Team:ETH Zurich/Modeling

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|style="font-size:2em; height: 30px" class="modeling"|Modeling Overview
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|colspan="2"|'''We created a computational model of our system in order to check whether our ideas might work in reality. We investigated questions such as for which range of acetaldehyde input we get a GFP band from the band-pass filter, or what the most suitable channel dimensions are in silico. Only afterwards we started with the actual experimental channel design.'''
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= Modeling overview =
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== Roundup==
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[[File:ETH codeExample.png|350px|left|thumb|Most of the modeling was done in Matlab. The 3D diffusion models were implemented using COMSOL.]]
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Our goal was to create a large, spatiotemporal 3D reaction-diffusion model not only reliably reproducing the molecular dynamics of a single cell, but also the population dynamics arising from the intra-cell communication with acetaldehyde, xylene and AHL. Since the gradients of the signaling molecules are not only sensed, but also created by the SmoColi cells in a cooperative manner, a single cell model alone would not have been able to fully capture the dynamics of the microfluidic cellular sensor device.
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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 adjustments, so that they meet the biological conditions under which we create our system in the lab.
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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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First, we simulated the '''[[Team:ETH_Zurich/Modeling/SingleCell|single cell model]]''' 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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Then, we used the COMSOL software to simulate diffusion of the toxic molecules to see whether a realistic gradient could be created '''[[Team:ETH_Zurich/Modeling/Microfluidics|(diffusion model)]]'''. 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 '''[[Team:ETH_Zurich/Modeling/Combined| 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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'''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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'''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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