Representation, Simulation, and Hypothesis Generation in Graph and Logical Models of Biological Networks

Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 759)

Abstract

This chapter presents a discussion of metabolic modeling from graph theory and logical modeling perspectives. These perspectives are closely related and focus on the coarse structure of metabolism, rather than the finer details of system behavior. The models have been used as background knowledge for hypothesis generation by Robot Scientists using yeast as a model eukaryote, where experimentation and machine learning are used to identify additional knowledge to improve the metabolic model. The logical modeling concept is being adapted to cell signaling and transduction biological networks.

Key words

Graph theory logical models metabolic networks machine learning 

References

  1. 1.
    Kitano, H. (2002) Systems biology: a brief overview. Science 295, 1662–1664.PubMedCrossRefGoogle Scholar
  2. 2.
    Csete, M. E., and Doyle, J. C. (2002) Reverse engineering of biological complexity. Science 295, 1664–1669.PubMedCrossRefGoogle Scholar
  3. 3.
    Chong, L., and Ray, L. B. (2002) Introduction to special issue. Whole-istic biology. Science 295, 1661.CrossRefGoogle Scholar
  4. 4.
    Davidson, E. H., Rast, J. P., and Oliveri, P., et al. (2002) A genomic regulatory network for development. Science 295, 1669–1678.PubMedCrossRefGoogle Scholar
  5. 5.
    Kanehisa, M., and Goto, S. (2000) KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27–30.PubMedCrossRefGoogle Scholar
  6. 6.
    Kanehisa, M. (1997) A database for post-genome analysis. Trends Genet. 13, 375–376.PubMedCrossRefGoogle Scholar
  7. 7.
    Karp, P. D., Riley, M., Paley, S. M., Pellegrini-Toole, A., and Krummenacker, M. (1996) EcoCyc: encyclopedia of Escherichia coli genes and metabolism. Nucleic Acids Res. 24, 32–39.PubMedCrossRefGoogle Scholar
  8. 8.
    Feng, X., and Rabitz, H. (2004) Optimal identification of biochemical reaction networks. Biophys. J. 86, 1270–1281.PubMedCrossRefGoogle Scholar
  9. 9.
    Bratko I. (1986) Prolog Programming for Artificial Intelligence. Reading, MA: Addison Wesley International Computer Science Series.Google Scholar
  10. 10.
    Lemke, N., Heredia, F., Barcellos, C. K., Dor Reis A. N., and Mombach, J. C. (2004) Essentiality and damage in metabolic networks. Bioinformatics 20, 115–119.PubMedCrossRefGoogle Scholar
  11. 11.
    Lemke, N., Heredia, F., Barcellos, C. K., and Mombach, J. C. (2003). A method to identify essential enzymes in the metabolism: application to Escherichia coli. In: Priami, C. (ed.), Proceedings of the First International Workshop on Computational Methods in Systems Biology (pp. 142–148). London, UK: Springer.CrossRefGoogle Scholar
  12. 12.
    Fages, F., Soliman, S., and Chabrier-Rivier, N. (2004) Modeling and querying interaction networks in the biochemical abstract machine BIOCHAM. J. Biol. Phys. Chem. 4, 64–73.CrossRefGoogle Scholar
  13. 13.
    Gershenson, C.(2002) Classification of Random Boolean Networks. In: Standish, R., Abbas, H., and Bedau, M. (eds.), Artificial Life VIII. Proceedings of the Eighth International Conference on Artificial Life (pp. 1–8). Cambridge, MA: MIT Press.Google Scholar
  14. 14.
    Kauffman, S., Peterson, C., Samuelsson, B., and Troein, C. (2003) Random Boolean network models and the yeast transcriptional network. Proc. Natl. Acad. Sci. USA 100, 14796–14799.PubMedCrossRefGoogle Scholar
  15. 15.
    King, R. D., Whelan, K. E., Jones, F. M., et al. (2004) Functional genomic hypothesis generation and experimentation by a robot scientist. Nature 427, 247–252.PubMedCrossRefGoogle Scholar
  16. 16.
    King, R. D., Rowland, J. J. R., Oliver, S. G., et al. (2009) The automation of science. Science 324, 85–89.PubMedCrossRefGoogle Scholar
  17. 17.
    Klamt, S., Haus, U. U., and Theis, F. (2005) Hypergraphs and cellular networks. PLoS Comput. Biol. 5, 1–6.Google Scholar
  18. 18.
    Chartrand, G., and Lesniak, L. (2004) Graphs and Digraphs. New York, NY: Chapman and Hall/CRC.Google Scholar
  19. 19.
    Christiensen, T. S., Oliviera, A. P., and Nielsen, J. (2009) Reconstruction and logical modeling of the glucose repression signaling pathways in Saccharomyces cerevisiae. BMC Syst. Biol. 3, 7.CrossRefGoogle Scholar
  20. 20.
    Rokach, L., and Maimon, O. (2005) Top down induction of decision trees classifiers: a survey. IEEE Trans. Syst. Man. Cybern. C Appl. Rev. v35 i4, 476–487.CrossRefGoogle Scholar
  21. 21.
    Montgomery, D. C., Peck, E. A., and Vining, G. G. (2001) Introduction to Linear Regression Analysis. New York, NY: Wiley.Google Scholar
  22. 22.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P. and Witten, I. H. (2009) The WEKA data mining software: an update. SIGKDD Explorations 11(1).Google Scholar
  23. 23.
    Muggleton, S., and DeRaedt, L. (1994) Inductive logic programming: theory and methods. J. Logic Programming 19(20), 629–679.CrossRefGoogle Scholar
  24. 24.
    Ray, O., Clare, A., Liakata, M., Soldatova, L., Whelan, K., and King, R. D. (2009) Towards the automation of scientific method. In: Proceedings of IJCAI'09 Workshop on Abductive and Inductive Knowledge Development (pp. 27–33). Pasadena, CA.Google Scholar
  25. 25.
    Whelan, K. E., and King, R. D. (2008) Using a logical model to predict the growth of yeast. BMC Bioinformatics 9, 97.Google Scholar
  26. 26.
    Muggleton, S., and Bryant, C. (2000) Theory completion using inverse entailment. In: Cussens, J. and Frisch, A. (eds.), Inductive Logic Programming, LNCS 1866 (pp. 130–146). London, UK: Springer.Google Scholar
  27. 27.
    Ray, O. (2009) Nonmonotonic abductive inductive learning. J. Appl. Logic 7, 329–340.CrossRefGoogle Scholar
  28. 28.
    Förster, J., Famili, I., Fu, P., Palsson B. Ø., and Nielsen, J. (2003) Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network. Genome Res. 13, 244–253.PubMedCrossRefGoogle Scholar

Copyright information

© Humana Press 2011

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of AberystwythCeredigionUK
  2. 2.Department of Computer ScienceUniversity of BristolBristolUK

Personalised recommendations