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<div class="mop"><figure> <img src="https://static.igem.org/mediawiki/2017/7/7a/T-SICAU-modeloptizimation_figure1c.jpg" /><figcaption>the background for the 10 ^ -5 </figcaption></figure></div> | <div class="mop"><figure> <img src="https://static.igem.org/mediawiki/2017/7/7a/T-SICAU-modeloptizimation_figure1c.jpg" /><figcaption>the background for the 10 ^ -5 </figcaption></figure></div> | ||
<div class="mop"><figure> <img src="https://static.igem.org/mediawiki/2017/5/50/T-SICAU-modeloptimization_figure1d.jpg" /><figcaption> the background for the 10 ^ -3 </figcaption></figure> </div> | <div class="mop"><figure> <img src="https://static.igem.org/mediawiki/2017/5/50/T-SICAU-modeloptimization_figure1d.jpg" /><figcaption> the background for the 10 ^ -3 </figcaption></figure> </div> | ||
− | <div class="p- | + | <div class="clear"></div> |
+ | <div class="p-size2">Figure 1</div> | ||
<div class="clear"></div> | <div class="clear"></div> | ||
<li>Effects of Bacterial Growth on Threshold Time</li> | <li>Effects of Bacterial Growth on Threshold Time</li> | ||
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<div class="mop"><figure> <img src="https://static.igem.org/mediawiki/2017/4/46/T-SICAU-modeloptimization_figure2a.jpg" /><figcaption> Not Consider the growth situation </figcaption></figure></div> | <div class="mop"><figure> <img src="https://static.igem.org/mediawiki/2017/4/46/T-SICAU-modeloptimization_figure2a.jpg" /><figcaption> Not Consider the growth situation </figcaption></figure></div> | ||
<div class="mop"><figure> <img src="https://static.igem.org/mediawiki/2017/5/50/T-SICAU-modeloptimization_figure2b.jpg" /><figcaption> Consider the growth situation </figcaption></figure></div> | <div class="mop"><figure> <img src="https://static.igem.org/mediawiki/2017/5/50/T-SICAU-modeloptimization_figure2b.jpg" /><figcaption> Consider the growth situation </figcaption></figure></div> | ||
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+ | <div class="p-size2">Figure 2</div> | ||
<div class="clear"></div> | <div class="clear"></div> | ||
<li>The Influence of Introducing Sensor on System Detection</li> | <li>The Influence of Introducing Sensor on System Detection</li> | ||
<div class="mop"><figure> <img src="https://static.igem.org/mediawiki/2017/b/bb/T-SICAU-modeloptimization_figure3a.jpg" /><figcaption>Sensor- </figcaption></figure></div> | <div class="mop"><figure> <img src="https://static.igem.org/mediawiki/2017/b/bb/T-SICAU-modeloptimization_figure3a.jpg" /><figcaption>Sensor- </figcaption></figure></div> | ||
<div class="mop"><figure> <img src="https://static.igem.org/mediawiki/2017/d/df/T-SICAU-modeloptimization_figure3b.jpg" /><figcaption> Sensor+ </figcaption></figure></div> | <div class="mop"><figure> <img src="https://static.igem.org/mediawiki/2017/d/df/T-SICAU-modeloptimization_figure3b.jpg" /><figcaption> Sensor+ </figcaption></figure></div> | ||
− | <div class="p- | + | <div class="clear"></div> |
+ | <div class="p-size2">Figure 3</div> | ||
<div class="clear"></div> | <div class="clear"></div> | ||
According to the following comparison chart, we can see that when the sensor is introduced, the linear range of the concentration of AHL and time reached threshold is greatly reduced, and cause 2 problems: (1) When the concentration of addde AHL is low, the time to reach the threshold will be greatly extended; (2) When the concentration is haigh, the time to reach the threshold will be no significant gap. This is why we get this result: no fluorescence or fluorescence is too strong after we add AHL. But it is certain that the introduction of the sensor can reduce the time to reach the threshold. Thus we add AiiA to offset this part and the background expression produced by the AHL later. <br/> | According to the following comparison chart, we can see that when the sensor is introduced, the linear range of the concentration of AHL and time reached threshold is greatly reduced, and cause 2 problems: (1) When the concentration of addde AHL is low, the time to reach the threshold will be greatly extended; (2) When the concentration is haigh, the time to reach the threshold will be no significant gap. This is why we get this result: no fluorescence or fluorescence is too strong after we add AHL. But it is certain that the introduction of the sensor can reduce the time to reach the threshold. Thus we add AiiA to offset this part and the background expression produced by the AHL later. <br/> |
Revision as of 00:53, 2 November 2017
Introduction
We aim to convert the antibiotic signal into an AHL molecule signal by using a specific promoter in combination with LUX.And set up a positive feedback system based on the population induction system of Vibrio califlora.The input AHL molecular signal is amplified by a positive feedback system,then outputs fluorescent signal.The previous detection system is mostly between “0” and“1”,only detect the presence of the measured object whlie cannot measured on the quantitative. The fluorescence signal reaches the threshold time is different in contrast to inputting different concentrations of AHL signal molecular .Based on this we can build a relationship between the threshold time and the input signal like the qPCR, achieving quantitative effect.
We build a forecasting model and optimize it on the base of that principle.Compared with most biological systems, our system has an effect of local expression.So we made a modeling prediction of the impact of background expression and the stability of the system, proving the feasibility of our system.
Result one:Forecast ModelModeling
- 1.assumption
- 2.theoretical Basis
- Character definition
- Hill function
- Derivation process of discrete forecasting model
- 3.model building
1) The prediction model is an experimental analysis which based on the experimental principle and the Hill function by drawing up the relevant parameters.
2) It is assumed that there is less attenuation of the AHL when it is in low concentration.
3) The molecular weight of AHL-LuxR does not vary with time and remains stable.
4) The fixed parameters used in the model are based on the experimental principle and related literature hypothesis, for there may be about the predicted trend of curve and the problems which may arise in the process of experiment.
5) The model does not consider the impact of environmental factors on the change of natural causes.
6) The threshold can be chosen according to the experimental phenomena, and the threshold in ours prediction model is chosen as [LR]/2.
7) The model does not consider the impact of background expression on GFP accumulation.
2) It is assumed that there is less attenuation of the AHL when it is in low concentration.
3) The molecular weight of AHL-LuxR does not vary with time and remains stable.
4) The fixed parameters used in the model are based on the experimental principle and related literature hypothesis, for there may be about the predicted trend of curve and the problems which may arise in the process of experiment.
5) The model does not consider the impact of environmental factors on the change of natural causes.
6) The threshold can be chosen according to the experimental phenomena, and the threshold in ours prediction model is chosen as [LR]/2.
7) The model does not consider the impact of background expression on GFP accumulation.
1) The effect of different initial AHL concentration:
2)The effect of background expression on AHL accumulation: