Skip to main content
SpringerLink
Log in
Book cover

International Conference on Bioinformatics and Biomedical Engineering

IWBBIO 2016: Bioinformatics and Biomedical Engineering pp 304–314Cite as

Calculating Elementary Flux Modes with Variable Neighbourhood Search

  • Conference paper
  • First Online:

  • 1839 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 9656))

Abstract

In this work, we calculate Elementary Flux Modes (EFMs) from metabolic networks using a trajectory-based metaheuristic, Variable Neighbourhood Search (VNS). This method is based on the local exploration around an incumbent solution and the subsequent visits to “neighbourhoods” (i.e., other areas of the search space) when the exploration is not successful on improving an objective function. This strategy ensures a suitable balance between exploration and exploitation, which is the key point in metaheuristic-based optimization. Making use of linear programming and the Simplex method, a VNS-based metaheuristic has been designed and implemented. This algorithm iteratively solves the linear programs resulting from the formulation of different hypotheses about the metabolic network. These solutions are, when feasible, EFMs. The application of the proposed method on a benchmark problem corroborates its efficacy.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Behre, J., Wilhelm, T., von Kamp, A., Ruppin, E., Schuster, S.: Structural robustness of metabolic networks with respect to multiple knockouts. J. Theor. Biol. 252(3), 433–441 (2008)

    Article  Google Scholar 

  2. Berkelaar, M., et al.: lpSolve: Interface to Lp_solve v. 5.5 to Solve Linear/Integer Programs (2015), rpackageversion5.6.11. http://CRAN.R-project.org/package=lpSolve

  3. Blum, T., Kohlbacher, O.: Using atom mapping rules for an improved detection of relevant routes in weighted metabolic networks. J. Comput. Biol. 15(6), 565–576 (2008)

    Article  MathSciNet  Google Scholar 

  4. Carlson, R., Srienc, F.: Fundamental Escherichia coli biochemical pathways for biomass and energy production: identification of reactions. Biotechnol. Bioeng. 85(1), 1–19 (2004)

    Article  Google Scholar 

  5. Croes, D., Couche, F., Wodak, S.J., van Helden, J.: Inferring meaningful pathways in weighted metabolic networks. J. Mol. Biol. 356(1), 222–236 (2006)

    Article  Google Scholar 

  6. Dávid, L., Bockmayr, A.: Computing elementary flux modes involving a set of target reactions. IEEE/ACM Trans. Comput. Biol. Bioinform. 11(6), 1099–1107 (2014)

    Article  Google Scholar 

  7. Feist, A.M., Palsson, B.Ø.: The growing scope of applications of genome-scale metabolic reconstructions using Escherichia coli. Nat. Biotech. 26(6), 659–667 (2008)

    Article  Google Scholar 

  8. de Figueiredo, L.F., Schuster, S., Kaleta, C., Fell, D.A.: Can sugars be produced from fatty acids? a test case for pathway analysis tools. Bioinformatics 25(1), 152–158 (2009)

    Article  Google Scholar 

  9. Hansen, P., Mladenović, N., Moreno-Pérez, J.: Variable neighborhood search: methods and applications. Ann. Oper. Res. 175(1), 367–407 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  10. Céspedes, J.F.H., De Asís Guil Asensio, F., Carrasco, J.M.G.: A new approach to obtain EFMs using graph methods based on the shortest path between end nodes. In: Ortuño, F., Rojas, I. (eds.) IWBBIO 2015, Part I. LNCS, vol. 9043, pp. 641–649. Springer, Heidelberg (2015)

    Google Scholar 

  11. Kaleta, C., de Figueiredo, L.F., Schuster, S.: Can the whole be less than the sum of its parts? pathway analysis in genome-scale metabolic networks using elementary flux patterns. Genome Res. 19(10), 1872–1883 (2009)

    Article  Google Scholar 

  12. Kaleta, C., de Figueiredo, L.F., Schuster, S.: EFMEvolver: computing elementary flux modes in genome-scale metabolic networks. LNCS 157, 179–189 (2009)

    Google Scholar 

  13. Klamt, S., Stelling, J.: Combinatorial complexity of pathway analysis in metabolic networks. Mol. Biol. Rep. 29(1–2), 233–236 (2002)

    Article  Google Scholar 

  14. Luque, G., Alba, E.: Enhancing parallel cooperative trajectory based metaheuristics with path relinking. In: Proceedings of the 16th Annual Conference on Genetic and Evolutionary Computation (GECCO14), pp. 1039–1046 (2014)

    Google Scholar 

  15. Mladenović, N., Hansen, P.: Variable neighborhood search. Comput. Oper. Res. 24(11), 1097–1100 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  16. Nash, J.C.: The (Dantzig) simplex method for linear programming. Comput. Sci. Eng. 2(1), 29–31 (2000)

    Article  Google Scholar 

  17. Pey, J., Planes, F.J.: Direct calculation of elementary flux modes satisfying several biological constraints in genome-scale metabolic networks. Bioinform. Accepted (2015). doi:10.1093/bioinformatics/btu193

    Google Scholar 

  18. Pey, J., Villar, J.A., Tobalina, L., Rezola, A., García, J.M., Beasley, J.E., Planes, F.J.: TreeEFM: calculating elementary flux modes using linear optimization in a tree-based algorithm. Bioinformatics 31(6), 897–904 (2015)

    Article  Google Scholar 

  19. Planes, F.J., Beasley, J.E.: A critical examination of stoichiometric and path-finding approaches to metabolic pathways. Briefings Bioinform. 9(5), 422–436 (2008)

    Article  Google Scholar 

  20. Quek, L.E., Nielsen, L.K.: A depth-first search algorithm to compute elementary flux modes by linear programming. BMC Syst. Biol. 8(1), 94 (2014)

    Article  Google Scholar 

  21. R Development Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2008). http://www.R-project.org, ISBN 3-900051-07-0

  22. Rahman, S.A., Advani, P., Schunk, R., Schrader, R., Schomburg, D.: Metabolic pathway analysis web service (pathway hunter tool at cubic). Bioinformatics 21(7), 1189–1193 (2005)

    Article  Google Scholar 

  23. Schuster, S., Dandekar, T., Fell, D.A.: Detection of elementary flux modes in biochemical networks: a promising tool for pathway analysis and metabolic engineering. Trends Biotechnol. 17(2), 53–60 (1999)

    Article  Google Scholar 

  24. Stelling, J., Klamt, S., Bettenbrock, K., Schuster, S., Gilles, E.D.: Metabolic network structure determines key aspects of functionality and regulation. Nature 420(6912), 190–193 (2002)

    Article  Google Scholar 

  25. Stephanopoulos, G.N., Aristidou, A.A., Nielsen, J.H.: Metabolic engineering : principles and methodologies. California Academic Press, San Diego (1998)

    Google Scholar 

  26. Szallasi, Z., Stelling, J., Periwal, V. (eds.): System Modeling in Cellular Biology: From Concepts to Nuts and Bolts, 1st edn. The MIT Press, Canada (2006)

    MATH  Google Scholar 

  27. Terzer, M., Stelling, J.: Large-scale computation of elementary flux modes with bit pattern trees. Bioinformatics 24(19), 2229–2235 (2008)

    Article  Google Scholar 

  28. Trinh, C.T., Unrean, P., Srienc, F.: Minimal Escherichia coli cell for the most efficient production of ethanol from hexoses and pentoses. Appl. Environ. Microbiol. 74(12), 3634–3643 (2008)

    Article  Google Scholar 

  29. Yeung, M., Thiele, I., Palsson, B.Ø.: Estimation of the number of extreme pathways for metabolic networks. BMC Bioinformatics 8(1), 363 (2007)

    Article  Google Scholar 

  30. Zanghellini, J., Ruckerbauer, D.E., Hanscho, M., Jungreuthmayer, C.: Elementary flux modes in a nutshell: properties, calculation and applications. Biotechnol. J. 8(9), 1009–1016 (2013)

    Article  Google Scholar 

Download references

Acknowledgments

Author Jose A. Egea acknowledges the funding received from the Spanish Ministry of Economy and Competitiveness through the project “Multi-Scales” (DPI2011-28112-C04-04). This work was jointly supported by the Fundación Séneca (Agencia Regional de Ciencia y Tecnología, Región de Murcia) under grant 15290/PI/2010 and the Spanish MEC and European Commission FEDER under grant TIN2012-31345.

Author information

Authors and Affiliations

  1. Department of Applied Mathematics and Statistics, Technical University of Cartagena, C/ Dr. Fleming s/n, 30202, Cartagena, Spain

    Jose A. Egea

  2. Parallel Computer Architecture Group, Facultad de Informática, University of Murcia, Campus Universitario de Espinardo, 30100, Murcia, Spain

    José M. García

Authors
  1. Jose A. Egea
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. José M. García
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jose A. Egea .

Editor information

Editors and Affiliations

  1. Universidad de Granada, Granada, Spain

    Francisco Ortuño

  2. Universidad de Granada, Granada, Spain

    Ignacio Rojas

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Egea, J.A., García, J.M. (2016). Calculating Elementary Flux Modes with Variable Neighbourhood Search. In: Ortuño, F., Rojas, I. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2016. Lecture Notes in Computer Science(), vol 9656. Springer, Cham. https://doi.org/10.1007/978-3-319-31744-1_27

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-31744-1_27

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-31743-4

  • Online ISBN: 978-3-319-31744-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation