IISER Pune x UBC iGEM :D
After revealing our idea on the iGEM Global Slack and Instagram, UBC iGEM from Vancouver reached out to us; we noticed that our ideas were pretty similar. Both teams were working on tackling abiotic plant stress by introducing the acdS gene into our respective chassis organisms and breaking down ACC before it gets converted to ethylene.
The collaboration very soon turned into a rewarding partnership in different aspects such as Modelling, Human Practices, and Wet Lab. We worked together on these aspects for nearly half of the iGEM cycle.
The UBC team was working on introducing the acdS gene (along with a few others!) into Triticum aestivum protoplasts to develop heat-resistant wheat crops. Our team was working on introducing the acdS gene into Azospirillum brasilense in order to alleviate waterlogging stress in plants. Broadly both teams were working on tackling climate-induced plant problems using the same stress signalling pathway.
Human Practices
In June, our human practices teams met theirs and we discussed more in depth about what we were individually planning as teams, and what we could do together.
Both our projects revolved around food security and crop yield, something which is challenging for countries that have fewer financial and industrial resources. However, due to the expenses related to creating and purchasing synthetic biology innovations like the ones we are creating. the people who need our projects the most would be the ones with the least access to it. Therefore, we decided to work on a webinar together revolving around the accessibility of synthetic biology in different parts of the world; synthetic biology is a possible solution to the dire situation of food insecurity and making people aware about what they could do in regards to this was important to us.
Over the course of the next month and a half—up until right before the webinar which was held in August—we had multiple meetings and discussions to plan this webinar. We decided on speakers, topics, questions, and finalised the logistics of the webinar.
The panel discussion based webinar had 4 speakers from all around their world giving their thoughts and views. Our panellists had varying experiences coming from developing countries—India, Peru, Uganda—and developed countries—Canada.
To read more about this, check out our Human Practices and Communication page!
Modelling
In July, our modelling teams had their first in-depth discussion, to brainstorm areas where we could help each other out.
Both teams were using kinetic modelling as a major aspect of their computational modelling and met regularly to give updates. Our team was also working on looking at soil elevation, water table depths, proximity to water-bodies among a few more parameters to find the places in the country where water logging is likely to happen. We were using remote sensing satellite data and running it through our algorithm to predict the same.
The UBC iGEM team was interested in our climate model as their project focussed on heat stress and could be enriched with a similar heatwave hotspot map. As our team had already started working on the map for waterlogging, we offered to also make a heatwave map. We were able to model a few parameters from satellite data (which can give information about parameters such as precipitation and temperature). Though we weren’t able to model all the parameters, we did manage to model a heatwave map which can predict when the next heatwave will occur.
We have achieved this by creating a Markov Chain model which gives us the return period of the heatwave; in simpler terms, it gives the number of days it takes for the heatwave to reoccur.
A Markov chain is a mathematical system that experiences transitions from one state to another according to certain probabilistic rules. The defining characteristic of a Markov chain is that no matter how the process arrived at its present state, the possible future states are fixed. In other words, the probability of transitioning to any particular state is dependent solely on the current state and time elapsed.
To construct the model, we take daily data of maximum temperature from a weather forecast station. Here we have used data from KELOWNA A, Canada weather station for the period of 1968-1996. Then we calculated a heat index for each month according to the above mentioned data, and the maximum temperatures which were more than the defined index were determined as the heat wave. Then the Markov Chain model was used to calculate the transition probabilities and reliability probabilities.
The return period of n-day heat waves was then calculated by the following formula:
In the above formula, the variables used are:
- RH is the return period of n-day heat wave
- P10 is the probability of the particular day having a heat wave given that the previous day did not have a heat wave
- P01 is the probability of the particular day not having a heat wave given that the previous day had a heat wave
Wet Lab
The UBC iGEM team was working on modifying wheat protoplasts and we were working on modifying a soil bacterium but what tied us together was that we both used ethylene as an output.
As we didn’t have the resources or time to perform ethylene assays, the UBC iGEM tried to help out with these assays. We needed access to plants and had neither the time nor the resources to perform experiments with plants at this stage. As the UBC iGEM team was working on wheat protoplasts, we were hoping they would be able to perform the preliminary assays on our behalf as these would benefit both teams. Even though they couldn’t perform the assays for us, they have given us a detailed experimental plan.
Check out our Model page for a more detailed description of the ethylene assay!
