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: 2.85). 06/2012; 6:73. DOI: 10.1186/1752-0509-6-73
Source: PubMed

ABSTRACT 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.

Download full-text


Available from: Ettore Murabito, Jun 16, 2015
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Microbial communities play important roles in health, industrial applications and earth's ecosystems. With current molecular techniques we can characterize these systems in unprecedented detail. However, such methods provide little mechanistic insight into how the genetic properties and the dynamic couplings between individual microorganisms give rise to their dynamic activities. Neither do they give insight into what we call "the community state", that is the fluxes and concentrations of nutrients within the community. This knowledge is a prerequisite for rational control and intervention in microbial communities. Therefore, the inference of the community structure from experimental data is a major current challenge. We will argue that this inference problem requires mathematical models that can integrate heterogeneous experimental data with existing knowledge. We propose that two types of models are needed. Firstly, mathematical models that integrate existing genomic, physiological, and physicochemical information with metagenomics data so as to maximize information content and predictive power. This can be achieved with the use of constraint-based genome-scale stoichiometric modeling of community metabolism which is ideally suited for this purpose. Next, we propose a simpler coarse-grained model, which is tailored to solve the inference problem from the experimental data. This model unambiguously relate to the more detailed genome-scale stoichiometric models which act as heterogeneous data integrators. The simpler inference models are, in our opinion, key to understanding microbial ecosystems, yet until now, have received remarkably little attention. This has led to the situation where the modeling of microbial communities, using only genome-scale models is currently more a computational, theoretical exercise than a method useful to the experimentalist.
    Frontiers in Microbiology 03/2015; 6:213. DOI:10.3389/fmicb.2015.00213 · 3.94 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Methane, as natural gas or biogas, is the least expensive source of carbon for (bio)chemical synthesis. Scalable biological upgrading of this simple alkane to chemicals and fuels can bring new sustainable solutions to a number of industries with large environmental footprints, such as natural gas/petroleum production, landfills, wastewater treatment, and livestock. Microbial biocatalysis with methane as a feedstock has been pursued off and on for almost a half century, with little enduring success. Today, biological engineering and systems biology provide new opportunities for metabolic system modulation and give new optimism to the concept of a methane-based bio-industry. Here we present an overview of the most recent advances pertaining to metabolic engineering of microbial methane utilization. Some ideas concerning metabolic improvements for production of acetyl-CoA and pyruvate, two main precursors for bioconversion, are presented. We also discuss main gaps in the current knowledge of aerobic methane utilization, which must be solved in order to release the full potential of methane-based biosystems. Copyright © 2015 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.
    Metabolic Engineering 03/2015; 29. DOI:10.1016/j.ymben.2015.03.010 · 8.26 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Sustainable production of target compounds such as biofuels and high-value chemicals for pharmaceutical, agrochemical, and chemical industries is becoming an increasing priority given their current dependency upon diminishing petrochemical resources. Designing these strains is difficult, with current methods focusing primarily on knocking-out genes, dismissing other vital steps of strain design including the overexpression and dampening of genes. The design predictions from current methods also do not translate well-into successful strains in the laboratory. Here, we introduce RobOKoD (Robust, Overexpression, Knockout and Dampening), a method for predicting strain designs for overproduction of targets. The method uses flux variability analysis to profile each reaction within the system under differing production percentages of target-compound and biomass. Using these profiles, reactions are identified as potential knockout, overexpression, or dampening targets. The identified reactions are ranked according to their suitability, providing flexibility in strain design for users. The software was tested by designing a butanol-producing Escherichia coli strain, and was compared against the popular OptKnock and RobustKnock methods. RobOKoD shows favorable design predictions, when predictions from these methods are compared to a successful butanol-producing experimentally-validated strain. Overall RobOKoD provides users with rankings of predicted beneficial genetic interventions with which to support optimized strain design.
    Frontiers in Cell and Developmental Biology 03/2015; 3(17). DOI:10.3389/fcell.2015.00017