Hostname: page-component-7b7567c8c-tnzcf Total loading time: 0 Render date: 2026-11-14T10:02:50.282Z Has data issue: false hasContentIssue false
  • English
  • Français

Hybrid metabolic network completion

Published online by Cambridge University Press:  09 November 2018

CLÉMENCE FRIOUXOpen the ORCID record for CLÉMENCE FRIOUX[Opens in a new window],
TORSTEN SCHAUBOpen the ORCID record for TORSTEN SCHAUB[Opens in a new window],
SEBASTIAN SCHELLHORN,
ANNE SIEGEL and
PHILIPP WANKO
CLÉMENCE FRIOUX
Affiliation:
Univ Rennes, Inria, CNRS, IRISA, F-35000 Rennes, France (e-mail: clemence.frioux@inria.fr)
TORSTEN SCHAUB
Affiliation:
Inria, Rennes, FranceUniversität Potsdam, Potsdam, Germany (e-mail: torsten@cs.uni-potsdam.de)
SEBASTIAN SCHELLHORN
Affiliation:
Universität Potsdam, Germany (e-mail: seschell@cs.uni-potsdam.de)
ANNE SIEGEL
Affiliation:
Univ Rennes, Inria, CNRS, IRISA, F-35000 Rennes, France (e-mail: anne.siegel@irisa.fr)
PHILIPP WANKO
Affiliation:
Universität Potsdam, Potsdam, Germany (e-mail: wanko@cs.uni-potsdam.de)
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button.

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 answer set programming (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.

Keywords

answer set programmingmetabolic networkgap-fillinglinear programminghybrid solvingbioinformatics

Information

Type
Technical Note
Information
Theory and Practice of Logic Programming , Volume 19 , Issue 1 , January 2019, pp. 83 - 108
Copyright
Copyright © Cambridge University Press 2018 

Footnotes

This work was partially funded by DFG grant SCHA 550/9 and 11 and benefited from the support of the French Government via the National Research Agency investment expenditure program IDEALG ANR-10-BTBR-04.

References

Ansótegui, C., Bonet, M. and Levy, J. 2013. SAT-based MaxSAT algorithms. Artificial Intelligence 196, 77–105.Google Scholar
Baral, C. 2003. Knowledge Representation, Reasoning and Declarative Problem Solving. Cambridge University Press.Google Scholar
Becker, S., Feist, A., Mo, M., Hannum, G., Palsson, B. and Herrgard, M. 2007. Quantitative prediction of cellular metabolism with constraint-based models: The COBRA toolbox. Nature Protocols 2, 3, 727–738.Google Scholar
Caspi, R., Billington, R., Ferrer, L., Foerster, H., Fulcher, C. A., Keseler, I. M., Kothari, A., Krummenacker, M., Latendresse, M., Mueller, L. A., Ong, Q., Paley, S., Subhraveti, P., Weaver, D. S. and Karp, P. D. 2016. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Research 44, D1, D471–80.Google Scholar
Collet, G., Eveillard, D., Gebser, M., Prigent, S., Schaub, T., Siegel, A. and Thiele, S. 2013. Extending the metabolic network of Ectocarpus siliculosus using answer set programming. In Proc. of the 12th International Conference on Logic Programming and Nonmonotonic Reasoning, Cabalar, P. and Son, T., Eds. Lecture Notes in Artificial Intelligence, vol. 8148. Springer-Verlag, 245–256.Google Scholar
Dantzig, G. 1963. Linear Programming and Extensions. Princeton University Press.Google Scholar
Ebrahim, A., Lerman, J., Palsson, B. and Hyduke, D. 2013. COBRApy: COnstraints-based reconstruction and analysis for Python. BMC Systems Biology 7, 74.Google Scholar
Gebser, M., Kaminski, R., Kaufmann, B., Ostrowski, M., Schaub, T. and Wanko, P. 2016. Theory solving made easy with clingo 5. In Technical Communications of the Thirty-second International Conference on Logic Programming, Carro, M. and King, A., Eds., vol. 52. Open Access Series in Informatics (OASIcs), 2:1–2:15.Google Scholar
Gebser, M., Kaminski, R., Kaufmann, B., Romero, J. and Schaub, T. 2015. Progress in clasp series 3. In Proc. of the 13th International Conference on Logic Programming and Nonmonotonic Reasoning, Calimeri, F., Ianni, G. and Truszczyński, M., Eds. Lecture Notes in Artificial Intelligence, vol. 9345. Springer-Verlag, 368–383.Google Scholar
Gelfond, M. and Lifschitz, V. 1991. Classical negation in logic programs and disjunctive databases. New Generation Computing 9, 365–385.Google Scholar
Handorf, T., Ebenhöh, O. and Heinrich, R. 2005. Expanding metabolic networks: Scopes of compounds, robustness, and evolution. Journal of Molecular Evolution 61, 4, 498–512.Google Scholar
Janhunen, T., Kaminski, R., Ostrowski, M., Schaub, T., Schellhorn, S. and Wanko, P. 2017. Clingo goes linear constraints over reals and integers. Theory and Practice of Logic Programming 17, 5–6, 872–888.Google Scholar
Latendresse, M. 2014. Efficiently gap-filling reaction networks. BMC Bioinformatics 15, 1, 225.Google Scholar
Maranas, C. and Zomorrodi, A. 2016. Optimization Methods in Metabolic Networks. John Wiley & sons.Google Scholar
Orth, J. and Palsson, B. 2010. Systematizing the generation of missing metabolic knowledge. Biotechnology and Bioengineering 107, 3, 403–412.Google Scholar
Ostrowski, M. and Schaub, T. 2012. ASP modulo CSP: The clingcon system. Theory and Practice of Logic Programming 12, 4–5, 485–503.Google Scholar
Prigent, S., Collet, G., Dittami, S., Delage, L., Ethis de Corny, F., Dameron, O., Eveillard, D., Thiele, S., Cambefort, J., Boyen, C., Siegel, A. and Tonon, T. 2014. The genome-scale metabolic network of ectocarpus siliculosus (ectogem): A resource to study brown algal physiology and beyond. The Plant Journal 80, 2, 367–381.Google Scholar
Prigent, S., Frioux, C., Dittami, S., Thiele, S., Larhlimi, A., Collet, G., Gutknecht, F., Got, J., Eveillard, D., Bourdon, J., Plewniak, F., Tonon, T. and Siegel, A. 2017. Meneco, a topology-based gap-filling tool applicable to degraded genome-wide metabolic networks. PLOS Computational Biology 13, 1, e1005276.Google Scholar
Reed, J., Vo, T., Schilling, C. and Palsson, B. 2003. An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR). Genome Biology 4, 9, R54.Google Scholar
Satish Kumar, V., Dasika, M. and Maranas, C. 2007. Optimization based automated curation of metabolic reconstructions. BMC Bioinformatics 8, 1, 212.Google Scholar
Schaub, T. and Thiele, S. 2009. Metabolic network expansion with ASP. In Proc. of the 25th International Conference on Logic Programming, Hill, P. and Warren, D., Eds. Lecture Notes in Computer Science, vol. 5649. Springer-Verlag, 312–326.Google Scholar
Simons, P., Niemelä, I. and Soininen, T. 2002. Extending and implementing the stable model semantics. Artificial Intelligence 138, 1–2, 181–234.Google Scholar
Thiele, I., Vlassis, N. and Fleming, R. 2014. fastGapFill: Efficient gap filling in metabolic networks. Bioinformatics 30, 17, 2529–2531.Google Scholar
Vitkin, E. and Shlomi, T. 2012. MIRAGE: A functional genomics-based approach for metabolic network model reconstruction and its application to cyanobacteria networks. Genome Biology 13, 11, R111.Google Scholar