Hybrid Metabolic Network Completion

  • Clémence Frioux
  • Torsten Schaub
  • Sebastian Schellhorn
  • Anne Siegel
  • Philipp Wanko
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10377)


Metabolic networks play a crucial role in biology since they capture all chemical reactions in an organism. While there are networks of high quality for many model organisms, networks for less studied organisms are often of poor quality and suffer from incompleteness. To this end, we introduced in previous work an ASP-based approach to metabolic network completion. Although this qualitative approach allows for restoring moderately degraded networks, it fails to restore highly degraded ones. This is because it ignores quantitative constraints capturing reaction rates. To address this problem, we propose a hybrid approach to metabolic network completion that integrates our qualitative ASP approach with quantitative means for capturing reaction rates. We begin by formally reconciling existing stoichiometric and topological approaches to network completion in a unified formalism. With it, we develop a hybrid ASP encoding and rely upon the theory reasoning capacities of the ASP system clingo for solving the resulting logic program with linear constraints over reals. We empirically evaluate our approach by means of the metabolic network of Escherichia coli. Our analysis shows that our novel approach yields greatly superior results than obtainable from purely qualitative or quantitative approaches.


  1. 1.
    Ansótegui, C., Bonet, M., Levy, J.: SAT-based MaxSAT algorithms. Artif. Intell. 196, 77–105 (2013)MathSciNetCrossRefMATHGoogle Scholar
  2. 2.
    Baral, C.: Knowledge Representation, Reasoning and Declarative Problem Solving. Cambridge University Press, New York (2003)CrossRefMATHGoogle Scholar
  3. 3.
    Becker, S., Feist, A., Mo, M., Hannum, G., Palsson, B., Herrgard, M.: Quantitative prediction of cellular metabolism with constraint-based models: the COBRA toolbox. Nat. Protoc. 2(3), 727–738 (2007)CrossRefGoogle Scholar
  4. 4.
    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). doi:10.1007/978-3-642-40564-8_25 CrossRefGoogle Scholar
  5. 5.
    Dantzig, G.: Linear Programming and Extensions. Princeton University Press, Princeton (1963)CrossRefMATHGoogle Scholar
  6. 6.
    Ebrahim, A., Lerman, J., Palsson, B., Hyduke, D.: COBRApy: COnstraints-Based Reconstruction and Analysis for Python. BMC Syst. Biol. 7, 74 (2013)CrossRefGoogle Scholar
  7. 7.
    Gebser, M., Kaminski, R., Kaufmann, B., Ostrowski, M., Schaub, T., Wanko, P.: Theory solving made easy with clingo 5. In: Technical Communication of ICLP, pp. 2:1–2:15. OASIcs (2016)Google Scholar
  8. 8.
    Gebser, M., Kaminski, R., Kaufmann, B., Romero, J., Schaub, T.: Progress in clasp series 3. In: Calimeri, F., Ianni, G., Truszczynski, M. (eds.) LPNMR 2015. LNCS (LNAI), vol. 9345, pp. 368–383. Springer, Cham (2015). doi:10.1007/978-3-319-23264-5_31 CrossRefGoogle Scholar
  9. 9.
    Gelfond, M., Lifschitz, V.: Classical negation in logic programs and disjunctive databases. New Gener. Comput. 9, 365–385 (1991)CrossRefMATHGoogle Scholar
  10. 10.
    Handorf, T., Ebenhöh, O., Heinrich, R.: Expanding metabolic networks: scopes of compounds, robustness, and evolution. J. Mol. Evol. 61(4), 498–512 (2005)CrossRefGoogle Scholar
  11. 11.
    Latendresse, M.: Efficiently gap-filling reaction networks. BMC Bioinform. 15(1), 225 (2014)CrossRefGoogle Scholar
  12. 12.
    Maranas, C., Zomorrodi, A.: Optimization Methods in Metabolic Networks. Wiley, Hoboken (2016)CrossRefGoogle Scholar
  13. 13.
    Orth, J., Palsson, B.: Systematizing the generation of missing metabolic knowledge. Biotechnol. Bioeng. 107(3), 403–412 (2010)CrossRefGoogle Scholar
  14. 14.
    Ostrowski, M., Schaub, T.: ASP modulo CSP: the clingcon system. Theory Pract. Logic Program. 12(4–5), 485–503 (2012)MathSciNetCrossRefMATHGoogle Scholar
  15. 15.
    Prigent, S., Collet, G., Dittami, S., Delage, L., Ethis de Corny, F., Dameron, O., Eveillard, D., Thiele, S., Cambefort, J., Boyen, C., Siegel, A., Tonon, T.: The genome-scale metabolic network of ectocarpus siliculosus (ectogem): a resource to study brown algal physiology and beyond. Plant J. 80(2), 367–381 (2014)CrossRefGoogle Scholar
  16. 16.
    Prigent, S., Frioux, C., Dittami, S., Thiele, S., Larhlimi, A., Collet, G., Gutknecht, F., Got, J., Eveillard, D., Bourdon, J., Plewniak, F., Tonon, T., Siegel, A.: Meneco, a topology-based gap-filling tool applicable to degraded genome-wide metabolic networks. PLOS Comput. Biol. 13(1), e1005276 (2017)CrossRefGoogle Scholar
  17. 17.
    Reed, J., Vo, T., Schilling, C., Palsson, B.: An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR). Genome Biol. 4(9), R54 (2003)CrossRefGoogle Scholar
  18. 18.
    Satish Kumar, V., Dasika, M., Maranas, C.: Optimization based automated curation of metabolic reconstructions. BMC Bioinform. 8(1), 212 (2007)CrossRefGoogle Scholar
  19. 19.
    Schaub, T., Thiele, S.: Metabolic network expansion with ASP. In: Proceedings ICLP, pp. 312–326. Springer (2009)Google Scholar
  20. 20.
    Simons, P., Niemelä, I., Soininen, T.: Extending and implementing the stable model semantics. Artif. Intell. 138(1–2), 181–234 (2002)MathSciNetCrossRefMATHGoogle Scholar
  21. 21.
    Thiele, I., Vlassis, N., Fleming, R.: fastGapFill: efficient gap filling in metabolic networks. Bioinformatics 30(17), 2529–2531 (2014)CrossRefGoogle Scholar
  22. 22.
    Vitkin, E., Shlomi, T.: MIRAGE: a functional genomics-based approach for metabolic network model reconstruction and its application to cyanobacteria networks. Genome Biol. 13(11), R111 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Clémence Frioux
    • 1
    • 2
  • Torsten Schaub
    • 1
    • 3
  • Sebastian Schellhorn
    • 3
  • Anne Siegel
    • 2
  • Philipp Wanko
    • 3
  1. 1.InriaRennesFrance
  2. 2.IRISAUniversité de Rennes 1RennesFrance
  3. 3.Universität PotsdamPotsdamGermany

Personalised recommendations