Before our wet lab team could begin in the lab to start gathering data for us to work with, the dry lab team did some preliminary research into different differential equations describing various variables present in the experiment including viral vector concentration and tumor cell proliferation rates. After some logistical delays in our experiment, the dry lab team opted to pursue a more predictive approach to our work. We decided to focus on predicting the accuracy of our sgRNA while taking into account the potential for off target mutations. In our preliminary research for this we discovered a software coined the ODIGOS algorithm, which is an improvement on the Weissman algorithm based out of the Weissman Lab at UCSF (Horlbeck et al., eLife 2016) (https://elifesciences.org/content/5/e19760). This ODIGOS algorithm created by the iGEM team GunnVistaPingry_US in 2018 improved upon the Weissman algorithm and updated it to Python version 3.8. We decided that this algorithm would have been a great resource for us to predict which guide RNA to use for our CRISPR complex. However, this resource was discovered too late into our wet lab process, so we found another potential use for it.
As a dry lab team, we decided to produce a user guide for the code in order to help future teams have easier access to this resource if needed. There is currently not much instruction on how to operate the software and it would not be readily available to those with less dry lab experience. In order to produce this user guide and troubleshoot this process, each of our dry lab team members split into groups to attempt to set it up separately. Then, during this process, any issues and missteps were documented. Then, each of these documented issues and missteps were compiled into a thorough user guide that not only guides a user into properly using the software, but provides troubleshooting steps for the most frequent issues we found.
Another task taken on earlier on in our Dry Lab division’s efforts was putting together a document that compiled different literature of relevant mathematical models into a single collaborative document. While we were never able to implement this document as we had initially hoped, those doing experiments with glioblastomas, or cancer cells in general, may find the document useful. The document contains the equations we thought were most useful to our project, along with descriptions of the different variables. Hopefully these summaries and compiled literature can help save future iGEM teams time and effort when going about their modeling processes.