Selection of gRNA, design and construction of pgRNA-Csg.

Re-design and construction of pgRNA-Csg, since the previous homologous fragment went wrong.

Construct CsgA-his-BL21.

Construct CsgA-trucR5.

Confirmation of ECNΔCsg by colony PCR.

The first time to express CsgA-histag in BL21, but failed, because the bacteria died when cultured.

Construct CsgBtruncR5-his-BL21.

Construct ECN1917genome-konck down _mt.

Construct pBbB8k-mutCsgA_F97TAG_mt.

All iGEM members underwent a 1-hour experimental safety training.

The first time to express and purify the CsgBtruncR5.

Discard of plasmids inside ECNΔCsg and confirmed.

Attending molecular training course for dynamics simulation software GROMACS.

Attend the Capital Biomedical Innovation and Entrepreneurship Elite Exchange Meeting.

Communicate with Tao Liu and report on the progress of the experiment.

Engage in an exchange with Southern Medical University.

Arrival of the purchased plasmid pBbB9k-CsgA-nbGFP.

The purification of CsgBtrucR5-His protein has been preliminarily completed by denaturing purification. However the purity and yield were relatively poor.

Purification of CsgBtruncR5-histag by non-denaturing purification from the supernant was preliminarily completed. However, the purity and yield were relatively poor.

Construction of multiple plasmids.

Construct PBbB8K-Csg opreon-F-delete.

Preparation of several competent cells including: ECNΔCsg, ECNΔCsg+pUltra-Tet v2.0 (ECNΔCsg+OTS), ECNΔCsg+pBbB8k-CsgAWT.

"Five-Team Exchange Meeting".

Communicate with Professor Lv.

Construct CsgBtrucR5-Flag tag.

Preparation of several competent cells including: ECNΔCsg, ECNΔCsg+pUltra-Tet v2.0 (ECNΔCsg+OTS), ECNΔCsg+pBbB8k-CsgAWT.

Construct CsgBtrucR4R5&CsgA(-).

Construction of plasmids: S89TAG, F97TAG.

Solve the key issue for nonnatral amino acid TeT molecular dynamics simulation with GROMACS. Successfully conducted force field topology file.

Construct pBbB8k-δCsgA.

Literature survey on proteins that CsgA can bind.

Questionnaire survey.

Construct Csg-operon-δFδB(with nb His).

Literature research on protein purification conditions.

Purification of CsgBtruncR5-histag.

Construct CsgAtrucR5(PBbB8K)&Csg-MMP-nb.

Fill out the safety form.

Insertion of Tetv2.0 into sfGFP-151TAG, whose fluorescence was supposed to be quenced by Tet v2.0. MS of sfGFP is sent to verify the insertion of Tet v2.0.

Check the safety form and ask for advice.

Construct pET22b-deltaFdeltaB-CsgAtrucR5-His&pET22b-deltaFdeltaB-CsgA-MMP-nb-His.

Complete the molecular dynamics simulation of the csga protein with TeT. Analysis Done.

Complete the first part of ODE.

Construct pBbB8k-mutCsgA_S89_mt.

Construct pET22b-deltaFdeltaB-CsgAtrucR5-His&pET22b-deltaFdeltaB-CsgA-MMP-nb-His.

Purification of CsgAtruncR5-histag.

Consult the safety literature.

Attending the webinar on "Modeling and Analysis of Synbio Systems" presented by Mathworks. Learned about Simbiology, a biological system modeling tool, and determined to use it as the main method for ODE modeling and subsequent optimization.

Congo Red and ThT assay of curli fibre including CsgAWT, S89TAG, F97TAG, finding that insertion of amber codon at site 89 and 97 didn't end the formation of curli fibre, it can be formed with only R1 and R2. Furthermore confirmation of curlie fibre formation needs to be completed.

Purification of pET22b-CsgAtruncR5-his.

Complete the second part of ODE, make a presentation of past work.

Second phase simulation of nucleation. Attempt to construct a mem-protein system but failed.

Contact the pharmaceutical company.

Purification of CsgA-S89TAG, Csg-F97TAG and CsgAWT in ECNΔCsg. SDS-PAGE was done to verify the protein. However, the purity and rate of expression of proteins with Tet v2.0 needs to be further improved. A preliminary try of verification of the insertion of Tetv2.0 of the protein is done using TCO-cy5 and tested by Typhoon.

Exchange with Beijing Normal University.

Communicate with Professor Liu Zhuang.

Communicate with the public mutual fund researcher.

Verification of the formation of Curli fibre is done by SEM. It was confirmed that, without Tet v2.0, under the induction of arabinose, curli fibre was still formed.

Collaborate with Jilin University.

Cy5-TCO to mark Tet v2.0. S89TAG and F97TAG all displayed significant rise in signal, while S89TAG did a much better job. It was considered that S89TAG was chosen as the better candidate.

Purification of CsgA-His& CsgBtrucR5.

Teaching experiments to international high school igem team students for four days.

Presentation in CCiC.

Communicate with iGEM team leader of Jilin University.

Purification of CsgB& CsgBtrucR5.

Visit the ACROBiosystems company and communicate with the company manager.

Contact Kai Zheng from innoModels.

Communicate with Chen Zihao from Southern University of Science and Technology.

Exchanged the details of MD simulation with instructor Zhengwei Xie and senior Lisheng Zhang, and solved the problem of lack of computational resources.Exchange views with senior students, and obtain guidance on more comprehensive molecular dynamics simulation methods of protein polymerization; Got high performance server help

Growth Curve of E.coli in existence of Tet. Previous experts reflected their worries about the toxity of Tet v2.0, so we constructed experiments of E.coli growing in 2YT with 1mM Tet v2.0 inside. The problem is that, the experiment is conducted under 25℃ rather than 37℃, but the growth curve can surely prove the safety of Tet v2.0.

It was decided to build Deepro, a neural network based promoter strength prediction system. searched for data, constructed the dataset and completed the skeleton part of Deepro (data loading module, training module, testing module, plotting module, etc.).

Communicate with Director Qi.

Completed construction of the MLP portion of the Deepro model for initial parameter optimization, which performed poorly. Next step is to try using 1dCNN model.

Completion of the Deepro 1dCNN model part of the construction, compared to the MLP parameter amount is greatly increased, in the case of training the same number of rounds in the training set of better performance compared to the MLP model, but in the test set of the performance of the performance of the improvement is not obvious, in general, unsatisfactory. The next step is to try the LSTM model and XGBoost model, and at the same time hope to carry out the extended dataset.

Communicate with Zhang Lisheng.

Synthesis of TCO-coumarine. It was successfully done at the third time. First failure was because incomplete protection of water, Beijing was really rainy at that moment. The molecule was successfully synthesized at the second time, and synthesized more at the third time.

Flow cytometry of EcN. The bacteria was labled if Tet v2.0 was inserted into the curli fibre. The experiment was accomplished in one single attempt. The problem of the experiment, is that the bacteria unlabled cannot be distinguished from impurity. However, the amout of labled bateria was so outstanding, that we didn't think that it would influence the results much.

Create a stakeholder map for the commercial version.

Prepare a batch of competent cells for electroporation.

Try to use embedded vector instead of one hot vector as CNN model input. The new model performs weaker than the original model under the same training conditions, which is to be expected since word embedding matrices tend to struggle to exhibit DNA base features initially, and thus the model requires longer training rounds. Increasing the number of training rounds to 300 shows a slight but insignificant improvement in the model's performance on the test set, with hyperparameters to be optimized. Meanwhile I think the main problem at the moment is centered on low quality and small datasets.

Purification of CsgA-His and BCA quantification.

The construction of the LSTM model was completed, and the training was extremely time consuming and the model generalization ability was quite limited, and it is not intended to be used as a skeleton part of the hybrid model in the future.

Visit Jingfeng Pharmaceutical.

Purification of CsgA-His and BCA quantification.

By searching the literature, two datasets with sizes of 936 sequences and 3512 sequences were added to the model. However, it still faces the same problem as before, i.e., data from different sources cannot be merged with each other. The data characteristics of the two datasets are quite different, with the smaller dataset having lower DNA sequence homology and the larger dataset having higher sequence homology, a distribution that can have a serious impact on the generalization ability of the model.

Based on our teacher's advice, we decided to try it out using some relatively simple models.The XGBoost model was added and tuned, which was the fastest to train and performed the best of all the current models, which may be related to the limited size of our data. This model and the subsequent models were chosen to be trained on the third dataset, which is the largest dataset, and of course our program supports training the model from scratch on the first and second datasets.

Using the API provided by Scikit-learn, AdaBoost, SVR, RandomForest, and GBDT models are added to Deepro, where the AdaBoost model performs poorly, GBDT and XGBoost perform close to each other, and the rest of the models perform moderately well.

Visit Xinhua Medicine.

Visit HICOOL.

ECN Conference.

Visit Intco.

A joint CNN-LSTM model was completed, with improved performance compared to CNN and LSTM models, but with a significant increase in training time.

Completed the Transformer model (Encoder only), which is the largest and most structurally complex of all models to date, with a significant increase in training time compared to the other models, and a final performance similar to XGBoost. Our concern is that the high homology of the dataset will limit the generalization ability of the model, especially in the prediction of the strength of promoters from other organisms or artificial promoters modified based on promoters from other organisms, and the model lacks the consideration of the external environmental conditions, for example, the strength of the promoter expression under different oxygen conditions may be significantly different.

Dry Lab Conference.

Communicate with Yan Zeyu.

Decage verification of TCO-coumarine in level of molecule. We wanted to observe the complete decaging time. Suggested by Yanbo Jing, later decaging experiment conducted in culture medium rather than ddH2O would be conducted.

Refactor Deepro project code to increase its readability and optimize code structure.

Write two white papers.

Decage verification of TCO-coumarine in level of bacteria. A really tough experiment, which I have to handle about 54 samples at the same time. Fortunately, the results came out well.

Communicate with You Siying.

Communicate with Wang Ge.

Participating in the MindSpore SPONGE summer course, hoping to master MD simulation methods based on machine learning and augmented sampling for use in projects.

Communicate with Song Renxiu.

Synthesis of TCO-dox. It was accomplished in 3 attempts. The first failure was because technical error, which all the products were leaked out from Flash. The second time, I mistakenly collected the wrong product, and it was successful for the third time.

Click-to-Release verification of TCO-Dox performed by HPLC.

The condition of MC38 went worse, so we decided to focus on CT26

Project Exchange Meeting.

Participate in an online forum on the theme "Navigating the Future of AI in Synthetic Biology: A Multi Stakeholder Dialogue".

Started to culture CT26 and MC38, and explored ways to conduct cytoxicity experiment.

Cell Cytoxicity of Dox and TCO-Dox on CT26 was done.

Cell Cytoxicity of TCO-Dox decaging on molecular level on CT26 was done.

Write an HP article to participate in building a wiki.

Cell Cytoxicity of TCO-Dox decaging on protein and bacteria level on CT26 was done.

Consult Mr. Hou for legal advice.