TY - JOUR
T1 - Identifying a gene knockout strategy using a hybrid of the bat algorithm and flux balance analysis to enhance the production of succinate and lactate in Escherichia coli
AU - Chua, Pooi San
AU - Salleh, Abdul Hakim Mohamed
AU - Mohamad, Mohd Saberi
AU - Deris, Safaai
AU - Omatu, Sigeru
AU - Yoshioka, Michifumi
N1 - Publisher Copyright:
© 2015, The Korean Society for Biotechnology and Bioengineering and Springer-Verlag Berlin Heidelberg.
PY - 2015/4/22
Y1 - 2015/4/22
N2 - The current problem for metabolic engineering is how to identify a suitable set of genes for knockout that can improve the production of certain metabolites and sustain the growth rate from the thousands of metabolic networks which are complex and combinatorial. Some approaches, such as OptKnock and OptGene, are developed to enhance the production of desired metabolites. However, the performances of these approaches are suboptimal and the obtained results are unsatisfactory because of computational limitations such as local minima. In this paper, we propose a hybrid of Bat Algorithm and Flux Balance Analysis (BATFBA) to enhance succinate and lactate production by identifying a set of genes for knock out. The Bat Algorithm is an optimisation algorithm, whereas Flux Balance Analysis (FBA) is a mathematical approach to analyse the flow of metabolites through a metabolic network. The Escherichia coli iJR904 dataset was used to determine optimal knockout genes, production rate, and growth rate. By applying this hybrid method to the iJR904 dataset, we found that BATFBA yielded better results than existing methods, such as OptKnock and a hybrid of Artificial Bee Colony algorithms and Flux Balance Analysis (ABCFBA), at predicting succinate and lactate production.
AB - The current problem for metabolic engineering is how to identify a suitable set of genes for knockout that can improve the production of certain metabolites and sustain the growth rate from the thousands of metabolic networks which are complex and combinatorial. Some approaches, such as OptKnock and OptGene, are developed to enhance the production of desired metabolites. However, the performances of these approaches are suboptimal and the obtained results are unsatisfactory because of computational limitations such as local minima. In this paper, we propose a hybrid of Bat Algorithm and Flux Balance Analysis (BATFBA) to enhance succinate and lactate production by identifying a set of genes for knock out. The Bat Algorithm is an optimisation algorithm, whereas Flux Balance Analysis (FBA) is a mathematical approach to analyse the flow of metabolites through a metabolic network. The Escherichia coli iJR904 dataset was used to determine optimal knockout genes, production rate, and growth rate. By applying this hybrid method to the iJR904 dataset, we found that BATFBA yielded better results than existing methods, such as OptKnock and a hybrid of Artificial Bee Colony algorithms and Flux Balance Analysis (ABCFBA), at predicting succinate and lactate production.
KW - artificial intelligence
KW - bat algorithm
KW - bioinformatics
KW - Escherichia coli
KW - flux balance analysis
KW - gene knockout strategy
KW - lactate
KW - metabolic engineering
KW - succinate
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U2 - 10.1007/s12257-014-0466-x
DO - 10.1007/s12257-014-0466-x
M3 - Article
AN - SCOPUS:84929458586
SN - 1226-8372
VL - 20
SP - 349
EP - 357
JO - Biotechnology and Bioprocess Engineering
JF - Biotechnology and Bioprocess Engineering
IS - 2
M1 - 19
ER -