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
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.

Figures

Full-text

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 . "
    [Show abstract] [Hide abstract] ABSTRACT: Metabolic pathways can be engineered to maximize the synthesis of various products of interest. With the advent of computational systems biology, this endeavour is usually carried out through in silico theoretical studies with the aim to guide and complement further in vitro and in vivo experimental efforts. Clearly, what counts is the result in vivo, not only in terms of maximal productivity but also robustness against environmental perturbations. Engineering an organism towards an increased production flux, however, often compromises that robustness. In this contribution, we review and investigate how various analytical approaches used in metabolic engineering and synthetic biology are related to concepts developed by systems and control engineering. While trade-offs between production optimality and cellular robustness have already been studied diagnostically and statically, the dynamics also matter. Integration of the dynamic design aspects of control engineering with the more diagnostic aspects of metabolic, hierarchical control and regulation analysis is leading to the new, conceptual and operational framework required for the design of robust and productive dynamic pathways.
    Full-text · Article · Apr 2016 · Journal of The Royal Society Interface
    • "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 . "
    [Show abstract] [Hide abstract] ABSTRACT: The recent advances in high-throughput ‘omics technologies have enabled researchers to explore the intricacies of the human microbiome. On the clinical front, the gut microbial community has been the focus of many biomarker-discovery studies. While the recent deluge of high-throughput data in microbiome research has been vastly informative and groundbreaking, we have yet to capture the full potential of ‘omics-based approaches. Realizing the promise of multi-omics data will require integration of disparate ‘omics data, as well as a biologically relevant, mechanistic framework – or metabolic model - on which to overlay these data. Also, a new paradigm for metabolic model evaluation is necessary. Herein, we outline the need for multi-omics data integration, as well as the accompanying challenges. Furthermore, we present a framework for characterizing the ecology of the gut microbiome based on metabolic network modeling.
    Full-text · Article · Feb 2016 · Applied and Translational Genomics
    • "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. "
    [Show abstract] [Hide abstract] ABSTRACT: Recent progress in high-throughput data acquisition has shifted the focus from data generation to processing and understanding of how to integrate collected information. Context specific reconstruction based on generic genome scale models like ReconX or HMR has the potential to become a diagnostic and treatment tool tailored to the analysis of specific individuals. The respective computational algorithms require a high level of predictive power, robustness and sensitivity. Although multiple context specific reconstruction algorithms were published in the last 10 years, only a fraction of them is suitable for model building based on human high-throughput data. Beside other reasons, this might be due to problems arising from the limitation to only one metabolic target function or arbitrary thresholding. This review describes and analyses common validation methods used for testing model building algorithms. Two major methods can be distinguished: consistency testing and comparison based testing. The first is concerned with robustness against noise, e.g., missing data due to the impossibility to distinguish between the signal and the background of non-specific binding of probes in a microarray experiment, and whether distinct sets of input expressed genes corresponding to i.e., different tissues yield distinct models. The latter covers methods comparing sets of functionalities, comparison with existing networks or additional databases. We test those methods on several available algorithms and deduce properties of these algorithms that can be compared with future developments. The set of tests performed, can therefore serve as a benchmarking procedure for future algorithms.
    Full-text · Article · Jan 2016 · Frontiers in Physiology
Show more

Similar publications

Discover more