iGEM Groningen 2021: Bye-Monia
In 2021, I was a part of the iGEM team of Groningen. iGEM is a global competition focusing on finding solutions to local societal problems using synthetic microbiology. For our iGEM project, our team decided to contribute to mitigating the Dutch nitrogen crisis. This crisis is a result of the Netherlands emitting too much nitrogen containing compounds into the air. Once in the air, these compounds are deposited into nature reserves where they cause eutrophication and acidification, resulting in a loss of biodiversity in these areas. Our project was eventually nominated for 5 awards, including best model and best project presentation. We reached top 10 within the overgraduate category and, as icing on top of the cake, we won the best environmental project in the overgraduate category.
One of the main emitters of nitrogen containing compounds is the Dutch agricultural industry, with the cattle industry in particular emitting almost half of the total ammonia emissions within the Netherlands. We decided to modify a Saccharomyces spp. such that it is able to excrete alpha-amylase. This enzyme hydrolyses glycosidic bonds in complex sugars. We reasoned that by adding this enzyme to the feed of cattle, the animal would be better able to digest polysaccharides, increasing the digestive efficiency and cause it to excrete less ammonia.
In order to maximize the activity of the alpha-amylase excreted by our alpha-amylase producing Saccharomyces spp. (from now on also referred to as our ‘device’), we looked at the effect of 10 different promoters, 4 secretion signals, 4 alpha-amylase genes and 4 chassis, creating a combinatorial library with 640 different combinations of parts. We set out to test 64 of these combinations and use machine learning (ML) in order to make predictions on the untested combinations. The tool used for this is called the automated recommendation tool (ART). The predictions made by this tool were both exploratory, giving an indication on which combinations should be tested in order to reduce the uncertainty within the ML model, and exploitative, giving an indication on which combinations are expected to yield the highest activity based on the training data. Designing and analysing the ML approach to our project was one of my main contributions to the project.
In the end, we managed to produce usable activity measures for 47 devices. After training the ART, it became apparent that using one specific secretion signal and one alpha-amylase gene would most likely result in the highest activity. Furthermore, one promoter also appeared to be outcompeting the others. No clear preference for chassis was observed, and thus we decided to use the chassis which showed the highest growth rate. Unfortunately, we did not have enough time to test the final device and verify the predictions made by the model.
Besides wet and dry lab work, our team also put substantial efforts in outreach, both in the form of human practice (HP) and in the form of education. With our human practice work, reached out to stakeholders and used their advice in order to make adjustments to our product such that their needs are taken into account. My role in this part of the project was supporting Milou, as she was our HP manager. This support consisted of brainstorming with her, filling in various HP tools, reaching out to stakeholders and conducting stakeholder interviews.
For the education part of our project, our education team created several comics aimed at teaching elementary school children (aged 6 to 12) basic concepts within synthetic biology, along with a lesson plan on how to use these comics in a classroom setting. Additionally, several webpages were created where the actual science behind each of the comics is explained. These webpages targeted an older audience, mainly focussing on the parents of the elementary school children. Besides translating some materials and brainstorming on how to set up the webpages, my contribution to this part of the project was limited.
My final contribution to the project was being in charge of the finances. I made the budget, kept track of our income and expenses, directed sponsoring requests to Chienes from the GBB, and explained to the other team members how they can declare the expenses they made for the project.