Team:Kyoto/Criteria
Criteria
to Judging Form
1. Medal Criteria
| Bronze Criteria | ||||
|---|---|---|---|---|
| Number | Criteria | Check | URL | Contents |
| 1 | Competition Deliverables | None | We've completed all the Wiki, Presentation Video, and Judging Form. | |
| 2 | Attributions | https://2021.igem.org/Team:Kyoto/Attributions | We described the team members' roles and the contributions of the people who helped us. | |
| 3 | Project Description | https://2021.igem.org/Team:Kyoto/Description | We described an overview of our projects. | |
| 4 | Contribution | https://2021.igem.org/Team:Kyoto/Contribution | We described our contribution to future iGEM, mainly through adding the new parts to the iGEM registry. | |
| Silver Criteria | ||||
| Number | Criteria | Check | URL | Contents |
| 1 | Engineering Success | https://2021.igem.org/Team:Kyoto/Engineering | We succeeded in applying the “Design→Build→Test→Learn” cycle throughout the project creation. | |
| 2 | Collaboration | https://2021.igem.org/Team:Kyoto/Collaborations | This is a list of the many interactions we have had with iGEM teams during the process of our project and what we have learned from each interaction. | |
| 3 | Human Practices | https://2021.igem.org/Team:Kyoto/Human_Practices | We learned the problem at the flower industry by listening to the voices of the people in the field and consulting with experts in various fields. In order to indicate the cultural importance of flowers, we also described what we learned from our teacher about Ikebana, a Japanese cultural art form. | |
| 4 | Proposed Implementation | https://2021.igem.org/Team:Kyoto/Implementation | We described the application of our project in the real world. | |
| Gold Criteria | ||||
| Number | Check | Criteria | URL | Contents |
| 1 | Integrated Human Practices | https://2021.igem.org/Team:Kyoto/Human_Practices | We repeatedly listen to various people in the flower industry to evaluate our solutions. By doing this, we understood our project more deeply and the project itself was improved in many ways than projects we thought at first. | |
| 2 | Improvement of an Existing Part | To extend the general applicability of the production system we developed, we improved it by mutating the sequence of the proteolytic tag, SsrA tag. This improvement is based on the suggestions obtained from modeling. Although the SsrA tag usually induces very fast proteolysis in E. coli, we have created many variants and established a set of variants with different degradation rates. The collection of these parts is a resource that can be used extensively to control the rate of protein turnover within E. coli. Degradation activity was evaluated by sfGFP intensity and compared to the original parts (wild-type SsrA tag). | ||
| 3 | Project Modeling | https://2021.igem.org/Team:Kyoto/Model | We simulated the production system we developed, and confirmed that the production system based on asymmetric plasmid partitioning behaves as intended. We also used this model to evaluate the effect of varying parameters and predicted that the "repressor degradation rate" is the most sensitive parameter to control the timing of reporter expression in our production system. This prediction provided valuable suggestions for the improvement experiments of the parts described in Improvement of an Existing Part above. In addition to the two-plasmid system that was actually used in this project, a similar model was constructed for a three-plasmid system, and we confirmed that the two-step continuous asymmetric partitioning can occur as intended. By doing this, we were able to clarify the scalability of our production system. We have also simulated the application to actual biomolecular production. This quantitative result demonstrated its superiority to ordinary continuous culture systems. |
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| 4 | Proof of Concept | https://2021.igem.org/Team:Kyoto/Proof_Of_Concept | Toward the implementation of our project in a relevant context, we completed proof of concept experiments. First, to solve the problem of viral infection in flowering plants, our experiments and prototypes include an enzymatic detection method, image-based software AI, and an integrated hardware system. Additionally, we performed experiments to address the control of plant pests, as well as conceptualized and modeled an improved biomolecule production system with broad applications in our own project and general bioprocesses. The following results establish proof of concept for each component and the project as a whole. | |
| 5 | Partnership | https://2021.igem.org/Team:Kyoto/Partnership | We described our efforts throughout the year in collaboration with other iGEM teams. Not only did we share information about the progress of their projects, but we actually conducted experiments to complement each other's projects by exchanging DNA, peptides, enzymes, and other molecules. These collaborations played a major role in making the projects of both teams more valuable. | |
| 6 | Education & Communication | https://2021.igem.org/Team:Kyoto/Communication | We described the educational activities we have conducted to promote synthetic biology. This activity was not only an opportunity to provide new knowledge to high school students interested in synthetic biology but also an opportunity for our team to understand the significance of synthetic biology more deeply by giving lectures to students. The linked page provides the actual materials used for education for future teams to develop similar educational activities. | |
| 7 | Excellence in Another Area | https://2021.igem.org/Team:Kyoto/Gift_from_Synthetic_Biology | We visited new startups founded by the members of iGEM Kyoto graduate to find out how their experience at iGEM has changed their lives and helped them to launch their startups. By listening to and thinking about their stories, the meaning of our activities at iGEM, which we hadn't noticed before, gradually began to emerge. | |
2. Special Prizes
| Number | Prize | Check | URL | Contents |
|---|---|---|---|---|
| 1 | Best Education | https://2021.igem.org/Team:Kyoto/Education | Our team is eligible for this prize because we created teaching materials of synthetic biology which promote mutual learning and implemented materials to high school students. Since the term "synthetic biology" is not very well known in Japan even today, we created slides on the basics of synthetic biology that could be easily understood by people who do not know much about biology in order to promote the term and its contents. On the other hand, we also created different versions of the slides for those who have some knowledge of biology, to provide materials that are more suitable for the needs and levels of each person. In order to improve these materials, we conducted a survey of the participants throughout the educational activities to see how well they met the actual needs of the participants, and then added information to meet the needs. Finally, by making these slides available on our wiki, we have made our materials accessible to people around the world, and thus have contributed to the spread of synthetic biology. | |
| 2 | Best Hardware | https://2021.igem.org/Team:Kyoto/Hardware | We developed a method to detect the infection of flower viruses and viroids by isothermal amplification that can be carried out on-site by farmers. In addition, we created the hardware named “DLAMI” that makes it easy to measure the fluorescence of samples. Farmers are the intended end-user of this device, so we listened to the needs of farmers to improve our hardware. We shared the schematics and images of our hardware to enable reproduction, so this hardware will contribute to various iGEM projects which need fluorescent measurement on-site. | |
| 4 | Best Integrated Human Practices | https://2021.igem.org/Team:Kyoto/Human_Practices | We visited farmers, distribution centers, and florists. We also held many Zoom meetings with many
professors. What we learned from these improved our project. One of the good points of our human practice is
visiting many stakeholders with very diverse background and connecting them to one project. We interviewed
people on site more than 10 times and we held online meeting more than 20 times. The increase in online
meetings after the pandemic enabled continuous feedback to our ideas from professors, which proved to be
very efficient to improve the project. We also learned Ikebana, the culture of flower arranging in Japan, from a famous teacher. We wrote in our wiki about our fascination with Ikebana as one of the ways people enjoy flowers. We intended this to enrich the reader's experience about flowers. |
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| 6 | Best Model | https://2021.igem.org/Team:Kyoto/Model | Our modeling team accurately understood our device "BLOOM", based on a complicated Asymmetric Plasmid Partitioning (APP) system, by building a model using the stochastic algorithm "Gillespie algorithm". Gillespie algorithm is the most proper algorithm to reproduce chemical reactions which are caused at random timing. Our model helped our wet team to determine how to do experiments to control degradation rates of repressors, and helped our team to grasp BLOOM and improve our project. Thanks to our model, we demonstrated that our system is useful and widely applicable in the future. Our model can be used not only to simulate BLOOM but also to help to optimize production systems and to comprehend various systems containing APP. If you try to use the APP system in your project, this model will help you to improve your experiments and to control your system. | |
| 7 | Best New Basic Part | https://2021.igem.org/Team:Kyoto/Parts | ||
| 9 | Best Part Collection | https://2021.igem.org/Team:Kyoto/Part_Collection | ||
| 12 | Best Presentation | None | Look forward to it! | |
| 13 | Safety and Security Award | https://2021.igem.org/Team:Kyoto/Safety | In this project, we are proposing solutions using organic materials without cells such as peptides and RNA to make flowers last longer. So in these ways, we minimize the risk of GMOs being released into the environment. To put these approaches to practical use, we need to devise a way to increase production efficiency and decrease the cost of the biological materials used. Therefore, we have also developed an intelligent biomolecule production system using asymmetric plasmid partitioning. This shows that it is possible to improve the efficiency of production in synthetic biology while maintaining the security and safety of closed systems. | |
| 14 | Best Software Tool | https://2021.igem.org/Team:Kyoto/Software | Our software deserves to be awarded, because it has many excellent features both as a complete tool and as a science and technology. We created two kinds of software. One is an AI-based software to diagnose whether a leaf is diseased, and the other is a software to extract the brightness of the well area from the fluorescence emitted by RT_LAMP. The former boasts an accuracy of 99.8%, which increases the prior probability of RT_LAMP, while the latter assists in post-test analysis, which meshes with synthetic biology experiments. In addition, both are intuitive and easy to use, and their details are described in the wiki. Furthermore, the AI in the former is highly versatile in terms of informatics, as it is easy to create diagnostic imaging AI for other plants by fine tuning it. This makes it easier to create an AI for disease detection, and will greatly help the projects of other iGEM teams. | |
| 16 | Best Sustainable Development Impact | https://2021.igem.org/Team:Kyoto/Sustainable | The FAO estimates that globally, annual crop losses to plant pests are between 20-40 percent of production. In terms of economic value, plant diseases alone cost the global economy around US$220 billion annually and invasive insects around US$70 billion. The impetus for the project was to cut flowers, and through Human Practices, we have arrived at the problem of disease and insect pests to cut flowers, crops, and any other plant that people may find valuable. The integrated package for virus detection and the pest control method we have developed will avert $290 billion in total in food production losses, making a significant contribution to the SDGs “2 ZERO HUNGER” and “8 DECENT WORK AND ECONOMIC GROWTH”. In addition, “BLOOM”, a biomolecular production platform that is expected to be used and developed by many iGEM teams and synthetic biologists, will contribute to "9 INDUSTRY INNOVATION AND INFRASTRUCTURE”. We addressed 10 of the 17 SDGs through interviews with people in the flower industry and experts in various fields. | |
| 17 | Best Wiki | https://2021.igem.org/Team:Kyoto | Our wiki is a comprehensive description of the activities of iGEM Kyoto in 2021, and is the fruit of the members' efforts: each person, from those who think about the overall layout of the wiki to those who think about the text of the wiki, has used their own strengths to create a colorful palette of the wiki. The menu is displayed on the flutter side across all pages of the wiki, which allows readers to know where they are reading at any time and to quickly get to where they want to read. This should be obvious if you read the code of the wiki, but it should also set a good design example for the other iGEM team. |
3. Community-Awarded Prizes
| Number | Prize | Check | URL |
|---|---|---|---|
| 1 | iGEMers' Prize | None | |
| 2 | Best Project Promotion Video Prize | https://video.igem.org/w/81DxS3o5zQHNXhgBQUHG8t |