A critical aspect of research is developing one’s knowledge while pursuing novel avenues of study in the field of synthetic biology. On the McMasterU team, we believe learning is a fundamental part of developing one’s research skills and background, as well as building their character as someone who faces adversity to better understand a specific topic. To aid in this process, our team has developed models that will aid future iGEM teams with projects involving therapeutics.
One major contribution we have made is our design of the fdGPS phage we plan to use in our vaccine. The current model, fdGPS2.1, was inspired by Dr. George P. Smith and his work with the fd filamentous phage, using it as a backbone for our plasmid. It was designed on Benchling and its production was attempted in the lab using Golden Gate assembly. Our plasmid is a novel therapeutic agent precursor that generates bacteriophage particles that can be used in drug delivery or targeted removal of harmful cells in the human body. An advantage of this design is its modularity, allowing iGEM teams to swap sequences out for ones that fit their project. As well, the plasmid contains a truncated pIII coat protein sequence along with an RGD-4C sequence, reducing the size of the overall plasmid while allowing the phage to specifically target human cells for drug delivery. Of course, the RGD-4C sequence can be replaced with another genetic sequence that codes for a ligand that favors the project in question, or be taken out completely and replaced with another component.
Figure 1: Current design schematic of fdGPS2.1(RGD-4C) with ampicillin resistance genes. Plasmid design developed by team member, William Pihlainen-Bleecker.
Beyond our work with the plasmid, our dry lab team has developed a vaccine distribution model that can be used to predict the maximum number of vaccines that can be delivered to a given area based on the parameters and constraints specified. This model can provide a foundation for estimating the resources needed to make vaccines and other therapeutics available within a population, and can also aid iGEM teams in foreseeing potential obstacles that must be overcome during delivery. Again, an advantage of our model is its versatility regarding the parameters and constraints that can be used for it. Teams can adjust variables within the model to simulate their target area and the factors associated with them, allowing for accurate predictions of distribution.
In our education page, you can see that each initiative we did had something we left behind for future teams (or that we plan to showcase at the Jamboree), to help guide their outreach work as well. While the full details can be found on that page, here are some of the highlights: