Abstract
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.
Highlights
Computational system biology has gained attention from many researchers and become an important research area
genetic algorithm (GA) loses its effectiveness when it is applied in a complex metabolic pathway as the representation of the solution becomes complex and difficult to evaluate
An improved method for in silico optimization of metabolic pathway production known as Newton Cooperative Genetic Algorithm (NCGA) has been proposed
Summary
Computational system biology has gained attention from many researchers and become an important research area. Applying only the Newton method is not sufficient for the optimization process because the variables in the nonlinear equations system need to be tuned This is because all the metabolic pathway components are represented by many variables in the nonlinear equations system. An improved method, namely the Newton Cooperative Genetic Algorithm (NCGA), was utilized for the in silico optimization of metabolic pathway production. The in silico optimization of metabolic pathway production can be considered as a method for solving a nonlinear equations system. This is done to ensure that the representatives from all the sub-populations will combine with each other in order to minimize the total component concentrations involved This step is referred to as the sub-chromosome evaluation. At the initial steady state, all the fluxes in the model were formulated in the following form: Vin 1⁄4 0:8122X2À0:2344Y1
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