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.
