NSF NPGI Postdoctoral Fellowship in Biology FY 2014

Markelz Robert J, Davis CA

Investigators

Abstract

This action funds an NSF National Plant Genome Initiative Postdoctoral Research Fellowship in Biology for FY 2014. The fellowship supports a research and training plan in a host laboratory for the Fellow who also presents a plan to broaden participation in biology. The title of the research and training plan for this fellowship to R.J. Cody Markelz is "Develop a systems level model of resource allocation and partitioning in Brassica rapa to predict growth across multiple genotypes and environments". The host institution for the fellowship is the University of California, Davis and the sponsoring scientists are Drs. Julin Maloof and Daniel Kliebenstein. If successful, this project will significantly advance classical plant growth modeling by connecting it to quantitative genetics and metabolic network modeling. Training objectives include statistical genetics, mathematical modeling, metabolic network reconstruction, and systems biology. Broader impacts include training undergraduates and high school students through small, hands-on QTL mapping projects. Additionally, a module for middle school teachers about shade avoidance will be developed for inclusion in the Plants iView outreach program. This module will include a detailed lesson plan, the key biological concepts and national education standards addressed, and a practical guide for teaching the lessons in the classroom. The research will take a systems biology approach to investigate the interactions among genotype, response to shade, and response to nutrient availability. This project will be conducted in a genetic mapping population of Brassica rapa, which is segregating for the response to shade and nutrient availability. The objectives are to: (1) determine if shade-induced changes in nutrient use efficiency are genetically controlled; (2) link tissue-level physiological traits with the underlying metabolic networks; (3) integrate quantitative trait loci (QTL) and metabolic networks into a mathematical model of whole plant resource partitioning. The model will then be used to predict genotype-specific growth and yield responses to shade and nutrient availability. The model will be validated against datasets collected by undergraduate researchers. The whole-plant systems level dataset will be made available through a relational database that will be linked to an open-access data analysis workflow utilizing the free cloud computing resources at iPlant Collaborative.