Abstract
Bacillus subtilis strains can be manipulated to improve product yield and 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. Bacillus subtilis 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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
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. In: 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)
Orth, J.D., Fleming, R.M.T., Palsson, B.Ø.: Reconstruction and Use of Microbial Metabolic Networks: the Core Escherichia coli Metabolic Model as an Educational Guide. ASM Press, Washington, DC (2009)
Henry, C.S., Zinner, J.F., Cohoon, M.P., Stevens, R.L.: iBsu1103: a new genome-scale metabolic model of Bacillus subtilis based on SEED annotations. Genome Biology 10, 69 (2009)
Bohl, K., de Figueiredo, L.F., 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)
Kim, J.W., Chin, Y.W., Park, Y.C., Seo, J.H.: 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 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Choon, Y.W., Mohamad, M.S., Deris, S., Illias, R.M., Chai, L.E., Chong, C.K. (2013). Identifying Gene Knockout Strategy Using Bees Hill Flux Balance Analysis (BHFBA) for Improving the Production of Ethanol in Bacillus Subtilis. In: Sidhu, A., Dhillon, S. (eds) Advances in Biomedical Infrastructure 2013. Studies in Computational Intelligence, vol 477. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37137-0_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-37137-0_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37136-3
Online ISBN: 978-3-642-37137-0
eBook Packages: EngineeringEngineering (R0)