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− | <li><a href=" | + | <li><a href="/Team:Fudan/Demonstrate">Demonstration</a></li> |
− | <li><a href=" | + | <li><a href="/Team:Fudan/Antigen_Receptors">Antigen, Receptors</a></li> |
− | <li><a href=" | + | <li><a href="/Team:Fudan/Results">Transmembrane logic</a></li> |
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<ul id="dropdown2" class="dropdown-content"> | <ul id="dropdown2" class="dropdown-content"> | ||
− | <li><a href=" | + | <li><a href="/Team:Fudan/Addon#ribo">Addon: ribo</a></li> |
− | <li><a href=" | + | <li><a href="/Team:Fudan/Addon#TALE">Addon: TALE</a></li> |
− | <li><a href=" | + | <li><a href="/Team:Fudan/Addon#T2">Addon: T2</a></li> |
− | <li><a href=" | + | <li><a href="/Team:Fudan/Model#Transcriptional_Amplifer">Model: transcriptional amplifer</a></li> |
− | <li><a href=" | + | <li><a href="/Team:Fudan/Model#NotchLigandKinetics">Model: Notch-ligand kinetics</a></li> |
− | <li><a href=" | + | <li><a href="/Team:Fudan/Software">Software</a></li> |
</ul> | </ul> | ||
<ul id="dropdown3" class="dropdown-content"> | <ul id="dropdown3" class="dropdown-content"> | ||
− | <li><a href=" | + | <li><a href="/Team:Fudan/InterLab">iGEM interLab</a></li> |
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− | <li><a href=" | + | <li><a href="/Team:Fudan/Optimization">Optimization</a></li> |
− | <li><a href=" | + | <li><a href="/Team:Fudan/Parts_Collection">Parts collection</a></li> |
− | <li><a href=" | + | <li><a href="/Team:Fudan/Improve">Parts improvement</a></li> |
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− | <li><a href=" | + | <li><a href="/Team:Fudan/Design_Intention">Design intention</a></li> |
− | <li><a href=" | + | <li><a href="/Team:Fudan/Human_Practices">Human practices</a></li> |
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− | <li><a href=" | + | <li><a href="/Team:Fudan/Demonstrate">Demonstration</a></li> |
− | <li><a href=" | + | <li><a href="/Team:Fudan/Antigen_Receptors">Antigen, Receptors</a></li> |
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− | <li><a href=" | + | <li><a href="/Team:Fudan/Addon#ribo">Addon: ribo</a></li> |
− | <li><a href=" | + | <li><a href="/Team:Fudan/Addon#TALE">Addon: TALE</a></li> |
− | <li><a href=" | + | <li><a href="/Team:Fudan/Addon#T2">Addon: T2</a></li> |
− | <li><a href=" | + | <li><a href="/Team:Fudan/Model#Transcriptional_Amplifer">Model: transcriptional amplifer</a></li> |
− | <li><a href=" | + | <li><a href="/Team:Fudan/Model#NotchLigandKinetics">Model: Notch-ligand kinetics</a></li> |
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− | <li><a href=" | + | <li><a href="/Team:Fudan/Notebook">Our notebook</a></li> |
− | <li><a href=" | + | <li><a href="/Team:Fudan/Primers">Primers used</a></li> |
− | <li><a href=" | + | <li><a href="/Team:Fudan/Protocols">Protocols</a></li> |
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− | <li><a href=" | + | <li><a href="/Team:Fudan/Composite_Parts">Composite parts</a></li> |
− | <li><a href=" | + | <li><a href="/Team:Fudan/Optimization">Optimization</a></li> |
− | <li><a href=" | + | <li><a href="/Team:Fudan/Parts_Collection">Parts collection</a></li> |
− | <li><a href=" | + | <li><a href="/Team:Fudan/Improve">Parts improvement</a></li> |
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− | <li><a href=" | + | <li><a href="/Team:Fudan/Collaborations">Collaborations</a></li> |
− | <li><a href=" | + | <li><a href="/Team:Fudan/Design_Intention">Design intention</a></li> |
− | <li><a href=" | + | <li><a href="/Team:Fudan/Human_Practices">Human practices</a></li> |
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− | <li><a href=" | + | <li><a href="/Team:Fudan/Attributions">Attributions</a></li> |
− | <li><a href=" | + | <li><a href="/Team:Fudan/Heritage">Heritage</a></li> |
− | <li><a href=" | + | <li><a href="/Team:Fudan/Team">Members</a></li> |
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<p style="margin-top:0;text-indent: 0;"><b>Figure 1. Flow cytometry characterization of SynNotch receptors.</b> TRE3GV-EGFP circuit was set to indicate the activation level of SynNotch receptors. It is shown that αCD19-mN1c-tTAA has the highest signal-to-noise ratio.</p> | <p style="margin-top:0;text-indent: 0;"><b>Figure 1. Flow cytometry characterization of SynNotch receptors.</b> TRE3GV-EGFP circuit was set to indicate the activation level of SynNotch receptors. It is shown that αCD19-mN1c-tTAA has the highest signal-to-noise ratio.</p> | ||
− | <p>For more details, please check <a href=" | + | <p>For more details, please check <a href="/Team:Fudan/Parts_Collection">our parts collection page</a>. |
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− | <li><a href=" | + | <li><a href="/Team:Fudan/Antigen_Receptors">Antigen, Receptors</a></li> |
− | <li><a href=" | + | <li><a href="/Team:Fudan/Results">Transmembrane logic</a></li> |
<li><a href="https://2017.igem.org/Team:Fudan">2017.iGEM</a></li> | <li><a href="https://2017.igem.org/Team:Fudan">2017.iGEM</a></li> | ||
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− | <li><a href=" | + | <li><a href="/Team:Fudan/Addon#TALE">Addon: TALE</a></li> |
− | <li><a href=" | + | <li><a href="/Team:Fudan/Addon#T2">Addon: T2</a></li> |
− | <li><a href=" | + | <li><a href="/Team:Fudan/Model#Transcriptional_Amplifer">Model: transcriptional amplifer</a></li> |
− | <li><a href=" | + | <li><a href="/Team:Fudan/Model#NotchLigandKinetics">Model: Notch-ligand kinetics</a></li> |
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− | <li><a href=" | + | <li><a href="/Team:Fudan/Notebook">Our notebook</a></li> |
− | <li><a href=" | + | <li><a href="/Team:Fudan/Primers">Primers used</a></li> |
− | <li><a href=" | + | <li><a href="/Team:Fudan/Protocols">Protocols</a></li> |
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− | <li><a href=" | + | <li><a href="/Team:Fudan/Composite_Parts">Composite parts</a></li> |
− | <li><a href=" | + | <li><a href="/Team:Fudan/Optimization">Optimization</a></li> |
− | <li><a href=" | + | <li><a href="/Team:Fudan/Parts_Collection">Parts collection</a></li> |
− | <li><a href=" | + | <li><a href="/Team:Fudan/Improve">Parts improvement</a></li> |
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− | <li><a href=" | + | <li><a href="/Team:Fudan/Collaborations">Collaborations</a></li> |
− | <li><a href=" | + | <li><a href="/Team:Fudan/Design_Intention">Design intention</a></li> |
− | <li><a href=" | + | <li><a href="/Team:Fudan/Human_Practices">Human practices</a></li> |
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− | <li><a href=" | + | <li><a href="/Team:Fudan/Acknowledgement">Acknowledgement</a></li> |
− | <li><a href=" | + | <li><a href="/Team:Fudan/Attributions">Attributions</a></li> |
− | <li><a href=" | + | <li><a href="/Team:Fudan/Heritage">Heritage</a></li> |
− | <li><a href=" | + | <li><a href="/Team:Fudan/Team">Members</a></li> |
− | <li><a href=" | + | <li><a href="/Team:Fudan/Sponsors">Sponsors</a></li> |
</ul> | </ul> | ||
</div> | </div> |
Latest revision as of 15:11, 7 November 2018
- Addon: ribo
- Addon: TALE
- Addon: T2
- Model: transcriptional amplifer
- Model: Notch-ligand kinetics
- Software
Composite parts
Part:BBa_K2549021 αCD19-mN1c-tTAA
Introduction
This year we have provided 7 versions of SynNotch receptors in our BioBrick submission, enabling others to wire their contact-dependent signal transduction in mammalian cells. Multiple combinations of extracellular domains, transmembrane core regions and intracellular domains make it even easier for others to readily assemble their own desirable genetic circuits.
Among the 7 SynNotch receptors, our favorite one is αCD19-mN1c-tTAA (BBa_K2549021)
How αCD19-mN1c-tTAA works
It receives ligand-dependent signal via the CD19 scFv and undergoes a cleavage process in which the tTA advance is released, then entering into the nucleus to activate the expression of TRE3GV promotor. Thus it can be served as a signal input module.
Advantages of αCD19-mN1c-tTAA
We have conducted flow cytometry experiments to test our SynNotch receptors and after testing, αCD19-mN1c-tTAA have stood out for showing the highest signal-to-noise ratio. We have also discovered that it has the highest activation ratio when activated by surface-expressed CD19 antigen. Moreover, it also shows only a few amount of false activation which can be tolerated. As it performs great modularity and has a great potential to be utilized by others to assemble their own CD19-dependent signal transduction module, this especially enables the possibility of the clinical application of SynNotch receptors.
Figure 1. Flow cytometry characterization of SynNotch receptors. TRE3GV-EGFP circuit was set to indicate the activation level of SynNotch receptors. It is shown that αCD19-mN1c-tTAA has the highest signal-to-noise ratio.
For more details, please check our parts collection page.
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.