Regulation of Global Transcription Dynamics During Cell Division and Root Development

The successful completion of many critical biological processes depends on the proper execution of complex spatial and temporal gene expression programs. With the advent of high-throughput microarray technology, it is now possible to measure the dynamics of these expression programs on a genome-wide level. In this thesis we present work focused on utilizing this technology, in combination with novel computational techniques, to examine the role of transcriptional regulatory mechanisms in controlling the complex gene expression programs underlying two fundamental biological processes---the cell cycle and the development and differentiation of an organ.

We generate a dataset describing the genomic expression program which occurs during the cell division cycle of Saccharomyces cerevisiae. By concurrently measuring the dynamics in both wild-type and mutant cells that do not express either S-phase or mitotic cyclins we quantify the relative contributions of cyclin-CDK complexes and transcriptional regulatory networks in the regulation the cell cell expression program. We show that CDKs are not the sole regulators of periodic transcription as contrary to previously accepted models; and we hypothesize an oscillating transcriptional regulatory network which could work independent of, or in tandem with, the CDK oscillator to control the cell cell expression program.

To understand the acquisition of cellular identity, we generate a nearly complete gene expression map of the Arabidopsis Thaliana root at the resolution of individual cell-types and developmental stages. An analysis of this data reveals a representative set of dominant expression patterns which are used to begin defining the spatiotemporal transcriptional programs that control development within the root.

Additionally, we develop computational tools that improve the interpretability and power of these data. We present CLOCCS, a model for the dynamics of population synchrony loss in time-series experiments. We demonstrate the utility of CLOCCS in integrating disparate datasets and present a CLOCCS based deconvolution of the cell-cycle expression data. A deconvolution method is also developed for the Arabidopsis dataset, increasing its resolution to cell-type/section subregion specificity. Finally, a method for identifying biological processes occurring on multiple timescales is presented and applied to both datasets.

It is through the combination of these new genome-wide expression studies and computational tools that we begin to elucidate the transcriptional regulatory mechanisms controlling fundamental biological processes.