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Team:Peking/Modeling/Luminesensor/Simulation
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From the figure above, we discovered that the activation and decay of <i>Luminesensor</i> are the pioneers of progress, and the activating rate is the most completely switched variable as lighting varies. The promoter sequences in the DNA are repressed even though the <i>Luminesensor</i> has not all dimered. | From the figure above, we discovered that the activation and decay of <i>Luminesensor</i> are the pioneers of progress, and the activating rate is the most completely switched variable as lighting varies. The promoter sequences in the DNA are repressed even though the <i>Luminesensor</i> has not all dimered. | ||
| + | </p> | ||
| + | </div> | ||
| + | <div class="PKU_context floatR"> | ||
| + | <h3 id="title3">Stochastic Simulation</h3> | ||
| + | <p> | ||
| + | In order to check the working stability of Luminesensor, we simulated this reaction network with a <a href="/Team:Peking/Modeling/Appendix/Stochastic">stochastic model</a>. By estimating the volume of a cell, we converted the concentration of a component into the number of molecules by 1 n mol/L : 1. The result are shown below: | ||
| + | <p> | ||
| + | <div class="floatC"> | ||
| + | <img src="/wiki/images/2/2d/Peking2012_sto(h).png" alt="Simulation Result" style="width:600px;"/> | ||
| + | <div> | ||
| + | <p class="description"> | ||
| + | Fig 3. Stochastic Simulation Result of the prototype Luminesensor. | ||
| + | </p> | ||
| + | </div> | ||
| + | </div> | ||
| + | <p> | ||
| + | According to the figure above, the noise did not influence this system. Thus, the <i>Luminesensor</i> is expected to work theoretically. Besides, the average value of stochastic simulation is coupled with the result of ODE model, which in turn proves the self-consistency of our ODE model. | ||
</p> | </p> | ||
</div> | </div> | ||
</html>{{Template:Peking2012_Color_Epilogue}} | </html>{{Template:Peking2012_Color_Epilogue}} | ||
Revision as of 02:04, 23 September 2012
ODE model
According to the previous network and ODE model, we listed all the differential equations (detail here) and simulated this system with MATLAB. We specifically watched three expressions to understand the mechanism for Luminesensor.
| Expression | Description | Remark |
| rb = 1 - [DL]/[DT] | This indicates the repressing degree. | DT indicates the total specific binding sites, while the DL indicates the free ones among DT. |
| rd = 2[LA2X]/[LT] | This indicates the dimerizing degree. | LT indicates the total Luminesensor molecules, and LA2X indicates all dimered Luminesensor molecules, i.e. LA2 + LA2DL. |
| ra = ( [LAX] + 2[LA2X] ) /[LT] |
This indicates the activating degree. | LAX indicates all monomer Luminesensor molecules, i.e. LA + LADL. |
The simulation result is shown below:
.png)
Fig 2. ODE Simulation Result of the prototype Luminesensor.
From the figure above, we discovered that the activation and decay of Luminesensor are the pioneers of progress, and the activating rate is the most completely switched variable as lighting varies. The promoter sequences in the DNA are repressed even though the Luminesensor has not all dimered.
Stochastic Simulation
In order to check the working stability of Luminesensor, we simulated this reaction network with a stochastic model. By estimating the volume of a cell, we converted the concentration of a component into the number of molecules by 1 n mol/L : 1. The result are shown below:
.png)
Fig 3. Stochastic Simulation Result of the prototype Luminesensor.
According to the figure above, the noise did not influence this system. Thus, the Luminesensor is expected to work theoretically. Besides, the average value of stochastic simulation is coupled with the result of ODE model, which in turn proves the self-consistency of our ODE model.
