Combining Microarray and Location Data for Reconstructing Gene Regulatory Networks with Multi-time Delay

Abstract

To understand the regulating principle of gene expression is one task of molecular biology. Reconstruction of regulatory networks is essential to model this mechanism. Most research in constructing gene regulatory networks assumed that there was either no time delay in gene expression or a constant time delay. A general framework was provided for combining time series microarray and transcription factor binding location data. It aimed at reconstructing a regulatory network with multi-time delay by using a continuous dynamic Bayesian networks model. This approach was more accurate in determining gene structure as compared to the traditional methods. It was evaluated using time series expression data and location data measured during yeast cell cycle. The experimental results show that combinatorial approach can filter false positives better to increase the accuracy of reconstructed networks. The regulatory relationship described by the reconstructed network also mostly agrees with those in earlier studies.