Methods to Elucidate the Dynamics of Transcriptional Regulation and Chromatin

项目摘要 在细胞内，虽然基因组的序列基本上是固定的，但它的状态是不断变化的。两个方面 在给定的时间点上，这种变化状态的一个重要特征是无数蛋白质复合物沿着蛋白质复合物的特定排列，这些蛋白质复合物沿着 以染色质形式存在的基因组，以及每个基因的转录产物的速率。每一个都是 另一个，每一个也会随着细胞的内部或外部环境而变化，建立一个复杂的 支持细胞功能和适应的动力系统。一个基本的研究目标是 理解这两者之间的动态关系，全基因组：转录是如何被基因组所影响的。 染色质景观，以及染色质景观如何受到转录的影响。 我们研究小组的中心目标是开发能够预测细胞全基因组转录的模型 从其全基因组染色质状态的知识。要建立这样的模型，需要同时分析 细胞的全基因组染色质和转录状态在不同的时间和不同的条件下： 观察两者在应对不断变化的环境时如何共同变化，特别是在 定向扰动，提供了建立预测模型所需的统计杠杆， 因果和机械解释。我们的模型最初将通过监测和验证 不同条件下芽殖酵母的动态染色质占据和转录：随着它们的进展 通过细胞周期（一系列控制细胞增殖的高度调节的事件， 这可能导致癌症），对环境压力的反应，以及跨遗传菌株，包括突变体 破坏染色质重塑或TF表达。我们还可以获得大量的数据， 在人类激素反应和染色质重塑的背景下，转录和染色质的动力学。 不同的酵母和人类环境提供了一个机会，开发方法，广泛适用 并为基因组调控中的基础问题提供了机制性的见解。 拟议的研究将产生基于贝叶斯概率的计算和统计方法 图形建模方法可以（1）更准确，全面，可扩展地描述染色质 占据和转录调控，因为它们随着时间的推移而变化，和（2）推断机械之间的联系， 两个阐明细胞如何动态调节其全基因组转录程序和染色质 组织应对不断变化的条件。 更一般地说，随着先进的实验技术和分析方法继续以快速的步伐开拓，我们 需要同时开发复杂的新的计算和统计方法，而不仅仅是存储 或处理不断增长的数据量，但要制定模型，提供机械接地 解释数据，开发算法，更有效地利用数据，揭示更深层次的生物学 洞察力，并做出可以通过实验测试的因果预测，以促进我们的科学理解。

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.