Difference between revisions of "Team:Tongji China/Collaborations"

Tongji-China & SSTi-SZGD

To make our engineered bacteria safer, we want them to kill themselves after being expelled from the human body. The SSTi-SZGD team helped us solve this problem. They provided us with a light-activated plasmid expressing a protein which, when stimulated by blue light, promoted oxidative stress and apoptosis of the bacteria. Thank the SSTi-SZGD team for their help.

Tongji-China & SKLMT-China

We also hope to help other teams with their projects. During an exchange meeting, we found that team SKLMT-China needed other teams to help them evaluate the strength of their promoter. So we got the job. We believe that our work can be of help to their project.

Tongji-China & SJTU-BioX-Shanghai

In the course of the project, we wanted to find a suitable capsule for our oral bacteria by visiting and learning from local pharmaceutical factories. And we found that the SJTU-BioX-Shanghai team had the same needs. Therefore, after contacting roche pharmaceuticals, we invited the students of SJTU-BioX-Shanghai team to visit together. During this visit, we also shared some project experience. After the visit, we completed the capsules we wanted with the help of roche. And SJTU-BioX-Shanghai team helped us to complete the production of bacterial freeze-dried powder.

  

The 5th CCiC

We hope to make our project more complete through extensive communication. So we participated in the fifth Conference of China iGEMer Communication held by ShanghaiTech University at the end of August. This exchange activity brought us great harvest. We learned some experimental ideas from other teams, sought advice from some instructors on how to solve our problems, and also found many opportunities for cooperation.

  

Tongji-China & CPU
The modeling department of our project was completed with the help of CPU_CHINA. They use Bayesian statistics to predict which type of mutation is most likely to product MHC strong binding peptides with the sum of the affinity of each mutation site and each allele type for us. Using this model, we can predict which mutation of a colorectal cancer patient is playing the strongest role in cancer and which site can be the best one for peptide making for this certain patient. Thanks to the help of the CPU_CHINA igem team.
To know about our collaboration of modeling, you can go to our Modeling page.