NSF Award Abstract #1021619
Project Summary: The long-term goal of this project is to determine the dynamics of the gene regulatory networks that control root growth and differentiation in Arabidopsis. This will be analyzed both under normal conditions and when subjected to environmental stress. A central aim of systems biology is the identification of connections among cellular components to form networks. Because biological processes undergo continuous change, a second aim of systems biology is to understand how the dynamics of life relate to the dynamics of information flow through networks. To understand the dynamics of a network it is necessary to perturb it and see how the system responds. In this project the root of Arabidopsis will be used as an ideal model in which to identify and perturb networks. The simplifying aspects of root growth and anatomy will be exploited to identify networks involved in development, which will then be perturbed with environmental stimuli. Current work on determining the mRNA expression profile in every cell type of the root and within fine sections along the developmental axis will be greatly enhanced through the use of a new set of markers that are specific for both cell type and developmental stage and through use of next-generation sequencing to profile expression (RNA-Seq). In a second aim, the expression data will be combined and extended by chromatin immunoprecipitation followed by sequencing (ChIP-Seq) and by yeast one-hybrid analysis to infer regulatory networks controlling biological processes central to agriculture and bioenergy production. The third aim builds on ongoing work showing that perturbation by nutrient deprivation results in dramatically different responses in different cell types and at different developmental stages. The response at cellular resolution will be determined for other types of abiotic stresses including temperature and drought as well as biotic stresses such as bacteria. The data quality will be enhanced through time-course analysis of specific cell populations combined with RNA-Seq. To obtain parameters for the modeling of the dynamics of network responses to environmental stimuli, the RootArray platform will be used. This technology allows the simultaneous in vivo observation of expression responses of more than 60 seedlings. The fourth aim will take advantage of the remarkable resources developed over the last decade to investigate differential responses to environmental stimuli among natural Arabidopsis accessions. The aim will be to identify one or more key alleles that influence a quantitative trait such as response to an abiotic stress. This will help elucidate the topology and dynamics of the networks in which these alleles function. All of these efforts will be informed by and analyzed in concert with a theory and modeling team, which will lead the effort on network identification, modeling dynamical responses and image analysis.
Broader Impact: To achieve continued improvement in plant traits for food security and bioenergy production will require a sophisticated understanding of the networks that control plant growth and differentiation. This research will generate high-resolution datasets from which regulatory networks controlling biological processes central to real-world agricultural and bioenergy productivity can be identified and characterized. Another important part of the project will be to train the next generation of plant scientists in systems biology, which integrates quantitative and experimental approaches. Postdoctoral fellows, graduate and undergraduate students will be mentored in this research. The experience of all trainees will be enhanced by cross-training opportunities between computational and experimental biology within the Duke Center for Systems Biology as well as participation in outreach efforts such as helping to teach a course on Complex Genetic Traits at North Carolina Central University, a historically black university in Durham, NC and participating in summer undergraduate research programs for underrepresented minorities.
PI: Philip N. Benfey (Biology Department and Center for Systems Biology, Duke University)
Co-PI: Uwe Ohler (Institute for Genome Sciences and Policy, Duke University)
Last updated: May 9, 2011