During this iGEM season we were fortunate to be able to form three fruitful partnerships with four teams in total. These partnerships added more value to our project than our other collaborations (see collaborations). All partnerships were formed in the spring and lasted until the wiki freeze.
Our partnership started as a result of a message we posted on the iGEM Global Slack workspace in the spring calling for teams working on the same topics or using the same techniques as us. Team TAU contacted us as their project, TrigGate, also centered around toehold switches. They were an ideal partnership for us since we would have to create multiple toehold switches to be able to detect various pathogens and they were aiming to create an optimized design software for them.
We met on Zoom shortly after initial contact to discuss our projects in more detail. In the meeting, we saw a clear benefit for both teams. Team TAU would create alternative designs for the toehold switches we would create and we would give them modeling and experimental data about their toehold switches. This excited us, as we knew we would all gain a deeper understanding on toehold switches and their design.
Team TAU’s project, TrigGate, focuses on creating a universal software tool for toehold switch design. Toehold switches are modules of the 5’ UTR of mRNA that have been engineered to allow translation only in the presence of their specific trigger. Toehold switches have been used successfully in prokaryotes, but their implementation in eukaryotes has been lacking and thus, there is no efficient design software. TrigGate aims to create a universal and user-friendly toehold switch design tool that can be used for both eukaryotes and prokaryotes. More information can be found on team TAU’s wiki page.
Toehold switches are de novodesigned riboregulators that can detect virtually any sequence depending on their design. As our project centered around toehold-based detection, we needed an optimized design algorithm to create novel switches. We created an algorithm ourselves, which we used to create the first iterations of our toehold switches we wanted to test in the lab. As team TAU’s TrigGate focused on creating this optimized algorithm, we decided we would want to partner up in the creation. As we generated our toehold switches with our own algorithm, team TAU also provided alternative designs for each of our toehold switches.
We assembled sensor plasmids for each of our selected toehold switches and TAU’s best ranking alternative designs, according to our model. We also confirmed the correct assembly of the sensor plasmids for each toehold with colony PCR. Our aim was to test the viability of each toehold and provide additional experimental data about their toehold switches’ performance. We analyze this data with them to screen for any possible improvements or unexpected results so we could further optimize the design algorithms. We would also get feedback on our model’s capability of predicting the performance of the toehold switches. However, as we faced difficulties measuring the performance of toehold switches in our cell-free expression system, we unfortunately were not able to provide the experimental data from the measurements.
During the beginning of our projects we created a design algorithm in NUPACK 184.108.40.206 utilizing the python module’s structure prediction capabilities to create novel toehold switches and team TAU were creating their first iteration of their design algorithm. After our first meeting, we provided them our preliminary designs and they created their alternative designs with their program. After we analyzed the results from both of our programs we saw some clear improvements we could make. We created our second iteration of our algorithm and created new designs, which we would use in our wet lab experiments. As we selected our best designs according to our model and took them to testing in the lab, team TAU were working hard on their design algorithm. When they had finished their algorithm’s second iteration, they created alternative designs for the toehold switches we had selected for our wet lab experiments.
As we analyzed the results together, we again saw clear improvements that could be made to both of the algorithms. We both had created toehold switches with two distinct design principles, the A- and B-series toehold switches from Pardee et al. (2016). From analyzing the designs with our model, we saw that it predicted TAU’s B-series toehold switches to be superior to ours, while our A-series toehold switches were predicted to perform better. This meant that we’d both need to optimize our programs more. Our program needed an extensive overhaul, to which we got help and ideas from team TAU. We modified our program to create more unique designs and we integrated our model into the design algorithm so we could more easily filter the best designs. Team TAU also patched their program based on the analyzing results and decided to integrate a model similar to ours into their design algorithm. In the end, we both had optimized our program extensively based on each others’ feedback.
During our partnership, both teams learnt more about the design and modeling of toehold switches. Although the performance of the design and modeling algorithms could not be verified experimentally, we can conclude with confidence that both our projects benefited from this partnership. Both of our design programs were optimized beyond what we would have been capable of on our own and the impact of this partnership can clearly be seen from the predicted performance of different toehold switches designed at different times during our projects. We were in contact with team TAU from the spring up to the wiki freeze, and hopefully beyond, via virtual meetings, emails and text messages. We gave each other constructive criticism and ideas to improve our projects as well as supported each other throughout the year.
Similarly to our partnership with team TAU, this partnership also began as a result of the same iGEM Global Slack message that we posted in the spring. Alongside TAU, the teams Patras from Greece and TecCEM from Mexico were among the first ones to react to our post and seeing that they were also working with an agricultural topic, we instantly saw them as interesting partnership candidates for us. We initiated scheduling a joint meeting with both teams to discuss our projects in more detail and the potential for forming a partnership. Doing this was, however, easier said than done due to all of our busy schedules and time differences, so the partnership ended up starting by continuing email correspondence before we could properly meet on Zoom.
In the first meeting we briefly presented our project topics and ideas followed by a discussion on how we could best join forces to take our projects to the next level. We realized that one of the most valuable and interesting prospects in our partnership would be the opportunity to learn about other methods to enhance agricultural production. Together we could provide farmers and others interested in the agricultural sector multiple options for increasing crop health. The partnership would also teach us to look at our problem from new angles and increase our awareness of different challenges in the field.
After a successful initiation meeting we kept in contact through the messaging platform WhatsApp, frequent emails and online meetings roughly every other week. During the more intense phases we met every week.
PAGGAIA, the project of team Patras, provides an innovative precision agriculture approach using genomics, artificial intelligence and aero-transportable equipment. The problem they are tackling with their project is the challenge of collecting data on soil quality for agricultural purposes. With their project they aim to optimize the use of fertilizers and seek increase in both quality and quantity of yields through the lens of machine learning. For more information see the wiki of team Patras.
On the other hand, the project of TecCEM centers around protecting crops against endocrine disrupting chemicals (EDCs). These chemicals have detrimental effects on ecosystems and even human health. The objective of their project is to contribute to quality regulations through the development of an electrochemical sensor for EDC quantification and to detoxify the EDCs with laccase enzymes. The team also addresses antibiotic resistance concerns with their choice of selection markers. For more information see the wiki of team TecCEM.
Making our projects accessible also for our product users, i.e. local farmers, was important to all of our teams. To do this we decided to create a short handbook as it would allow for more information to be shared than we could fit on flyers yet be easy to distribute and read. We divided the handbook into chapters including an introduction to the handbook itself, its purpose and about iGEM, followed by a brief description of synthetic biology and introducing our topics. We then discuss various plant diseases and sustainable agriculture, without forgetting a glossary at the end containing simple explanations for more complex words. The contents and layout of the handbook were all done as a collaborative effort from all teams. These were discussed over various meetings throughout the summer.
The core of the handbook is also summarized on its last page: “This handbook is made for you who are working with agriculture, and are interested in synthetic biology. Here we introduce you to the basic principles of synthetic biology, and how it can be utilized in agriculture. We want to spread the knowledge about synthetic biology and its applications all over the world.”
As part of our project this year we created a library of toehold switches for different plant pathogens to showcase system modularity. Since one of the main motivations for generating it was to demonstrate the global impact our project can have, we asked the other teams to help us. Patras did some research on their local crops and diseases and provided us with a list of their findings. From this list we decided to include the pathogens potato virus Y and tobacco mosaic virus. The introductory texts of these viruses were written in collaboration with Patras, and can be found on our design page. We then went on to design toehold switches for these pathogens using our optimized design algorithm, which was generated in our TAU partnership. The resulting toehold designs can be accessed in our part collection.
To further improve our integrated human practices work and our sustainable development impact, TecCEM organized a meeting with three Sustainable Development Goals experts on 26.9.2022 for our teams. This meeting gave us many new ideas and changed the way we perceived the goals. It, thus, led to a revised outlook on the SDGs and inspired us to think further about ways to make our project sustainable. The meeting also helped us to better formulate our Sustainable Development Goals page.
For more information please visit our sustainable development goals and integrated human practices pages.
We helped Patras by providing them with conserved regions of a few viruses they can use in their soil analysis. We sent them conserved sequences of tomato brown rugose virus, cucumber green mottle virus, wheat dwarf virus, tomato chlorosis virus and potato virus Y. By sending conserved sequences of their plant pathogens we contributed to their project by improving their soil analysis and making it more accurate.
We influenced the project design of TecCEM by providing information about different laccase enzymes. TecCEM needed to learn more about laccases and laccase functions for their EDC degradation filter and choose a suitable laccase to do the job. As our team had competed with a laccase project last year and had team members that continued to the ABOA 2022 team we were able to help them with this task. Our input turned out to be crucial for their choice of laccase.
This partnership with Patras and TecCEM lasted from the spring all the way to wiki freeze with regular meetings and other forms of communication. Our project topics were all related to agriculture and improving the life of farmers from the plant disease, soil quality or water quality perspectives, which enabled collaborations primarily within integrated human practices. Together we created a handbook for farmers detailing synthetic biology approaches to their problems. The partnership also influenced our dry lab work as our library of toehold switches now includes some common pathogens found in Greece, as an example of the modularity of CropFold and its easy application in different parts of the world. We also met to discuss the sustainability of our projects with SDG experts and helped TecCEM to choose the best laccase for their EDC degrading filter. Summarized, this partnership improved our project on many levels and ended up becoming an integral part of our iGEM journey this season.
This partnership was also formed as a result of our message on Slack. They contacted us, as both our projects focused on creating diagnostic kits utilizing RNA-based sensing methods. We scheduled a meeting shortly after and we both presented our projects in more detail. After discussing our projects, we realized that although our projects differ on the surface, we can still form a fruitful partnership, as the projects share some properties. We discussed our plans further and realized that we both are planning on using the NUPACK module to design novel RNA sequences that fold into predetermined secondary structures. As we both had some struggles using the program, we decided that we’d help each other figuring out the functionalities, as neither of our teams had little experience using these kinds of programs. We also realized that although NUPACK and other similar programs can be immensely useful in synthetic biology, many other teams might also have struggles using them. Thus, we decided to create a guide for NUPACK, so more teams with little experience could feel more comfortable dipping their toes into computational design and modeling.
Team IISER-Tirupati’s AptaSteles focuses on creating a novel detection kit for Polycystic Ovarian Syndrome (PCOS). The prevalence of PCOS has risen dramatically in the past few years due to lifestyle shifts. The lack of accurate diagnosis methods have led to people being susceptible to chronic metabolic and cardiovascular ailments. Aptasteles aims to combine the detection of multiple biomarkers in one microfluid kit using an innovative approach to facilitate accurate diagnosis. More information can be found in the wiki of team IISER-Tirupati.
Our project, CropFold, utilizes toehold switches to detect the target sequence in a pathogen’s genome to produce a visible signal. APTASTELES however, combines both toehold switch like structures and light-up aptamers to detect biomarkers and produce fluorescent signals. Toehold switches and aptamers are RNA sequences that rely on their structure for their function. They need to be carefully designed so that they actually fold into these predetermined structures to express their desired functionality. As neither of our teams had any expertise in the field of RNA designing, we wanted to tackle the challenges posed by these requirements together. We both used NUPACK to design these RNA sequences and so we worked together closely to take advantage of the program’s functionalities.
NUPACK has a powerful web app, but the full benefits of the program can be utilized in the python module. Because the installation of this module can be confusing for novices, we worked together on this. When we had our first meeting, our team had finally managed to install the module and so we helped team IISER-Tirupati through some problems in the installation phase. After successful installation, we began discovering the properties of the program and we had regular chats and meetings when writing our programs and helped each other through many issues.
As we had some trouble utilizing the full potential of the NUPACK python module, we realized that other teams in the future might face similar issues. Computational calculations and simulations are of growing importance in synthetic biology and life sciences in general, yet they can be quite intimidating for beginners. To lower the barrier of entry for novices, we wanted to create a guide (see Contribution page) for using the NUPACK python module and web app. Although NUPACK provides a user guide, we wanted to provide a simpler version in which we cover all the necessary information for starting and even provide example programs. The guide covers the basics of analyzing and designing nucleic acid sequences with NUPACK and compiles different programs created by us and other life science researchers, so anyone could find inspiration for designing a program for their specific needs or even find one ready-made for them.
Although this guide was meant to help future teams in their endeavors, we also learnt a lot when compiling the guide. As we dove deeper into the user guide for NUPACK, we discovered new features that we did not think of implementing into our programs and therefore managed to improve their functionalities. Writing this guide together helped our projects and hopefully it will make the first steps easier for future iGEM teams.
While working with a lot of different types of aptamers, Team IISER Tirupati realized that there are few aptamers that are not well characterized in the iGEM Registry and there is a scope to improve these parts using NUPACK, along with other computational tools like MD simulations. They were able to produce in silico results. However, they were struggling to order and test these parts. So they discussed this with us and we realized that we could help them as our team has some experience with in-vitro transcription and handling RNA samples. Additionally, we realized that these light up aptamers could also be implemented in our project to potentially simplify the method of readout. So we proposed to help them in characterizing these parts. We discussed and prepared in detail the protocols and reagents required. This helped us understand the potential of aptamers in detail.
Unfortunately, due to limitations of time and few reagents, we couldn’t perform the experiments. However, the brainstorming and troubleshooting we did during these meetings was extremely fruitful for both teams.
Through our partnership during the whole year, we were able to learn new skills in computational design phases of our projects. We also helped each other to create the design programs, which play a key role in both of our projects. As we formed the partnership in the design phases in the spring, we were able to accelerate the creation of these programs and therefore progress into the later phases of the projects much faster. We stayed in contact through the year up until wiki freeze through regular video meetings and a WhatsApp group.
Please see our collaborations page for information about all of our smaller collaborations with other iGEM teams.