In this talk we will focus on biclustering, a technique that has recently shown to be remarkably effective in a variety of applications in biological data analysis and other data mining tasks. The importance of biclustering in the identification of groups of genes with coherent expression patterns (in a subset of the experimental conditions), and its advantages (when compared to clustering) in the discovery of local expression patterns has been extensively studied and documented. The use of these techniques is therefore critical to identify the dynamics of biological systems as well as the different groups of genes involved in each biological process. This talk is organized in three main parts. First, we will talk about gene expression data analysis and its general goals and challenges. We will then talk about biclustering and its applications to expression data analysis. Finally, we will focus on biclustering in time-series expression data and its applications to the discovery of gene regulatory modules and networks.