Advertisement

Computational Tools for Strain Optimization by Adding Reactions

  • Sara Correia
  • Miguel Rocha
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 154)

Abstract

This paper introduces a new plug-in for the OptFlux Metabolic Engineering platform, aimed at finding suitable sets of reactions to add to the genomes of microbes (wild type strain), as well as finding complementary sets of deletions, so that the mutant becomes able to overproduce compounds with industrial interest, while preserving their viability. The optimization methods used are Evolutionary Algorithms and Simulated Annealing. The usefulness of this plug-in is demonstrated by a case study, regarding the production of vanillin by the bacterium E. coli.

Keywords

Simulated Annealing Metabolic Network Flux Balance Analysis Metabolic Flux Analysis System Biology Markup Language 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bäck, T.: Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms. Oxford University Press, Dortmund (1996)zbMATHGoogle Scholar
  2. 2.
    Edwards, J.S., Covert, M., Palsson, B.: Metabolic modelling of microbes: the flux-balance approach. Environ. Microbiol. 4(3), 133–140 (2002)CrossRefGoogle Scholar
  3. 3.
    Hucka, M., Finney, A., Sauro, H.M., Bolouri, H., et al.: The systems biology markup language (sbml): a medium for representation and exchange of biochemical network models. Bioinformatics 19(4), 524–531 (2003)CrossRefGoogle Scholar
  4. 4.
    Kauffman, K.J., Prakash, P., Edwards, J.S.: Advances in flux balance analysis. Curr. Opin. Biotechnol. 14(5), 491–496 (2003)CrossRefGoogle Scholar
  5. 5.
    Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by Simulated Annealing. Science 220(4598), 671–680 (1983)MathSciNetzbMATHCrossRefGoogle Scholar
  6. 6.
    Nielsen, J.: Metabolic engineering. Applied Microbiology and Biotechnology 55(3), 263–283 (2001)CrossRefGoogle Scholar
  7. 7.
    Patil, K.R., Rocha, I., Förster, J., Nielsen, J.: Evolutionary programming as a platform for in silico metabolic engineering. BMC Bioinformatics 6, 308 (2005)CrossRefGoogle Scholar
  8. 8.
    Pharkya, P., Burgard, A.P., Maranas, C.D.: Optstrain: a computational framework for redesign of microbial production systems. Genome Res. 14(11), 2367–2376 (2004)CrossRefGoogle Scholar
  9. 9.
    Rocha, I., Maia, P., Evangelista, P., Vilaca, P., Soares, S., Pinto, J.P., Nielsen, J., Patil, K.R., Ferreira, E.C., Rocha, M.: OptFlux: an open-source software platform for in silico metabolic engineering. BMC Syst. Biol. 4, 45 (2010)CrossRefGoogle Scholar
  10. 10.
    Rocha, M., Maia, P., Mendes, R., Pinto, J.P., Ferreira, E.C., Nielsen, J., Patil, K.R., Rocha, I.: Natural computation meta-heuristics for the in silico optimization of microbial strains. BMC Bioinformatics 9, 499 (2008)CrossRefGoogle Scholar
  11. 11.
    Satish Kumar, V., Dasika, M.S., Maranas, C.D.: Optimization based automated curation of metabolic reconstructions. BMC Bioinformatics 8, 212 (2007)CrossRefGoogle Scholar
  12. 12.
    Segrè, D., Vitkup, D., Church, G.M.: Analysis of optimality in natural and perturbed metabolic networks. Proc. Natl. Acad. Sci. USA 99(23), 15112–15117 (2002)CrossRefGoogle Scholar
  13. 13.
    Shlomi, T., Berkman, O., Ruppin, E.: Regulatory on/off minimization of metabolic flux changes after genetic perturbations. Proc. Natl. Acad. Sci. USA 102(21), 7695–7700 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.CCTCUniversity of MinhoBragaPortugal

Personalised recommendations