ITR:"Regulography"- Quantitative Reconstruction of Transcriptional Regulatory Networks

ITR：“Regulography”——转录调控网络的定量重建

• 批准号: 0326605
• 负责人: James Liao
• 金额: --
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-12-01 至 2008-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0326605&HistoricalAwards=false
• 关键词: ITR; Regulography; Quantitative; Reconstruction; Transcriptional

"Regulography"- Quantitative Reconstruction of Transcriptional Regulatory NetworksJames C. Liao, Vwani Roychowdhury, Chiara Sabatti, David Eisenberg, Fuyuhiko Tamanoi, University of California, Los AngelesGoal and Tasks: The goal of this project is to reconstruct the hidden structure and dynamics of transcriptional regulatory networks based on massive gene expression data (generated from DNA microarray) and regulatory models under the constraints of various ancillary information, such as protein interactions with DNA, other proteins, and RNA. The ultimate outcome of the project will be an integrated framework (incorporating both methodology and the required databases) for deducing transcriptional network structure and dynamic, which can be applied to define the signal transduction pathways perturbed by unknown drug effects, toxic compound challenges, and mutations. We are using Saccharomyces cerevisiae as the model eukaryote for verification of our paradigm. Toward this end, we are pursuing the following specific tasks: (1) Develop an analytical and IT-based framework for quantitative and dynamic reconstructions of various pre-translational regulatory networks. This includes transcriptional regulation (as governed by protein-DNA and protein-protein interactions), and processing, transport, and stability regulation of mRNA (as governed by various protein-mRNA interactions). This work builds on a novel system-reconstruction methodology called Network Component Analysis. (2) Acquire and organize data for protein-DNA, protein-protein interaction, protein-RNA interactions, mRNA stability, and gene-expression noise. (3) Experimental verification of the paradigm of the framework developed in (1) and (2). (4) Disseminate the results through a composite "regulographic" database.Team Organization: The PIs of this project come from five different fields: Dr. Liao is a chemical/biochemical engineer specialized in metabolic engineering and DNA microarray analysis, Dr. Roychowdrury is an electrical engineer/computer scientist specialized in networks and systems theories, Dr. Tamamoi is a microbiologist specialized in yeast genetics, Dr. Eisenberg is a renowned biochemist/crystallographer specialized in protein structure, function, and protein-protein interactions, and Dr.Sabatti is a statistician specialized in genetic and microarray analyses.Broader Impact: In addition to the fundamental scientific discoveries proposed here, we are pursuing the following information technology related activities: (1) As part of the interdisciplinary bioinformatics program at UCLA, we plan to develop a two-part course on intracellular regulatory networks (targeted toward upper-level undergraduate students and beginning graduate students in both engineering and biological fields) . (2) We will leverage the organizational infrastructure of a number of interdisciplinary research institutes on UCLA campus (including, the NSF Institute for Pure and Applied Mathematics, the California Nano-Science Institute, and the Institute for Cell Mimetic Space Exploration) to attract both undergraduate and graduate minority students. (3) We will integrate our experimental and analytical results into a dynamic database, and make it available to the larger community, which we believe will spur and aid research efforts on intracellular process modeling at a much larger scale, involving academic and commercial institutions nation wide.

转录调控网络的定量重建。Liao，Vwani Roychowdhury，Chiara Sabatti，大卫艾森伯格，Fuyuhiko Tamanoi，加州大学，洛杉矶目标和任务：本项目的目标是基于海量基因表达数据，重建转录调控网络的隐藏结构和动力学（由DNA微阵列产生）和各种辅助信息约束下的调控模型，如蛋白质与DNA、其他蛋白质和RNA的相互作用。该项目的最终成果将是一个综合框架（包括方法和所需的数据库），用于推导转录网络结构和动态，可用于定义受未知药物作用，毒性化合物挑战和突变干扰的信号转导途径。我们使用酿酒酵母作为模型真核生物来验证我们的范例。为此，我们正在进行以下具体任务：（1）开发一个基于IT的分析框架，用于定量和动态重建各种翻译前调控网络。这包括转录调节（由蛋白质-DNA和蛋白质-蛋白质相互作用控制），以及mRNA的加工，运输和稳定性调节（由各种蛋白质-mRNA相互作用控制）。这项工作建立在一种新的系统重建方法，称为网络组件分析。(2)获取和组织蛋白质-DNA、蛋白质-蛋白质相互作用、蛋白质-RNA相互作用、mRNA稳定性和基因表达噪声的数据。(3)对（1）和（2）中开发的框架的范例进行实验验证。 (4)通过一个复合的“regulographic”数据库传播结果。团队组织：该项目的PI来自五个不同的领域：廖博士是一位化学/生化工程师，专门从事代谢工程和DNA微阵列分析，罗伊乔德鲁里博士是一位电子工程师/计算机科学家，专门从事网络和系统理论，玉茂博士是一位微生物学家，专门从事酵母遗传学，Eisenberg博士是一位著名的生物化学家/晶体学家，专门研究蛋白质结构、功能和蛋白质-蛋白质相互作用，Sabatti博士是一位统计学家，专门研究基因和微阵列分析。更广泛的影响：除了这里提出的基础科学发现外，我们正在进行以下与信息技术相关的活动：（1）作为UCLA跨学科生物信息学项目的一部分，我们计划开发一个由两部分组成的细胞内调节网络课程（针对工程和生物领域的高年级本科生和初级研究生）。(2)我们将利用加州大学洛杉矶分校校园内的一些跨学科研究机构的组织基础设施（包括NSF纯数学和应用数学研究所，加州纳米科学研究所和细胞模拟太空探索研究所）来吸引本科生和研究生少数民族学生。 (3)我们将把我们的实验和分析结果整合到一个动态数据库中，并将其提供给更大的社区，我们相信这将刺激和帮助在更大规模的细胞内过程建模的研究工作，涉及全国范围内的学术和商业机构。