Abstract:

We constructed gene regulatory networks adopting recurrent neural network model. We proposed a twostep procedure for genetic regulatory network inference. At first we used simulated annealing algorithm to search network structure space and found meaningful weights that indicate the regulatory relations. Secondly we adopted improved particle swarm optimization algorithm based on immune principle to determine the network parameters. Our approach has been applied to both artificial data set and data set of Desoxyribonucleic acid (DNA) Repair System of Escherichia coli. The results demonstrate that the method can provide a meaningful insight into potential regulatory interactions between genes, which is revealed by the nonlinear dynamics of the gene expression time series. Thereby we have provided a new approach to solve the biological problem of constructing gene regulatory networks.

Key words: gene regulatory network, recurrent neural network, simulate annealing algorithm, immune system, particle swarm optimization