Computational Tools for Strain Optimization by Tuning the Optimal Level of Gene Expression

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


In this work, a plug-in for the OptFlux Metabolic Engineering platform is presented, implementing methods that allow the identification of sets of genes to over/under express, relatively to their wild type levels. The optimization methods used are Simulated Annealing and Evolutionary Algorithms, working with a novel representation and operators. This overcomes the limitations of previous approaches based solely on gene knockouts, bringing new avenues for Biotechnology, fostering the discovery of genetic manipulations able to increase the production of certain compounds using a host microbe. The plug-in is made freely available together with appropriate documentation.


Simulated Annealing Mutation Operator Gene Knockout Mixed Integer Linear Programming Flux Balance Analysis 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Stephanopoulos, G., Aristidou, A., Nielsen, J.: Metabolic Engineering. Acad. Press (1998)Google Scholar
  2. 2.
    Reed, J., Vo, T., Schilling, C., Palsson, B.: An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR). Genome Biology 4, R54 (2003)Google Scholar
  3. 3.
    Edwards, J., Covert, M.: Minireview Metabolic modelling of microbes: the flux-balance approach. Environmental Microbiology 4, 133–140 (2002)CrossRefGoogle Scholar
  4. 4.
    Lewis, N., Hixson, K., Conrad, T., et al.: Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models. Molec. Syst. Biol. 6(390) (2010)Google Scholar
  5. 5.
    Burgard, A., Pharkya, P., Maranas, C.: Optknock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnology and Bioengineering 84, 647–657 (2003)CrossRefGoogle Scholar
  6. 6.
    Patil, K., Rocha, I., Förster, J., Nielsen, J.: Evolutionary programming as a platform for in silico metabolic engineering. BMC Bioinformatics 6(308) (2005)Google Scholar
  7. 7.
    Rocha, M., Maia, P., Mendes, R., et al.: Natural computation meta-heuristics for the in silico optimization of microbial strains. BMC Bioinformatics 9(499) (2008)Google Scholar
  8. 8.
    Vilaça, P., Maia, P., Rocha, I., Rocha, M.: Metaheuristics for Strain Optimization Using Transcriptional Information Enriched Metabolic Models. In: Pizzuti, C., Ritchie, M.D., Giacobini, M. (eds.) EvoBIO 2010. LNCS, vol. 6023, pp. 205–216. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  9. 9.
    Pharkya, P., Maranas, C.: An optimization framework for identifying reaction activation/inhibition or elimination candidates for overproduction in microbial systems. Metabolic Engineering 8, 1–13 (2006)CrossRefGoogle Scholar
  10. 10.
    Rocha, I., Maia, P., Evangelista, P., Vilaça, P., Soares, S., et al.: OptFlux: an open-source software platform for in silico metabolic engineering. BMC Systems Biology (2010)Google Scholar
  11. 11.
    Glez-Peña, D., Reboiro-Jato, M., Maia, P., Rocha, M., Dìaz, F., Fdez-Riverola, F.: AIBench: a rapid application development framework for translational research in biomedicine. Computer Methods and Programs in Biomedicine 98, 191–203 (2010)CrossRefGoogle Scholar
  12. 12.
    Kim, J., Reed, J.: OptORF: Optimal metabolic and regulatory perturbations for metabolic engineering of microbial strains. BMC Systems Biology 4, 53 (2010)CrossRefGoogle Scholar
  13. 13.
    Gonçalves, E., Pereira, R., Rocha, I., et al.: Optimization approaches for the in silico discovery of optimal targets for gene over/underexpression. J. Computational Biology (in press)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Emanuel Gonçalves
    • 1
  • Isabel Rocha
    • 2
  • Miguel Rocha
    • 1
  1. 1.CCTCUniversity of MinhoBragaPortugal
  2. 2.CEB / IBBUniversity of MinhoBragaPortugal

Personalised recommendations