New Supercomputing Tools to Understand Plant Adaptation
Understanding how plants evolve and adapt to changing environmental conditions is important to managing various agricultural and environmental problems, including assisted migrations of native plant populations and breeding crops that will thrive in a changing climate. It is difficult, however, to discover how genetics interact with the environment to affect how well a plant will thrive.
Dr. Bryan Runck (GEMS Informatics Center https://gems.umn.edu/ ; Department of Geography, Environment and Society), Dr. Tom Kono (Minnesota Supercomputing Institute, Bioinformatics group), and Dr. Amber Nashoba (University of Alaska Anchorage) are heading a project called “Refactoring Aster for Robust Variance Estimation,” which will address this problem. The project includes two parts:
The researchers will advance an established method from quantitative genetics (Aster models) by building new supercomputing tools that can help understand the evolutionary fitness of plants in changing environments.
The researchers will develop curriculum and software documentation to support plant biologists in the use of these new tools.
This work will establish a foundation for future work on understanding climate change impacts on economically and culturally important plant species.
This project recently received a UMII Seed Grant. UMII Seed Grant funds are intended to promote, catalyze, accelerate and advance UMN-based informatics research in areas related to the MnDRIVE initiative, so that U of M faculty and staff are well prepared to compete for longer term external funding opportunities. This Seed Grant falls under the Securing the Global Food Supply research area of the MnDRIVE initiative.
Image description: (A) Illustrates a graphical model used for aster analysis. Adapted from Goldsmith and Nashoba 2021. (B) Illustrates how the aster model is run in parallel on Minnesota Supercomputing Institute resources to estimate fitness. (C) Describes specific project outcomes.