Abstract
The study of systems biology through inductive logic programming (ILP) aims at improving the understanding of the physiological state of the cell by reasoning with rules and relations instead of ordinary differential equations. This paper presents a method for enabling the ILP framework to deal with quantitative information from some experimental data in systems biology. The method consist in both discretizing the evolution of concentrations of metabolites during experiments and transcribing enzymatic kinetics (for instance Michaelis-Menten kinetics) into logic rules. Kinetic rules are added to background knowledge, along with the topology of the metabolic pathway, whereas discretized concentrations are observations. Applying ILP allows for abduction and induction in such a system. A logical model of the glycolysis and pentose phosphate pathways of E. Coli is proposed to support our method description. Logical formulae on concentrations of some metabolites, which could not be measured during the dynamic state, are produced through logical abduction. Finally, as this results in a large number of hypotheses, they are ranked with an expectation maximization algorithm working on binary decision diagrams.
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References
Kitano, H.: Systems biology toward system-level understanding of biological systems. Science 295, 1662–1664 (2002)
Baral, C., Chancellor, K., Tran, N., Tran, N., Joy, A., Berens, M.: A knowledge based approach for representing and reasoning about signaling networks. In: Proc. of the 12th Int. Conf. on Intelligent Systems for Molecular Biology, pp. 15–22 (2004)
Juvan, P., Demsar, J., Shaulsky, G., Zupan, B.: Genepath: from mutations to genetic networks and back. Nucleic Acids Res. 33 (2005)
King, R., Whelan, K., Jones, F., Reiser, P., Bryant, C., Muggleton, S., Kell, D., Olivier, S.: Functional genomic hypothesis generation and experimentation by a robot scientist. Nature 427, 247–252 (2004)
King, R., Garrett, S., Coghill, G.: On the use of qualitative reasoning to simulate and identify metabolic pathways. Bioinformatics 21, 2017–2026 (2005)
Tamaddoni-Nezhad, A., Chaleil, R., Kakas, A., Muggleton, S.: Application of abductive ILP to learning metabolic network inhibition from temporal data. Machine Learning 64, 209–230 (2006)
Tiwari, A., Talcott, C., Knapp, M., Lincoln, P., Laderoute, K.: Analyzing Pathways Using SAT-Based Approaches. In: Anai, H., Horimoto, K., Kutsia, T. (eds.) Ab 2007. LNCS, vol. 4545, pp. 155–169. Springer, Heidelberg (2007)
Doncescu, A., Yamamoto, Y., Inoue, K.: Biological systems analysis using Inductive Logic Programming. In: IEEE International Symp. on Bioinf. and Life Science Computing (2007)
Dworschak, S., Grell, S., Nikiforova, V., Schaub, T., Selbig, J.: Modeling biological networks by action languages via answer set programming. Constraints 13, 21–65 (2008)
Fages, F., Soliman, S., France, I.R.: Model Revision from Temporal Logic Properties in Computational Systems Biology. In: De Raedt, L., Frasconi, P., Kersting, K., Muggleton, S.H. (eds.) Probabilistic Inductive Logic Programming. LNCS (LNAI), vol. 4911, pp. 287–304. Springer, Heidelberg (2008)
Inoue, K., Sato, T., Ishihata, M., Kameya, Y., Nabeshima, H.: Evaluating abductive hypotheses using and EM algorithm on BDDs. In: Proc. of IJCAI 2009, pp. 815–820. AAAI Press (2009)
Gauvain, J.L., Lee, C.H.: Maximum a posteriori estimation for multivariate gaussian mixture observations of markov chains. IEEE Transactions on Speech and Audio Processing 2, 291–298 (1994)
Ji, S., Krishnapuram, B., Carin, L.: Variational bayes for continuous hidden markov models and its application to active learning. IEEE Transactions on Pattern Analysis and Machine Intelligence 28, 522–532 (2006)
Kanehisa, M., Goto, S.: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30 (2000)
Kanehisa, M., Araki, M., Goto, S., Hattori, M., Hirakawa, M., Itoh, M., Katayama, T., Kawashima, S., Okuda, S., Tokimatsu, T., Yamanishi, Y.: KEGG for linking genomes to life and the environment. Nucleic Acids Res. 36, 480–484 (2008)
Nabeshima, H., Iwanuma, K., Inoue, K.: SOLAR: A consequence finding system for advanced reasoning. In: Proc. of the 11th International Conference TABLEAUX 2003. LNCS (LNAI), vol. 2786, pp. 257–263 (2003)
Ishihata, M., Kameya, Y., Sato, T., Minato, S.: Propositionalizing the EM algorith by BDDs. Technical report, TR08-0004, Dept. Comp. Sc., Tokyo Instute of Technology (2008)
Benhamou, F.: Interval Constraint Logic Programming. In: Podelski, A. (ed.) Constraint Programming: Basics and Trends. LNCS, vol. 910, pp. 1–21. Springer, Heidelberg (1995)
Geurts, P.: Pattern Extraction for Time Series Classification. In: Siebes, A., De Raedt, L. (eds.) PKDD 2001. LNCS (LNAI), vol. 2168, pp. 115–127. Springer, Heidelberg (2001)
Keogh, E., Lin, J., Fu, A.: HOT SAX: efficiently finding the most unusual time series subsequence. In: 5th IEEE International Conference on Data Mining (2005)
Rabiner, L.: A tutorial on hidden markov models and selected applications in speech recognition. Proc. of the IEEE 77, 257–286 (1989)
Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978)
Cheeseman, P., Stutz, J.: Bayesian classification (autoclass): Theory and results. In: Advances in Knowledge Discovery and Data Mining, pp. 153–180. The MIT Press (1995)
Beal, M.: Variational Algorithms for Approximate Bayesian Inference. PhD thesis, Gatsby Comp. Neurosc. Unit, University College London (2003)
De Raedt, L.: Logical and Relational Learning. Springer, Heidelberg (2008)
Kameya, Y., Synnaeve, G., Doncescu, A., Inoue, K., Sato, T.: A bayesian hybrid approach to unsupervised time series discretization. In: International Conference on Technologies and Applications of Artificial Intelligence, pp. 342–349 (2010)
Chassagnole, C., Rodrigues, J., Doncescu, A., Yang, L.T.: Differential evolutionary algorithms for in vivo dynamic analysis of glycolysis and pentose phosphate pathway in Escherichia Coli. A. Zomaya (2006)
Mooney, R.: Integrating abduction and induction in machine learning. In: Working Notes of the IJCAI 1997 Workshop on Abduction and Induction in AI, pp. 37–42 (1997)
Inoue, K.: Linear resolution for consequence finding. Artificial Intelligence 56, 301–353 (1992)
Muggleton, S.: Inverse entailment and progol. New Generation Computing 13, 245–286 (1995)
Inoue, K.: Induction as consequence finding. Machine Learning 55, 109–135 (2004)
Peters-Wendisch, P., Schiel, B., Wendisch, V., Katsoulidis, E., et al.: Pyruvate carboxylase is a major bottleneck for glutamate and lysine production by corynebacterium glutamicum. Molecular Microbiol. Biotechnol. 3 (2001)
Ray, O., Whelan, K., King, R.: A nonmonotonic logical approach for modelling and revising metabolic networks. In: IEEE Complex, Intelligent and Software Intensive Systems (2009)
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Synnaeve, G. et al. (2013). Discretized Kinetic Models for Abductive Reasoning in Systems Biology. In: Fred, A., Filipe, J., Gamboa, H. (eds) Biomedical Engineering Systems and Technologies. BIOSTEC 2011. Communications in Computer and Information Science, vol 273. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29752-6_11
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