Methods to Elucidate the Dynamics of Transcriptional Regulation and Chromatin

阐明转录调控和染色质动力学的方法

• 批准号: 10205905
• 负责人: Alexander J Hartemink
• 金额: $ 42.97万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10205905
• 关键词: Algorithmic Software; Algorithms; Biological; Biological Assay; Cell Cycle; Cell Proliferation; Cell division; Cell physiology; Cells; Chromatin; Complex; Computing Methodologies; Data; Diabetes Mellitus; Environment; Event; Foundations; Genes; Genetic Transcription; Genome; Genomics; Goals; Hormones; Human; Knowledge; Lead; Link; Malignant Neoplasms; Methods; Modeling; Monitor; Organism; Process; Production; Regulation; Research; Saccharomycetales; Series; Signal Transduction; Statistical Methods; Technology; Testing; Time; Transcript; Transcriptional Regulation; Yeasts; base; biological adaptation to stress; cell growth; chromatin remodeling; clinically relevant; developmental disease; dynamic system; environmental stressor; fundamental research; genetic strain; genome-wide; insight; mutant; predictive modeling; programs; protein complex; response

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.

项目摘要 在细胞内，尽管基因组的序列基本上是固定的，但其状态仍在不断变化。两个方面 在给定时间点不断变化的状态的是沿着的指定布置 染色质形式的基因组和每个基因的转录率。这些影响 另一个，每个都会根据单元的内部或外部环境而发生变化，建立一个复杂 动态系统，该系统涉及细胞功能和适应性。一个基本的研究目标是 了解这两个，全基因组之间的动态关系：转录如何影响 染色质景观以及染色质景观如何受到转录的影响。 我们研究小组的一个核心目标是开发能够预测细胞全基因组转录的模型 了解其全基因组染色质状态的知识。要构建这样的模型，需要简单地进行填充 细胞在不同时间和不同条件下的细胞全基因组染色质和转录状态： 观察两者在响应不断变化的环境时如何一起改变，尤其是在上下文中 定向扰动，提供了建立能够提供的预测模型所需的统计杠杆作用 因果解释。我们的模型最初将通过监视开发和验证 在各种条件下发芽酵母中的动态染色质占用和转录：随着它们的进展 通过细胞周期（控制细胞增殖的一系列临时高度调节的事件， 这可能导致癌症），响应环境应力以及跨遗传菌株，包括突变体 这种破坏染色质重塑或TF表达。我们还可以访问大量数据分析 在人雌激素反应和染色质重塑的背景下，转录和染色质动力学。 独特的酵母和人类环境为开发广泛适用的方法提供了机会 在此范围内，并提供了对基因组调控基础问题的机械洞察力。 拟议的研究将基于贝叶斯概率产生计算和统计方法 图形建模方法可以（1）更准确，全面，可固定地填充两种染色质 随着时间的流逝，占用和转录调节的变化，以及（2）推断 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的两个 响应不断变化的条件。 更普遍的是，随着先进的实验技术和测定继续以快速的速度开创性，我们 需要同时开发复杂的新计算和统计方法，而不仅仅是存储 或处理不断增长的数据，但要制定提供机械接地的模型 数据的解释，以开发更有效地使用数据的算法来揭示更深的生物学 洞察力，并做出可以通过实验测试的因果预测，以提高我们的科学理解。