Difference between revisions of "Team:Tokyo Tech/Achievement"
| Line 70: | Line 70: | ||
<div id="best-new-application"> | <div id="best-new-application"> | ||
| − | <h6 style="line-height: | + | <h6 style="line-height: 30px">As our project title “Finding Flavi” implies, we assume that our Dengue-specific detection system and prediction model can be applied to other flaviviruses. |
In our detection system, since we use fluorescence protein which can be substituted for any other color more viruses can be identified by matching it with different colors. This system is high-throughput, cost-efficient, highly sensitive and has the potential to be widely used in the market.</h6> | In our detection system, since we use fluorescence protein which can be substituted for any other color more viruses can be identified by matching it with different colors. This system is high-throughput, cost-efficient, highly sensitive and has the potential to be widely used in the market.</h6> | ||
| − | <h6 style="line-height: | + | <h6 style="line-height: 30px">Our prediction model works robustly and the accuracy increases in proportion to the amount of input timescale data. We successfully predicted the change in the number of patients in a specific area and also corrected the gap between actual data and predicted data with environmental factors.</h6> |
</div> | </div> | ||
Revision as of 13:14, 14 October 2018
Achievements
| Medal | Criteria | Detail |
|---|---|---|
| Bronze | Register and Attend | We registered for iGEM, had a great summer, and attended the Giant Jamboree. |
| Deliverables | We met all deliverables on the requirements page. | |
| Attribution | We created an "Attributions" page on our team wiki with clear attribution of each aspect in our project. | |
| Characterization / Contribution | We participated in the Interlab Measurement Study. | |
| Silver | Validated Part | We experimentally validated and documented new BioBrick Parts (BBa_K2729001, BBa_K2729006, BBa_K2729007) of our own design and construction that work as expected. |
| Collaboration | We hosted iGEM East Japan Meetup on August 8th. We mentored ASIJ Tokyo Team about the modeling and qualitative analysis. We held a practice session with Botchan Lab Team (Tokyo University of Science) at Boston University. We shipped M13 phage plasmid (BBa_K1139025, pBAD-M13) to iGEM Buenos Aires Team (Click here to read more). |
|
| Human Practices | We held a workshop at a high school to raise the awareness of infectious diseases among younger generations. We discussed our project with virology researchers and received valuable advice. (Click here to read more) |
|
| Gold | Integrated Human Practices | We integrated human practice activities with analysis from both wet lab experiments and modeling to design our project in accordance with comments from the general public and experts from various institutions through this summer. (Click here to read more) |
| Model Your Project | We developed a brand-new model to predict change in the number of dengue-infected patients of each serotype. We successfully extracted the pattern and predicted the change in the near future. This model gave insights to the wet lab side and is applicable to other areas. (Click here to read more) | |
| Demonstration of Your Work | We designed our project based on iGEM's biosafety standard. Since we split up the structural region and non-structural region, the pseudoviruses produced in our system can infect host cells only once. Thus, it will not spread out of the system and will not be harmful.(Click here to read more) |
Best New Application
As our project title “Finding Flavi” implies, we assume that our Dengue-specific detection system and prediction model can be applied to other flaviviruses. In our detection system, since we use fluorescence protein which can be substituted for any other color more viruses can be identified by matching it with different colors. This system is high-throughput, cost-efficient, highly sensitive and has the potential to be widely used in the market.
Our prediction model works robustly and the accuracy increases in proportion to the amount of input timescale data. We successfully predicted the change in the number of patients in a specific area and also corrected the gap between actual data and predicted data with environmental factors.
