Development of a Gene-Based Ecophysiology Model

PI: C. Eduardo Vallejos (University of Florida)

CoPIs: James W. Jones, Kenneth J. Boote, and Melanie J. Correll (University of Florida), Arthur Berg and Rongling Wu (Pennsylvania State University), and Juan Osorno (North Dakota State University)

Key Collaborators: William Farmerie and Fahong Yu (University of Florida), Steve Beebe and Idupulapati Rao [International Center for Tropical Agriculture (CIAT), Colombia], and James S. Beaver and Elvin Roman (University of Puerto Rico)

Two systems biology approaches have been implemented to solve the genotype to phenotype problem: the bottom-up approach, which uses molecular level knowledge to simulate biochemical pathways, cells, organs, and embryos, and top-down approaches embodied by ecophysiology-crop simulation models which use environment and physiological knowledge to predict the phenotype of specific cultivars. The objective of this project is to generate a gene-based ecophysiology-crop model capable of using the genetic makeup of a plant to predict the phenotype under a range of environments. This goal will be accomplished via genetic analysis of a recombinant inbred (RI) family of Phaseolus vulgaris. This family was generated from a cross between parents with contrasting physiological and morphological characteristic, as well as DNA sequences. The family comprises 200 lines, each containing a unique combination of the parental versions of the genes, which will be determined using the latest genotyping technology. The growth and developmental phenotypes of the RI lines will be measured under five different environments. The genetic makeup of these lines along with their phenotypes will be analyzed by functional mapping to identify, locate, and quantify the effect of genes that control the dynamic traits that shape the phenotype throughout growth and development. Finally, genotypic and phenotypic data will be used to develop computer programs that can effectively predict the dynamic daily behavior and final yield of crops under a range of environments.

This project is framed in the spirit of the NSF-funded iPlant Collaborative project which promotes the use of 'computational thinking' to address biological questions. The expected outcome of this project is a gene-based model which can have a wide range of applications in plant biology including genetics, functional genomics, ecology, and plant breeding. The modular nature of the model will make it possible to interconnect with bottom-up modeling approaches. In addition, this project will provide a platform for the education and training of students at the undergraduate, graduate and post-graduate levels through summer internships for undergraduate students and high school teachers as well as through two hands-on workshops that will deal with training in both crop modeling and genotyping technologies. All data generated in this project can be accessed through project websites (to be determined) and through long-term repositories that include GenBank and the Phaseolus Genomics Resource (http://phaseolus.genomics.purdue.edu/). Germplasm will be made available through the University of Florida, the University of Puerto Rico and CIAT. Software and statistical and analytical tools can be accessed through the project websites and eventually through the iPlant Collaborative (http://www.iplantcollaborative.org/).