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: Metabolic engineering San Diego: Academic Press 1998.
- Nielsen J: Metabolic Engineering. Applied Microbiology and Biotechnology 2001, 55:263–283. CrossRef
- Tomita M: Whole-cell simulation: a grand challenge of the 21st century. Trends in Biotechnology 2001, 19:205–210. CrossRef
- Kauffman KJ, Prakash P, Edwards JS: Advances in flux balance analysis. Curr Opin Biotechnol 2003, 14:491–496. CrossRef
- Ibarra RU, Edwards JS, Palsson BO:Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth. Nature 2002, 420:186–189. CrossRef
- Burgard AP, Pharkya P, Maranas CD: OptKnock: A bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnology and Bioengineering 2003, 84:647–657. CrossRef
- Patil KR, Rocha I, Forster J, Nielsen J: Evolutionary programming as a platform for in silico metabolic engineering. BMC Bioinformatics 2005, 6:308. CrossRef
- Segre D, Vitkup D, Church GM: Analysis of optimality in natural and perturbed metabolic networks. Proceedings of the National Academy of Sciences of the United States of America 2002, 99:15112–15117. 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: An analysis of the Bahavior of a Class of Genetic Adaptive Systems PhD thesis: University of Michigan 1975.
- Kirkpatrick S, Gellatt CD Jr, Vecchi MP: Optimization by Simulated Annealing. Science 1983, 220:671–680. CrossRef
- Lee SY, Hong SH, Moon SY:In Silico metabolic pathway analysis and design: succinic acid production by metabolically engineered Escherichia coli as an example. Genome Informatics 2002, 13:214–223.
- Hofvendahl K, Hahn-Hagerdal B: Factors affecting the fermentative lactic acid production from renewable resources. Enzyme and Microbial Technology 2000, 26:87–107. CrossRef
- John RP, Nampoothiri KM, Pandey A: Fermentative production of lactic acid from biomass: an overview on process developments and future perspectives. Applied Microbiology and Biotechnology 2007, 74:524–534. CrossRef
- Chang DE, Jung HC, Rhee JS, Pan JG: Homofermentative production of D- or L-lactate in metabolically engineered Escherichia coli RR1. Appl Environ Microbiol 1999,65(4):1384–1389.
- Zhou SD, Causey TB, Hasona A, Shanmugam KT, Ingram LO: Production of optically pure D-lactic acid in mineral salts medium by metabolically engineered Escherichia coli W3110. Applied and Environmental Microbiology 2007, 69:399–407. CrossRef
- Forster J, Famili I, Fu P, Palsson BO, Nielsen J: Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network. Genome Res 2003, 13:244–253. CrossRef
- Reed JL, Vo TD, Schilling CH, Palsson BO: An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR). Genome Biology 2003, 4:R54. CrossRef
- Lin H, Bennett GN, San KY: Genetic reconstruction of the aerobic central metabolism in Escherichia coli for the absolute aerobic production of succinate. Biotechnol Bioeng 2005, 89:148–156. CrossRef
- Alper H, Miyaoku K, Stephanopoulos G: Construction of lycopene-overproducing E. coli strains by combining systematic and combinatorial gene knockout targets. Nature Biotechnololy 2005, 23:612–616. CrossRef
- Klamt S, Stelling J: Combinatorial complexity of pathway analysis in metabolic networks. Mol Biol Rep 2002, 29:233–236. CrossRef
- Stelling J, Klamt S, Bettenbrock K, Schuster S, Gilles ED: Metabolic network structure determines key aspects of functionality and regulation. Nature 2002, 420:190–193. CrossRef
- Deutscher D, Meilijson I, Kupiec M, Ruppin E: Multiple knockout analysis of genetic robustness in the yeast metabolic network. Nature Genetics 2006, 38:993–998. 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