Internationally Genetically Engineered Machine – Toronto (iGEM Toronto)

Laboratory Research and Innovation - The funding supported the purchase of critical reagents and supplies for plasmid design and testing, including high-fidelity polymerases, restriction enzymes, competent E. coli, purification kits, antibiotic-selective media, and protein expression tools. These materials enabled cloning and expression experiments of antimicrobial peptide (AMP) sequences and resistance-modifying enzymes, selected and optimized using our AI model. Our wet-lab experiments ran from May through September 2024 in research labs at U of T, offering hands-on training to 20+ students, most of whom were conducting research for the first time.

Education, Outreach, and Capacity Building - CPSIF funding enabled us to organize some internal skill-building workshops open to the broader U of T community on topics such as synthetic biology fundamentals, generative AI, and molecular cloning. Participant feedback praised these events as “eye-opening” and “a rare opportunity to engage in multidisciplinary STEM topics outside the classroom.” We organized a cloning workshop in May 2024 that was open to all UofT students where they learned foundation wetlab techniques that assist in molecular cloning in the IBBME lab. We held speaker series events in collaboration with CUBE and UofT Neurotech where students could learn about the impact of artificial intelligence in the healthcare industry. In February 2025, we organized BMEC, which was a 2 day hackathon in collaboration with the same aforementioned groups. Finally the Grand Jamboree in October 2024 was an opportunity for all UofT students to attend in Paris and hear about the latest developments in synthetic biology worldwide.

Global Recognition at the iGEM Grand Jamboree - Crucially, CPSIF funds helped cover travel and registration costs for our team to present Plasmid.AI at the iGEM Grand Jamboree, held in Paris from October 23–27, 2024. Our project combined wet-lab validated plasmid designs was capable of predicting gene expression burden and plasmid stability. Our work was recognized with a Gold Medal, and we won two special prizes: Best Model for our machine learning platform and Best Entrepreneurship for our commercialization strategy involving partnerships with biotech accelerators.