Abstract
The identification of a set of genetic manipulations that result in a microbial strain with improved production capabilities of a metabolite with industrial interest is a big challenge in Metabolic Engineering. Evolutionary Algorithms and Simulated Annealing have been used in this task to identify sets of reaction deletions, towards the maximization of a desired objective function. To simulate the cell phenotype for each mutant strain, the Flux Balance Analysis approach is used, assuming organisms have maximized their growth along evolution.
In this work, transcriptional information is added to the models using gene-reaction rules. The aim is to find the (near-)optimal set of gene knockouts necessary to reach a given productivity goal. The results obtained are compared with the ones reached using the deletion of reactions, showing that we obtain solutions with similar quality levels and number of knockouts, but biologically more feasible. Indeed, we show that several of the previous solutions are not viable using the provided rules.
This is a preview of subscription content, log in via an institution to check access.
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
Burgard, A.P., Pharya, P., Maranas, C.D.: Optknock: A bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol. Bioeng. 84, 647–657 (2003)
Covert, M.W., Schilling, C.H., Famili, I., Edwards, J.S., Goryanin, I.I., Selkov, E., Palsson, B.O.: Metabolic modeling of microbial strains in silico. Trends in Biochemical Sciences 26(3), 179–186 (2001)
Duarte, N.C., Herrgård, M.J., Palsson, B.Ø.: Reconstruction and validation of Saccharomyces cerevisiae ind750, a fully compartmentalized genome-scale metabolic model. Genome Res. 14(7), 1298–1309 (2004)
Hofvendahl, K., Hahn-Hagerdal, B.: Factors affecting the fermentative lactic acid production from renewable resources. Enzyme Microbial Technology 26, 87–107 (2000)
Ibarra, R.U., Edwards, J.S., Palsson, B.G.: Escherichia coli k-12 undergoes adaptive evolution to achieve in silico predicted optimal growth. Nature 420, 186–189 (2002)
Kauffman, K.J., Prakash, P., Edwards, J.S.: Advances in flux balance analysis. Curr. Opin. Biotechnol. 14, 491–496 (2003)
Lee, S.Y., Hong, S.H., Moon, S.Y.: In Silico metabolic pathway analysis and design: succinic acid production by metabolically engineered Escherichia coli as an example. Genome Informatics 13, 214–223 (2002)
Nielsen, J.: Metabolic engineering. Appl. Microbiol. Biotechnol. 55, 263–283 (2001)
Patil, K., Rocha, I., Forster, J., Nielsen, J.: Evolutionary programming as a platform for in silico metabolic engineering. BMC Bioinformatics 6(308) (2005)
Reed, J.L., Vo, T.D., Schilling, C.H., Palsson, B.O.: An expanded genome-scale model of Escherichia coli k-12 (ijr904 gsm/gpr). Genome Biology 4(9), R54.1–R54.12 (2003)
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 (2008)
Segre, D., Vitkup, D., Church, G.M.: Analysis of optimality in natural and perturbed metabolic networks. PNAS 99, 15112–15117 (2002)
Shlomi, T., Berkman, O., Ruppin, E.: Regulatory on/off minimization of metabolic flux changes after genetic perturbations. PNAS 102(21), 7695–7700 (2005)
Stephanopoulos, G., Aristidou, A.A., Nielsen, J.: Metabolic engineering principles and methodologies. Academic Press, San Diego (1998)
Thiele, I., Vo, T.D., Price, N.D., Palsson, B.Ø.: Expanded metabolic reconstruction of Helicobacter pylori (iit341 gsm/gpr): an in silico genome-scale characterization of single- and double-deletion mutants. J. Bacteriol. 187(16), 5818–5830 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Vilaça, P., Maia, P., Rocha, I., Rocha, M. (2010). Metaheuristics for Strain Optimization Using Transcriptional Information Enriched Metabolic Models. In: Pizzuti, C., Ritchie, M.D., Giacobini, M. (eds) Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. EvoBIO 2010. Lecture Notes in Computer Science, vol 6023. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12211-8_18
Download citation
DOI: https://doi.org/10.1007/978-3-642-12211-8_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12210-1
Online ISBN: 978-3-642-12211-8
eBook Packages: Computer ScienceComputer Science (R0)