Research Initiation Award: A Deeper Look at Transmembrane Permeability in Plants and its Implications to Food Security

The Historically Black Colleges and Universities Undergraduate Program (HBCU-UP) through Research Initiation Awards provide support for junior and mid-career faculty at Historically Black Colleges and Universities who are building new research programs or redirecting and rebuilding existing research programs. It is expected that the award helps to further the faculty member's research capability and effectiveness and improve research and teaching at the home institution. This award to Savannah State University supports faculty and undergraduate research experiences to develop tools that will predict a plant’s ability to transport contaminants from the soil and subsequent translocation throughout the plant. Specifically, utilizing computer simulations and biological validation techniques, the project may reveal mechanistic insights in plant biology. Furthermore, uptake of environmental contaminants by plant roots and their subsequent translocation to the edible parts may have serious implications to the food security. Plants provide a fundamental fraction of the human food and are the primary source of nutrition for livestock. This research project advances the fundamental knowledge of plant uptake and translocation of emerging contaminants by considering the role of physicochemical properties and fundamental plant materials. The project offers new approaches for assessing root-water partitioning and transmembrane permeability at molecular and large scales. At the molecular level, the interactions of contaminants with plant roots will be examined using molecular dynamics simulation. At the large level, artificial intelligence (AI) and machine learning (ML) techniques will be used to determine the uptake variability in different plant species based on transmembrane permeability. Neural networks, fuzzy logic, and clustering algorithms are some of the AI/ML techniques that will be employed to perform the simulations. The plant species with potential for phytoremediation and plant species with higher possibility of contamination in their edible tissues will be determined using these analyses. A tiny machine learning (TinyML) system will be developed to detect emerging contaminants in plants through image processing. This system uses a computer vision model to find plants impacted by emerging contaminants. This project has the potential to provide an easy-to-use tool to analyze crop contamination and plant uptake of the emerging contaminants. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.