Difference between revisions of "Team:Fudan/Acknowledgement"

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                                 We thank <a href="https://2017.igem.org/Team:Fudan/Teamers" target="_blank">Yiheng Wang</a> for teaching us to model
 
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                                 <a href="/model#1">DNA and transcription factor binding</a> using his previous \[model](link to 2017.model).
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                                 <a href="/model#1">DNA and transcription factor binding</a> using his previous </a> using his previous \ <a href="https://2017.igem.org/Team:Fudan/Model" target="blank">model</a>.
 
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Revision as of 15:40, 17 October 2018

Acknowledgement

Let us express the most sincere thanks to those who rendered their warm help to us. A successful team can’t be constructed without them.

Acknowledgement

Let us express the most sincere thanks to those who rendered their warm help to us. A successful team can’t be constructed without them.

Acknowledgement

  • We thank Yiheng Wang for teaching us to model DNA and transcription factor binding using his previous using his previous \ model.
  • We thank Tian Huang for providing us with a Materialize based wiki template and teaching us with basic knowledge about HTML and CSS.
  • We thank Yiming Cai for giving us advice on purifying EGFP protein, which was used in \[the beads assay](link to others) at the early stage of the project.
  • We thank Yijie Pan for giving us practical suggestions on \[repeated fragment](link to protocol) molecular cloning and \[BioBrick parts](link to parts) documentation.
  • We thank Xinxuan Xiong and Xinyi Xu for giving us useful advice about the \[Bio-ART](link to the page) display and actively helping us in preparation for the display.
  • We thank Ao Feng for helpful discussions on human practice.
  • We thank Jingyi Hu for helping us taking bacteria on the morning, and playing an active role in some of our human practice.
  • We thank Haotian Guo for incisive comments on refining our presentation during and immediately after \[CCiC](link to).
  • We thank Professor [Xu Wei]’s advice on performing flow cytometry and doing the flow data analysis.
  • We thank Professor [Yufang Zheng] for helpful discussions on the known Notch regulation and unpublished insight from her study with metalloprotease [ADAM10] and [ADAM17].
  • We thank Professor [Stephen Blacklow] on email and face-to-face discussions on engineered Notch receptor optimization.
  • We thank Doctor Lixin Yang, Doctor Ganjun Yu, Doctor Xiaoying Bi, and Doctor Pengfei Luo for sparing precise time to have the discussion on xxxx. 需要链接到对应的HP页面部分。医生有主页要放
  • We thank Professor \[Xin Yuan](link to), in the Department of Philosophy, for attending our Bio-Ethic大小写统一 debate and his comments enriched our understanding.
  • We thank Professor Jiang Zhong, Professor Xiaoming Ding, Professor Qiang Huang, and Professor Ting Ni for giving us valuable suggestions and feeds at different stages of the project, from the initial brainstorm to the middle project progress and the pre-departure presentations. 需要添加各个老师的学院网站链接
  • We thank \[the Club of FDU Bertalanffy Society](link to page or wx) for helping to rent the space for [Bio-ART] display.
  • We thank teachers in Qibao Dwight High School, Shanghai Jincai High School, the High School Affiliated to Fudan University for offering us with opportunities to implement \[our HP](link to HP). 需要添加各个学校网址
  • We thank \[Team:Fudan\_China](link to) for teaching us how to use the xxxx brand plate reader.

Photos

People

Listed by alphabetical order of surnames.

Xiaoying Bi
Stephen Blacklow
Yiming Cai
Xiaoming Ding
Ao Feng
Haotian Guo
Jingyi Hu
Qiang Huang
Tian Huang
Pengfei Luo
Ting Ni
Yijie Pan
Yiheng Wang
Xinxuan Xiong
Wei Xu
Xinyi Xu
Lixing Yang
Shunhua Yang
Ganjun Yu
Xin Yuan
Jiang Zhong
Yufang Zheng

Labs, Organizations & Schools

Changhai Hospital
Jincai High School
High School Affiliated to Fudan University
Shanghai Qibao Dwight High School

iGEM Teams

Fudan-CHINA 2018
Fudan 2017
Fudan_China 2017

Abstract

Contact-dependent signaling is critical for multicellular biological events, yet customizing contact-dependent signal transduction between cells remains challenging. Here we have developed the ENABLE toolbox, a complete set of transmembrane binary logic gates. Each gate consists of 3 layers: Receptor, Amplifier, and Combiner. We first optimized synthetic Notch receptors to enable cells to respond to different signals across the membrane reliably. These signals, individually amplified intracellularly by transcription, are further combined for computing. Our engineered zinc finger-based transcription factors perform binary computation and output designed products. In summary, we have combined spatially different signals in mammalian cells, and revealed new potentials for biological oscillators, tissue engineering, cancer treatments, bio-computing, etc. ENABLE is a toolbox for constructing contact-dependent signaling networks in mammals. The 3-layer design principle underlying ENABLE empowers any future development of transmembrane logic circuits, thus contributes a foundational advance to Synthetic Biology.