KINETIC MODELS AND QUALITATIVE ABSTRACTION FOR RELATIONAL LEARNING IN SYSTEMS BIOLOGY

Gabriel Synnaeve, Katsumi Inoue, Andrei Doncescu, Hidetomo Nabeshima, Yoshitaka Kameya, Masakazu Ishihata, Taisuke Sato

2011

Abstract

This paper presents a method for enabling the relational learning or inductive logic programming (ILP) framework to deal with quantitative information from experimental data in systems biology. The study of systems biology through ILP aims at improving the understanding of the physiological state of the cell and the interpretation of the interactions between metabolites and signaling networks. A logical model of the glycolysis and pentose phosphate pathways of E. Coli is proposed to support our method description. We explain our original approach to building a symbolic model applied to kinetics based on Michaelis-Menten equation, starting with the discretization of the changes in concentration of some of the metabolites over time into relevant levels. We can then use them in our ILP-based model. 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.

References

  1. Baral, C., Chancellor, K., Tran, N., Tran, N., Joy, A., and Berens, M. (2004). A knowledge based approach for representing and reasoning about signaling networks. In Proc. of the 12th Int. Conf. on Intelligent Systems for Molecular Biology, pages 15-22.
  2. Beal, M. (2003). Variational Algorithms for Approximate Bayesian Inference. PhD thesis, Gatsby Comp. Neurosc. Unit, University College London.
  3. Benhamou, F. (1994). Interval constraint logic programming. Lecture Notes in Computer Science, 910.
  4. Chassagnole, C., Rodrigues, J., Doncescu, A., and Yang, L. T. (2006). Differential evolutionary algorithms for in vivo dynamic analysis of glycolysis and pentose phosphate pathway in Escherichia Coli. A. Zomaya.
  5. Cheeseman, P. and Stutz, J. (1995). Bayesian classification (autoclass): Theory and results. In Advances in Knowledge Discovery and Data Mining, pages 153- 180. The MIT Press.
  6. De Raedt, L. (2008). Logical and Relational Learning. Springer.
  7. Doncescu, A., Yamamoto, Y., and Inoue, K. (2007). Biological systems analysis using Inductive Logic Programming. In IEEE International Symp. on Bioinf. and Life Science Computing.
  8. Dworschak, S., Grell, S., Nikiforova, V., Schaub, T., and Selbig, J. (2008). Modeling biological networks by action languages via answer set programming. Constraints, 13(1/2):21-65.
  9. Fages, F., Soliman, S., and France, I. R. (2008). Model revision from temporal logic properties in systems biology. In In: Probabilistic Inductive Logic Programming. LNAI, volume 4911, pages 287-304.
  10. Gauvain, J.-L. and Lee, C.-H. (1994). Maximum a posteriori estimation for multivariate gaussian mixture observations of markov chains. IEEE Transactions on Speech and Audio Processing, 2(2):291-298.
  11. Geurts, P. (2001). Pattern extraction for time-series classification. Lecture Notes in Artificial Intelligence, 2168:115-127.
  12. Inoue, K. (1992). Linear resolution for consequence finding. Artificial Intelligence, 56:301-353.
  13. Inoue, K. (2004). Induction as consequence finding. Machine Learning, 55:109-135.
  14. Inoue, K., Sato, T., Ishihata, M., Kameya, Y., and Nabeshima, H. (2009). Evaluating abductive hypotheses using and EM algorithm on BDDs. In Proc. of IJCAI-09, pages 820-815. AAAI Press.
  15. Ishihata, M., Kameya, Y., Sato, T., and Minato, S. (2008). Propositionalizing the EM algorith by BDDs. Technical report, TR08-0004, Dept. Comp. Sc., Tokyo Instute of Technology.
  16. Ji, S., Krishnapuram, B., and Carin, L. (2006). Variational bayes for continuous hidden markov models and its application to active learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(4):522-532.
  17. Juvan, P., Demsar, J., Shaulsky, G., and Zupan, B. (2005). Genepath: from mutations to genetic networks and back. Nucleic Acids Res., 33.
  18. Kanehisa, M., Araki, M., Goto, S., Hattori, M., Hirakawa, M., Itoh, M., Katayama, T., Kawashima, S., Okuda, S., Tokimatsu, T., and Yamanishi, Y. (2008). KEGG for linking genomes to life and the environment. Nucleic Acids Res., 36:480-484.
  19. Kanehisa, M. and Goto, S. (2000). Kyoto encyclopedia of genes and genomes. Nucleic Acids Res., 28(1):27-30.
  20. Keogh, E., Lin, J., and Fu, A. (2005). HOT SAX: efficiently finding the most unusual time series subsequence. In 5th IEEE International Conference on Data Mining.
  21. King, R., Garrett, S., and Coghill, G. (2005). On the use of qualitative reasoning to simulate and identify metabolic pathways. Bioinformatics, 21(9):2017- 2026.
  22. King, R., Whelan, K., Jones, F., Reiser, P., Bryant, C., Muggleton, S., Kell, D., and Olivier, S. (2004). Functional genomic hypothesis generation and experimentation by a robot scientist. Nature, 427:247-252.
  23. Kitano, H. (2002). Systems biology toward systemlevel understanding of biological systems. Science, 295(5560):1662-1664.
  24. Mooney, R. (1997). Integrating abduction and induction in machine learning. In Working Notes of the IJCAI97 Workshop on Abduction and Induction in AI, pages 37-42.
  25. Muggleton, S. (1995). Inverse entailment and progol. New Generation Computing, 13(3/4):245-286.
  26. Nabeshima, H., Iwanuma, K., and Inoue, K. (2003). SOLAR: A consequence finding system for advanced reasoning. In Proc. of the 11th International Conference TABLEAUX 2003, LNAI, volume 2786, pages 257-263.
  27. Peters-Wendisch, P., Schiel, B., Wendisch, V., and et al., E. K. (2001). Pyruvate carboxylase is a major bottleneck for glutamate and lysine production by corynebacterium glutamicum. Molecular Microbiol. Biotechnol., 3(2).
  28. Rabiner, L. (1989). A tutorial on hidden markov models and selected applications in speech recognition. Proc. of the IEEE, 77(2):257-286.
  29. Ray, O., Whelan, K., and King, R. (2009). A nonmonotonic logical approach for modelling and revising metabolic networks. Complex, Intelligent and Software Intensive Systems, IEEE.
  30. Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6(2):461-464.
  31. Tamaddoni-Nezhad, A., Chaleil, R., Kakas, A., and Muggleton, S. (2006). Application of abductive ILP to learning metabolic network inhibition from temporal data. Machine Learning, 64:209-230.
  32. Tiwari, A., Talcott, C., Knapp, M., Lincoln, P., and Laderoute, K. (2007). Analyzing pathways using SATbased approaches. In Proc of the 2nd Int. Conf. on Algebraic Biology, pages 155-169.
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Paper Citation


in Harvard Style

Synnaeve G., Inoue K., Doncescu A., Nabeshima H., Kameya Y., Ishihata M. and Sato T. (2011). KINETIC MODELS AND QUALITATIVE ABSTRACTION FOR RELATIONAL LEARNING IN SYSTEMS BIOLOGY . In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2011) ISBN 978-989-8425-36-2, pages 47-54. DOI: 10.5220/0003166300470054


in Bibtex Style

@conference{bioinformatics11,
author={Gabriel Synnaeve and Katsumi Inoue and Andrei Doncescu and Hidetomo Nabeshima and Yoshitaka Kameya and Masakazu Ishihata and Taisuke Sato},
title={KINETIC MODELS AND QUALITATIVE ABSTRACTION FOR RELATIONAL LEARNING IN SYSTEMS BIOLOGY},
booktitle={Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2011)},
year={2011},
pages={47-54},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003166300470054},
isbn={978-989-8425-36-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2011)
TI - KINETIC MODELS AND QUALITATIVE ABSTRACTION FOR RELATIONAL LEARNING IN SYSTEMS BIOLOGY
SN - 978-989-8425-36-2
AU - Synnaeve G.
AU - Inoue K.
AU - Doncescu A.
AU - Nabeshima H.
AU - Kameya Y.
AU - Ishihata M.
AU - Sato T.
PY - 2011
SP - 47
EP - 54
DO - 10.5220/0003166300470054