A hybrid of bees algorithm and flux balance analysis with OptKnock as a platform for in silico optimization of microbial strains

Bioprocess and Biosystems Engineering

Volumn 37, Issue 3, 2014, Pages 521-532

  Choon, Yee Wen ^a   Mohamad, Mohd Saberi ^a   Deris, Safaai ^a   Illias, Rosli Md ^a   Chong, Chuii Khim ^a   Chai, Lian En ^a  

^a UNIVERSITI TEKNOLOGI MALAYSIA   (Malaysia)

Author keywords

Bees algorithm; Flux balance analysis; Metabolic engineering; Microbial strains; Optimization

Indexed keywords

ALGORITHMS; ESCHERICHIA COLI; METABOLIC ENGINEERING; STRAIN;

BEES ALGORITHMS; CLOSTRIDIUM THERMOCELLUM; COMBINATORIAL PROBLEM; COMPUTATIONAL EXPERIMENT; FLUX BALANCE ANALYSIS; GENETIC MODIFICATIONS; MICROBIAL STRAIN; TECHNOLOGICAL PROPERTIES;

OPTIMIZATION;

ALCOHOL; LACTIC ACID; SUCCINIC ACID;

ARTICLE; BACILLUS SUBTILIS; BACTERIAL STRAIN; BACTERIUM MUTANT; CLOSTRIDIUM THERMOCELLUM; COMPUTER MODEL; ESCHERICHIA COLI; GENE INACTIVATION; HYBRID OF BEES ALGORITHM AND FLUX BALANCE ANALYSIS; INFORMATION PROCESSING; NONHUMAN; PREDICTION; PRIORITY JOURNAL; ALGORITHM; BACTERIAL GENE; COMPUTER SIMULATION; GENETICS; METABOLISM; VALIDATION STUDY;

ALGORITHMS; BACILLUS SUBTILIS; CLOSTRIDIUM THERMOCELLUM; COMPUTER SIMULATION; ESCHERICHIA COLI; GENES, BACTERIAL;

EID: 84898548508     PISSN: 16157591     EISSN: 16157605     Source Type: Journal    
DOI: 10.1007/s00449-013-1019-y     Document Type: Article

References (26)

1. Feist AM, Herrgård MJ, Thiele I, Reed JL, Palsson BØ (2009) Reconstruction of biochemical networks in microorganisms. Nat Rev Microbiol 72:129-143
2. Rocha M, Maia P, Mendes R, Pinto JP, Ferreira EC, Nielsen J, Patil KR, Rocha I (2008) Natural computation meta-heuristics for the in silico optimization of microbial strains. BMC Bioinform 9:499
3. Boghigian BA, Shi H, Lee K, Pfeifer BA (2010) Utilizing elementary mode analysis, pathway thermodynamics, and a genetic algorithm for metabolic flux determination and optimal metabolic network design. BMC Syst Biol 4:49-65
4. Tomita M (2001) Whole-cell simulation: a grand challenge of the 21st century. Trends Biotechnol 19:205-210
5. Kauffman KJ, Prakash P, Edwards JS (2003) Advances in flux balance analysis. Curr Opin Biotechnol 14:491-496 (Pubitemid 37298033)
6. Ibarra RU, Edwards JS, Palsson BØ (2002) Esherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth. Nature 420:186-189 (Pubitemid 35340123)
7. Bourguignon PY, Samal A, Képès F, Jost J, Martin OC (2010) Challenges in experimental data integration within genome-scale metabolic models. Algorithm Mol Biol 5:20
8. Burgard AP, Pharkya P, Maranas CD (2003) OptKnock: a bilevel programming framework for identifying gene knockout strategies for microbial strains optimization. Biotechnol Bioeng 84: 647-657 (Pubitemid 37420336)
9. Patil KR, Rocha I, FO{combining diaeresis}rster J, Nielsen J (2005) Evolutionary programming as a platform for in silico metabolic engineering. BMC Bioinform 6:308
10. Hwang SF, He RS (2006) A hybrid real-parameter genetic algorithm for function optimization. Adv Eng Inform 20(1):7-21
11. Aydin ME, Fogarty TC (2004) A distributed evolutionary simulated annealing algorithm for combinatorial optimisation problems. J Heuristics 10(3):269-292
12. Carr R (1999) Simulated annealing. From MathWorld-A Wolfram Web Resource, created by Eric W. Weisstein. http://mathworld.wolfram.com/SimulatedAnnealing. html, Accessed on 05.March.2012
13. Choon YW, Mohamad MS, Deris S, Chong CK, Chai LE, Ibrahim Z, Omatu S (2012) Identifying gene knockout strategies using a hybrid of bees algorithm and flux balance analysis for in silico optimization of microbial strains. Adv Intell Soft-Comput 151:371-378
14. Choon YW, Mohamad MS, Deris S, Illias RM (2013) A hybrid of bees algorithm and flux balance analysis (BAFBA) for the optimization of microbial strains. Int J Data Min Bioinform (IJDMB) (in press)
15. Pham DT, Ghanbarzadeh A, Koç E, Otri S, Zaidi M (2006) The bees algorithm - a novel tool for complex optimization problems. In: Proceedings of the Second International Virtual Conference on Intelligent Production Machines and Systems. July 3-14
16. Pham DT, Darwish AH, Eldukhri EE (2009) Optimisation of a fuzzy logic controller using the bees algorithm. Int J Comput Aided Eng Technol 1(2):250-264
17. Olague G, Puente C (eds) (2006) The honeybee search algorithm for three-dimensional reconstruction. Springer, Heidelberg
18. Pham, D.T., Ghanbarzadeh, A. (2007) Multi-objective optimisation using the bees algorithm. In: Proceedings of the Third International Virtual Conference on Intelligent Production Machines and Systems, July 2-13
19. Reed JL, Vo TD, Schilling CH, Palsson BO (2003) An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR). Genome Biol 4(9):R54.1-R54.12
20. Henry CS, Zinner JF, Cohoon MP, Stevens RL (2009) iBsu1103: a new genome-scale metabolic model of Bacillus subtilis based on SEED annotations. Genome Biol 10:69
21. Roberts SB, Gowen CM, Brooks JP, Fong SS (2010) Genomescale metabolic analysis of Clostridium thermocellum for bioethanol production. BMC Syst Biol 4:31
22. 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 Inform 13:214-223
23. Hofvendahl K, Hahn-Hagerdal B (2000) Factors affecting the fermentative lactic acid production from renewable resources. Enzym Microb Technol 26:87-107
24. Bohl K, Figueiredo LF, Hadicke O, Klamt S, Kost C, Schuster S, Kaleta C (2010) CASOP GS: computing intervention strategies targeted at production improvement in genome-scale metabolic networks. Fifth German Conference on Bioinformatics, September 20-22, Braunschweig, Germany
25. Kim JW, Chin YW, Park YC, Seo JH (2012) Effects of deletion of glycerol-3-phosphate dehydrogenase and glutamate dehydrogenase genes on glycerol and ethanol metabolism in recombinant Saccharomyces cerevisiae. Bioprocess Biosyst Eng 35:49-54
26. Li Y, Tschaplinski TJ, Engle NL, Hamilton CY, Rodriguez M, Liao JC, Schadt CW, Guss AM, Yang Y, Graham DE (2012) Combined inactivation of the Clostridium cellulolyticum lactate and malate dehydrogenase genes substantially increases ethanol yield from cellulose and switchgrass fermentations. Biotechnol Biofuels 5:2