Team:PKU Beijing/Modeling/Improvement

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Latest revision as of 17:52, 21 October 2009

Modeling

ODE

Improvement

Modeling > Improvement

You may wonder: our model doens't consider base expression. What if we consider the base expression? Will AND Gate 2 be in high state forever? To answer these questions, we modify our ODE model. Here, we'll demonstrate how base expression will affect the simulation result of the T3 model.

We set the base expression rate 0.005, which can be considered as appropiate. In the next few screens, the new simulation result will be presented. This time, we'll concentrate on how the behaviour of AND Gate 2 changes after the modification to the model.

	Here's a figure showing how GFP(yellow), CI(blue) and CI434( react under both food stimulus and bell stimulus present. You may see, after the training, the GFP production does NOT decrease to ground level. Instead, it keeps in a higher level. What cause this phenomenon? We believe this is because of the change of behaviour of the second AND gate.
	You may see how AND Gate 2's behaviour changes. After the training, the AND Gate 2 expression(Blue) does NOT decrease to ground level, which we believe causes the changing output dynamics of GFP. Then why does AND Gate 2 change its behaviour? The answer is T3 mRNA. We also plot T3 mRNA in this figure, which is represented by pink curve. You may see, as a natural consequence of bistable turning from CI434 state to CI state, the concentration of T3 mRNA increases to a high level. Considering the equation describing AND Gate 2, we can see that the production of the second AND gate is proportional to that of T3 mRNA. From these facts, the changing behaviour of the whole system is explanable and predictable.
	For example, at 750 min, we train the dog again. Since CI has already been in high state before that second training, the T3 mRNA production won't change much, which means the GFP output will decrease to the same level between these two trainings. Here we plot the dynamics of GFP, CI and CI434 under this circumstance, which is exactly the same with our prediction.

After this modification to the model, we conclude that despite a few changes, our model DOES work with base expression.

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 You may wonder: our model doens't consider base expression. What if we consider the base expression? Will AND Gate 2 be in high state forever? To answer these questions, we modify our ODE model. Here, we'll demonstrate how base expression will affect the simulation result of the T3 model.
-We set the base expression rate 0.005, which can be considered as appropiate. In the next few lines, the new simulation results will be presented. You may find out some differences! And don't forget what these curves represent! Just a reminder, CI:blue, CI434: pink, GFP: yellow.
+We set the base expression rate 0.005, which can be considered as appropiate. In the next few screens, the new simulation result will be presented. This time, we'll concentrate on how the behaviour of AND Gate 2 changes after the modification to the model.
+{|cellpadding=5
+|[[Image:PKU_Base_nomemory_both.PNG|300px]]
+||Here's a figure showing how GFP(yellow), CI(blue) and CI434( react under both food stimulus and bell stimulus present. You may see, after the training, the GFP production does NOT decrease to ground level. Instead, it keeps in a higher level. What cause this phenomenon? We believe this is because of the change of behaviour of the second AND gate.
+|-
+|[[Image:PKU_base_Andgate2.PNG|300px]]
+||You may see how AND Gate 2's behaviour changes. After the training, the AND Gate 2 expression(Blue) does NOT decrease to ground level, which we believe causes the changing output dynamics of GFP. Then why does AND Gate 2 change its behaviour? The answer is T3 mRNA. We also plot T3 mRNA in this figure, which is represented by pink curve. You may see, as a natural consequence of bistable turning from CI434 state to CI state, the concentration of T3 mRNA increases to a high level. Considering the equation describing AND Gate 2, we can see that the production of the second AND gate is proportional to that of T3 mRNA. From these facts, the changing behaviour of the whole system is explanable and predictable.
+|-
+|[[Image:PKU_Base_withmemory_both.PNG|300px]]
+||For example, at 750 min, we train the dog again. Since CI has already been in high state before that second training, the T3 mRNA production won't change much, which means the GFP output will decrease to the same level between these two trainings. Here we plot the dynamics of GFP, CI and CI434 under this circumstance, which is exactly the same with our prediction.
+|}
+After this modification to the model, we conclude that despite a few changes, our model DOES work with base expression.
 {{PKU_Beijing/Foot}}
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