TY - GEN
T1 - A gene knockout strategy for succinate production using a hybrid algorithm of bees algorithm and minimization of metabolic adjustment
AU - Koo, Ching Lee
AU - Mohamad, Mohd Saberi
AU - Ornatu, Sigeru
AU - Salleh, Abdul Hakim Mohamed
AU - Deris, Safaai
AU - Yoshioka, Michifumi
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/12/11
Y1 - 2014/12/11
N2 - Recently, metabolic engineering has gained popularity in the area of system biology due to its potential of capable to increase production. It works by manipulating the genes that can restructure the metabolic networks that have the potential to increase the yield of metabolite production. This metabolic network model has become the foundation for the development of computational procedures to suggest genetic manipulations that eventually leads to optimizing the metabolite production. This research focuses on optimizing the metabolite production of succinate in Escherichia coli. Previous works on rational modelling framework failed in optimizing the metabolite production and tended to suggest unrealistic flux distributions. Hence, in this paper, a hybrid algorithm of Bees Algorithm and Minimization of Metabolic Adjustment (BAMOMA) is proposed to overcome the problems found in the previous works. By developing this hybrid algorithm, it helps to identify a set of gene in Escherichia coli dataset that can be deleted and eventually leads to overproduction of succinate. Experimental results show that BAMOMA is better than previous works in term of production rates.
AB - Recently, metabolic engineering has gained popularity in the area of system biology due to its potential of capable to increase production. It works by manipulating the genes that can restructure the metabolic networks that have the potential to increase the yield of metabolite production. This metabolic network model has become the foundation for the development of computational procedures to suggest genetic manipulations that eventually leads to optimizing the metabolite production. This research focuses on optimizing the metabolite production of succinate in Escherichia coli. Previous works on rational modelling framework failed in optimizing the metabolite production and tended to suggest unrealistic flux distributions. Hence, in this paper, a hybrid algorithm of Bees Algorithm and Minimization of Metabolic Adjustment (BAMOMA) is proposed to overcome the problems found in the previous works. By developing this hybrid algorithm, it helps to identify a set of gene in Escherichia coli dataset that can be deleted and eventually leads to overproduction of succinate. Experimental results show that BAMOMA is better than previous works in term of production rates.
KW - Bees Algorithm
KW - Bioinformatics
KW - Escherichia coli
KW - Gene Knockout Strategy
KW - Minimization of Metabolic Adjustmen
KW - Succinate
UR - http://www.scopus.com/inward/record.url?scp=84920736360&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84920736360&partnerID=8YFLogxK
U2 - 10.1109/GRC.2014.6982821
DO - 10.1109/GRC.2014.6982821
M3 - Conference contribution
AN - SCOPUS:84920736360
T3 - Proceedings - 2014 IEEE International Conference on Granular Computing, GrC 2014
SP - 131
EP - 136
BT - Proceedings - 2014 IEEE International Conference on Granular Computing, GrC 2014
A2 - Kudo, Yasuo
A2 - Tsumoto, Shusaku
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE International Conference on Granular Computing, GrC 2014
Y2 - 22 October 2014 through 24 October 2014
ER -