Michigan State University receives grant to help predict plant phenotype
Michigan State University molecular plant scientist Dan Chitwood has received a $640,000 grant from the National Science Foundation to learn how to predict a plant’s phenotype.
The grant is part of a larger $1.5 million collaborative award from the National Science Foundation, according to a news release.
The research funded aims to learn how to predict a plant’s phenotype — its observable physical traits — from its genetic makeup or molecular profile, the release said.
While current sequencing technologies allow the extraction of nearly all information from the genome, Chitwood said in the release said that measuring what an organism is or what it eventually becomes has not advanced as far as genomic sequencing.
Scientists have not yet been able to measure the totality of information embedded in an organism’s physical form, like they have in genomes, according to the release.
“If we could extract all the information that is contained within organisms, we would be able to create a model predicting what the organism is from its genomic information — which remains a 'Grand Challenge' in biology,” he said in the release.
To tackle this challenge, Chitwood said the research team will use a mathematical approach not yet fully explored in biology called Topological Data Analysis (TDA). This form of analysis, typically deployed within mathematics, recognizes that all shapes are data, and all data have shapes, the release said.
The project will be performed collaboratively among several universities. Researchers from MSU also include Elizabeth Munch, a CMSE associate professor, and Robert VanBuren, an assistant professor in the department of horticulture. Emily Josephs, an assistant professor in the department of plant biology, is also collaborating on the project, the release said.
Researchers outside of MSU are Aman Husbands, an assistant professor at the University of Pennsylvania, Arjun Krishnan, an associate professor at the University of Colorado, and Alejandra Rougon-Cardoso, a professor at the Universidad Nacional Autónoma de México, according to the release.
The purpose behind analyzing and coding the data is to develop a model for predicting what an organism could be based on its complete set of genes. The novel model within this project will be used to predict the shape of a leaf, according to the release.
“It’s called a ‘Grand Challenge’ amongst the scientific community because nobody knows how to do that,” Chitwood said in the release.
Currently, the only way breeders can know exactly what a plant will be or how it’ll respond to its environment based on its gene combination is to plant it. Chitwood said n the release that a model like this would accelerate the pace of breeding by eliminating the need to plant every time there’s a question about what an organism’s genes will give rise to.
“If you could predict what an organism would be given any gene expression profile, you would bypass that whole process [of having to test gene combinations by planting,]” Chitwood said in the release.
