A Systems Approach to the NPK Nutriome and its Effect on Biomass

智力优势：该项目旨在利用系统生物学方法通过感知和整合对土壤养分的反应来识别控制植物生物量生产的基因。研究的重点是氮、磷和钾（NPK）的相互作用，这是商业肥料中使用的主要营养化学物质。优化植物生长以响应氮磷钾营养相互作用，有可能增加生物量，同时减少这些化学物质（特别是氮和磷）渗入地表水和地下水的毒性，从而影响环境和人类健康。在现在的经典实验中，Murashige和Skoog（1962）表明，氮磷钾的相互作用可以导致低氮输入条件下生物量的增加。本项目旨在通过结合基因组、表型和网络推断方法，确定“氮磷钾相互作用效应”对生物量的基因网络。我们的实验和分析策略是生物学家和计算机科学家之间高度成功合作的结果，涉及实验和计算的迭代循环，这是系统生物学的标志。该项目的目标是：1。确定在低氮输入下能产生高生物量的氮磷钾处理组合；2. 定义作为生物量预测因子的“早期”分子标记基因；3. 开展全基因组RNA分析和建模，识别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。最终目标是揭示氮磷钾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。更广泛的影响：该项目的长期优势在于确定和瞄准控制养分利用效率的关键调控成分，以创造出能产生高生物量的作物，减少肥料用量，从而减少浸出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在贫瘠或营养贫乏的土壤上的种植。该项目将包括在计算生物学和实验生物学领域培养研究生和博士后水平的科学家。由于这项研究需要系统生物学方法的发展，生物学家将从事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的工作。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拓展计划，为高中生提供在生物学和计算机科学界面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为2012年的英特尔竞赛产生了4名半决赛选手和2名全国决赛选手（40名选手中）。其中一名英特尔决赛选手是由该项目的PI指导的。该项目的pi致力于增加多样性，并将继续积极寻找和招募来自代表性不足的少数民族的科学家参与研究和外联部分。该项目已经并将继续涉及一个多元化的研究团队。该项目已经成为许多女科学家的导师，并将在未来继续发挥这一作用。

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.