Nonmonotonic Learning in Large Biological Networks

  • Stefano Bragaglia
  • Oliver Ray
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9046)


This paper introduces a new open-source implementation of a nonmonotonic learning method called XHAIL and shows how it can be used for abductive and inductive inference on metabolic networks that are many times larger than could be handled by the preceding prototype. We summarise several implementation improvements that increase its efficiency and we introduce an extended form of language bias that further increases its usability. We investigate the system’s scalability in a case study involving real data previously collected by a Robot Scientist and show how it led to the discovery of an error in a whole-organism model of yeast metabolism.


ILP ALP ASP Metabolic networks Completion Revision 



This work is supported by EPSRC grant EP/K035959/1.


  1. 1.
    Förster, J., Famili, I., Fu, P., Palsson, B., Nielsen, J.: Genome-scale reconstruction of the saccharomyces cerevisiae metabolic network. Gen. Res. 13(2), 244–53 (2003)CrossRefGoogle Scholar
  2. 2.
    Gebser, M., Kaufmann, B., Neumann, A., Schaub, T.: clasp: A conflict-driven answer set solver. In: Logic Programming and Nonmonotonic Reasoning, pp. 260–265. Springer (2007)Google Scholar
  3. 3.
    Gelfond, M., Lifschitz, V.: Classical negation in logic programs and disjunctive databases. New Generation Comp. 9(3/4), 365–386 (1991)CrossRefzbMATHGoogle Scholar
  4. 4.
    Heavner, B.D., et al.: Yeast 5 – an expanded reconstruction of the saccharomyces cerevisiae metabolic network. BMC Syst. Biol. 6, 55 (2012)CrossRefGoogle Scholar
  5. 5.
    Kakas, A., Kowalski, R., Toni, F.: Abductive logic programming. J. Logic Comput. 2(6), 719–770 (1992)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    King, R.D., Rowland, J., Oliver, S.G., Young, M., Aubrey, W., Byrne, E., Liakata, M., Markham, M., Pir, P., Soldatova, L.N.: The automation of science. Science 324(5923), 85–89 (2009)CrossRefGoogle Scholar
  7. 7.
    King, R.D., Whelan, K.E., Jones, F.M., Reiser, P.G., Bryant, C.H., Muggleton, S.H., Kell, D.B., Oliver, S.G.: Functional genomic hypothesis generation and experimentation by a robot scientist. Nature 427(6971), 247–252 (2004)CrossRefGoogle Scholar
  8. 8.
    Lehninger, A.: Biochemistry: The Molecular Basis of Cell Structure and Function, 2nd edn. Worth Publishers, New York (1979)Google Scholar
  9. 9.
    Muggleton, S.: Inverse entailment and Progol. New Gen. Comp. 13, 245–286 (1995)CrossRefGoogle Scholar
  10. 10.
    Muggleton, S.H., Bryant, C.H.: Theory completion using inverse entailment. In: Cussens, J., Frisch, A.M. (eds.) ILP 2000. LNCS (LNAI), vol. 1866, pp. 130–146. Springer, Heidelberg (2000) CrossRefGoogle Scholar
  11. 11.
    Muggleton, S., De Raedt, L.: Inductive logic programming: theory and methods. J. Logic Program. 19(20), 629–679 (1994)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Ogata, H., Goto, S., Sato, K., Fujibuchi, W., Bono, H., Kanehisa, M.: Kegg: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 27(1), 29–34 (1999)CrossRefGoogle Scholar
  13. 13.
    Ray, O.: Hybrid Abductive-Inductive Learning. Ph.D. thesis, Department of Computing, Imperial College London, UK (2005)Google Scholar
  14. 14.
    Ray, O., Whelan, K., King, R.: A nonmonotonic logical approach for modelling and revising metabolic networks. In: Proceedings of 3rd International Conference on Complex, Intelligent and Software Intensive Systems, pp. 825–829. IEEE (2009)Google Scholar
  15. 15.
    Ray, O.: Nonmonotonic abductive inductive learning. J. Appl. Logic 7(3), 329–340 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Ray, O., Whelan, K., King, R.: Automatic revision of metabolic networks through logical analysis of experimental data. In: De Raedt, L. (ed.) ILP 2009. LNCS, vol. 5989, pp. 194–201. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  17. 17.
    Simons, P., Niemelä, I., Soininen, T.: Extending and implementing the stable model semantics. Artificial Intel. 138(1–2), 181–234 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Tamaddoni-Nezhad, A., Kakas, A.C., Muggleton, S.H., Pazos, F.: Modelling inhibition in metabolic pathways through abduction and induction. In: Camacho, R., King, R., Srinivasan, A. (eds.) ILP 2004. LNCS (LNAI), vol. 3194, pp. 305–322. Springer, Heidelberg (2004) CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of BristolBristolUK

Personalised recommendations