Building gene networks with time-delayed regulations

We propose a method to build gene regulatory networks (GRN) capable of representing time-delayed regulations. The gene expression data is represented in two types of graphical models: a linear model using a dynamic Bayesian network (DBN) and a skip model using a hidden Markov model. The linear model is designed to find short-delays and skip model for long-delays. The algorithm was tested on time-series data obtained on yeast cell-cycle and validated against protein–protein interaction data. The proposed method better fits expression profiles compared to classical higher-order DBN and found core genes that are crucial in cell-cycle regulation.