S-system model has been considered suitable to characterize biochemical network systems and capable to analyze the regulatory system dynamics. Essentially, the inference of S-system models of genetic networks is a large-scale optimization problem consisting of 2N (N + 1) parameters to be optimized where N is the number of genes in the genetic network. This thesis proposes an intelligent two-stage evolutionary algorithm (iTEA) to solve the optimization problem using a divide-and-conquer strategy in each of two stages. The original problem can be decomposed into N individual 2(N + 1) dimensional subproblems if the measurement noise is small. In stage 1, an intelligent genetic algorithm is used to solve the individual subproblems independently without further estimating gene expression levels of other genes. Our simulations demonstrated that the proposed IGA-based method is effective in solving the subproblem of inferring S-system models of genetic networks. In stage 2, OSA can refine the combined solution quality in terms of fitness value where 3% and 5% Gaussian noises were added. From simulation results, it has shown that the proposed algorithm iTEA performs well from noise-free and small-noise gene expression profiles. Our future work is to use iTEA to identify the dynamic pathway
from actual gene expression profiles with measurement noise where biological knowledge is incorporated to improve solution quality.
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