Requirements for a Bronze Medal

1. Competition Deliverables: Wiki & Project Promotion Video & Team Presentation & Judging Form

2. Project Attributions Project Attributions page

3. Project Description Project Description page

4. Contribution Contribution page

Additional Requirements for a Silver Medal

1. Engineering Success Engineering Success page

2. Collaborations Collaborations page

3. Human Practices Human Practices page

4. Proposed Implementation Proposed Implementation page

Additional Requirements for a Gold Medal

1. Integrated Human Practices Integrated Human Practices page

2. Improvement of an Existing Part BBa_K322921→BBa_K4176004

3. Project Modeling Project Modeling page

4. Proof of Concept Proof of Concept page

5. Partnership Partnership page

6. Education & Communication Education & Communication page

Special Prizes

Best Integrated Human Practices

Best Integrated Human Practices

At the beginning of the project, we collected problems about gene editing through literature reviews, interviews with laboratory and enterprise personnel, and handing out questionnaires. The project design was carried out through the analysis of the problems. With the help of experts in related fields, we improved the design further and solved the wet lab problems. We also actively communicated with the scholars on biosafety and environmental friendliness to ensure that the project is "safe and harmless". At the same time, by understanding the actual needs of enterprises and society, we identified future application scenarios of our project as providing gene editing tools and biological products. We not only tried to solve the problems but focused on practice as well. We also carried out the popularization of science education at the same time. We want to bring our tools to the actual use and solve practical problems.

Best Modeling

Best Modeling

Selection of sgRNA can influence the on-target knockout efficacy of CRISPR/Cas9 system immensely. Thus, we conducted a model to predict the off-target propensity of sgRNA through deep learning. DenseNet is an outstanding neural network model based on CNN. We encoded each sgRNA-DNA sequence by one-hot encoding to form a 4×23 matrix as part of our input. Besides, most models only calculated the score according to the mismatch of sequences in crispr-cas9.We used python package PyBioMed to extract physicochemical descriptors and generate matrix according to sgRNA-DNA sequence. On the basis of the gene library, we screened out sgRNAs with high efficiency to help our wet experiment. In our wet experiments, we found that the toxicity of sacB in our strainer system is too high. To this end, we sought to use dry-lab experiment to design a sacB mutant with lower toxicity for E. coli, and this sacB mutant can increase the CFU/ug of the strainer method with high editing efficiency. We speculated that if S164 is mutated to THR, the -CH3 would change the orientation of the -OH and would effectively form new hydrogen bonds. Thus, the position of the D86 carboxyl group of D86 is restricted by hydrogen bonding, and the hydrolysis rate is reduced, and then the toxicity is reduced. We modeled the new hydrogen bond formations and the position of the D86 carboxyl group by molecular dynamics, and test our conjecture in our wet experience.