#research #biotech #machinelearning
Graphic by Natalie Chan
We are a student-led undergraduate synthetic biology research group based in the University of Toronto. Every year we design and execute a research project and compete with teams from across the world at the International Genetically Engineered Machine (iGEM) exhibition. iGEM is an international synthetic biology competition open to high school, undergraduate, and graduate students. Each year’s competition begins in January and culminates with the year’s Grand Jamboree expo in October of the same year.
Our current project is focused on the design of a cell-free detection system to detect oak wilt, a fungal disease caused by Bretziella fagacearum.
“For the past two years we’ve been designing enzymes for plastic degradation using machine learning. We envision a circular plastics economy where the cost of recycled plastics is lower than producing virgin plastic. Further, we hope to increase the value of plastic waste and transform our trash into a critical sustainable resource.”
Over 100 talented students from the University of Toronto have already contributed to making our goal possible. In the process, we foster interdisciplinary collaboration and a goal-driven research mindset in our members. With guidance of faculty and PhD students from the University of Toronto, MIT, and Stanford, our three Subteams are dedicated to making this vision a reality. You can support our research by joining us or sponsoring us!
Initial proof of concept
Validation with molecular simulations
Scalable bio-recycling solution
Protein graphics by Natalie Chan
We proudly announce that
Our 2019–2020 project won gold 🏅 and was nominated as the best project in the undergraduate manufacturing track. In 2021, we continued the pursuit towards a highly stable while active enzyme for plastic waste recycling.
This year, we are designing a cell-free detection system to detect oak wilt, a fungal disease caused by Bretziella fagacearum. We will disseminate our results at the Giant Jamboree in Paris and as a preprint for submission to NeurIPS workshops.