Team:PKU Beijing/Modeling/Result
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(→Result - T3 RNA Polymerase) |
(→Result - P2) |
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==='''Result - T3 RNA Polymerase'''=== | ==='''Result - T3 RNA Polymerase'''=== | ||
- | Here we'll demonstrate dynamics of GFP(output,shows how strongly the dog reacts), CI and CI434(bistable switch, represents the memory). In the figures below, CI was represented by pink line, CI434 was represented by blue line while yellow line shows the dynamics of GFP output. | + | Here we'll demonstrate dynamics of GFP(output,shows how strongly the dog reacts), CI and CI434(bistable switch, represents the memory). In the figures below, CI was represented by pink line, CI434 was represented by blue line while yellow line shows the dynamics of GFP output. Since no input, no output. We'll skip the No food+No bell situation. |
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Now some fun:] Let's mimic a situation. We train the dog, and dog initially drools because of the food. However, after several minutes, the dog thinks, why there was a bell ring? Maybe it's because it means the food comes! The dog thinks of food by then, and drools.<br> | Now some fun:] Let's mimic a situation. We train the dog, and dog initially drools because of the food. However, after several minutes, the dog thinks, why there was a bell ring? Maybe it's because it means the food comes! The dog thinks of food by then, and drools.<br> | ||
2. The concentration of CI increases while the concentration of CI434 decreases. This phenomenon indicates that the dog has remembered the relationship between food and bell. | 2. The concentration of CI increases while the concentration of CI434 decreases. This phenomenon indicates that the dog has remembered the relationship between food and bell. | ||
+ | |- | ||
+ | |With Memory||Food||[[Image:PKU_T3_withmemory_food.PNG|200px]] | ||
+ | ||Now the dog has memory. We give food stimulus at 750 min and the dog reacts similarly, just like the situation with no memory. | ||
+ | |- | ||
+ | | ||Bell||[[Image:PKU_T3_withmemory_bell.PNG|200px]] | ||
+ | ||Here's the big part. After the training, we give the dog a bell stimulus at 750 min. As we can see in the figure, CI is in high state. Since the promoters of T3 is exactly the same with these of CI, the transcription of T3 is also in high level. Then as the bell stimulus comes, the circuit generates large quantities of Aa-tRNA, which enable T3 to translate as the signals pass through AND Gate 2. For the result of these situation, we can see a very large GFP peak in the figure. | ||
+ | |- | ||
+ | | ||Food+Bell||[[Image:PKU_T3_withmemory_both.PNG|200px]] | ||
+ | ||If we give both stimuli, the output will be two GFP peaks. However, we may notice there's also a small peak of CI. From the view of non-linear dynamics, CI tries to escape the attractor to reach a higher level, which fails in the end. From a dog's point of view, the dog strengthens its memory after the second training for a short while. After that, its memory goes back to normal and stable level. | ||
|} | |} | ||
+ | |||
+ | Now let's see how well this result fit the standards. | ||
+ | *'''Truth Table''' YES! | ||
+ | *'''Fit Dog's Reaction''' YES! | ||
+ | *'''VALUE NOT TOO EXTREME''' YES! | ||
+ | That means, our model is very successful! | ||
==='''Result - P2'''=== | ==='''Result - P2'''=== | ||
+ | |||
+ | Then comes the result of P2 model. Just like the case of T3, pink curve represents CI434, blue curve represent CI while yellow curve shows the dynamics of GFP output. | ||
+ | {|cellpadding=5 | ||
+ | |colspan=2|Situations | ||
+ | |Simulation Result||Remarks and Explanations | ||
+ | |- | ||
+ | |No Memory||Food||[[Image:PKU_P2_nomemory_food.PNG|200px]] | ||
+ | ||CI: low state, CI434: high state, GFP: caused by food stimulus | ||
+ | |- | ||
+ | | ||Bell||[[Image:PKU_P2_nomemory_bell.PNG|200px]] | ||
+ | ||CI: low state, CI434: high state, GFP: too small to see. | ||
+ | |- | ||
+ | | || ||[[Image:PKU_P2_nomemory_bell2.PNG|200px]] | ||
+ | ||The output of GFP is even lower here. | ||
+ | |- | ||
+ | | ||Food+Bell||[[Image:PKU_P2_nomemory_both.PNG|200px]] | ||
+ | ||CI: from low state to high state, CI434: from high state to low state, GFP: two peaks | ||
+ | |- | ||
+ | |With Memory||Food||[[Image:PKU_P2_withmemory_food.PNG|200px]] | ||
+ | ||CI: high state, CI434: low state, GFP: caused by food stimulus | ||
+ | |- | ||
+ | | ||Bell||[[Image:PKU_P2_withmemory_bell.PNG|200px]] | ||
+ | ||CI: high state, CI434: low state, GFP: caused by memory and bell stimulus | ||
+ | |- | ||
+ | | ||Food+Bell||[[Image:PKU_P2_withmemory_both.PNG|200px]] | ||
+ | ||CI: high state with a small peak, CI434: low state, GFP: two peaks | ||
+ | |} | ||
+ | Then let's see how this model achieve the goal. | ||
+ | *'''Truth Table''' YES! | ||
+ | *'''Fit Dog's Reaction''' YES! | ||
+ | *'''VALUE NOT TOO EXTREME''' No. The value for AND Gate 2 is too extreme, which may cause difference between our model and actual process in cells. | ||
+ | Overall, this model is still able to perform well under all the circumstances, which means the P2 model is acceptable. | ||
{{PKU_Beijing/Foot}} | {{PKU_Beijing/Foot}} | ||
__NOTOC__ | __NOTOC__ |
Latest revision as of 16:23, 21 October 2009
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