TY - JOUR
T1 - A newton cooperative genetic algorithm method for In Silico optimization of metabolic pathway production
AU - Ismail, Mohd Arfian
AU - Deris, Safaai
AU - Mohamad, Mohd Saberi
AU - Abdullah, Afnizanfaizal
N1 - Publisher Copyright:
© 2015 Ismail et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2015/5/11
Y1 - 2015/5/11
N2 - This paper presents an in silico optimization method of metabolic pathway production. The metabolic pathway can be represented by a mathematical model known as the generalized mass action model, which leads to a complex nonlinear equations system. The optimization process becomes difficult when steady state and the constraints of the components in the metabolic pathway are involved. To deal with this situation, this paper presents an in silico optimization method, namely the Newton Cooperative Genetic Algorithm (NCGA). The NCGA used Newton method in dealing with the metabolic pathway, and then integrated genetic algorithm and cooperative co-evolutionary algorithm. The proposed method was experimentally applied on the benchmark metabolic pathways, and the results showed that the NCGA achieved better results compared to the existing methods.
AB - This paper presents an in silico optimization method of metabolic pathway production. The metabolic pathway can be represented by a mathematical model known as the generalized mass action model, which leads to a complex nonlinear equations system. The optimization process becomes difficult when steady state and the constraints of the components in the metabolic pathway are involved. To deal with this situation, this paper presents an in silico optimization method, namely the Newton Cooperative Genetic Algorithm (NCGA). The NCGA used Newton method in dealing with the metabolic pathway, and then integrated genetic algorithm and cooperative co-evolutionary algorithm. The proposed method was experimentally applied on the benchmark metabolic pathways, and the results showed that the NCGA achieved better results compared to the existing methods.
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U2 - 10.1371/journal.pone.0126199
DO - 10.1371/journal.pone.0126199
M3 - Article
C2 - 25961295
AN - SCOPUS:84949143547
SN - 1932-6203
VL - 10
JO - PLoS ONE
JF - PLoS ONE
IS - 5
M1 - e0126199
ER -