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Automating Mathematical Modeling of Biochemical Reaction Networks

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Systems Biology for Signaling Networks

Part of the book series: Systems Biology ((SYSTBIOL))

Abstract

In this chapter we introduce a five-step modeling pipeline that ultimately leads to a mathematical description of a biochemical reaction system. We discuss how to automate each individual step and how to put these steps together. First, we create a topology of interconversion processes and mutual influences between reactive species. The Systems Biology Markup Language (SBML) encodes the model in a computer-readable form and allows us to add semantic information to each component of the model. Second, from such an annotated network, the procedure known as SBMLsqueezer generates kinetic equations in a context-sensitive manner. The resulting model can then be combined with already existing models. Third, we estimate the values of all newly introduced parameters in each created rate law. This procedure requires that a time series of quantitative measurements of the reactive species within this system be available, because we calibrate the parameters with the aim that the model will fit these experimental data. Fourth, an experimental validation of the resulting model is advisable. Fifth, a model report is generated automatically to document the model with all of its components. For a better understanding, we will begin with an introduction to current standardization attempts in systems biology and generalized approaches for common rate equations before discussing computer-aided modeling, parameter estimation, and automatic report generation. We complete this chapter with a discussion of possible further improvements to our modeling pipeline.

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Notes

  1. 1.

    http://sbml.org/Software/KEGG2SBML

  2. 2.

    http://www.ebi.ac.uk/sbo

  3. 3.

    http://sbgn.org

  4. 4.

    Since August 2008, CellDesigner has been available in version 4.0.1 at http://celldesigner.org.

  5. 5.

    Systems Biology Workbench (SBW)

  6. 6.

    http://www.yeastgenome.org

  7. 7.

    http://www.genome.jp/dbget

  8. 8.

    http://www.ihop-net.org/UniPub/iHOP

  9. 9.

    http://www.ncbi.nlm.nih.gov/pubmed

  10. 10.

    http://www.ebi.ac.uk/biomodels-main

  11. 11.

    http://www.ra.cs.uni-tuebingen.de/software/SBMLsqueezer

  12. 12.

    Database of chemical entities of biological interest, http://www.ebi.ac.uk/chebi

  13. 13.

    http://www.ebi.ac.uk/miriam

  14. 14.

    http://www.semanticsbml.org

  15. 15.

    http://www.semanticsml.org

  16. 16.

    An optimization problem is called multimodal if it contains a large number of local optima.

  17. 17.

    A comprehensive introduction to naturally inspired heuristic optimization procedures can be found at http://www.biomedcentral.com/imedia/8946429342473639/supp1.pdf

  18. 18.

    http://www.ra.cs.uni-tuebingen.de/software/EvA2

  19. 19.

    The homepage http://www.sbtoolbox2.org freely provides SBToolbox2 for MATLAB™ including a comprehensive documentation.

  20. 20.

    http://sbml.org/Facilities/Validator

  21. 21.

    http://www.ra.cs.uni-tuebingen.de/software/SBML2LaTeX

  22. 22.

    http://sbml.org

  23. 23.

    SB.OS can be freely downloaded at http://www.sbos.eu for all non-commercial purposes.

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Acknowledgments

The authors are grateful to Michael J. Ziller, Marcel Kronfeld, Catherine Lloyd, Falko Krause, and Wolfram Liebermeister for helpful advice, discussion, and contribution. This work was funded by the German Federal Ministry of Education and Research (BMBF) in the two projects, National Genome Research Network (NGFN-II EP under grant number 0313323, later NGFN-Plus under grant number 01GS08134) and HepatoSys under grant number 0313080 L, and the German Federal State of Baden-Württemberg in the two projects Identifikation und Analyse metabolischer Netze aus experimentellen Daten under contract number 7532.22-26-18 and Tübinger Bioinformatik-Grid under contract number 23-7532.24-4-18/1.

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Correspondence to Andreas Zell .

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© 2010 Springer Science+Business Media, LLC

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Dräger, A., Schröder, A., Zell, A. (2010). Automating Mathematical Modeling of Biochemical Reaction Networks. In: Choi, S. (eds) Systems Biology for Signaling Networks. Systems Biology. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-5797-9_7

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