Difference between revisions of "Team:BIT-China/Model/GPCRpathway"
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<p class="my-content-p">T1R2-T1R3 receptor activation part:</p> | <p class="my-content-p">T1R2-T1R3 receptor activation part:</p> | ||
| − | <img src="https://static.igem.org/mediawiki/2017/3/3f/BIT-China_model-GPCR_fig1.png"/> | + | <img class="formula2"src="https://static.igem.org/mediawiki/2017/3/3f/BIT-China_model-GPCR_fig1.png"/> |
</div> | </div> | ||
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<p class="my-content-p">G-protein cycle activation part:</p> | <p class="my-content-p">G-protein cycle activation part:</p> | ||
| − | <img src="https://static.igem.org/mediawiki/2017/4/45/BIT-China_model-GPCR_fig2.png"/> | + | <img class="formula2" src="https://static.igem.org/mediawiki/2017/4/45/BIT-China_model-GPCR_fig2.png"/> |
</div> | </div> | ||
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<p class="my-content-p">The MAPK cascade part:</p> | <p class="my-content-p">The MAPK cascade part:</p> | ||
| − | <img src="https://static.igem.org/mediawiki/2017/7/70/BIT-China_model-GPCR_fig3.png"/> | + | <img class="formula2" src="https://static.igem.org/mediawiki/2017/7/70/BIT-China_model-GPCR_fig3.png"/> |
</div> | </div> | ||
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<div class="my-content-box"> | <div class="my-content-box"> | ||
<p class="my-content-p">Gene expression part:</p> | <p class="my-content-p">Gene expression part:</p> | ||
| − | <img src="https://static.igem.org/mediawiki/2017/9/98/BIT-China_model-GPCR_fig4.png"/> | + | <img class="formula2" src="https://static.igem.org/mediawiki/2017/9/98/BIT-China_model-GPCR_fig4.png"/> |
</div> | </div> | ||
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<h3 class="title-h3">Primary simulation result</h3> | <h3 class="title-h3">Primary simulation result</h3> | ||
<div class="my-content-box"> | <div class="my-content-box"> | ||
| − | <img src="https://static.igem.org/mediawiki/2017/2/20/BIT-China_model-GPCR_fig5.png"/> | + | <img class="formula2" src="https://static.igem.org/mediawiki/2017/2/20/BIT-China_model-GPCR_fig5.png"/> |
</div> | </div> | ||
<p class="my-content-p">The result is calculated by our initial model. As we can see, RFP fluorescence intensity will saturate when sweetener’s concentration reaches 2uM. This result is consistent with what we see from the literature.</p> | <p class="my-content-p">The result is calculated by our initial model. As we can see, RFP fluorescence intensity will saturate when sweetener’s concentration reaches 2uM. This result is consistent with what we see from the literature.</p> | ||
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<p class="my-content-p">So we transform our system based on this model, the results are as follows;</p> | <p class="my-content-p">So we transform our system based on this model, the results are as follows;</p> | ||
<div class="my-content-box"> | <div class="my-content-box"> | ||
| − | <img src="https://static.igem.org/mediawiki/2017/6/64/BIT-China_model-GPCR_fig6.png"/> | + | <img class="formula2" src="https://static.igem.org/mediawiki/2017/6/64/BIT-China_model-GPCR_fig6.png"/> |
</div> | </div> | ||
<p class="my-content-p">Based on the experimental data and the result of simulation we can see that the concentration of 5-10 uM works best. Our analysis concludes that we have not yet achieved the results of yeast transformation.</p> | <p class="my-content-p">Based on the experimental data and the result of simulation we can see that the concentration of 5-10 uM works best. Our analysis concludes that we have not yet achieved the results of yeast transformation.</p> | ||
Revision as of 11:06, 24 October 2017
Purpose
By establishing the signal transduction model, we can intuitively understand how the sweetener signal conducts in the GPCR and the details of each step of the signal arrival thorough, which provides a powerful help for our transformation of the pathway, amplification of the signal, while the simulation results can also give us the future design of biological sweetness meter to provide theoretical help.
Method
Our GPCR model based on three presuppositions:
- 1. We assume that T1R2-T1R3 receptor does not have synergistic effect.
- 2. We hypothesize that the combination between s1
- 3. We assume that the combination of the rate constant and the initial concentration of the sweeteners and alpha pheromone are consistent.
We establish the equations among variable quantities based on Mass-action law; and we use ordinary differential equations (ODEs) that can be solved by MATLAB. We remake the pheromone signal system in yeast into four modules: T1R2-T1R3 receptor activation, G-protein cycle activation, the MAPK cascade, and gene expression.
T1R2-T1R3 receptor activation part:
reaction diagram
It can be modeled in a set of differential equations:
Rate equations:
G-protein cycle activation part:
It can be modeled in a set of differential equations:
Rate equations:
The MAPK cascade part:
It can be modeled in a set of differential equations:
Rate equations:
Gene expression part:
It can be modeled in a set of differential equations:
Rate equations
| Parameter | Description | Value | Unit |
|---|
Primary simulation result
The result is calculated by our initial model. As we can see, RFP fluorescence intensity will saturate when sweetener’s concentration reaches 2uM. This result is consistent with what we see from the literature.
So we transform our system based on this model, the results are as follows;
Based on the experimental data and the result of simulation we can see that the concentration of 5-10 uM works best. Our analysis concludes that we have not yet achieved the results of yeast transformation.
Discussion
It is clear that the results of our initial model and experimental model are different, indicating that our model is not accurate enough. We need to keep working, such as changing the value of certain parameters, rewriting some important equations, so that our model and our experimental can combine perfectly.
References
1.Grant E. DuBois, Molecular mechanism of sweetness sensation, Physiology & Behavior 164 (2016) 453–463.
2. Bente Kofahl1 and Edda Klipp2, Modelling the dynamics of the yeast pheromone< pathway, Published online in Wiley InterScience, Yeast 2004; 21: 831–850.
