Team:BIT-China/Collaborations
On July 24th, we were invited to take part a meeting upheld by International Teenager Competition and Communication Center on campus of USTB, during which 4 teams shared the experimental process about their project of iGEM. We found some problems in the design of each other's projects through communication, and discussed some solutions and ideas. Each team received a lot of pertinent suggestions from the others, which helped a lot to our project.
By the end of August, we had preliminarily completed all parts of experimental verification, constructed the relevant proteins, and verified the strength of each promoter. In order to further improve our project and make progress with other teams from communication, we participated in CCIC. All of the teams exchanged ideas about their project and original design and have a quick exhibition of the poster, give some simple but classic presentation. Among which we also try to find ways to better collaborate with other teams, especially in the modelling and outreach. We received more than 10 useful advises about our project and having a great time!
We determined the inhibitory effect of PLA (phenyllactic acid, a new type of natural antibacterial substance and preservative) on yeast CEN.PK2-1C. In this way, we knew more about the broad-spectrum antibacterial performance of PLA. (check here for more detail)
We provided them with the E.coli strain containing BBa_K2120006 for they decided to make an improvement on it. (check here for more detail)
They designed a handbook that contains an introduction of iGEM and tips for preventing bacterial infections in daily life, presenting useful information in a more concise and clearer way, focused on the prevention of harmful bacterial and how to maintain environmental sanitation. We learned the manual and promoted it to the community. Helping them broaden the scope of the promotion, namely the northeastern part of China (check here for more detail)
In the early days of collaborations, we explained to FJNU-China about the needs and difficulties of our modeling. After analyzing, communicating and consulting the literature, they helped us establish the ODE equation in the hydrogen peroxide decomposition model and provided us with guiding recommendations and feasibility verification strategies.(check here for more detail)
They shared us the right of using flow cytometer and helped us measure the related data of regulator group, finally got the fluorescence intensity of a single cell. (check here for more detail)
Besides, the two teams discussed each other's project content and problems, and raised some questions and views, solving existing problems of both projects.
Our work in 2017 made several fundamental changes to the pheromone pathway, facilitating the further transformation of biosensors in their team in 2018. So we shared the stain of modified CEN.pk2-1C, which used it to:
1. Exclude the possibility that the pheromone pathway is affected by the native ligand pheromone in yeast: (Knock out the Ste2 receptor);
2. Exclude upstream G-protein coupled receptor signaling attenuation (desensitization) that may result from sustained stimulation. (Knock out Sst2, the GTPase-activating protein for Gpa1p, as a receptor-desensitizing factors.)
3. Exclude side effects on cell life cycle, cell morphology, etc. due to enhanced MAPK pathway signaling in the Pheromone pathway. (Knock out Far1, the cyclin-dependent protein serine/threonine kinase inhibitor.)
We also shared our original strain CEN.PK2-1C which no knockout of Ste2, Sst2, Far1 was performed. And the results of ligand binding response before and after gene knockout of pheromone pathway were compared to verify the significance of the modification for knocking out side effects. (check here for more detail)
After understanding our needs for modeling, they helped us build a descriptive model based on the mechanism of action of fluorescent probes. The model validates this linear relationship through experimental data and predicts future possible outcomes by constructing a linear regression model. And they also analyzed the residuals of the model to ensure it works well. With this model, we can properly predict the output when the concentration of H2O2 is in certain interval. (check here for more details)
