A Hybrid of Simple Constrained Artificial Bee Colony Algorithm and Flux Balance Analysis for Enhancing Lactate and Succinate in Escherichia Coli
In the past decades, metabolic engineering has received great attention from different sectors of science due to its important role in enhancing the over expression of the target phenotype by manipulating the metabolic pathway. The advent of metabolic engineering has further laid the foundation for computational biology, leading to the development of computational approaches for suggesting genetic manipulation. Previously, conventional methods have been used to enhance the production of lactate and succinate in E. coli. However, these products are always far below their theoretical maxima. In this research, a hybrid algorithm is developed to seek optimal solutions in order to increase the overproduction of lactate and succinate by gene knockout in E. coli. The hybrid algorithm employed the Simple Constrained Artificial Bee Colony (SCABC) algorithm, using swarm intelligence as an optimization algorithm to optimize the objective function, where lactate and succinate productions are maximized by simulating gene knockout in E. coli. In addition, Flux Balance Analysis (FBA) is used as a fitness function in the SCABC algorithm to assess the growth rate of E. coli and the productivity of lactate and succinate. As a result of the research, the gene knockout list which induced the highest production of lactate and succinate is obtained.
KeywordsGene knockout strategies Escherichia coli Lactate Succinate Simple Constrained Artificial Bee Colony Flux Balance Analysis Computational intelligence
We would like to thank Malaysian Ministry of Higher Education and Universiti Teknologi Malaysia for supporting this research by the Fundamental Research Grant Schemes (grant number: R.J130000.7828.4F886 and R.J130000.7828.4F720). We would also like to thank Universiti Malaysia Pahang for sponsoring this research via the RDU Grant (Grant Number: RDU180307).
- 5.Salleh, A.H.M., Mohamad, M.S., Deris, S., Omatu, S., Fdez-Riverola, F., Corchado, J.M.: Gene knockout identification for metabolite production improvement using a hybrid of genetic ant colony optimization and flux balance analysis. Biotechnol. Bioprocess Eng. 20, 685–693 (2015)CrossRefGoogle Scholar
- 6.Choon, Y.W., Mohamad, M.S., Deris, S., Illias, R.M., En Chai, L., Chong, C.K.: Identifying gene knockout strategy using Bees Hill Flux Balance Analysis (BHFBA) for improving the production of ethanol in bacillus subtilis. In: Advances in Biomedical Infrastructure 2013. Studies in Computational Intelligence, vol. 477, pp. 117–126. Springer, Heidelberg (2013)Google Scholar
- 7.Choon, Y.W., Mohamad, M.S., Deris, S., Illias, R.M., Chong, C.K., Chai, L.E., Omatu, S., Corchado, J.M.: Differential bees flux balance analysis with OptKnock for in silico microbial strains optimization. PLoS One 9(7), e102744 (2014)Google Scholar
- 9.Martino, G.D.S., Cardillo, F.A., Starita, A.: A new swarm intelligence coordination model inspired by collective prey retrieval and its application to image alignment. In: Runarsson, T.P., Beyer, H.-G., Burke, E., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds.) PPSN 2006. LNCS, vol. 4193, pp. 691–700. Springer, Heidelberg (2006). https://doi.org/10.1007/11844297_70CrossRefGoogle Scholar
- 11.Raman, K., Chandra, N.: Flux balance analysis of biological systems: applications and challenges. Brief Bioinform. 10(4), 435–449 (2009)Google Scholar
- 12.Brajevic, I., Tuba, M., Subotic, M.: Performance of the improved artificial bee colony algorithm on standard engineering constrained problems. Int. J. Math. Comput. Simul. 5, 135–143 (2011)Google Scholar