CAREER: Inference of transcriptional regulation under environmental perturbations

基因组定义了细胞正常运作所必需的蓝图，对于复杂的生物体，如脊椎动物，构成个体的大多数细胞的蓝图几乎是相同的。尽管如此，细胞在形状和功能上差异很大。了解单个细胞如何形成并维持其身份和功能的机制，以及它们如何相互沟通以协调整个生物体的发育，是发育生物学家非常感兴趣的问题。这个项目有两个目标。首先，它开发了计算工具，用于1)表征细胞间通讯对分子功能的影响，2)测量从不同组织或个体收集的细胞之间分子功能的生物学变异，以及3)预测操纵细胞身份和功能的实验策略。其次，它训练高中生、本科生和研究生使用这些工具和数据分析技术，并开发方法让学生参与跨学科团队的基因组学研究。因此，该项目将实现更广泛的目标，即培养下一代数据科学家，利用基因组学技术解决生物学中的重要问题。DNA测序技术的最新发展使得在广泛的生物体中测量基因调控的不同动态方面成为可能。对于基因组中的每个DNA片段，我们现在可以测量其物理可达性的快照，测量其转录成RNA的相对速率，确定DNA或其锚定蛋白的可逆修饰的位置，甚至确定与其物理接触的其他远端DNA片段。本项目的研究目标是定量表征来自内在和外在因素的信号整合驱动基因和染色质调控变异的机制，并最终定义细胞身份及其动力学。它专门开发了基于深度神经网络的工具，以对细胞进行硅扰动，以识别转录细胞状态的调节因子，识别细胞对刺激反应的调节途径，并表征细胞间通信对基因调节的影响。该项目的教育目标是制定可扩展的策略，以培养高中、本科和研究生阶段的下一代基因组数据科学家，使他们能够使用这些工具，以跨学科团队为基础的科学方法解决生物学中的各种问题。这项工作的结果可以在http://qlab.faculty.ucdavis.edu.This上找到，该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

The genome defines blueprints necessary for the proper functioning of cells, and for complex organisms, such as vertebrates, it is nearly identical for most cells that make up the individual. Despite this, cells vary widely in shape and function. Understanding the mechanisms by which individual cells are set on the path to, and then maintain, their identity and function, and how they communicate with each other in order to coordinate development of the whole organism, is of keen interest to developmental biologists. The goals of this project are two-fold. First, it develops computational tools for 1) characterizing the impact of cell-cell communication on molecular function, 2) measuring biological variation in molecular function between cells collected from different tissues or individuals, and 3) predicting experimental strategies for manipulating cell identity and function. Second, it trains high school, undergraduate and graduate students in the use of these tools and data analysis techniques, and develops approaches to engage students in interdisciplinary team-based genomics research. The project will thus achieve the broader goal of training the next generation of data scientists to address important problems in biology using genomics technologies. Recent developments in DNA sequencing technologies enable the measurement of different dynamic aspects of gene regulation across a wide spectrum of organisms. For each segment of DNA in a genome, we can now measure a snapshot of its physical accessibility, measure its relative rate of transcription into RNA, identify the location of reversible modifications to the DNA or its anchoring proteins, and even identify other distal DNA segments that are in physical contact with it. The research goal of this project is to quantitatively characterize the mechanisms by which signals from both intrinsic and extrinsic factors are integrated to drive variation in gene and chromatin regulation, and ultimately define cell identity and its dynamics. It specifically develops tools based on deep neural networks to perform in silico perturbations to cells in order to identify the regulators of transcriptional cell state, identify regulatory pathways underlying cellular responses to stimuli, and characterize the effect of cell-cell communication on gene regulation. The educational goal of this project is to develop scalable strategies to train the next generation of genome data scientists at the high school, undergraduate and graduate levels of education to use these tools to address diverse problems in biology in an interdisciplinary team-based science approach. The results of this work can be found at http://qlab.faculty.ucdavis.edu.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.