Optimization of bacterial strains with variable-sized evolutionary algorithms

2007 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology, CIBCB 2007

Volumn , Issue , 2007, Pages 331-337

  Rocha, Miguel ^a   Pinto, José P ^a   Rocha, Isabel ^a   Ferreira, Eugénio ^a  

^a UNIVERSITY OF MINHO   (Portugal)

Author keywords

Evolutionary Algorithms; Flux balance analysis; Metabolic engineering; Set based representations; Variable size chromosomes

Indexed keywords

ARTIFICIAL INTELLIGENCE; BIOINFORMATICS; ESCHERICHIA COLI; EVOLUTIONARY ALGORITHMS; GENES; GENETIC ALGORITHMS; LACTIC ACID; METABOLISM;

APPROPRIATE MODELS; AUTOMATIC DISCOVERY; EVOLUTIONARY ALGORITHMS (EAS); FLUX BALANCE ANALYSIS; GENETIC MANIPULATIONS; OBJECTIVE FUNCTIONS; SET BASED REPRESENTATIONS; VARIABLE SIZE CHROMOSOMES;

METABOLIC ENGINEERING;

EID: 38349027647     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/cibcb.2007.4221241     Document Type: Conference Paper

References (17)

1. I. Borodina and J. Nielsen. From genomes to in silico cells via metabolic networks. Current Opinion in Biotechnology, 16(3):350.355, 2005.
2. A.P. Burgard, P. Pharya, and C.D. Maranas. Optknock: A bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol Bioeng, 84:647.657, 2003.
3. D.E. Chang, S. Shin, J. Rhee, and J. Pan. Homofermentative production of d- or l-lactate in metabolically engineered escherichia coli rr1. Appl. Environ. Microbiol, 65:1384.1389, 1999.
4. C. Chassagnole, N. Noisommit-Rizzi, J.W. Schmid, K. Mauch, and M. Reuss. Dynamic modeling of the central carbon metabolism of escherichia coli. Biotechnology and Bioengineering, 79(1):53.73, 2002.
5. M.W. Covert, C.H. Schilling, I. Famili, J.S. Edwards, I.I. Goryanin, E. Selkov, and B.O. Palsson. Metabolic modeling of microbial strains in silico. Trends in Biochemical Sciences, 26(3):179.186, 2001.
6. K. Hofvendahl and B. Hahn-Hagerdal. Factors affecting the fermentative lactic acid production from renewable resources. Enzyme Microbial Technology, 26:87.107, 2000.
7. R.U. Ibarra, J.S. Edwards, and B.G. Palsson. Escherichia coli k-12 undergoes adaptive evolution to achieve in silico predicted optimal growth. Nature, 420:186.189, 2002.
8. K.J. Kauffman, P. Prakash, and J.S. Edwards. Advances in flux balance analysis. Curr Opin Biotechnol, 14:491.496, 2003.
9. S.Y. Lee, S.H. Hong, and S.Y. Moon. In silico metabolic pathway analysis and design: succinic acid production by metabolically engineered escherichia coli as an example. Genome Informatics, 13:214.223, 2002.
10. J. Nielsen. Metabolic engineering. Appl Microbiol Biotechnol, 55:263. 283, 2001.
11. K. Patil, I. Rocha, J. Forster, and J. Nielsen. Evolutionary programming as a platform for in silico metabolic engineering. BMC Bioinformatics, 6(308), 2005.
12. J.L. Reed, T.D. Vo, C.H. Schilling, and B.O. Palsson. An expanded genome-scale model of escherichia coli k-12 (ijr904 gsm/gpr). Genome Biology, 4(9):R54.1.R54.12, 2003.
13. D. Segre, D. Vitkup, and G.M. Church. Analysis of optimality in natural and perturbed metabolic networks. Proc National Acad Sciences USA, 99:15112.15117, 2002.
14. T. Shlomi, O. Berkman, and E. Ruppin. Regulatory on/off minimization of metabolic flux changes after genetic perturbations. Proc National Acad Sciences USA, 102:7695.7700, 2005.
15. G. Stephanopoulos, A.A. Aristidou, and J. Nielsen. Metabolic engineering principles and methodologies. Academic Press, San Diego, 1998.
16. Gilbert Syswerda. Uniform Crossover in Genetic Algorithms. In J. Schaffer, editor, Proc. of the 3rd Intern. Conference on Genetic Algorithms, pages 2.9, San Mateo, CA, 1991. Morgan Kaufmann.
17. M. Tomita. Whole-cell simulation: a grand challenge for the 21st century. Trends Biotechnol, 19:205.210, 2001.