Analysis of genome wide transcriptional control in yeast

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Analysis of genome-wide transcriptional control in yeast The long term goal of this project is to develop a comprehensive and predictive model of a functional transcriptional regulatory network in a eukaryotic cell. We seek to understand genome-wide transcriptional changes that are triggered in response to stress conditions, in terms of the quantitative contributions of individual transcriptional regulators, including sequence-specific DNA binding transcription factors as well as transcriptional regulators that affect gene expression through modulating chromatin structure. We will use yeast as a model system to address several questions in this area through the following specific aims. First, we will comprehensively identify every transcriptional regulator and chromatin factor that potentially regulates stress responses in yeast. This will be accomplished through phenotypic growth assays of yeast strains in which transcription factor function is modulated by deletion or overexpression. Transcription factor deletion and overexpression strains will be screened for growth defects by spotting on plates and growth in liquid, under three different stress conditions - heat shock, nutrient starvation and DNA damage. Second, we will identify the downstream targets of key regulators under stress conditions. We will identify the direct binding targets of a prioritized set of transcription regulators during the stress responses that they are shown to be required for, using chromatin immunoprecipitation combined with microarrays (ChIP-chip). We will also identify the genes that are actively and functionally regulated by these factors, by carrying out gene expression profiling in strains deleted for the selected transcription factors, under the stress conditions that these factors are known to be required for. Finally, we will integrate our experimental genomic data to build a predictive and causal regulatory network to explain the transcriptional regulation of all yeast genes under stress. We will use a Bayesian framework to model and reconstruct this transcriptional regulatory network, which will minimally contain the targets of every stress-related transcriptional regulator in yeast, and ideally explain the regulation of every yeast gene under physiological stress perturbations. We will test the predictive ability of our network using both external experimental data and gene functional annotations. Selected predicted regulatory relationships in the network will be verified by directed experiments.