Systematic integration of experimental data and models in systems biology

School of Chemistry, The University of Manchester, Manchester M13 9PL, UK.
BMC Bioinformatics (Impact Factor: 2.58). 11/2010; 11:582. DOI: 10.1186/1471-2105-11-582
Source: PubMed


The behaviour of biological systems can be deduced from their mathematical models. However, multiple sources of data in diverse forms are required in the construction of a model in order to define its components and their biochemical reactions, and corresponding parameters. Automating the assembly and use of systems biology models is dependent upon data integration processes involving the interoperation of data and analytical resources.
Taverna workflows have been developed for the automated assembly of quantitative parameterised metabolic networks in the Systems Biology Markup Language (SBML). A SBML model is built in a systematic fashion by the workflows which starts with the construction of a qualitative network using data from a MIRIAM-compliant genome-scale model of yeast metabolism. This is followed by parameterisation of the SBML model with experimental data from two repositories, the SABIO-RK enzyme kinetics database and a database of quantitative experimental results. The models are then calibrated and simulated in workflows that call out to COPASIWS, the web service interface to the COPASI software application for analysing biochemical networks. These systems biology workflows were evaluated for their ability to construct a parameterised model of yeast glycolysis.
Distributed information about metabolic reactions that have been described to MIRIAM standards enables the automated assembly of quantitative systems biology models of metabolic networks based on user-defined criteria. Such data integration processes can be implemented as Taverna workflows to provide a rapid overview of the components and their relationships within a biochemical system.

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    • "Ultimately, the generation of quantitative multivariate measurements enables the application of statistical tests and correlation analyses that allow scientists to report detailed information about their own experiments in a more prescribed and systematised manner. Systems biology generates prodigious amounts of complex, integrated data from the fields of functional genomics, proteomics and metabolomics and has started to emerge as a driving force at the interface of biology, biochemistry, mathematics, computing engineering, and physics [4]. It has become accepted, albeit also widely ignored, that the complexity of a biological organism cannot be described by a static listing of even the most wellcharacterised components such as DNA sequence, genetic and epigenetic modifications, levels of coding and noncoding RNAs, proteins and their many isoforms or the range of metabolites being synthesised or entering individual cells. "

    10/2014; 2(1). DOI:10.1016/j.bdq.2014.09.001
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    • "Given the topology of a network, and the stoichiometric and thermodynamic constraints under which metabolic networks must operate (Palsson 2006; Kell 2006a; b), it is possible to use generalised kinetics to predict metabolic fluxes (Liebermeister and Klipp 2006; Smallbone et al. 2007; Smallbone and Simeonidis 2008; Smallbone et al. 2010). The accuracy of these predictions can of course be enhanced by the use of known kinetic rate equations (Li et al. 2010), and even by expression profiles alone (Lee et al. 2012). Such an approach has been applied, exploiting both transcriptomics and fluxomics data, to constrain models derived from a precursor of Recon 2 in order to elucidate and validate new drug targets in renal-cell cancer (Frezza et al. 2011). "
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    ABSTRACT: Following a strategy similar to that used in baker's yeast (Herrgård et al. Nat Biotechnol 26:1155-1160, 2008). A consensus yeast metabolic network obtained from a community approach to systems biology (Herrgård et al. 2008; Dobson et al. BMC Syst Biol 4:145, 2010). Further developments towards a genome-scale metabolic model of yeast (Dobson et al. 2010; Heavner et al. BMC Syst Biol 6:55, 2012). Yeast 5-an expanded reconstruction of the Saccharomyces cerevisiae metabolic network (Heavner et al. 2012) and in Salmonella typhimurium (Thiele et al. BMC Syst Biol 5:8, 2011). A community effort towards a knowledge-base and mathematical model of the human pathogen Salmonella typhimurium LT2 (Thiele et al. 2011), a recent paper (Thiele et al. Nat Biotechnol 31:419-425, 2013). A community-driven global reconstruction of human metabolism (Thiele et al. 2013) described a much improved 'community consensus' reconstruction of the human metabolic network, called Recon 2, and the authors (that include the present ones) have made it freely available via a database at and in SBML format at Biomodels ( This short analysis summarises the main findings, and suggests some approaches that will be able to exploit the availability of this model to advantage.
    Metabolomics 08/2013; 9(4):757-764. DOI:10.1007/s11306-013-0564-3 · 3.86 Impact Factor
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    • "The toolset includes support for enzyme kinetics [41], quantitative proteomics [49], and quantitative metabolomics data [62–63]. Furthermore, workflows have been generated to automate the generation and parameterisation of kinetic models [64–66]. "
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    ABSTRACT: We present an experimental and computational pipeline for the generation of kinetic models of metabolism, and demonstrate its application to glycolysis in Saccharomyces cerevisiae. Starting from an approximate mathematical model, we employ a "cycle of knowledge" strategy, identifying the steps with most control over flux. Kinetic parameters of the individual isoenzymes within these steps are measured experimentally under a standardised set of conditions. Experimental strategies are applied to establish a set of in vivo concentrations for isoenzymes and metabolites. The data are integrated into a mathematical model that is used to predict a new set of metabolite concentrations and reevaluate the control properties of the system. This bottom-up modelling study reveals that control over the metabolic network most directly involved in yeast glycolysis is more widely distributed than previously thought.
    FEBS letters 07/2013; 587(17). DOI:10.1016/j.febslet.2013.06.043 · 3.17 Impact Factor
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