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，组成个体的大多数细胞的基因组几乎是相同的。尽管如此，细胞在形状和功能上差异很大。了解单个细胞被设置在通往并维持其身份和功能的路径上的机制，以及它们如何相互通信以协调整个生物体的发育，是发育生物学家感兴趣的问题。这个项目的目标有两个。首先，它开发了计算工具，用于1)表征细胞间通信对分子功能的影响，2)测量从不同组织或个人收集的细胞之间分子功能的生物学差异，以及3)预测操纵细胞身份和功能的实验策略。其次，它培训高中生、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基于团队的跨学科基因组学研究。因此，该项目将实现更广泛的目标，即培训下一代数据科学家，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人们能够在广泛的生物体中测量基因调控的不同动态方面。对于基因组中的每一段DNA，我们现在可以测量其物理可及性的快照，测量其转录成RNA的相对速度，确定对DNA或其锚定蛋白进行可逆修饰的位置，甚至识别与其有物理接触的其他DNA末端片段。这个项目的研究目标是定量描述来自内在和外在因素的信号整合以驱动基因和染色质调节变异的机制，并最终确定细胞特性及其动力学。它专门开发基于深度神经网络的工具来对细胞进行电子干扰，以确定转录细胞状态的调节因子，确定潜在的细胞对刺激的反应的调控途径，并表征细胞间通信对基因调控的影响。该项目的教育目标是制定可扩展的战略，以培训高中、本科和研究生教育水平的下一代基因组数据科学家，以便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，该奖项反映了国家科学基金会的法定使命，并通过使用基金会的智力优势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.