International Conference on Computational Methods in Systems Biology

CMSB 2015: Computational Methods in Systems Biology pp 145-156 | Cite as

Automating the Development of Metabolic Network Models

  • Robert Rozanski
  • Stefano Bragaglia
  • Oliver Ray
  • Ross King
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9308)

Abstract

Although substantial progress has been made in the automation of many areas of systems biology, from data processing and model building to experimentation, comparatively little work has been done on integrated systems that combine all of these aspects. This paper presents an active learning system, “Huginn”, that integrates experiment design and model revision in order to automate scientific reasoning about Metabolic Network Models. We have validated our approach in a simulated environment using substantial test cases derived from a state-of-the-art model of yeast metabolism. We demonstrate that Huginn can not only improve metabolic models, but that it is able to both solve a wider range of biochemical problems than previous methods, and to utilise a wider range of experiment types. Also, we show how design of extended crucial experiments can be automated using Abductive Logic Programming for the first time.

References

  1. 1.
    Aung, H.W., Henry, S.A., Walker, L.P.: Revising the representation of fatty acid, glycerolipid, and glycerophospholipid metabolism in the consensus model of yeast metabolism. Ind. Biotechnol. 9(4), 215–228 (2013)CrossRefGoogle Scholar
  2. 2.
    Bechtel, W., Richardson, R.C.: Discovering Complexity: Decomposition and Localization as Strategies in Scientific Research. MIT Press, Cambridge (2010) Google Scholar
  3. 3.
    Collet, G., Eveillard, D., Gebser, M., Prigent, S., Schaub, T., Siegel, A., Thiele, S.: Extending the metabolic network of ectocarpus siliculosus using answer set programming. In: Cabalar, P., Son, T.C. (eds.) LPNMR 2013. LNCS, vol. 8148, pp. 245–256. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  4. 4.
    Craver, C., Darden, L.: Discovering mechanisms in neurobiology. In: Machamer, P.K., et al. (eds.) Theory and Method in the Neurosciences, pp. 112–137. University of Pitt Press, Pittsburgh (2001)Google Scholar
  5. 5.
    Craver, C.F., Darden, L.: In Search of Mechanisms: Discoveries Across the Life Sciences. University of Chicago Press, Chicago (2013)CrossRefGoogle Scholar
  6. 6.
    Darden, L.: Reasoning in Biological Discoveries. Cambridge University Press, Cambridge (2006)CrossRefGoogle Scholar
  7. 7.
    Džeroski, S., Todorovski, L.: Discovering dynamics: from inductive logic programming to machine discovery. J. Intell. Inf. Syst. 4(1), 89–108 (1995)CrossRefGoogle Scholar
  8. 8.
    Gebser, M., Kaufmann, B., Neumann, A., Schaub, T.: clasp: A conflict-driven answer set solver. In: Baral, C., Brewka, G., Schlipf, J. (eds.) LPNMR 2007. LNCS (LNAI), vol. 4483, pp. 260–265. Springer, Heidelberg (2007) CrossRefGoogle Scholar
  9. 9.
    Gebser, M., Schaub, T., Thiele, S.: Gringo: a new grounder for answer set programming. In: Baral, C., Brewka, G., Schlipf, J. (eds.) LPNMR 2007. LNCS (LNAI), vol. 4483, pp. 266–271. Springer, Heidelberg (2007) CrossRefGoogle Scholar
  10. 10.
    King, R., Rowland, J., Oliver, S., Young, M., Aubrey, W., Byrne, E., Liakata, M., Markham, M., Pir, P., Soldatova, L., et al.: The automation of science. Science 324(5923), 85–89 (2009)CrossRefGoogle Scholar
  11. 11.
    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
  12. 12.
    Langley, P.: Scientific Discovery: Computational Explorations of the Creative Processes. MIT Press, Cambridge (1987) Google Scholar
  13. 13.
    Langley, P.: Lessons for the computational discovery of scientific knowledge. In: Proceedings of First International Workshop on Data Mining Lessons Learned, pp. 9–12. University of New South Wales (2002)Google Scholar
  14. 14.
    Machamer, P., Darden, L., Craver, C.F.: Thinking about mechanisms. Philos. Sci. 67, 1–25 (2000)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Ray, O.: Nonmonotonic abductive inductive learning. J. Appl. Logic 7(3), 329–340 (2009)CrossRefMATHGoogle 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.
    Schmidt, M., Lipson, H.: Distilling free-form natural laws from experimental data. Science 324(5923), 81–85 (2009)CrossRefGoogle Scholar
  18. 18.
    Thagard, P.: Computational Philosophy of Science. MIT Press, Cambridge (1993)Google Scholar
  19. 19.
    Todorovski, L., Bridewell, W., Shiran, O., Langley, P.: Inducing hierarchical process models in dynamic domains. In: Proceedings of the National Conference on Artificial Intelligence, vol. 20, p. 892. AAAI Press, MIT Press, Menlo Park, Cambridge (1999, 2005)Google Scholar
  20. 20.
    Valdés-Pérez, R.E.: Machine discovery in chemistry: new results. Artif. Intell. 74(1), 191–201 (1995)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Robert Rozanski
    • 1
  • Stefano Bragaglia
    • 2
  • Oliver Ray
    • 2
  • Ross King
    • 1
  1. 1.School of Computer ScienceUniversity of ManchesterManchesterUK
  2. 2.Department of Computer ScienceUniversity of BristolBristolUK

Personalised recommendations