Contribution | SDU-CHINA

Overview

This year we have successfully made contributions in many areas. We made parts contributions on 5 existing parts. And we provide detailed information about how to use the Three-layer dynamic regulation model that developed by our team. What is more, we developed a A glucose automatic monitoring and supplementation system based on STM32. In additionally, we provide 20 variants of EsaR that were predicted using Alphafold.

01 Parts

1.1 BBa_K3989009

Characterization and improving

We provide detailed characterization results and protocols for PesaS and compare its expression time with three other promoters. We used an RBS(BBa_B0034) and GFP-LVA(BBa_K082003) to characterize it.

1.2 BBa_K3989008

Characterization and improving

We provide detailed characterization results and protocols for PesaR-C and compare its expression time with PesaS. We used an RBS(BBa_B0034) and mkate (BBa_K076006) to characterize it.t.

1.3 BBa_ K2152003

Characterization and improving

We designed an auto-lysis system that will express at the late stationary phase (40h) based on this part. And we provide our failure experience and advice for the future iGEM team.

1.4 BBa_K4153004

Characterization and improving

We designed an auto-lysis system that will express at the late stationary phase (40h) based on this part. And we provide our failure experience and advice for the future iGEM team. And we also provide useful experience for.

1.5 BBa_ K3989003

Characterization and improving

We provide the 3D structure of EsaR protein and the interaction between it and AHL..

02 Three-layer Dynamic Regulation Model

We have developed the Three-Layer Dynamic Regulation Model (see Design), which is a model that separates the growth stage, production stage and product release stage. The application of this model can effectively increase yield, reduce costs (including production costs and downstream treatment costs) and reduce environmental pollution. The three-layer dynamic regulation model we have developed can be applied to more than just PHB production. Theoretically, any use of E. coli as a platform for the production of a specific product, such as amino acids, can be applied to our model. Next, we will explain how to apply the model well and how to adjust the model so that future iGEM teams can easily cope with it.

2.1 Growth Phase

The genetic circuit of Growth Phase is shown in Fig. Using the promoter PesaS can upregulate the gene in the first serval hours and down regulate it before the bacteria enter the stationary phase. We use RBS BBa_B0034 in this part and the gene that we regulated was gltA in TCA cycle, and has been proved useful by our team. You can change the RBS strength and the downstream gene as you like.

Fig.1 | Genetic Circuit of Growth Phase

2.2 Production Phase

The genetic circuit of Production Phase is shown in Fig. Using the promoter PesaR can turn on the gene when the cell density has reached a specific level. PesaR has serval variants and the express time of them are different. In our project, we test all three promoters, and you can find more details in our Results page. You can choose the one that suit for your project. The RBS we used is BBa_B0034, and you can change it to others.

Fig.2 | Genetic Circuit of Production Phase.

2.3 Product-release Phase

The genetic circuit of Product-release Phase is shown in Fig. We tested the late stationary phase promoter PYU3, PYU7, PYU16, and PYU92 in our project. The lysis gene we used is SRRz (BBa_K4153004) and E (BBa_K2152003).

Fig.3 | Genetic Circuit of Product-release Phase

03 A glucose automatic monitoring and supplementation system based on human-computer interaction

We have designed a glucose automatic monitoring and supplementation system based on STM32, which can accurately and timely achieve glucose supplementation (see Hardware). Future iGEM teams can use this hardware to control the fermentation process. We have also developed corresponding software that facilitates remote, real-time viewing of fermentation (See Software).

Fig.4 | The apperance of our hardware

Fig.5 | The app operation interface

04 Model

We predicted the structures of a series of EsaR and its variants using alphafold, filling in the gaps concerning the structure of the EsaR protein. What's even more noteworthy is that, through molecular docking, we obtained the different binding capacities of various variants with AHL. These results can serve as a reference for iGEM teams planning to use the EsaI/R system in the future.

Fig.6 | Binding energy of proteins with AHL.

05 How to conduct questionnaire research

For conducting a better questionnaire survey, we designed a circular feedback questionnaire design model.

Fig.7 | The circular feedback process of our questionnaire.

Firstly, the internal members discuss the first draft of the questionnaire, then consults the relevant professors/technicians about the professionalism of the questionnaire. After that we complete the revision. Then submit it to the relevant professors for review. After the questionnaire is correct, we look for professors related to humanities research and ask for help about the questionnaire's way of asking questions, design of thinking, privacy protection, legality and so on, and then we will modify the questionnaire and review it to get the final version of the questionnaire to be distributed to the community.

Tips on questionnaire question setting:

Reduce the non-essential information collection in questionnaires and desensitize the questionnaires

Reduce the percentage of the question of right and wrong.

Options cannot be leading in numerically related topics

Reduce the use of proper names which is unfamiliar to the general public