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Systematic integration of experimental data and models in systems biology
Li, P., Dada, J.O., Jameson, D., Spasic, I., Swainston, N., Carroll, K., Dunn, W., Khan, F.,Naglis, M., Messiha, H.L., Simeonidis, E., Weichart, D., Winder, C., Whisart, J., Broomhead, D., Goble, C.A., Gaskell, S.J., Kell, D.B., Westerhoff, H.V., Mendes, P., Paton, N.W
BMC Bioinformatics. 2010;11:art582.
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Abstract
BackgroundThe 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.ResultsTaverna 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.ConclusionsDistributed 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.