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<p>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.</p><p>We generate
a dataset describing the genomic expression program which occurs during the cell division
cycle of <italic>Saccharomyces cerevisiae</italic>. 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.</p><p>To understand the acquisition of cellular
identity, we generate a nearly complete gene expression map of the <italic>Arabidopsis
Thaliana</italic> 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.</p><p>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 <italic>Arabidopsis</italic> 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.</p><p>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.</p>
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