TY - GEN
T1 - A hybrid of simple constrained artificial bee colony algorithm and flux balance analysis for enhancing lactate and succinate in Escherichia Coli
AU - Hon, Mei Kie
AU - Mohamad, Mohd Saberi
AU - Salleh, Abdul Hakim Mohamed
AU - Choon, Yee Wen
AU - Remli, Muhammad Akmal
AU - Ismail, Mohd Arfian
AU - Omatu, Sigeru
AU - Corchado, Juan Manuel
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Computational intelligence
KW - Escherichia coli
KW - Flux balance analysis
KW - Gene knockout strategies
KW - Lactate
KW - Simple constrained artificial bee colony
KW - Succinate
UR - http://www.scopus.com/inward/record.url?scp=85052937725&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85052937725&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-98702-6_1
DO - 10.1007/978-3-319-98702-6_1
M3 - Conference contribution
AN - SCOPUS:85052937725
SN - 9783319987019
T3 - Advances in Intelligent Systems and Computing
SP - 1
EP - 8
BT - Practical Applications of Computational Biology and Bioinformatics, 12th International Conference
A2 - Rocha, Miguel
A2 - Mohamad, Mohd Saberi
A2 - De Paz, Juan F.
A2 - Fdez-Riverola, Florentino
A2 - Gonzalez, Pascual
PB - Springer Verlag
T2 - 12th International Conference on Practical Applications of Computational Biology and Bioinformatics, PACBB 2018
Y2 - 20 June 2018 through 22 June 2018
ER -