Team:ETH Zurich/Modeling/Overview

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<h1>Modeling part overview</h1>
<h1>Modeling part overview</h1>
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<p align="justify">We developed a 2D spatio-temporal model of the Colisweeper bacterial game to evaluate our network, validate the design and get some insights of how we can improve our system.  Essentially, we studied the diffusion of [https://2013.igem.org/Team:ETH_Zurich/Experiments_2 AHL] in a hexagonal grid setup with mine and non-mine colonies.  To solve the system of partial differential equations we used finite element methods, and simulations were carried out in Comsol Multiphysics.  Most model parameters are taken from literature supported by experimental evidence, but we continually refine their accuracy for our system by estimating them from our own experimental data. To evaluate the circuit and get biological meaningful initial conditions, we built a [https://2013.igem.org/Team:ETH_Zurich/Modeling/InitialStates single-cell model]. Additionally, we made some [https://2013.igem.org/Team:ETH_Zurich/Modeling/Analytical_Approximations analytic derivations] for the AHL gradient and for the optimal P<sub>LuxR</sub> promoter affinity<br></p>
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<p align="justify">We developed a 2D spatio-temporal model of the Colisweeper bacterial game to evaluate our network, validate the design and get some insights of how we can improve our system.  Essentially, we studied the diffusion of [https://2013.igem.org/Team:ETH_Zurich/Experiments_2 AHL] in a hexagonal grid setup with mine and non-mine colonies.  To solve the system of partial differential equations we used finite element methods, and simulations were carried out in Comsol Multiphysics.  Most model parameters are taken from literature supported by experimental evidence, but we continually refine their accuracy for our system by estimating them from our own experimental data. To evaluate the circuit and get biological meaningful initial conditions, we built a [https://2013.igem.org/Team:ETH_Zurich/Modeling/InitialStates single-cell model]. Additionally, we made some analytic derivations for the AHL gradient and for the optimal P<sub>LuxR</sub> promoter affinity<br></p>
<p align="justify">It needs to be mentioned that there was a close interplay with the model and experiments in our project which facilitated the improvement of our system. Based on this premise, we generate data from experiments to validate the model, but also use the model to predict the behavior of our system and help further experimental designs. </p>
<p align="justify">It needs to be mentioned that there was a close interplay with the model and experiments in our project which facilitated the improvement of our system. Based on this premise, we generate data from experiments to validate the model, but also use the model to predict the behavior of our system and help further experimental designs. </p>

Latest revision as of 00:09, 29 October 2013

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Modeling part overview

We developed a 2D spatio-temporal model of the Colisweeper bacterial game to evaluate our network, validate the design and get some insights of how we can improve our system. Essentially, we studied the diffusion of AHL in a hexagonal grid setup with mine and non-mine colonies. To solve the system of partial differential equations we used finite element methods, and simulations were carried out in Comsol Multiphysics. Most model parameters are taken from literature supported by experimental evidence, but we continually refine their accuracy for our system by estimating them from our own experimental data. To evaluate the circuit and get biological meaningful initial conditions, we built a single-cell model. Additionally, we made some analytic derivations for the AHL gradient and for the optimal PLuxR promoter affinity

It needs to be mentioned that there was a close interplay with the model and experiments in our project which facilitated the improvement of our system. Based on this premise, we generate data from experiments to validate the model, but also use the model to predict the behavior of our system and help further experimental designs.


  • OHHL Diffusion concentration gradient after 24 hours.

  • Level of GFP expression, after 11 hours of incubation, depending on the number of mine cells surrounding a colony. On the left is shown the experimental result (fluorescence) and on the right the simulation results are displayed, corresponding to concentration of GFP (mol/m3). Mine colonies are delimited in green.

Note: For parameter values and references click on the parameter of interest or have a look at the parameters section.