chapter  2
Transcription Regulatory Networks in Yeast Cell Cycle
Pages 12

The functional genomics techniques for mapping transcription regulatory networks have evolved based on advances in experimental approaches and the kinds of data generated. Studies in yeast have emphasized powerful genetic approaches that are not available in other higher eukaryotic organisms. As a consequence, yeast is particularly amenable for analyz­ ing transcriptional regulatory mechanisms in vivo under true physiological conditions. With its small genome (predicted to encode roughly about 6200 proteins) and its tractable genetics, Saccharomyces cerevisiae has played a prominent role in the development of many methodolo­ gies for functional genomics.1 Various high throughput expression techniques, such as SAGE and microarrays, have been developed that exploit the huge body of transcription data and provide rapid, parallel surveys of gene-expression patterns for hundreds of thousands of genes in a single assay. Several computational algorithms have been developed and applied to uncover coregulated genes or causal relationships from the large-scale gene expression data. As tran­ scription is mainly controlled and regulated by the binding of transcription factors (TFs) to the promoter DNA sequence, significant progress has also been made in identifying these c/r-regulatory elements in the promoters, giving more insights to gene function and regulation pathways.2 Recendy, other high-throughput methods have been developed for measuring the interactions between DNA and TFs in vivo. Microarray-based chromatin immunoprecipitation assays (ChIP-chip), have enabled genome-wide location analysis of TF-binding in vivo, offering another powerful tool in dissecting the global regulatory networks. Also, sequencing of multiple yeast species have provided an opportunity to look for conserved functional mod­ ules. In this chapter, we discuss the functional genomics approaches to map regulatory net­ works from combinations of sequence data, genome-wide gene expression data and ChIP data in the context of the cell cycle regulation of the budding yeast, Saccharomyces cerevisiae. These approaches extract key aspects of regulatory mechanisms such as identifying target genes and cis-regulatory elements important for a TF or combination of TFs under a particular condition or perturbation. They also help mapping interactions between trans-and cis-transcription modules (defined by a TF and target genes) - giving a more systematic view of the mechanistic underpinnings of gene expression networks.