Review
InformaticsSystems biology, metabolic modelling and metabolomics in drug discovery and development
Informatics
Section snippets
Systems biology and metabolic modelling in the 21st Century
Although there are many individual definitions, most commentators (including this one 1, 2) take it that systems biology involves an iterative interplay between more or less high-throughput and high-content ‘wet’ experiments, technology development, theory and computational modelling, and that it is the involvement of computational modelling, in particular, in the process that sets systems biology apart from the more traditional and more reductionist molecular biology. Metabolomics illustrates
Metabolism is more discriminating
It has long been known, and proven through the formalism of metabolic control analysis 7, 10, 14, 15, 16, that whereas small changes in the concentrations of enzymes (and the transcripts that encode them) have only small effects on the fluxes through metabolic pathways, they have substantial effects on the concentrations of metabolic intermediates. Because the metabolome (nominally the concentrations of ‘all’ the metabolites measured in a system of interest [17]) is downstream of the proteome,
Metabolic reconstruction is now mature and timely
An attractive feature for the purposes of modelling is that metabolism, in contrast to signalling pathways, is subject to direct thermodynamic and in particular stoichiometric constraints 23, 24. As the product of one reaction is usually the substrate of another, and we know a considerable amount at a baseline level [25], the starting point for metabolic reconstruction is thus the genome itself. A combination of automated and manual procedures can help turn a genome sequence into a metabolic
Generalised representation of metabolic and other biochemical models
SBML ([33]; http://www.sbml.org/; see also Table 1 for other Internet resources) is an eXtensible Markup Language (XML) that, in its present version, enables one straightforwardly to describe a biological network and its local equations in a manner that can be exchanged between any number of modelling systems, including what is probably [34] the most popular modelling software, viz. Gepasi [35]. A simple example of a model of glycolysis [36] is given in Figure 1. SBML therefore encodes the
Systems parameters and systems variables
Systems biology models make explicit the relationship between the elements of a system, namely the parameters (here the fixed or starting concentrations of proteins and controlled metabolites, and all the kinetic constants of the proteins for their substrates, products and effectors) and the variables (the time-dependent metabolite concentrations and fluxes). Therefore, a major requirement is the measurement of parameters [2], but much of our energy is expended on the measurements of the
Metabolomics and technologies for its measurement
Metabolomics seeks to measure the concentrations of nominally all of the [small molecular weight (MW)] metabolites in a particular system, for example, a body fluid such as serum or an ensemble of cells 17, 37, 38, although normally a more restricted subset, the ‘metabolic profile’ is measured in practice. This is because of the huge chemical diversity, especially in terms of polarity, among different metabolites. As part of the emphasis on technology development above (and see Ref. [39]), the
Sizes of the human and other metabolomes
An attraction of the metabolome has always been that it is numerically smaller, and thus more tractable, than the transcriptome or proteome [37]. In the case of baker's yeast (Saccharomyces cerevisiae), the latest models (e.g. Ref. [26]) give some 1200 reactions and 650 metabolites, with slightly smaller but broadly similar numbers for bacteria such as Escherichia coli 54, 55, 56 and Streptomyces coelicolor [28], most with a MW <500 27, 55. The curated human metabolome [as reconstructed
What and where to measure?
One question that arises for those contemplating a metabolome project using measurements on biofluids, is whether to study urine or plasma and/or serum (there seems little difference between the latter; Dunn, W.B. et al., unpublished). Overall, the general feeling is that urine reflects a more short-term state of the organism, whereas fasting serum and/or plasma changes represent more chronic or long-term snapshots of the system. There is also the influence of ethnicity, diet, diurnal rhythm,
Biomarker detection
In a certain sense, the metabolome is chemical pathology writ large, and just as many disease conditions are now assessed by measuring small molecule concentrations in biofluids, we can expect the metabolome to be of significant utility in various kinds of diagnosis 21, 64. Of course, the entire field of ‘inborn errors of metabolism’ is based on seeking diagnostic changes in the metabolome, and these are now measured routinely 65, 66. Such diagnostic biomarkers can of course be surrogates [67],
Integrating metabolomics and metabolic modelling for systems biology
The methods for carrying out metabolic modelling, and the means for collecting, storing and analysing metabolomic data are considerably different, will normally be performed by individuals or in laboratories with different skill sets, and yet necessarily will deal with the same molecules. It is therefore extremely timely to bring together the known or inferred metabolic maps of suitable organisms with measurements of their metabolomes to provide a systems-level understanding of the metabolic
Future directions: bringing cheminformatics to metabolic systems biology
It is often the case that what are intellectually reasonably closely related subjects or disciplines can develop with little overlap, and two subjects that pertain closely to metabolic systems biology are cheminformatics and chemical genetics. Cheminformatics 109, 110, 111 is the application of informatics methods to solve chemical problems. Although it has largely been driven by the interests of the pharmaceutical industry whose concerns lie with xenobiotics, it is obvious that the same
Acknowledgements
I thank the BBSRC, EPSRC and BHF for financial support of several projects, and AstraZeneca and GlaxoSmithKline for support of the HUSERMET project. Many individuals, especially David Broadhurst, Rick Dunn, Carole Goble, Roy Goodacre, Peter Li, Steve Oliver, Bernhard Palsson and Norman Paton are thanked for useful discussions. I thank Phil Baker, David Broadhurst, Rick Dunn and Louise Kenny for Figure 3. This is a contribution from the Manchester Centre for Integrative Systems Biology (//www.mcisb.org/
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