Natural computation meta-heuristics for the in silico optimization of microbial strains
One of the greatest challenges in Metabolic Engineering is to develop quantitative models and algorithms to identify a set of genetic manipulations that will result in a microbial strain with a desirable metabolic phenotype which typically means having a high yield/productivity. This challenge is not only due to the inherent complexity of the metabolic and regulatory networks, but also to the lack of appropriate modelling and optimization tools. To this end, Evolutionary Algorithms (EAs) have been proposed for in silico metabolic engineering, for example, to identify sets of gene deletions towards maximization of a desired physiological objective function. In this approach, each mutant strain is evaluated by resorting to the simulation of its phenotype using the Flux-Balance Analysis (FBA) approach, together with the premise that microorganisms have maximized their growth along natural evolution.
This work reports on improved EAs, as well as novel Simulated Annealing (SA) algorithms to address the task of in silico metabolic engineering. Both approaches use a variable size set-based representation, thereby allowing the automatic finding of the best number of gene deletions necessary for achieving a given productivity goal. The work presents extensive computational experiments, involving four case studies that consider the production of succinic and lactic acid as the targets, by using S. cerevisiae and E. coli as model organisms. The proposed algorithms are able to reach optimal/near-optimal solutions regarding the production of the desired compounds and presenting low variability among the several runs.
The results show that the proposed SA and EA both perform well in the optimization task. A comparison between them is favourable to the SA in terms of consistency in obtaining optimal solutions and faster convergence. In both cases, the use of variable size representations allows the automatic discovery of the approximate number of gene deletions, without compromising the optimality of the solutions.
- Stephanopoulos, G, Aristidou, A, Nielsen, J (1998) Metabolic engineering. Academic Press, San Diego
- Nielsen, J (2001) Metabolic Engineering. Applied Microbiology and Biotechnology 55: pp. 263-283 CrossRef
- Tomita, M (2001) Whole-cell simulation: a grand challenge of the 21st century. Trends in Biotechnology 19: pp. 205-210 CrossRef
- Kauffman, KJ, Prakash, P, Edwards, JS (2003) Advances in flux balance analysis. Curr Opin Biotechnol 14: pp. 491-496 CrossRef
- Ibarra, RU, Edwards, JS, Palsson, BO (2002) Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth. Nature 420: pp. 186-189 CrossRef
- Burgard, AP, Pharkya, P, Maranas, CD (2003) OptKnock: A bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnology and Bioengineering 84: pp. 647-657 CrossRef
- Patil, KR, Rocha, I, Forster, J, Nielsen, J (2005) Evolutionary programming as a platform for in silico metabolic engineering. BMC Bioinformatics 6: pp. 308 CrossRef
- Segre, D, Vitkup, D, Church, GM (2002) Analysis of optimality in natural and perturbed metabolic networks. Proceedings of the National Academy of Sciences of the United States of America 99: pp. 15112-15117 CrossRef
- Michalewicz Z: Genetic Algorithms + Data Structures = Evolution Programs Springer Verlag 1996.
- Syswerda G: Uniform crossover in Genetic Algorithms. Proc. 3rd Intl Conference on Genetic Algorithms 1989 San Mateo: Morgan Kaufmann 2–9.
- De Jong, K (1975) An analysis of the Bahavior of a Class of Genetic Adaptive Systems. University of Michigan, PhD thesis
- Kirkpatrick, S, Gellatt, CD, Vecchi, MP (1983) Optimization by Simulated Annealing. Science 220: pp. 671-680 CrossRef
- Lee, SY, Hong, SH, Moon, SY (2002) In Silico metabolic pathway analysis and design: succinic acid production by metabolically engineered Escherichia coli as an example. Genome Informatics 13: pp. 214-223
- Hofvendahl, K, Hahn-Hagerdal, B (2000) Factors affecting the fermentative lactic acid production from renewable resources. Enzyme and Microbial Technology 26: pp. 87-107 CrossRef
- John, RP, Nampoothiri, KM, Pandey, A (2007) Fermentative production of lactic acid from biomass: an overview on process developments and future perspectives. Applied Microbiology and Biotechnology 74: pp. 524-534 CrossRef
- Chang, DE, Jung, HC, Rhee, JS, Pan, JG (1999) Homofermentative production of D- or L-lactate in metabolically engineered Escherichia coli RR1. Appl Environ Microbiol 65: pp. 1384-1389
- Zhou, SD, Causey, TB, Hasona, A, Shanmugam, KT, Ingram, LO (2007) Production of optically pure D-lactic acid in mineral salts medium by metabolically engineered Escherichia coli W3110. Applied and Environmental Microbiology 69: pp. 399-407 CrossRef
- Forster, J, Famili, I, Fu, P, Palsson, BO, Nielsen, J (2003) Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network. Genome Res 13: pp. 244-253 CrossRef
- Reed, JL, Vo, TD, Schilling, CH, Palsson, BO (2003) An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR). Genome Biology 4: pp. R54 CrossRef
- Lin, H, Bennett, GN, San, KY (2005) Genetic reconstruction of the aerobic central metabolism in Escherichia coli for the absolute aerobic production of succinate. Biotechnol Bioeng 89: pp. 148-156 CrossRef
- Alper, H, Miyaoku, K, Stephanopoulos, G (2005) Construction of lycopene-overproducing E. coli strains by combining systematic and combinatorial gene knockout targets. Nature Biotechnololy 23: pp. 612-616 CrossRef
- Klamt, S, Stelling, J (2002) Combinatorial complexity of pathway analysis in metabolic networks. Mol Biol Rep 29: pp. 233-236 CrossRef
- Stelling, J, Klamt, S, Bettenbrock, K, Schuster, S, Gilles, ED (2002) Metabolic network structure determines key aspects of functionality and regulation. Nature 420: pp. 190-193 CrossRef
- Deutscher, D, Meilijson, I, Kupiec, M, Ruppin, E (2006) Multiple knockout analysis of genetic robustness in the yeast metabolic network. Nature Genetics 38: pp. 993-998 CrossRef
- Natural computation meta-heuristics for the in silico optimization of microbial strains
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- Available under Open Access This content is freely available online to anyone, anywhere at any time.
- Online Date
- November 2008
- Online ISSN
- BioMed Central
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- Author Affiliations
- 1. Department of Informatics/CCTC, University of Minho, Campus de Gualtar, 4710-057, Braga, Portugal
- 2. IBB-Institute for Biotechnology and Bioengineering/Centre of Biological Engineering, Universidade do Minho, 4710-057, Campus de Gualtar, Braga, Portugal
- 4. Systems Biology, Dept. Chemical and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-412 96, Gothenburg, Sweden
- 3. Department of Systems Biology, Center for Microbial Biotechnology,Building 223, Technical University of Denmark, DK-2800 Kgs, Lyngby, Denmark