Metabolic Network Expansion with Answer Set Programming

  • Torsten Schaub
  • Sven Thiele
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5649)

Abstract

We propose a qualitative approach to elaborating the biosynthetic capacities of metabolic networks. In fact, large-scale metabolic networks as well as measured datasets suffer from substantial incompleteness. Moreover, traditional formal approaches to biosynthesis require kinetic information, which is rarely available. Our approach builds upon a formal method for analyzing large-scale metabolic networks. Mapping its principles into Answer Set Programming (ASP) allows us to address various biologically relevant problems. In particular, our approach benefits from the intrinsic incompleteness-tolerating capacities of ASP. Our approach is endorsed by recent complexity results, showing that the reconstruction of metabolic networks and related problems are NP-hard.

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References

  1. 1.
    Savageau, M.: Biochemical system analysis: a study of function and design in molecular biology. Addison-Wesley, Reading (1976)MATHGoogle Scholar
  2. 2.
    Kompala, D., Ramkrishna, D., Jansen, N., Tsao, G.: Investigation of bacterial-growth on mixed substrates. Biotechnology and Bioengineering 28(7), 1044–1055 (1986)CrossRefGoogle Scholar
  3. 3.
    Bonarius, H., Schmid, G., Tramper, J.: Flux analysis of underdetermined metabolic networks: The quest for the missing constraints. Trends Biotechnology 15, 308–314 (1997)CrossRefGoogle Scholar
  4. 4.
    Schilling, C., Schuster, S., Palsson, B., Heinrich, R.: Metabolic pathway analysis: Basic concepts and scientific applications in the post-genomic era. Biotechnology progress 15, 296–303 (1999)CrossRefGoogle Scholar
  5. 5.
    Wildermuth, M.: Metabolic control analysis: biological applications and insights. Genome Biology 1(6), 1031.1–1031.5 (2000)CrossRefGoogle Scholar
  6. 6.
    Baral, C.: Knowledge Representation, Reasoning and Declarative Problem Solving. Cambridge University Press, Cambridge (2003)CrossRefMATHGoogle Scholar
  7. 7.
    Nikoloski, Z., Grimbs, S., May, P., Selbig, J.: Metabolic networks are np-hard to reconstruct. Journal of Theoretical Biology 254, 807–816 (2008)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Nikoloski, Z., Grimbs, S., Selbig, J., Ebenhöh, O.: Hardness and approximability of the inverse scope problem. In: Crandall, K.A., Lagergren, J. (eds.) WABI 2008. LNCS (LNBI), vol. 5251, pp. 99–112. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  9. 9.
    Ebenhöh, O., Handorf, T., Heinrich, R.: Structural analysis of expanding metabolic networks. Genome Informatics 15(1), 35–45 (2004)Google Scholar
  10. 10.
    Handorf, T., Ebenhöh, O., Heinrich, R.: Expanding metabolic networks: Scopes of compounds, robustness, and evolution. Journal of Molecular Evolution 61(4), 498–512 (2005)CrossRefGoogle Scholar
  11. 11.
    Christian, N., May, P., Kempa, S., Handorf, T., Ebenhöh, O.: An integrative approach towards completing genome-scale metabolic networks (2008) (submitted for publication)Google Scholar
  12. 12.
    Handorf, T., Ebenhöh, O., Heinrich, R.: An environmental perspective on metabolism. Journal of Theoretical Biology 252(3), 498–512 (2008)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Simons, P., Niemelä, I., Soininen, T.: Extending and implementing the stable model semantics. Artificial Intelligence 138(1-2), 181–234 (2002)MathSciNetCrossRefMATHGoogle Scholar
  14. 14.
    Gebser, M., Kaufmann, B., Schaub, T.: Solution enumeration for projected boolean search problems. In: van Hoeve, W.-J., Hooker, J.N. (eds.) CPAIOR 2009. LNCS, vol. 5547, pp. 71–86. Springer, Heidelberg (2009)Google Scholar
  15. 15.
    Christian, N., May, P., Kempa, S., Handorf, T., Ebenhöh, O.: Personal communication (2008)Google Scholar
  16. 16.
    Gebser, M., Kaminski, R., Kaufmann, B., Ostrowski, M., Schaub, T., Thiele, S.: Engineering an incremental ASP solver. In: [23], pp. 190–205Google Scholar
  17. 17.
    Leone, N., Pfeifer, G., Faber, W., Eiter, T., Gottlob, G., Perri, S., Scarcello, F.: The DLV system for knowledge representation and reasoning. ACM TOCL 7(3), 499–562 (2006)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Baral, C., Chancellor, K., Tran, N., Tran, N., Joy, A., Berens, M.: A knowledge based approach for representing and reasoning about signaling networks. In: Proceedings ISMB 2004/ECCB 2004, pp. 15–22 (2004)Google Scholar
  19. 19.
    Dworschak, S., Grell, S., Nikiforova, V., Schaub, T., Selbig, J.: Modeling biological networks by action languages via answer set programming. Constraints 13(1-2), 21–65 (2008)MathSciNetCrossRefMATHGoogle Scholar
  20. 20.
    Gebser, M., Schaub, T., Thiele, S., Usadel, B., Veber, P.: Detecting inconsistencies in large biological networks with answer set programming. In: [23], pp. 130–144Google Scholar
  21. 21.
    Erdem, E., Türe, F.: Efficient haplotype inference with answer set programming. In: Proceedings AAAI 2008, pp. 436–441. AAAI Press, Menlo Park (2008)Google Scholar
  22. 22.
    Ray, O., Whelan, K., King, R.: A nonmonotonic logical approach for modelling and revising metabolic networks. In: Proceedings CISIS 2009. IEEE Press, Los Alamitos (to appear, 2009)Google Scholar
  23. 23.
    Garcia de la Banda, M., Pontelli, E. (eds.): ICLP 2008. LNCS, vol. 5366. Springer, Heidelberg (2008)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Torsten Schaub
    • 1
  • Sven Thiele
    • 1
  1. 1.Institut für InformatikUniversität PotsdamPotsdamGermany

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