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, promotes 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 the fifth Conference of China iGEMer Communication, we found that team SKLMT-China needed other teams to help them evaluate the strength of their promoter. So we took over the job. We believe that our work can be of great help to their project.

Tongji-China & SJTU-BioX-Shanghai

In the course of the project, we wanted to find a suitable capsule for orally intake of our bacterial by visiting and learning from local pharmaceutical factories. We found that the SJTU-BioX-Shanghai team had the same needs. Therefore, after contacting Shanghai Roche Ltd., we invited 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 Shanghai Roche Ltd.. Besides, 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 benefit us a lot. We learned some experimental ideas from other teams, sought advice from instructors on how to solve our problems, and found many opportunities for cooperation.

  

Tongji-China & CPU_CHINA

The modeling department of our project was completed with the help of CPU_CHINA. They used 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 could 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 more about our collaboration of modeling, you can go to our Modeling page.