Team:HZAU-China/Achievements
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- | 12. Beyond the lab work, we did lots of | + | 12. Beyond the lab work, we also did a lots of interesting things. You can see them in the <span style="font-weight:bold;"><a href="https://2014.igem.org/Team:HZAU-China/Outreach">outreach</a></span>. We had collaboration with other teams, spread iGEM to the public and social media. And we also paid an attention to the safety. <span style="font-weight:bold;">Specially</span>, we held the CCiC meetup in Wuhan in this summer with 16 teams joined. Also we <span style="font-weight:bold;">described and evaluated</span> our project from the viewpoints of humanities, aesthetics, economics and law. |
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Revision as of 02:10, 18 October 2014
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Judgement
We apply for a Gold Medal, Best Information Processing Project, Best Model, Best Policy &Practices Project.
As for the Gold Medal
We have met these requirements below:
As for Best Information Processing Project:
Conventional engineered circuits in cells have only a simple regulatory network to process information. Once the environment changed, the approach to handle the information might be invalid. We adopted an elegant means of systems integration to make the cells have adaptability and work well in complex environments. Our engineered cells can use different approaches to process information according to the environment; meanwhile we overcame some common challenges like host overload and crosstalk.
As for Best Model:
Different from most modeling work in iGEM, we first described the biological process related to our project rather than list the equations used before directly. In this way, we made sense of the necessary preconditions we needed and it became easier to perform stochastic simulation. Before simulation, we incorporated some information from wet lab to adjust some parameters and analyzed how some promoter properties influenced our devices. We found that the systems behaviors in the repressilator were closely related to the absolute promoter strength of the weakest promoter among the three genes. Many modeling approaches, including deterministic and stochastic simulations, phase analysis, parameter scanning, theoretical inference, were presented in our modeling part. Moreover, we introduced a novel design principle to help other researches to finish their custom designs.