IN THE NEXT DECADE, HUNDREDS OF SPECIES OF PLANTS WILL NEED TO BE BRED OR GENETICALLY MODIFIED TO WITHSTAND THE EFFECTS OF CLIMATE CHANGE. UNFORTUNATELY, MANY SPECIES DO NOT HAVE THE FINANCIAL RESOURCES TO PERFORM THE RESEARCH EFFORTS NEEDED TO PERFORM THIS SCALE OF BREEDING AND GENETIC ENGINEERING. IN THESE SPECIES, RESEARCHERS OFTEN LOOK TOWARD MATHEMATICAL MODELS THAT PREDICT THE EFFECTS OF MODIFYING GENETICS. CURRENT MODELS OFTEN REQUIRE LARGE AMOUNTS OF EXPENSIVE TRAINING DATA, EXTENDED COMPUTATIONAL TIME, AND RARELY USE INFORMATION FROM OTHER SPECIES TO INFORM PREDICTIONS. THIS PROJECT AIMS TO IMPROVE CURRENT MODELS BY LEVERAGING A MODERN, BIOLOGICALLY-INFORMED PREDICTION FRAMEWORK. THE PREDICTION FRAMEWORK, CALLED DEEP REASONING, HAS THE ABILITY TO USE DATA FROM MULTIPLE MORE-RESOURCED SPECIES IN A WAY THAT ENCODES GENERAL BIOLOGICAL RULES INTO THE MODEL'S PREDICTIONS. IN ADDITION TO LEVERAGING THIS MODEL INTO QUESTIONS ABOUT THE MODIFICATION OF GENETICS, THIS PROJECT WILL PROVIDE A COMPUTATIONAL WEB-TOOL THAT WILL ALLOW FOR BOTH PLANT BREEDERS AND THE GREATER BIOLOGICAL COMMUNITY TO EXPLORE THE REGULATION OF GENETICS IN CONTEXTS SUCH AS CLIMATE CHANGE SCENARIOS. FURTHERMORE, THIS MODEL AND THE RESULTING TRAIT PREDICTION TOOL, CAN PROVIDE A LOW-COST, QUICK WAY FOR GENETIC ENGINEERS TO UNDERSTAND WHAT TYPES OF MODIFICATIONS A SPECIES MAY NEED IN ORDER TO FLOURISH IN A VARIETY OF CONDITIONS.