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.
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Kitano, H. (2002) Systems biology: a brief overview. Science 295, 1662–1664.
Csete, M. E., and Doyle, J. C. (2002) Reverse engineering of biological complexity. Science 295, 1664–1669.
Chong, L., and Ray, L. B. (2002) Introduction to special issue. Whole-istic biology. Science 295, 1661.
Davidson, E. H., Rast, J. P., and Oliveri, P., et al. (2002) A genomic regulatory network for development. Science 295, 1669–1678.
Kanehisa, M., and Goto, S. (2000) KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27–30.
Kanehisa, M. (1997) A database for post-genome analysis. Trends Genet. 13, 375–376.
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.
Feng, X., and Rabitz, H. (2004) Optimal identification of biochemical reaction networks. Biophys. J. 86, 1270–1281.
Bratko I. (1986) Prolog Programming for Artificial Intelligence. Reading, MA: Addison Wesley International Computer Science Series.
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.
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.
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.
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.
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.
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.
King, R. D., Rowland, J. J. R., Oliver, S. G., et al. (2009) The automation of science. Science 324, 85–89.
Klamt, S., Haus, U. U., and Theis, F. (2005) Hypergraphs and cellular networks. PLoS Comput. Biol. 5, 1–6.
Chartrand, G., and Lesniak, L. (2004) Graphs and Digraphs. New York, NY: Chapman and Hall/CRC.
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.
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.
Montgomery, D. C., Peck, E. A., and Vining, G. G. (2001) Introduction to Linear Regression Analysis. New York, NY: Wiley.
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).
Muggleton, S., and DeRaedt, L. (1994) Inductive logic programming: theory and methods. J. Logic Programming 19(20), 629–679.
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.
Whelan, K. E., and King, R. D. (2008) Using a logical model to predict the growth of yeast. BMC Bioinformatics 9, 97.
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.
Ray, O. (2009) Nonmonotonic abductive inductive learning. J. Appl. Logic 7, 329–340.
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.
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Whelan, K., Ray, O., King, R.D. (2011). Representation, Simulation, and Hypothesis Generation in Graph and Logical Models of Biological Networks. In: Castrillo, J., Oliver, S. (eds) Yeast Systems Biology. Methods in Molecular Biology, vol 759. Humana Press. https://doi.org/10.1007/978-1-61779-173-4_26
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DOI: https://doi.org/10.1007/978-1-61779-173-4_26
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