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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Stephanopoulos, G., Aristidou, A., Nielsen, J.: Metabolic Engineering. Acad. Press (1998)
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)
Edwards, J., Covert, M.: Minireview Metabolic modelling of microbes: the flux-balance approach. Environmental Microbiology 4, 133–140 (2002)
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)
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)
Patil, K., Rocha, I., Förster, J., Nielsen, J.: Evolutionary programming as a platform for in silico metabolic engineering. BMC Bioinformatics 6(308) (2005)
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)
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)
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)
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)
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)
Kim, J., Reed, J.: OptORF: Optimal metabolic and regulatory perturbations for metabolic engineering of microbial strains. BMC Systems Biology 4, 53 (2010)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
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
eBook Packages: EngineeringEngineering (R0)