Inference of gene regulatory networks from time series gene expression Data by transcriptional lagging time information

Abstract

Recent high-throughput technologies have produced numerous biological data by doing lots of experiments at a same time and genome-wide gene expression data is one of the outcomes. It can be used in various studies to understand biological system and inference of gene regulatory network using this gene expression data is an important issue in bioinformatics. Several algorithms have been introduced to infer the gene regulatory network, but these algorithms are not sufficiently accurate in case of time series gene expression data. Here, we propose an algorithm that predicts the gene regulatory network more accurately than the previous methods. We suggest lagging time information, which is a delay time when one gene regulates another gene, on the algorithm to suit for time series data. A new method finds relationship between a pair of genes by time-shifting the time series data of one gene against another when we compare the patterns of gene pair. In this process, we can find the lagging time between each gene pair. This lagging time information is used to increase the accuracy of the prediction by filtering out the interactions that cannot exist in real biological network based on newly defined time criterion. We tested the algorithm to several synthetic data, which is simulated from computer based on appropriate equations, and also applied the algorithm to real biological data, that is yeast cell cycle microarray gene expression data, to evaluated the effectiveness of our algorithm. As the results, we can find that the present algorithm significantly improves the accuracy of the inference of gene regulatory network. And also, we can verify that calculated lagging time information effectively used to infer the gene regulatory network in the case of time series gene expression data.