Dec. 2, 2019
High-performing undergrads turn green into gold at international competition
Chlorophyll solution for canola farmers earns accolade for UCalgary's iGEM 2019 team
It’s not easy being green — particularly when your livelihood is canola, and frosty fall weather is a regular headache that results in seed-spoiling chlorophyll.
That costly problem for one of Canada’s major crops has blossomed into an international award for a team of University of Calgary students committed to eradicating the curse of chlorophyll through a multi-pronged, multi-faculty strategy.
“Green seed causes millions of dollars in losses for the Canadian canola industry, and we’re working to change that,” explains Juan Sebastian Alvarez, chemical engineering student and one of the team’s leads.
From green to gold — gold medals, that is
Fighting chlorophyll was a golden decision, and UCalgary’s iGEM 2019 team left November’s International Genetically Engineered Machine (iGEM) Foundation’s Giant Jamboree in Boston with their best finish ever, as first runner-up in the undergraduate category.
The accolade places Calgary near the top of 353 teams from around the globe, including MIT, Cornell, Stanford, Princeton and Brown, plus Canadian universities like Concordia and Queen's.
The second-place finish means iGEM Calgary is the best-performing Canadian team in the history of the competition.
“It was a great moment. A year of innovation, discovery and hard work paid off and I couldn’t have been more proud of these talented students,” says Dr. Mayi Arcellana-Panlilio, PhD, who was lead supervisor of the team.
The team was nominated for a total of 10 awards, winning Best Food and Nutrition Project, Best Software Tool, and Best Integrated Human Practices.
Crushing chlorophyll, completely
UCalgary’s gold-medal project, entitled yOIL: An All Encompassing Solution to the Green Seed Problem, tackles all aspects of the chlorophyll issue, from automated seed grading to a method of removing the chlorophyll for reuse, and finally, an algorithm capable of predicting weather 180 days into the future.
“We wanted to tackle the entire issue, from sorting the seeds, to actually using the chlorophyll removed from the oil. We discovered that the captured chlorophyll can be repurposed into an antifungal agent, which can introduce new value back into the market,” says Alvarez.
The team sought advice from the Alberta Canola Council, Canola Council of Canada, and Alberta Canola Producers, who put the students in contact with farmers, agronomists, and crop pathologists. They were also in contact with a range of professors from the University of Calgary to improve their experiments.
Teamwork pays off
With 14 team members and four supervisors, the UCalgary iGEM team includes undergrad students from Cumming School of Medicine, Faculty of Science and Schulich School of Engineering.
Tasked with a mission of using synthetic biology to solve a global problem, Team Calgary decided to battle an issue estimated to cost Canadian farmers an average of $150 million a year, due to oil that is too green and prone to spoilage.
Frost is the key culprit, and canola exposed to an early chill will stop maturing, halting the natural removal of chlorophyll that would normally dissipate as the plant ages — and that means less profit for the farmer.
The award-winning iGEM solution included:
- Using chlorophyll-binding proteins to extract the chlorophyll from green seed canola oil using a water-in-oil emulsion approach. This efficient solution is intended to replace the costly and oil-wasting current method of chlorophyll extraction using clay filters.
- Repurposing chlorophyll, which is historically considered waste, into a fungus-fighting molecule called Pheophorbide A. This molecule has been previously used in anti-fungal and cancer treatments. The team has used it to fight sclerotinia, a fungal infection that degrades canola plants.
- For seed sorting, the team created the Mean Green Machine, a boxy contraption that automates and standardizes the grading of canola seeds within Canada using artificial lighting and computer algorithms, rather than the current method of eyeballing the seeds with colour cards.
- Finally, to help farmers know when a frost is coming, the team created Sunny Days, a weather predictive algorithm capable of predicting the weather 180 days into the future with a mean absolute error of 2.0 degrees.
Mayi Arcellana-Panlilio is a senior instructor in the Department of Biochemistry and Molecular Biology and a member of the Arnie Charbonneau Cancer Institute at the Cumming School of Medicine.