Arabidopsis 2010: Constructing and Analyzing a Model Gene Regulatory Network

Regents Of The University Of Michigan - Ann Arbor, Ann Arbor MI

Investigators

Abstract

Biological processes are controlled by sets of complex and interrelated regulatory events that determine when and where individual genes are able to act. A current challenge in biology is to organize genes into networks that accurately depict their regulatory relationships. In this project, a systems-based approach is being used to construct and analyze a relatively simple gene network that controls root epidermal cell differentiation in Arabidopsis. A cell-specific transcript profiling method has been used to define a set of root epidermis genes, and these are being assembled into a probabilistic network by systematically perturbing specific genes (nodes) and defining independence relationships from gene expression data using Bayesian network analysis. This network will be refined and tested by resolving uncertain parts of the network and by probing specific relationships using molecular, genetic, and biochemical experiments. The project is expected to provide new computational and experimental tools for gene network construction in Arabidopsis and to uncover fundamental features of plant gene network structure and circuitry. These data and biological resources will be accessible from the Arabidopsis Biological Resource Center, The Arabidopsis Information Resource (TAIR), and the Gene Expression Omnibus. Broader Impacts: The research will provide an unusually rich interdisciplinary training experience for undergraduates, graduate students, and postdoctoral fellows, and it will contribute to a summer research program for high school students. The project research will be incorporated into a case-study based biotechnology course for undergraduate and graduate students. In the long term, this project is expected to improve our understanding of plant gene regulatory mechanisms and enable the rational design of biological and synthetic networks for plant improvement.