expression Measurements

Department of Bioinformatics (CMBI), Centre for Molecular Life Sciences, Radboud University Nijmegen, The Netherlands
PLoS Computational Biology (Impact Factor: 4.62). 03/2013; 9(3):e1002988. DOI: 10.1371/journal.pcbi.1002988
Source: PubMed


The large size of metabolic networks entails an overwhelming multiplicity in the possible steady-state flux distributions that are compatible with stoichiometric constraints. This space of possibilities is largest in the frequent situation where the nutrients available to the cells are unknown. These two factors: network size and lack of knowledge of nutrient availability, challenge the identification of the actual metabolic state of living cells among the myriad possibilities. Here we address this challenge by developing a method that integrates gene-expression measurements with genome-scale models of metabolism as a means of inferring metabolic states. Our method explores the space of alternative flux distributions that maximize the agreement between gene expression and metabolic fluxes, and thereby identifies reactions that are likely to be active in the culture from which the gene-expression measurements were taken. These active reactions are used to build environment-specific metabolic models and to predict actual metabolic states. We applied our method to model the metabolic states of Saccharomyces cerevisiae growing in rich media supplemented with either glucose or ethanol as the main energy source. The resulting models comprise about 50% of the reactions in the original model, and predict environment-specific essential genes with high sensitivity. By minimizing the sum of fluxes while forcing our predicted active reactions to carry flux, we predicted the metabolic states of these yeast cultures that are in large agreement with what is known about yeast physiology. Most notably, our method predicts the Crabtree effect in yeast cells growing in excess glucose, a long-known phenomenon that could not have been predicted by traditional constraint-based modeling approaches. Our method is of immediate practical relevance for medical and industrial applications, such as the identification of novel drug targets, and the development of biotechnological processes that use complex, largely uncharacterized media, such as biofuel production.

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Available from: Sergio Rossell, Oct 06, 2015
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    • "By integrating transcriptomic data with a global human metabolic model using this method, they predicted tissue-specific metabolic activity in ten different tissues. A method called EXAMO (EXploration of Alternative Metabolic Optima) is an extended version of iMAT that builds a context-specific model [46]. Tailored gene expression using user-defined thresholds may avoid data normalization issues [33]. "
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    ABSTRACT: Several computational methods have been developed that integrate transcriptomic data with genome-scale metabolic reconstructions to infer condition-specific system-wide intracellular metabolic flux distributions. In this mini-review, we describe each of these methods published to date with categorizing them based on four different grouping criteria (requirement for multiple gene expression datasets as input, requirement for a threshold to define a gene's high and low expression, requirement for a priori assumption of an appropriate objective function, and validation of predicted fluxes directly against measured intracellular fluxes). Then, we recommend which group of methods would be more suitable from a practical perspective.
    Computational and Structural Biotechnology Journal 08/2014; 11(18). DOI:10.1016/j.csbj.2014.08.009
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    • "Flux-balance analysis is the most popular example of this strategy, but it becomes questionable once the steady-state assumption can no longer be upheld. Furthermore, as more data on enzyme abundance become available, we should attempt to include such information and the impact on metabolic processes (Colijn et al, 2009; Rossell et al., 2013). Here we provide a new framework that allows us to model metabolic fluxes and their dynamics, and which deals with the missing data problem in metabolic analysis in a flexible and consistent manner. "
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    ABSTRACT: One of the challenging questions in modelling biological systems is to characterise the functional forms of the processes that control and orchestrate molecular and cellular phenotypes. Recently proposed methods for the analysis of metabolic pathways, for example dynamic flux estimation, can only provide estimates of the underlying fluxes at discrete time-points but fail to capture the complete temporal behaviour. In order to describe the dynamic variation of the fluxes we additionally require the assumption of specific functional forms that can capture the temporal behaviour. But it also remains unclear how to address the noise which might be present in experimentally measured metabolite concentrations. Here we propose a novel approach to modelling metabolic fluxes: derivative processes that are based on Multiple-output Gaussian processes (MGPs), which are a flexible nonparametric Bayesian modelling technique. The main advantages that follow from MGPs approach include the natural nonparametric representation of the fluxes and ability to impute the missing data in between the measurements. Our derivative process approach allows us to model changes in metabolite derivative concentrations and to characterise the temporal behaviour of metabolic fluxes from time course data. Because the derivative of a Gaussian process is itself a Gaussian process we can readily link metabolite concentrations to metabolic fluxes and vice versa. Here we discuss how this can be implemented in an MGP framework and illustrate its application to simple models, including nitrogen metabolism in Escherichia coli.,
    Bioinformatics 02/2014; 30(13). DOI:10.1093/bioinformatics/btu069 · 4.98 Impact Factor
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    ABSTRACT: Constraint-based models of metabolism are a widely used framework for predicting flux distributions in genome-scale biochemical networks. The number of published methods for integration of transcriptomic data into constraint-based models has been rapidly increasing. So far the predictive capability of these methods has not been critically evaluated and compared. This work presents a survey of recently published methods that use transcript levels to try to improve metabolic flux predictions either by generating flux distributions or by creating context-specific models. A subset of these methods is then systematically evaluated using published data from three different case studies in E. coli and S. cerevisiae. The flux predictions made by different methods using transcriptomic data are compared against experimentally determined extracellular and intracellular fluxes (from 13C-labeling data). The sensitivity of the results to method-specific parameters is also evaluated, as well as their robustness to noise in the data. The results show that none of the methods outperforms the others for all cases. Also, it is observed that for many conditions, the predictions obtained by simple flux balance analysis using growth maximization and parsimony criteria are as good or better than those obtained using methods that incorporate transcriptomic data. We further discuss the differences in the mathematical formulation of the methods, and their relation to the results we have obtained, as well as the connection to the underlying biological principles of metabolic regulation.
    PLoS Computational Biology 04/2014; 10(4):e1003580. DOI:10.1371/journal.pcbi.1003580 · 4.62 Impact Factor
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