The increasing demand for food, feed and biofuel makes it very important to increase crop production, especially under adverse climate conditions. Plant steroid hormones, Brassinosteroids (BRs), have the potential to promote crop yield and improve plant performance against bacterial and fungal diseases and stresses such as drought and high temperature. Understanding BR functions in plants is essential to fully harvest its beneficial effects. Although BR signaling pathway has been well established, how BR regulates thousands of genes for various hormonal responses is not well understood. In this project, the investigators will use genetic, genomic, and computational approaches to establish gene regulatory networks through which BRs control the expression of the large number of genes for plant growth and stress tolerance. While the genetic and genomic studies will define the gene functions, the computational approach is required to build models for how hundreds to thousands of genes coordinate to carry out specific functions. The combination of functional genomic studies and computational modeling in establishing and validating transcriptional networks in hormone signaling is novel and is anticipated to provide unprecedented new insights into BR signaling network. The knowledge can be applied to agriculture to increase crop production. Moreover, the computational and bioinformatic tools developed and validated in the context of this project will be applicable to other systems biology studies. The research will also provide outstanding training opportunities at the interface of functional genomics and computational modeling for graduate and undergraduate students. High school science teachers will be recruited to participate in the research and to integrate such research into high school science education. The BR mutants will be introduced to an undergraduate laboratory course, in which a large number of undergraduate students each year will design and perform experiments to learn how plant steroid hormones control plant growth.
