Team:ETH Zurich/achievements

From 2013.igem.org

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<b>Pre-Processing:</b><br><br>
<b>Pre-Processing:</b><br><br>
- to characterize the AHL diffusion on agar plates and define, with predictions from the model and the [https://2013.igem.org/Team:ETH_Zurich/Experiments_2 experimental results], the distance between colonies, the strengh of the  promoter controlling LuxI production and the incubation time needed to establish a proper AHL gradient.<br>
- to characterize the AHL diffusion on agar plates and define, with predictions from the model and the [https://2013.igem.org/Team:ETH_Zurich/Experiments_2 experimental results], the distance between colonies, the strengh of the  promoter controlling LuxI production and the incubation time needed to establish a proper AHL gradient.<br>
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- to establish several AHL gradients from different mines on one plate, intersections of gradients result in higher AHL levels for the detection of 1, 2 or 3 mines.<br>
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- to establish several [https://2013.igem.org/Team:ETH_Zurich/Experiments_2#diffusion_experiment AHL gradients] from different mines on one plate, intersections of gradients result in higher AHL levels for the detection of 1, 2 or 3 mines.<br>
- to make a [https://2013.igem.org/Team:ETH_Zurich/Modeling/Reaction_Diffusion_OOHL 2D spatio-temporal model] for AHL, that encompasses local reactions and diffusion.<br><br>
- to make a [https://2013.igem.org/Team:ETH_Zurich/Modeling/Reaction_Diffusion_OOHL 2D spatio-temporal model] for AHL, that encompasses local reactions and diffusion.<br><br>
   
   
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- to find a [https://2013.igem.org/Team:ETH_Zurich/Modeling/Analytical_Approximations analytical solution] for the EC<sub>50</sub> of the promoter we need and therby choose the right candidate from the libary.<br>
- to find a [https://2013.igem.org/Team:ETH_Zurich/Modeling/Analytical_Approximations analytical solution] for the EC<sub>50</sub> of the promoter we need and therby choose the right candidate from the libary.<br>
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&rArr; '''a [https://2013.igem.org/Team:ETH_Zurich/Experiments_6 proof-of-principle] using a GFP reporter.'''<br><br>
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&rArr; '''a proof-of-principle using a GFP reporter.''' [https://2013.igem.org/Team:ETH_Zurich/Experiments_6 Experiment] / [https://2013.igem.org/Team:ETH_Zurich/GFP Model]<br><br>
   
   
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<br><br>
<br><br>
- the identification of the [https://2013.igem.org/Team:ETH_Zurich/Optimization leakiness source].<br>
- the identification of the [https://2013.igem.org/Team:ETH_Zurich/Optimization leakiness source].<br>
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- to model [https://2013.igem.org/Team:ETH_Zurich/Optimization different solution] to reduce the leakiness, such as destabilization of proteins and double negative feedback loop.<br>
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- to model [https://2013.igem.org/Team:ETH_Zurich/Optimization different solutions] to reduce the leakiness, such as destabilization of proteins and double negative regulation.<br>
- to [https://2013.igem.org/Team:ETH_Zurich/Optimization optimize the circuit] to reduce the leakiness by introduction of a positive feedback loop.<br>
- to [https://2013.igem.org/Team:ETH_Zurich/Optimization optimize the circuit] to reduce the leakiness by introduction of a positive feedback loop.<br>
   
   
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- to [https://2013.igem.org/Team:ETH_Zurich/Experiments_3 characterize] the hydrolases with Michaelis-Menten kinetics.<br>
- to [https://2013.igem.org/Team:ETH_Zurich/Experiments_3 characterize] the hydrolases with Michaelis-Menten kinetics.<br>
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&rArr; '''A preliminary game with the hydrolases.'''<br><br>
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<b>Information processing:</b><br><br>
<b>Information processing:</b><br><br>
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- to establish a spatio-temporal model of the [https://2013.igem.org/Team:ETH_Zurich/GFP proof-of-principle] and the [https://2013.igem.org/Team:ETH_Zurich/Modeling final game].
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- to establish a spatio-temporal model of the [https://2013.igem.org/Team:ETH_Zurich/Modeling final game].
<b>Human practice:</b><br><br>
<b>Human practice:</b><br><br>
- analyze the [https://2013.igem.org/Team:ETH_Zurich/Human relationship] between synthetic biology and games.<br>
- analyze the [https://2013.igem.org/Team:ETH_Zurich/Human relationship] between synthetic biology and games.<br>
- remote controled [https://2013.igem.org/Team:ETH_Zurich/World roboter playing] of the game.<br>
- remote controled [https://2013.igem.org/Team:ETH_Zurich/World roboter playing] of the game.<br>
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- set-up an [https://2013.igem.org/Team:ETH_Zurich/HEducational educational kit] containing all parts needed to built up Colisweeper.<br>
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- set-up an [https://2013.igem.org/Team:ETH_Zurich/HEducational educational kit] containing all parts needed to build  Colisweeper.<br>
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Latest revision as of 01:10, 29 October 2013

Header2.png
80px-Eth igem logo.png

We achieved

Pre-Processing:

- to characterize the AHL diffusion on agar plates and define, with predictions from the model and the experimental results, the distance between colonies, the strengh of the promoter controlling LuxI production and the incubation time needed to establish a proper AHL gradient.
- to establish several AHL gradients from different mines on one plate, intersections of gradients result in higher AHL levels for the detection of 1, 2 or 3 mines.
- to make a 2D spatio-temporal model for AHL, that encompasses local reactions and diffusion.


Processing:

- to create a mutant first mutant PLuxR variant form PLuxR wild-type promoter by site directed mutagenisis and a 2nd library by rational design with different EC50.
- to measure the dose-response of those promoters by mutant single cell analysis and determined the EC50 going from 0.02 nM to 6482 nM in liquid culture (for the wild-type and G1) and from 4.45 nM to 12'555 nM on agar plates (for the whole library).
- to find a analytical solution for the EC50 of the promoter we need and therby choose the right candidate from the libary.

a proof-of-principle using a GFP reporter. Experiment / Model


Optimization:

- the identification of the leakiness source.
- to model different solutions to reduce the leakiness, such as destabilization of proteins and double negative regulation.
- to optimize the circuit to reduce the leakiness by introduction of a positive feedback loop.


Output:

- to implement several reporters (hydrolases and fluorescent proteins) in one construct.
- to show different colorimetric responses (yellow, salmon, magenta, violett, green and blue) for five hydrolases.
- to show the orthogonality between hydrolase.
- to show a color overlay.
- to characterize the hydrolases with Michaelis-Menten kinetics.


Information processing:

- to establish a spatio-temporal model of the final game.

Human practice:

- analyze the relationship between synthetic biology and games.
- remote controled roboter playing of the game.
- set-up an educational kit containing all parts needed to build Colisweeper.