Difference between revisions of "Team:NYMU-Taipei/Model"
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<img src="https://static.igem.org/mediawiki/2018/6/6d/T--NYMU-Taipei--model-1.png" style="width:500px;"> | <img src="https://static.igem.org/mediawiki/2018/6/6d/T--NYMU-Taipei--model-1.png" style="width:500px;"> | ||
<div class="paragraphs"> | <div class="paragraphs"> | ||
| − | <h3> | + | <h3>Background</h3> |
<p >This model aims to find out how much FRET protein should be added into our screening system.</p> | <p >This model aims to find out how much FRET protein should be added into our screening system.</p> | ||
| − | <h3> | + | <h3>Methods</h3> |
<p >Chemical equilibrium is used to determine the florescence level and the minimal FRET protein activity required to produce the florescence that can be detected. This model assumes the portion of active protein in all protein is constant.</p> | <p >Chemical equilibrium is used to determine the florescence level and the minimal FRET protein activity required to produce the florescence that can be detected. This model assumes the portion of active protein in all protein is constant.</p> | ||
| − | <h3> | + | <h3>Results</h3> |
| + | <h3>Conclusion</h3> | ||
<p >The optimal ratio of the amount of one FRET protein to that of the other is the following:</p> | <p >The optimal ratio of the amount of one FRET protein to that of the other is the following:</p> | ||
<img src="https://static.igem.org/mediawiki/2018/e/e2/T--NYMU-Taipei--model-2.png"> | <img src="https://static.igem.org/mediawiki/2018/e/e2/T--NYMU-Taipei--model-2.png"> | ||
| Line 64: | Line 65: | ||
<div class="paragraphs"> | <div class="paragraphs"> | ||
| − | <h3> | + | <h3>Background</h3> |
<p >The efficiency of FRET is inversely proportional to the sixth power of the distance between donor and acceptor, making FRET extremely sensitive to small changes in distance. Therefore, simulating structure of protein-protein interactions is important. This model aims to predict the FRET efficiency in order to determine which molecules and which terminals should be used.</p> | <p >The efficiency of FRET is inversely proportional to the sixth power of the distance between donor and acceptor, making FRET extremely sensitive to small changes in distance. Therefore, simulating structure of protein-protein interactions is important. This model aims to predict the FRET efficiency in order to determine which molecules and which terminals should be used.</p> | ||
| − | <h3> | + | <h3>Methods</h3> |
<p >pyDockWEB is used for structural prediction of protein-protein interactions and the prediction of distances between donor and acceptor. Förster theory is used determine the FRET efficiency.</p> | <p >pyDockWEB is used for structural prediction of protein-protein interactions and the prediction of distances between donor and acceptor. Förster theory is used determine the FRET efficiency.</p> | ||
| − | <h3> | + | <h3>Results</h3> |
| + | <h3>Conclusion</h3> | ||
<p >The FRET efficiency is given by the following table.</p> | <p >The FRET efficiency is given by the following table.</p> | ||
<img src="https://static.igem.org/mediawiki/2018/b/b5/T--NYMU-Taipei--FRETEfficiencyResult.png" style="width:250px; float:left;"> | <img src="https://static.igem.org/mediawiki/2018/b/b5/T--NYMU-Taipei--FRETEfficiencyResult.png" style="width:250px; float:left;"> | ||
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<div class="paragraphs"> | <div class="paragraphs"> | ||
<p>[1]</p> | <p>[1]</p> | ||
| − | <h3> | + | <h3>Background</h3> |
<p >This project constructs a plasmid that connects DKK1 promoter to mCherry. The gene expression rate of DKK1 promoter is affected by testosterone activity and affects the expression level of mCherry. The expression level of mCherry should be greater than a threshold so that its florescence can be detected by devices. In order to achieve this threshold, sufficient amount of testosterone should be added into our screening system; this is the sensitivity of our screening system. This model aims to find out the sensitivity of our screening system.</p> | <p >This project constructs a plasmid that connects DKK1 promoter to mCherry. The gene expression rate of DKK1 promoter is affected by testosterone activity and affects the expression level of mCherry. The expression level of mCherry should be greater than a threshold so that its florescence can be detected by devices. In order to achieve this threshold, sufficient amount of testosterone should be added into our screening system; this is the sensitivity of our screening system. This model aims to find out the sensitivity of our screening system.</p> | ||
| − | <h3> | + | <h3>Methods</h3> |
<p >This model simulates the kinetics of transcription signal and then simulate the expression of DKK1. Florescence decay is not considered in this model because ……..</p> | <p >This model simulates the kinetics of transcription signal and then simulate the expression of DKK1. Florescence decay is not considered in this model because ……..</p> | ||
| − | <h3> | + | <h3>Results</h3> |
| + | <h3>Conclusion</h3> | ||
<p> This model has no result. </p> | <p> This model has no result. </p> | ||
<p >[DKK1] = 5 + 0.006 [DHT]^2 </p> | <p >[DKK1] = 5 + 0.006 [DHT]^2 </p> | ||
Revision as of 13:33, 8 October 2018
FRET Ratio Model
Background
This model aims to find out how much FRET protein should be added into our screening system.
Methods
Chemical equilibrium is used to determine the florescence level and the minimal FRET protein activity required to produce the florescence that can be detected. This model assumes the portion of active protein in all protein is constant.
Results
Conclusion
The optimal ratio of the amount of one FRET protein to that of the other is the following:
The figures in the table indicates the optimal ratio of the protein on the top over the protein on the left.
Click Here For More Info
FRET Efficiency Model
Background
The efficiency of FRET is inversely proportional to the sixth power of the distance between donor and acceptor, making FRET extremely sensitive to small changes in distance. Therefore, simulating structure of protein-protein interactions is important. This model aims to predict the FRET efficiency in order to determine which molecules and which terminals should be used.
Methods
pyDockWEB is used for structural prediction of protein-protein interactions and the prediction of distances between donor and acceptor. Förster theory is used determine the FRET efficiency.
Results
Conclusion
The FRET efficiency is given by the following table.
Click Here For More Info
mCherry Expression Model
[1]
Background
This project constructs a plasmid that connects DKK1 promoter to mCherry. The gene expression rate of DKK1 promoter is affected by testosterone activity and affects the expression level of mCherry. The expression level of mCherry should be greater than a threshold so that its florescence can be detected by devices. In order to achieve this threshold, sufficient amount of testosterone should be added into our screening system; this is the sensitivity of our screening system. This model aims to find out the sensitivity of our screening system.
Methods
This model simulates the kinetics of transcription signal and then simulate the expression of DKK1. Florescence decay is not considered in this model because ……..
Results
Conclusion
This model has no result.
[DKK1] = 5 + 0.006 [DHT]^2
where [DKK1] indicate activity in ng/ml and [DHT] indicates activity in nM.
[mCherry] = c_1 + c_2 [Testosterone]^2
where square brackets indicate activity in M, and c_1 and c_2 are constants to be determined.
Note: This model is accurate only when [DHT] is less than 50 nM.
Click Here For More Info
References
- Meehan KL1, Sadar MD. (2003). "Androgens and androgen receptor in prostate and ovarian malignancies." Front Biosci. 2003 May 1;8:d780-800.
- Jane Yuxia Qin (2010). "Systematic Comparison of Constitutive Promoters and the Doxycycline-Inducible Promoter." PLOS ONE. May 12, 2010. https://doi.org/10.1371/journal.pone.0010611
- Shicheng Liu, Hitoshi Yamauchi. (2008). "Different patterns of 5-alpha-reductase expression, cellular distribution, and testosterone metamolism in human follicular dermal papilla cells." Biochemical and Biophysical Research Communications 368 (2008) 858–864