INFERENCE OF GENE REGULATORY NETWORKS BY EXTENDED KALMAN FILTERING USING GENE EXPRESSION TIME SERIES DATA

In this paper. the Extended Kalman Entering (EKF) approach has been used to infer gene regulatory networks using time-series gene expression data. Gene expression values are considered stochastic processes and the gene regulator!, network, a dynamical nonlinear stochastic model. Using these values and a modified Kalman filtering approach, the model's parameters and consequently the interactions amongst genes are predicted. In this paper, each gene-gene interaction is modeled usury a linear term, a nonlinear one, and a constant term. The linear and nonlinear term coefficients are included in the state vector together with the gene expressions' true values. Through the extended Kalman filtering process, these coefficients are updated in such a way trait the predicted gene expressions follow the ones observed. Finally, connections between each two genes are inferred based on these coefficients.