- Registration and Giant Jamboree Attendance - UC San Diego iGEM has registered for iGEM and will be sending 7 team members to the Giant Jamboree in Boston in the upcoming weeks
- Competition Deliverables - We have completed this wiki, designed a PowerPoint and poster presentation, and have filled out the judging form as well.
- We have properly attributed everything that was done by the team and what was done by others. Check our attributions page.
- We have successfully completed the Interlab Study. See our results in the Interlab part of the wiki.
- Our team has completely validated Part:BBa_K2881006 and Part:BBa_K2881007 as our submissions for our MBD-GFP circuits. Please check in our depth work on our results page and on the associated Registry pages.
- Our team collaborated with ULaVerne for Interlab work in addition to attending a meetup with other California teams. We also learned more about microfluidic device design in our interactions with Boston University’s Hardware team. In addition, we worked to help strengthen Imperial College’s Communications Strategy Guide and gained feedback on our overall strategy for the Giant Jamboree. We also worked with the Stony Brook team to provide them with cyanobacteria vectors and necessary protocols for their project this year. Please read more on our collaborations page.
- Our team explored the importance of our project in a larger context, including a discussion about implementing our idea internationally with the TATA Institute of Genetics and Society. We also learned about the importance of sustainable design for our diagnostics platform by interacting with TIGS to gain a deeper understanding of the ASSURED criteria. We also asked some of the larger ethical questions surrounding our work including the implementation of a cell-free system and data privacy in a digital health platform. Check out our Human Practices Silver page to learn more.
Integrated Human Practices
Our team incorporated a novel communications paradigm in our interaction with stakeholders, including discussions with academic researchers, cancer patients, healthcare professionals, industry executives, and entrepreneurs. A holistic assessment of these interactions allowed us to make crucial technical decisions and tailor our design to specifically meet stakeholder needs. Please see the entire storyline at our Integrated Human Practices page.
ModelOur team used exploratory machine learning in order to gain a better assessment of the in silico tool that our team had designed. It guided our project implementation because it allowed us to design disease-specific methylation probes. Our model section of the wiki documents the foundational assumptions, data acquired, and a technical description about the theory that we were trying to accomplish.