Best Modeling

Note: All experiments, data, and results for these modelling methods can be found throughout the website.

After designing our theoretical gene insert for PFAS detection, we decided that it would be best to use computer simulations to verify its accuracy. These computer simulations started with the use of Virtual Cell (VCell). Using VCell, we were able to simulate the reaction kinetics of each part of our gene circuit. This included not only the creation of mRNA and proteins, but also the interactions between proteins and the degradation of these proteins. Using this simulation, we were able to determine the relative amount of GFP that would be produced from our circuit. Although not perfect at first, we were able to reiterate the reaction constants and the interactions between different parts of our circuit via extensive amounts of research into different literature surrounding the different genes and proteins. We were also able to run theoretical experiments in VCell that we didn’t have time to do practically, allowing us to better see the impacts of different situations on our gene circuit.

In addition, we used a reverse screening technique in a multitude of databases. Reverse screening includes using the SMILE string of a specific compound to see the potential docking sites for that compound on a cell. By utilizing several different databases, we were able to see different sites of potential PFAS binding, which helped us in coming up with our proposed biological mechanism of action on prmA.

Finally, we used an application known as OpenMM to run our simulations, which was used to find the potential impacts of PFAS on the receptor protein LuxR, and compare them to its natural ligands, such as AHL. OpenMM's capabilities allowed us to discover the possibilities of PFOA's interaction with our gene construct. The data generated by OpenMM, including potential energy, kinetic energy, total energy, temperature, box volume, and more, have aided our research. With this insight, we were able to refine our genetic system for a more efficient and effective approach.