Model
Our dynamic mathematical models
and computer simulations.
Partnerships
Throughout the course of our project we collaborated extensively with four teams: iGEM UCSC, iGEM EmpireGene, iGEM Ashesi-Ghana, and iGEM Bilkent-UNAM. We believe that each one of these teams was instrumental in the design and execution of our project, offering invaluable insight into the various different aspects of our research. By maintaining a consistent dialogue, meeting regularly, and offering iterative feedback through the competition cycle, they became crucial for the success of our team, and we aimed to give them the same support. In total, these teams helped shape the design and execution of our project significantly, and we similarly aided them, resulting in long-lasting and meaningful partnerships which we felt extended beyond a simple collaboration.
iGEM Ashesi-Ghana
We were intrigued by Ashesi-Ghana’s project which included developing a biosensor to improve gold-mining prospects in Africa. We collaborated extensively in the dry lab portion of our respective projects, and they helped us explore the idea of creating a biosensor for protein S detection.
How We Helped Them
Through multiple meetings and correspondence over email, we helped them develop a mathematical model for their biosensor. First, we asked them a series of questions over email about the specifics of their project. The questions were:
- What is your project (abstract) → include your methods, mechanisms.
- Are you using protein, chemical, fluorescence?
- If it is enzyme based, what is the enzyme? (catalyzed reaction or tag)
- How is your biosensor going to be used?
- What is the hydrogel for? → what polymers is it made of and what are the ratios?
- Literature review→ key papers you used to base your project on.
- What chemistry are you using?
- What is the end plan, hydrogel injection in soil?
In response, the Ghana team thoroughly answered each of our questions in the following document, which can be read at the link below.
Based on these answers, we were able to design a potential model for their project.
We shared our insights with the Ashesi-Ghana team, and held another meeting with them to discuss in greater detail. We recommended that they model their transcription circuits for gold, arsenic, and iron, as this seemed to be the most feasible. We decided to model one out of three of their ODE systems, which they could then replicate for two of their other constructs. We taught them the entire process: how to write an ODE, use MATLAB, and analyze their results. The guide that we made for them, as well as our model of one of their genetic circuits, can be seen below. This guide includes the derivation of the ODE’s, the MATLAB code, the theory behind the model, and the 3D graph that the model outputs.
How They Helped Us
To advance our project, the Ashesi-Ghana team further helped us explore the possibility of developing a biosensor for the detection of protein S in the bloodstream in order to reduce costs of tests and streamline the diagnosis process, which is currently very complex and inaccurate. They created a detailed design for an aptazyme-based biosensor that uses blood samples to diagnose protein S deficiency. We discussed this biosensor in detail with them, and they created an infographic with more information about the product. In the future, we plan on building upon their design to create a more efficient and accurate diagnostic test for protein S deficiency. You can see the infographic they created for the biosensor at the link below.
They also contributed to our scientific journal, writing an article titled, An Affordable & Fast Approach to Gold Prospecting in Ghana, and helped with distribution of the journal. You can read their article, as well as the entire journal, at the links below.
They also expressed how much they love our TikToks <3 :)
iGEM UCSC
We were contacted by the iGEM UCSC team in the early summer. Over the course of our project cycle, our two teams have collaborated in many aspects of our respective projects including in areas such as dry lab and wet lab. In particular, we extensively shared and compared our protocols.
How We Helped Them
Firstly, we provided the UCSC team with our mathematical modeling guide in order to help them get started with modeling protein production. In the partnership, we shared and explained our code so that they were able to adapt the model to their own project, and use it to predict and understand the behavior of their strains. We taught them about ODE’s, how to use online servers such as BioNumbers to find parameters and rates, and also helped them decide which equations and modeling systems they wanted to use to expand their modeling. In particular, after looking through the specifics of their project, we recommended looking into Monte Carlo simulations and molecular dynamics. We also gave them advice on some of their protocols, and compared our results in wet lab, particularly in the use of E.coli strains to express our respective proteins.
How They Helped Us
The UCSC team used E.coli and Saccharomyces cerevisiae. In contrast, we were using E.coli and SF9 cells. They shared some of their E.coli protocols with us, and helped us troubleshoot whenever we ran into any issues. The UCSC team also helped us model the possibility of using Saccharomyces cerevisiae to express protein S. We collected the rate constants and parameters, and they helped us analyze whether these cells could be used, in the future, to scale up production of our protein. Based on the results of this modeling, we learned that we learned that yeast might be a feasible option for future industrial-level production of protein S. Moreover, based on the results of expressing protein S in E. coli Origami B (DE3) cells for our project, we can conclude that expressing protein S in yeast cells along with glycosyl transferases is a promising future avenue for optimizing protein expression and quality. Yeast, being an eukaryotic organism, might be better suited for expressing recombinant human proteins because of its ability to perform certain post-translational modifications and forming disulfide bonds to increase protein stability and secretion. This represented a significant contribution to our team, and could help us expand the size and scale of our project in the future. The results of this work can be seen below.
Mentorship of iGEM EmpireGene
The EmpireGene iGEM team first reached out to us in mid-May to collaborate. Since then, we have engaged in numerous zoom calls, and even in-person meetings. Our teams collaborated extensively in our education and communication initiatives, particularly in a journal initiative, on a YouTube channel, in dry lab, and even in the wiki portion of our project. We helped mentor the younger high school team. Additionally, we collaborated closely with this team to create and distribute a children’s workbook about synthetic biology. Both teams curated content for the workbook in order to make synthetic biology more accessible and understandable for elementary to middle school-aged children. Then, both teams worked together to distribute the workbook to teachers, schools, and educational institutions around New York State. You can read more about the goals and design of the workbook on our Education and Communication Page.
How We Helped Them
We helped the EmpireGene team troubleshoot some of their wiki issues, particularly in uploading to Gitlab. Additionally, on August 19, we hosted a visit with a few of their members on the Stony Brook campus, showing the younger students around, introducing them to professors and faculty, and helping them take advantage of the resources that a college campus has to offer. Additionally, we helped validate and review some of their protein modeling. In particular, they sent us their homology modeling results, and after reviewing them, we were able to offer some feedback to help them fine-tune their modeling. In addition, our team helped add to their modeling by helping model their fusion proteins using Ab-Initio and Threading/Fold recognition methods. We created a document with these results and shared it with them to help give them better insight into their protein structure and function. We also recommended they use MolProbity to create Ramachandran plots for their models in order to assess their reliability. In this way we were able to extensively contribute to the dry lab portion of their project. The document with a rough draft of all this work can be seen below.
Finally, we also helped translate some of the videos on their educational YouTube channel into Chinese, increasing how accessible and understandable their videos are to a larger portion of people around the world. Their YouTube channel can be found at the link below, and the documents with our translations, which we provided them, can also be found below.
How They Helped Us
The EmpireGene team contributed to our journal, writing an article for the publication, and working extensively with us throughout the revisional writing process. They also helped us computationally model the structural similarities and differences between the COVID-19 virus and protein S, to shed some light on potentially why COVID-19 causes a decline in protein S levels. The results of their modeling can be found on our modeling page here, and gave us a lot of insight into the relationship between COVID-19 and protein S levels. In this way they helped us better characterize protein S activity and its role in diseases that extend beyond genetically acquired protein S deficiency. It also gave us a better understanding of the risk of an innate immune response that might occur when administering protein S directly into the bloodstream of patients. The results of this modeling can be seen on the document below, sent to us by the EmpireGene team, and on our modeling page.
iGEM Bilkent-UNAM
Disclaimer: This team withdrew from the iGEM competition, but is still planning on continuing their research independently. We would like to include them as a collaborator and partner because they have influenced our project significantly, and despite them not competing anymore, we also had an impact on their work. At the very least, we want to thank them for such a fruitful and engaging collaboration, and we wish them the best of luck with their project.
We participated in weekly meetings with this team in order to troubleshoot some of the issues we were facing in the wet lab and dry lab. In particular, we discussed the presence of ghost bands for both teams, exchanged protocol and buffer ingredients, translated each other’s works, and also helped review each other’s experiments and results. Each team strongly contributed to the project of the other team, which is why we consider this to be a partnership.
How We Helped Them
We translated their synthetic biology children’s book into languages including Polish, Chinese, Japanese, Gujarati, and Hindi, greatly increasing how accessible their storybook could be. In addition, both teams discussed the implications of the dry lab portion of their project. In order to help them, we gave them our mathematical modeling guide. In terms of mathematical modeling, we looked over their equations extensively and gave them significant feedback. In particular we mentioned:
- In terms of integration, the assumed values for P (protein) and R (receptor) cannot be constant. Both P+R are changing with time (t).
- A second assumption that was made in their initial model was that there will be a linear increase, but the curve should saturate.
In this way, we were able to validate their mathematical modeling.
Finally, we gave them our miniprep protocol and elution buffer ingredients for working with E.coli, since these were steps they were struggling with. We sent them a protocol from Thermofisher to help in protein purification, which they were also having some trouble with. These two protocols are attached below.
How They Helped Us
The iGEM Bilkent-UNAM team helped us translate a variety of our articles and guides into languages including Turkish and Russian. This was crucial in helping us increase the accessibility of our materials. In particular, they translated some of our journal articles and our algorithm for protein S deficiency testing. Their overall insight regarding our project proved invaluable and helped add to our project significantly. They also helped review our protein modeling work and gave us feedback on our methods and results. In this way, they validated our protein modeling results and analysis. The Turkish translation of our algorithm for streamlined protein S diagnosis can be found at the link below.
