Abstract
Microbial strains can be manipulated to improve product yield and improve growth characteristics. Optimization algorithms are developed to identify the effects of gene knockout on the results. However, this process is often faced the problem of being trapped in local minima and slow convergence due to repetitive iterations of algorithm. In this paper, we proposed Bees Hill Flux Balance Analysis (BHFBA) which is a hybrid of Bees Algorithm, Hill Climbing Algorithm and Flux Balance Analysis to solve the problems and improve the performance in predicting optimal sets of gene deletion for maximizing the growth rate and production yield of desired metabolite. Escherichia coli is the model organism in this paper. The list of knockout genes, growth rate and production yield after the deletion are the results from the experiments. BHFBA performed better in term of computational time, stability and production yield.
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References
Feist, A.M., Herrgård, M.J., Thiele, I., Reed, J.L., Palsson, B.Ø.: Reconstruction of biochemical networks in microorganisms. Nat. Rev. Microbiol. 72, 129–143 (2009)
Chandran, D., Copeland, W.B., Sleight, S.C., Sauro, H.M.: Mathematical modeling and synthetic biology. Drug Discovery Today Disease Models 5(4), 299–309 (2008)
Burgard, A.P., Pharkya, P., Maranas, C.D.: OptKnock: A bilevel programming framework for identifying gene knockout strategies for microbial strains optimization. Biotechnol. Bioeng. 84, 647–657 (2003)
Patil, K.R., Rocha, I., Förster, J., Nielsen, J.: Evolutionary programming as a platform for in silico metabolic engineering. BMC Bioinformatics 6, 308 (2005)
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, 499 (2008)
Pham, D.T., Ghanbarzadeh, A., Koç, E., Otri, S., Zaidi, M.: 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 (2006)
Choon, Y.W., Mohamad, M.S., Deris, S., Chong, C.K., Chai, L.E., Ibrahim, Z., Omatu, S.: Identifying Gene Knockout Strategies Using a Hybrid of Bees Algorithm and Flux Balance Analysis For in silico Optimization of Microbial Strains. In: The 9th International Symposium on Distributed Computing and Artificial Intelligence, DCAI 2012. University of Salamanca, Spain (2012)
Pham, D.T., Darwish, A.H., Eldukhri, E.E.: Optimisation of a fuzzy logic controller using the bees algorithm. International Journal of Computer Aided Engineering and Technology 1(2), 250–264 (2006)
Olague, G., Puente, C.: The Honeybee Search Algorithm for Three-Dimensional Reconstruction. In: Rothlauf, F., Branke, J., Cagnoni, S., Costa, E., Cotta, C., Drechsler, R., Lutton, E., Machado, P., Moore, J.H., Romero, J., Smith, G.D., Squillero, G., Takagi, H. (eds.) EvoWorkshops 2006. LNCS, vol. 3907, pp. 427–437. Springer, Heidelberg (2006)
Pham, D.T., Ghanbarzadeh, A.: Multi-objective optimisation using the bees algorithm. Paper Proceedings of the Third International Virtual Conference on Intelligent Production Machines and Systems, July 2-13 (2007)
Cheng, M.Y., Lien, L.C.: A Hybrid Swarm Intelligence Based Particle Bee Algorithm For Benchmark Functions And Construction Site Layout Optimization. In: Proceedings of the 28th ISARC, Seoul, pp. 898–904 (2011)
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, 54 (2003)
Bohl, K., Figueiredo, L.F.: d., Hadicke O., Klamt S., Kost C., Schuster S., Kaleta C: CASOP GS: Computing intervention strategies targeted at production improvement in genome-scale metabolic networks. In: The 5th German Conference on Bioinformatics 2010, September 20-22 (2010)
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Choon, Y.W., Bin Mohamad, M.S., Deris, S., Illias, R.M., Chai, L.E., Chong, C.K. (2013). Using Bees Hill Flux Balance Analysis (BHFBA) for in silico Microbial Strain Optimization. In: Selamat, A., Nguyen, N.T., Haron, H. (eds) Intelligent Information and Database Systems. ACIIDS 2013. Lecture Notes in Computer Science(), vol 7802. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36546-1_39
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DOI: https://doi.org/10.1007/978-3-642-36546-1_39
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