Team:ETH Zurich/Modeling

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|style="font-size:2em; height: 30px" class="modeling"|Modeling Overview
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|style="border-left: none;"|[[#Why modeling?|Why modeling?]]
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|[[#Results|Results]]
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|colspan="2"|'''blablabla... Intoduction... check and write more'''
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= Modeling overview =
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We created a computational model of our system in order to check whether our ideas will work in reality. For example, before starting with the channel design, we had to first find out in silico what the most suitable channel dimensions are. Most of the model equations and parameters we took from existing literature, which is supported by biological data. For some of them, we had to make assumptions or small adjusments, so that they meet the biological conditions under which we create our system in the laboratories.
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First, we simulated the single cell model (Link here) in MATLAB to see whether every module (sensor, band detector, filter) works properly. Most of the parameter manipulations and fine tuning of the system were done on the single sell model.
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'''If you want to get a better overview of the modeling, take a tour through our slides and click on them for detailed explanation of the respective sections.'''
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Then, we used the COMSOLE software to simulate diffusion of the toxic molecules to see whether a realistic gradient could be created. This is the point when we started to get some feeling about the channel dimensions.
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At the end, we combined the single cell model with the diffusion model to get the final (reaction-diffusion model) in COMSOL. By visualizing the diffusion and the movement of the GFP band, we saw that our system can actually work in practice.
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=A glimpse into our modeling=
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'''We created a computational three dimensional spatio-temporal model of the SmoColi smoke sensor in order to evaluate our network and channel designs.'''
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'''We started our modeling efforts in designing a [[Team:ETH_Zurich/Modeling/SingleCell|single-cell model]] in Matlab, based on ordinary differential equations, which can be reliably used to predict the sensor, band-pass filter and alarm behaviour. To analyze the robustness of the system we performed an extensive [[Team:ETH_Zurich/Modeling/Analysis|exploration of the parameter space]]. Because of the noisy nature of cellular processes we tested our circuit's noise tolerance by performing a [[Team:ETH_Zurich/Modeling/Stochastic|stochastic analysis]]. '''
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<br> <br>
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'''In parallel to this effort we created a [[Team:ETH_Zurich/Modeling/Microfluidics|three dimensional dynamic reaction-diffusion model]] using the mechanical engineering software platform COMSOL. It gives information on whether a gradient of the toxic molecule can be obtained.'''
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<br> <br>
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'''With these two separate models most of the questions regarding biological implementation and channel design could already be answered. However, the AHL alarm system depends neither only on the intra-cellular, nor only on the inter-cellular state of the overall network, but on both. Therefore, we [[Team:ETH_Zurich/Modeling/Combined|combined both models]] in a final step to create a three dimensional, temporal, molecular reaction diffusion model, which can reliably reproduce all important characteristics of our SmoColi smoke-detector.'''
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<br> <br>
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'''These modeling efforts were not only valuable in the decision process on how to [[Team:ETH_Zurich/Overview/CircuitDesign|design the overall network]], but also played a crucial role in the construction process of our [[Team:ETH_Zurich/Process/Microfluidics|microfluidic device]].'''
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Latest revision as of 20:20, 28 October 2011

Can you feel the smoke tonight?
 

Contents

Modeling overview

If you want to get a better overview of the modeling, take a tour through our slides and click on them for detailed explanation of the respective sections.

A glimpse into our modeling

We created a computational three dimensional spatio-temporal model of the SmoColi smoke sensor in order to evaluate our network and channel designs.

We started our modeling efforts in designing a single-cell model in Matlab, based on ordinary differential equations, which can be reliably used to predict the sensor, band-pass filter and alarm behaviour. To analyze the robustness of the system we performed an extensive exploration of the parameter space. Because of the noisy nature of cellular processes we tested our circuit's noise tolerance by performing a stochastic analysis.

In parallel to this effort we created a three dimensional dynamic reaction-diffusion model using the mechanical engineering software platform COMSOL. It gives information on whether a gradient of the toxic molecule can be obtained.

With these two separate models most of the questions regarding biological implementation and channel design could already be answered. However, the AHL alarm system depends neither only on the intra-cellular, nor only on the inter-cellular state of the overall network, but on both. Therefore, we combined both models in a final step to create a three dimensional, temporal, molecular reaction diffusion model, which can reliably reproduce all important characteristics of our SmoColi smoke-detector.

These modeling efforts were not only valuable in the decision process on how to design the overall network, but also played a crucial role in the construction process of our microfluidic device.

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