A Comparison Study of Reverse Engineering Gene Regulatory Network Modeling

The construction and understanding of Gene Regulatory Networks (GRNs) are among the hardest tasks faced by systems biology. To infer gene regulatory networks from gene expression data has been a vigorous research area. It aims to constitute an intermediate step from exploratory to gene expression analysis. In recent years, many reverse engineering methods have been proposed. In practice, different model approaches will generate different network structures. Therefore, it is very important for users to assess the performance of these algorithms. We present a comparative study with three different reverse engineering methods, including the S-system Parameter Estimation Method (SPEM), the Graphical Gaussian Model (GGM) and the TimeDelay-ARACNE. Our approach consists of the analysis of real gene expression data with the different methods, and the assessment of algorithmic performances by sensitivity, specificity, precision and F-score.