Location: Plant Gene Expression Center
Project Number: 2030-21210-001-002-S
Project Type: Non-Assistance Cooperative Agreement
Start Date: Aug 1, 2021
End Date: Jul 31, 2026
Objective:
Determine the molecular mechanisms by which plants perceive and respond to developmental, biotic and abiotic signals throughout the life cycle to enhance the quality and production efficiency of agriculturally important crops.
Approach:
A combination of molecular, genetic, genomic, and bioinformatic approaches will be used to address this multifaceted problem. Particular emphasis will be placed on identifying signaling components, regulatory genes and transcriptional
networks involved in controlling plant responses to developmental, biotic and abiotic signals. Genes that respond to light under control of the phytochrome photosensory system will be identified and the regulatory mechanisms defined. Genes involved in regulating the circadian clock will be identified and functionally defined. Genes controlling vegetative and reproductive development will be identified and characterized. Plant hormone function in mediating growth and developmental responses will be explored. Genes involved in plant responses to biotic and abiotic challenges will be identified and characterized. The impact of the microbiome on plant genotypes and farming practices will be determined. On an ongoing basis, cutting-edge strategies and technologies such as next generation sequencing and computational modeling, will be assimilated and developed, networks involved in controlling plant responses to developmental, biotic and abiotic signals. Genes and hormone networks controlling vegetative and reproductive development will be identified and characterized. Genes that respond to environmental signals such as light and temperature will be identified and functionally defined, as will those mediating plant responses to biotic and abiotic challenges. The interaction of the microbiome on plant genotypes and farming practices will be determined. On an ongoing basis, productivity-enhancing strategies and technologies, such as next generating sequencing and computational modeling, will be assimilated and employed to enhance predictive agriculture.
