Identification of robust adaptation gene regulatory network parameters using an improved particle swarm optimization algorithm.
Robust adaptation is a critical ability of gene regulatory network (GRN) to survive in a fluctuating environment, which represents the system responding to an input stimulus rapidly and then returning to its pre-stimulus steady state timely. In this paper, the GRN is modeled using the Michaelis-Menten rate equations, which are highly nonlinear differential equations containing 12 undetermined parameters. The robust adaption is quantitatively described by two conflicting indices. To identify the parameter sets in order to confer the GRNs with robust adaptation is a multi-variable, multi-objective, and multi-peak optimization problem, which is difficult to acquire satisfactory solutions especially high-quality solutions. A new best-neighbor particle swarm optimization algorithm is proposed to implement this task. The proposed algorithm employs a Latin hypercube sampling method to generate the initial population. The particle crossover operation and elitist preservation strategy are also used in the proposed algorithm. The simulation results revealed that the proposed algorithm could identify multiple solutions in one time running. Moreover, it demonstrated a superior performance as compared to the previous methods in the sense of detecting more high-quality solutions within an acceptable time. The proposed methodology, owing to its universality and simplicity, is useful for providing the guidance to design GRN with superior robust adaptation.
Shaanxi Key Laboratory of CSCIIP, Xi'an University of Technology, Xi'an, Shaanxi Province, China.|School of Physics and Electromechanical Engineering, HeXi University, Zhangye, Gansu Province, China.
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Huang
Shaanxi Key Laboratory of CSCIIP, Xi'an University of Technology, Xi'an, Shaanxi Province, China.
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Ren
D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D053263:Gene Regulatory Networks; D007700:Kinetics; D008957:Models, Genetic