A Systems Approach to the NPK Nutriome and its Effect on Biomass
NPK Nutriome 及其对生物质影响的系统方法
基本信息
- 批准号:1158273
- 负责人:
- 金额:$ 118.53万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Continuing Grant
- 财政年份:2012
- 资助国家:美国
- 起止时间:2012-04-01 至 2017-03-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Intellectual Merit: This project aims to use a systems biology approach to identify the genes that control plant biomass production by sensing and integrating responses to nutrients in soil. The research focuses on the interactions of Nitrogen, Phosphorus and Potassium (NPK), the main nutrient chemicals used in commercial fertilizers. Optimizing plant growth in response to NPK nutrient interactions has the potential to increase biomass production, while decreasing the toxic leaching of these chemicals (especially nitrogen and phosphorus) into surface and ground waters, thus impacting the environment and human health. In now classic experiments, Murashige and Skoog (1962) showed that the interactions of NPK could lead to an increase in biomass under low N-input conditions. This project seeks to identify the gene networks underlying the "NPK interaction effect" on biomass by combining genomic, phenotyping, and network inference approaches. Our experimental and analytical strategy is the result of a highly successful collaboration between biologists and computer scientists, and involves an iterative cycle of experimentation and computation, a hallmark of systems biology. The aims of the project are to: 1. Identify combinations of NPK treatments that result in high biomass under low N-input; 2. Define "early" molecular marker genes that act as predictors of biomass; 3. Conduct genome-wide RNA analysis and modeling to identify gene regulatory networks associated with low-N High-biomass state; and 4. Test candidate regulatory genes. The ultimate goal is to uncover genes and pathways that control plant growth under NPK treatments and to manipulate them to optimize N-use efficiency and biomass production.Broader Impacts: The long-term advantage of this project is to identify and target critical regulatory components controlling nutrient use efficiency to create crops that produce high biomass with a reduced amount of fertilizer, hence decreasing the health and ecological impacts of leached chemicals. In addition, the generation of nutrient-efficient crops would ameilorate their cultivation on impoverished or nutrient-poor soils. This project will involve the training of scientists at the graduate and postdoctoral level across computational and experimental biology. As the research entails the development of Systems Biology approaches, biologists will be engaged in teaching computer scientists about topics like genetics, experimental genomics, and the computational challenges of analyzing genomic data. In turn, computer scientists will be involved in developing and testing optimization as well as machine learning algorithms for network inference that predicting network states under untested conditions, the ultimate goal of systems biology. The project will also include an outreach program that provides high school students the opportunity to work in a laboratory environment at the interface of Biology and Computer Science. As part of this program, the NYU Center for Genomics and Systems Biology produced 4 semifinalists and 2 national finalists (out of 40) for the Intel Competition in 2012. One of these Intel finalists was mentored by the PI of this project. The PIs of this project are committed to increasing diversity and will continue to actively seek out and recruit scientists from under-represented minorities to participate in the research and outreach components. The project has and will continue to involve a diverse team of researchers. The PI has served as a mentor for many women scientists and will continue this role in the future.
智力优势:该项目旨在使用系统生物学方法,通过感知和整合对土壤中养分的反应来识别控制植物生物量生产的基因。研究重点是氮,磷和钾(NPK),商业肥料中使用的主要营养化学品的相互作用。优化植物生长以响应NPK营养相互作用具有增加生物量生产的潜力,同时减少这些化学品(特别是氮和磷)进入地表和地下沃茨的有毒沥滤,从而影响环境和人类健康。Murashige和Skoog(1962)的经典实验表明,在低氮输入条件下,氮磷钾的相互作用可导致生物量的增加。本项目旨在通过结合基因组学、表型分析和网络推理方法,确定氮磷钾互作效应对生物量影响的基因网络。我们的实验和分析策略是生物学家和计算机科学家之间非常成功的合作的结果,涉及实验和计算的迭代周期,这是系统生物学的标志。该项目的目标是:1。确定在低氮输入下产生高生物量的NPK处理组合; 2.定义“早期”分子标记基因,作为生物量的预测; 3。进行全基因组RNA分析和建模,以确定与低氮高生物量状态相关的基因调控网络;和4.测试候选调控基因。最终目标是揭示NPK处理下控制植物生长的基因和途径,并操纵它们以优化氮素利用效率和生物量生产。该项目的长期优势是确定和瞄准控制养分利用效率的关键监管成分,以培育出能以减少的肥料量生产高生物量的作物,从而减少沥滤化学品对健康和生态的影响。此外,生产营养效率高的作物将改善其在贫瘠或营养贫乏的土壤上的种植。该项目将涉及跨计算和实验生物学的研究生和博士后水平的科学家培训。由于这项研究需要系统生物学方法的发展,生物学家将参与教授计算机科学家有关遗传学,实验基因组学和分析基因组数据的计算挑战等主题。反过来,计算机科学家将参与开发和测试优化以及用于网络推理的机器学习算法,这些算法可以在未经测试的条件下预测网络状态,这是系统生物学的最终目标。该项目还将包括一个外展计划,为高中生提供在生物学和计算机科学接口的实验室环境中工作的机会。作为该计划的一部分,纽约大学基因组学和系统生物学中心在2012年英特尔竞赛中产生了4名半决赛选手和2名全国决赛选手(共40名)。其中一名英特尔决赛选手接受了该项目PI的指导。 该项目的主要研究者致力于增加多样性,并将继续积极寻找和招募代表性不足的少数群体的科学家参加研究和外联部分。该项目已经并将继续涉及不同的研究人员团队。PI已成为许多女科学家的导师,并将在未来继续发挥这一作用。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Gloria Coruzzi其他文献
Glutamate-receptor genes in plants
植物中的谷氨酸受体基因
- DOI:
10.1038/24066 - 发表时间:
1998-11-12 - 期刊:
- 影响因子:48.500
- 作者:
Hon-Ming Lam;Joanna Chiu;Ming-Hsiun Hsieh;Lee Meisel;Igor C. Oliveira;Michael Shin;Gloria Coruzzi - 通讯作者:
Gloria Coruzzi
Appointments and awards
任命和奖项
- DOI:
10.1007/bf02669258 - 发表时间:
1992-02-01 - 期刊:
- 影响因子:1.400
- 作者:
Philip N. Benfey;Gloria Coruzzi - 通讯作者:
Gloria Coruzzi
Gloria Coruzzi的其他文献
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{{ truncateString('Gloria Coruzzi', 18)}}的其他基金
RESEARCH-PGR: Uncovering the molecular mechanisms that integrate nutrient and water dose sensing and impact crop production
研究-PGR:揭示整合养分和水剂量传感并影响作物生产的分子机制
- 批准号:
1840761 - 财政年份:2019
- 资助金额:
$ 118.53万 - 项目类别:
Standard Grant
Gordon Research Conference on Plant Molecular Biology: Dynamic Plant Systems, Holderness, New Hampshire, June 10-15, 2018
戈登植物分子生物学研究会议:动态植物系统,霍尔德内斯,新罕布什尔州,2018 年 6 月 10-15 日
- 批准号:
1824578 - 财政年份:2018
- 资助金额:
$ 118.53万 - 项目类别:
Standard Grant
NutriNet: A Network Inspired Approach to Improving Nutrient Use Efficiency (NUE) in Crop Plants
NutriNet:一种提高作物养分利用效率 (NUE) 的网络方法
- 批准号:
1339362 - 财政年份:2014
- 资助金额:
$ 118.53万 - 项目类别:
Standard Grant
Prospecting for Resources: A Systems Integration of Local and Systemic Nutrient Signaling
资源勘探:局部和系统营养信号的系统集成
- 批准号:
1412232 - 财政年份:2014
- 资助金额:
$ 118.53万 - 项目类别:
Continuing Grant
Genomics of Comparative Seed Evolution
比较种子进化的基因组学
- 批准号:
0922738 - 财政年份:2010
- 资助金额:
$ 118.53万 - 项目类别:
Continuing Grant
Arabidopsis 2010: Nitrogen Networks in Plants
拟南芥 2010:植物中的氮网络
- 批准号:
0929338 - 财政年份:2009
- 资助金额:
$ 118.53万 - 项目类别:
Continuing Grant
Arabidopsis 2010: Nitrogen Networks in Plants
拟南芥 2010:植物中的氮网络
- 批准号:
0519985 - 财政年份:2005
- 资助金额:
$ 118.53万 - 项目类别:
Continuing Grant
Conceptual Data Integration for the VirtualPlant
VirtualPlant 的概念数据集成
- 批准号:
0445666 - 财政年份:2005
- 资助金额:
$ 118.53万 - 项目类别:
Continuing Grant
Genomics of Comparative Seed Evolution.
比较种子进化的基因组学。
- 批准号:
0421604 - 财政年份:2004
- 资助金额:
$ 118.53万 - 项目类别:
Continuing Grant
SGER Grant: Plant Evolutionary Genomics: Develop and Test Bioinformatic Tools to Automate Ortholog Identification for Phylogenomics and Functional Genomic Studies
SGER 资助:植物进化基因组学:开发和测试生物信息工具,以自动进行系统发育和功能基因组研究的直系同源鉴定
- 批准号:
0346436 - 财政年份:2003
- 资助金额:
$ 118.53万 - 项目类别:
Standard Grant
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