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: 10405481
• 负责人: Alexander J Hartemink
• 金额: $ 42.92万
• 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/10405481
• 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，尽管基因组的序列本质上是fix的，但它的状态是不断变化的。两个方面 在给定的时间点上，这种变化状态是无数蛋白质复合体的特殊fic排列。 染色质形式的基因组，以及每个基因的转录率。其中每一个都以flUse的形式出现 另一个，每一个也会随着细胞的内部或外部环境的变化而变化，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 了解这两者之间的动态关系，全基因组范围：转录是如何在fl中被 染色质景观，以及fl中染色质景观如何受到转录的影响。 我们研究小组的一个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。要构建这样的模型，需要同时支持fiLing 细胞在不同时间和不同条件下的全基因组染色质和转录状态： 观察两者如何在应对不断变化的环境时一起变化，特别是在 提供了构建预测模型所需的统计杠杆，能够提供 因果和机械的解释。我们的模型最初将通过监测来开发和验证 不同条件下芽殖酵母中染色质的动态占据和转录 通过细胞周期(控制细胞增殖的一系列高度调控的事件)， 这可能导致癌症)，对环境压力的反应，以及跨基因株，包括突变株 这会破坏染色质重塑或转铁蛋白的表达。我们还可以获得大量的数据来分析 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为开发广泛适用的方法提供了机会 并对基因组调控中的基础性问题提供机械性的见解。 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、更全面、更可伸缩地支持fi和染色质 占有率和转录调控随着时间的推移而变化，以及(2)推断 两个阐明了细胞如何动态地调节其全基因组转录程序和染色质 适应不断变化的条件的组织。 更广泛地说，随着先进的实验技术和分析继续以快速的速度领先，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 或者处理不断增长的数据量，但要制定模型，以机械地提供 解释数据，开发算法，更有效地利用数据来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，以促进我们对fic的科学理解。