For contributions, our promoter engineering have made a lot of effect on it. All of these may be helpful to other teams. We hope it will make some contribution to the iGEM community.

Wet lab

1.Measurement of the strength of 14 promoters

At the stage of promoter search and screening, 14 common promoters in various metabolic pathways of S. cerevisiae were compared in the production of Valencene by 64h shaker fermentation experiment, so as to characterize the intensity difference between them(Figure1).

One of the 14 promoters (PDC6p) could not control the expression of Valencene synthase, and the other promoters had different intensities. The PDC1 promoter had a strength of 100%, and the intensity distribution of different promoters was 3%-74%. The highest yield of Valencene was 4.39 mg/L Under the same conditions, its yield was nearly two times higher than that of the strongest promoter TEF1p(2.23 mg/L) commonly used by yeast. From the 14 natural promoters, we screened a promoter PDC1p with the highest strength. To classify the strength of these promoters:

highly expressed promoter :PDC1p SED1Lp SED1Sp CDC19p;

moderately expressed promoter: ALD4p ENO1p ALD4p TPI1p TEF2p TDH1p;

low-intensity promoter :SOL4p TKL2p PDC6p ALD3p.

More is known about the strength of these s. cerevisiae natural promoters through our measurements of valenciene production, which will be useful for future iGEM teams.

Figure 1 the VS expression differences of 14 promoter

2. the verification of the location of adding UAS

In our first round of modification, we compared two different locations of Adding UAS with hidden Markov model, and constructed two new heterozygous promoters M1 and M2 respectively. The results showed that location 2(M2) was more suitable for adding UAS (figure 2).

Figure 2 the different locations of adding UAS

3. the improvement of an existing part

Through our effective three rounds of promoter modification, we obtained better results of pM2, pM5 and pM8 on the basis of pPDC1, and the corresponding output of Valencene was increased by 18.9%,29.5% and 56.6% respectively(figure 3).

Figure 3 the Valencene yield of M2, M5, M8 and PDC1

In addition, by comparing the fermentation time curve, the yield of Valencene of pM8 with the best effect increased significantly better than that of pPDC1 in the late fermentation period, which reflected the success of the transformation (figure 4).

Figure 4 the time curve of M8 and PDC1

Human Practice

1.Interviewing skills

Interviewing is one of the important tools for hp implementation. In order to better standardize the interview process, we have compiled a summary report on interviewing techniques for different populations. The report includes several sections on the standardization of the interview process, consultation on the selection of interview timing, techniques for interviewing and writing scripts for different populations, and ways to secure information. This report will give future iGEM teams guidance on how to conduct interviews and help them get the information they need from their interviewees.

2. Proof on the effectiveness of Nootkatone

We have compiled the literature related to mosquito repellency/extermination with the known species that can be effectively repelled/killed by Nootkatone and listed the corresponding references. If there are iGEM teams doing mosquito repellent projects in the future, they can refer to the content we have compiled to help them better design their projects.

3. About the industrialization of Nootkatone

After research and interviews, we have come up with the following preliminary relationship diagram for industrialization, which consists of the "plant" part of our ongoing work to increase the production of Nootkatone, and the downstream market part. We hope to provide iGEMers who choose the manufacturing track in the future with ideas on how to move from the lab to the plant and then to industrialization.


1. Data analysis method

In processing the high school questionnaire data, we came up with a series of data by means of probability statistics. This way of analysis can reflect the effect of our science popularization more objectively, and we also hope that this way can provide an analysis idea for the iGEM team in the future.

2. Summary of science popularization experience

High school students are one of the subjects that most iGEMers will choose to popularize science, so we summarized a pdf of our experience and suggestions mainly for high school students to provide ideas for future iGEM teams on how to popularize science.

Dry Lab

1.Promoter optimizing software

This software serves to eukaryon promoters with UAS, while UAS has been discussing for decades. We set up a software to extremely optimize the promoter sequence with nucleosome affinity. The thing most worth mentioning is that the software was developed user-friendly that everyone can take up easily.

2.UP strategy

During the study of UAS through out the project, we gain lots of experience and useful conclution. Therefore we write a handbook for factory and researchers who would like to try promoter optimizing project especially with TFBS. And we have made fully proof of concept for this implementation.

3.RNN learning

Although the training result is not well, there’re few people trying to perform machine learning on biology database, which was attached great importance to by us. We decide to “Throw in a brick to draw in the jade”, hoping to inspire more teams to try on machine learning job.

4.Visualized Fermentation

Using cellular automata, and try our best to fit the growth curve of bacteria in fermenter, finally we build up an parameter-adjustable machine in order to promote science popularization about biology fermentation.