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Abstract

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.

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References

  1. Stephanopoulos, G., Aristidou, A., Nielsen, J.: Metabolic Engineering. Acad. Press (1998)

    Google Scholar 

  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. Edwards, J., Covert, M.: Minireview Metabolic modelling of microbes: the flux-balance approach. Environmental Microbiology 4, 133–140 (2002)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. 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. 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)

    Chapter  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  12. Kim, J., Reed, J.: OptORF: Optimal metabolic and regulatory perturbations for metabolic engineering of microbial strains. BMC Systems Biology 4, 53 (2010)

    Article  Google Scholar 

  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 

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Correspondence to Emanuel Gonçalves .

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Gonçalves, E., Rocha, I., Rocha, M. (2012). Computational Tools for Strain Optimization by Tuning the Optimal Level of Gene Expression. In: Rocha, M., Luscombe, N., Fdez-Riverola, F., Rodríguez, J. (eds) 6th International Conference on Practical Applications of Computational Biology & Bioinformatics. Advances in Intelligent and Soft Computing, vol 154. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28839-5_29

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  • DOI: https://doi.org/10.1007/978-3-642-28839-5_29

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28838-8

  • Online ISBN: 978-3-642-28839-5

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