Saturday, November 23, 2024
11/23/2024
Project Summary Within a cell, though the sequence of the genome is essentially fixed, its state is constantly changing. Two aspects of this changing state at a given point in time are the specific arrangement of myriad protein complexes along the genome in the form of chromatin, and the rate of transcript production for each gene. Each of these influences the other, and each also changes in response to the cell's internal or external environment, setting up a complex dynamical system that undergirds cellular function and adaptation. A fundamental research objective is to understand the dynamic relationship between these two, genome-wide: how transcription is influenced by the chromatin landscape, and how the chromatin landscape is influenced by transcription. A central goal of our research group is to develop models capable of predicting a cell's genome-wide transcription state from knowledge of its genome-wide chromatin state. To build such models requires simultaneously profiling a cell's genome-wide chromatin and transcription states at different times and under different conditions: Observing how the two change together as they respond to a changing environment, particularly in the context of directed perturbation, provides the statistical leverage needed to build predictive models capable of providing causal and mechanistic interpretations. Our models will initially be developed and validated by monitoring dynamic chromatin occupancy and transcription in budding yeast under various conditions: as they progress through the cell cycle (a temporal series of highly regulated events controlling cell proliferation, aberrations of which can lead to cancer), in response to environmental stresses, and across genetic strains, including mutants that disrupt chromatin remodeling or TF expression. We also have access to massive amounts of data assaying the dynamics of transcription and chromatin in the context of human hormone response and chromatin remodeling. The distinct yeast and human contexts offer an opportunity to develop methods that are broadly applicable across this spectrum and provide mechanistic insight into foundational questions in genomic regulation. The proposed research will produce computational and statistical methods based on Bayesian probabilistic graphical modeling approaches that can (1) more accurately, comprehensively, and scalably profile both chromatin occupancy and transcriptional regulation as they change over time, and (2) infer mechanistic links between the two that elucidate how the cell dynamically regulates its genome-wide transcription program and chromatin organization in response to changing conditions. More generally, as advanced experimental technologies and assays continue to be pioneered at a rapid pace, we need to concomitantly develop sophisticated new computational and statistical methods, not merely to store or process the ever-growing amounts of data, but to formulate models that provide mechanistically grounded explanations of the data, to develop algorithms that use the data more effectively to reveal deeper biological insight, and to make causal predictions that can be experimentally tested to advance our scientific understanding.
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