Reconstructing gene regulatory network with enhanced particle swarm optimization

This conference paper was presented in the 21st International Conference on Neural Information Processing, ICONIP 2014; Kuching; Malaysia; 3 November 2014 through 6 November 2014 [© Springer International Publishing Switzerland 2014]Inferring regulations among the genes is a well-known and significantly important problem in systems biology for revealing the fundamental cellular processes. Although computational models can be used as tools to extract the probable structure and dynamics of such networks from gene expression data, capturing the complex nonlinear system dynamics is a challenging task. In this paper, we have proposed a method to reverse engineering Gene Regulatory Network (GRN) from microarray data. Inspired from the biologically relevant optimization algorithm ‘Particle Swarm Optimization’ (PSO), we have enhanced the PSO incorporating two genetic algorithm operators, namely crossover and mutation. Furthermore, Linear Time Variant (LTV) Model is employed to modeling the GRN appropriately. In the evaluation, the proposed method shows superiority over the state-of-the-art methods when tested with synthetic network, both for the noise free and noise in data. The strength of the proposed method has also been verified by analyzing the real expression data set of SOS DNA repair system in Escherichia coli.Publishe