CAREER: Computational Identification of Gene Regulatory Networks in Multicellular Eukaryotes

职业：多细胞真核生物基因调控网络的计算识别

• 批准号: 0845823
• 负责人: Sheng Zhong
• 金额: $ 70.92万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-15 至 2014-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0845823&HistoricalAwards=false
• 关键词: CAREER; Computational; Identification; Gene; Regulatory

(This award is funded through the American Recovery and Reinvestment Act of 2009: Public Law 111-5).This is a CAREER award to support the research of Dr. Sheng Zhong, in the Department of bioengineering at University of Illinois at Urbana-Champaign. Dr. Zhong is a fourth-year, tenure-track Assistant Professor. Understanding cellular processes, such as development and differentiation, requires understanding how cells control the rate of protein production, especially gene transcription. A gene regulatory network (GRN) is a collection of DNA segments in a genome, which interact with each other and with other substances in the cell, thereby governing the rates at which genes in a network are transcribed into messenger RNA. Most computational tools for identification of eukaryote GRNs were developed for the single cell eukaryote, Saccharomyces cerevisiae (baker's yeast). The complexity of GRNs in multicellular organisms and especially in vertebrates is several magnitudes greater than that of the yeast GRNs. The greater complexity arises from how genes are controlled by complex patterns of DNA regulatory elements, called cis-regulatory modules; how larger genome size provides a greater template for cis-regulatory modules to evolve; and the stochastic process of transcription, which is activated by the interaction of regulatory proteins and cis-regulatory modules. A new framework for the identification of GRNs in multicellular organisms is being developed by Dr. Zhong. Quantitative evolution models and joint models of gene expression and protein-DNA interaction data are being developed in this project. The project is advancing the state of the art of systems biology through developing a theoretical framework of eukaryote GRN evolution, a variety of GRN identification and analysis methods, prototype systems for analysis of genomic data, and through discovery of engineering principles in cell biology. Databases and tools produced under this project will be accessible via the PI?s website at http://bioinformatics.bioen.uiuc.edu/As a part of his CAREER plan, Dr. Zhong is training a new generation of interdisciplinary researchers by engaging undergraduate students in research; developing new courses and participating in outreach activities with Illinois middle school and high school students, including a bioinformatics camp for middle school girls, as one of the Girls Adventures in Mathematics, Engineering, and Science (G.A.M.E.S) camp program.

(This该奖项通过2009年美国复苏和再投资法案：公法111-5）资助。这是一个职业奖，以支持伊利诺伊大学厄巴纳-香槟分校生物工程系的钟盛博士的研究。钟博士是第四年，终身助理教授。了解细胞过程，如发育和分化，需要了解细胞如何控制蛋白质的产生速度，特别是基因转录。基因调控网络（GRN）是基因组中DNA片段的集合，它们彼此相互作用并与细胞中的其他物质相互作用，从而控制网络中基因转录成信使RNA的速率。大多数用于鉴定真核生物GRNs的计算工具是针对单细胞真核生物酿酒酵母（Saccharomyces cerevisiae）（面包酵母）开发的。在多细胞生物体中，特别是在脊椎动物中，GRNs的复杂性比酵母GRNs的复杂性大几个数量级。更大的复杂性来自于基因如何被称为顺式调控模块的DNA调控元件的复杂模式控制;更大的基因组大小如何为顺式调控模块进化提供更大的模板;以及转录的随机过程，其由调控蛋白和顺式调控模块的相互作用激活。钟博士正在开发一种新的框架，用于识别多细胞生物中的GRNs。该项目正在开发基因表达和蛋白质-DNA相互作用数据的定量进化模型和联合模型。该项目通过开发真核生物GRN进化的理论框架，各种GRN识别和分析方法，用于分析基因组数据的原型系统，以及通过发现细胞生物学中的工程原理，推进系统生物学的最新技术。该项目下生成的数据库和工具将通过PI访问？http://bioinformatics.bioen.uiuc.edu/As作为他职业生涯计划的一部分，钟博士正在培养新一代的跨学科研究人员，方法是让本科生参与研究;开发新课程，并参加伊利诺伊州初中和高中学生的外联活动，包括为中学女生举办的生物信息学夏令营，作为数学、工程和科学女孩冒险（G.A.M.E.S）夏令营计划之一。