Team:TU-Munich/Modeling/Overview

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== Modeling Overview ==
== Modeling Overview ==
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!!!! Modeling !!!!!
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In our modeling efforts, we tried to cover a very wide range of different methods, reaching from simple and ordinary differential equations, over partial differential equations, to stochastic differential equations as well as bioinformatical methods. To gain the largest possible output, we stayed in close contact with our wetlab team, answered their design questions and fitted parameters which could then be used for implementation aspects.
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<div class="box-left overview"><html><img src="https://static.igem.org/mediawiki/2013/9/91/TUM13_modeling-1.jpg" /></html>
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===Protein Predictions===
===Protein Predictions===
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[[File:TUM13 Icon.jpg|thumb|right|200px| Figure 1:]]
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For the immobilization of effectors on the cell membrane, we needed to design a transmembrane domain. Using several bioinformatical methods we identified the transmembrane region of the SERK receptor which we later used as starting point for our constructs. [https://2013.igem.org/Team:TU-Munich/Modeling/Protein_Predictions Read More]
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Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. <br><br><br><br><br>
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<div class="box-right overview"><html><img src="https://static.igem.org/mediawiki/2013/2/28/TUM13_modeling-2.jpg" /></html>
===Enzyme Kinetics===
===Enzyme Kinetics===
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[[File:TUM13 Icon.jpg|thumb|right|200px| Figure 2:]]
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For an effective implementation of our filter system it is essential to analyze the enzymatic activity of our effectors. Using experimental data we fitted the respective kinetic parameters and carried out rigorous uncertainty analysis to assess the reliability of the fitted parameters. [https://2013.igem.org/Team:TU-Munich/Modeling/Enzyme Read More]
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Proper analysis of data is important to really understand them. For this reason we built small groups in which our wet lab and our dry lab people came together to understand our experiments from an experimental as well as mathematical point of view.
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===Kill Switch===
===Kill Switch===
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[[File:TUM13 Icon.jpg|thumb|right|200px| Figure 3:]]
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During the planning stage of our project, we had several different ideas on how to efficiently implement a kill-switch in our moss. In this section of the wiki we documented our mathematical train of thought that eventually led us to our final design.<br>[https://2013.igem.org/Team:TU-Munich/Modeling/Kill_Switch Read More]
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Especially during the planing we had several different ideas how the plant can detect that is has escaped from its intended envrionment, how the signal is triggered and how we should decrease the viablility.
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<div class="box-right overview"><html><img src="https://static.igem.org/mediawiki/2013/2/25/TUM13_modeling-4.jpg" /></html>
===Filter Model===
===Filter Model===
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[[File:TUM13 Icon.jpg|thumb|right|200px| Figure 4:]]
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The filter model is designed to simulate different remediation scenarios. It should be used to calculate the necessary amount of PhyscoFilters, referring to the environmental parameters.<br>[https://2013.igem.org/Team:TU-Munich/Modeling/Filter Read More]
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The filter model is a integrative model which is aimt to simulate different remediation scenarios and takes into account environmental conditions and simulates the implementation of a PhyscoFilter.
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Latest revision as of 03:47, 29 October 2013


Modeling Overview

In our modeling efforts, we tried to cover a very wide range of different methods, reaching from simple and ordinary differential equations, over partial differential equations, to stochastic differential equations as well as bioinformatical methods. To gain the largest possible output, we stayed in close contact with our wetlab team, answered their design questions and fitted parameters which could then be used for implementation aspects.

Protein Predictions

For the immobilization of effectors on the cell membrane, we needed to design a transmembrane domain. Using several bioinformatical methods we identified the transmembrane region of the SERK receptor which we later used as starting point for our constructs. Read More

Enzyme Kinetics

For an effective implementation of our filter system it is essential to analyze the enzymatic activity of our effectors. Using experimental data we fitted the respective kinetic parameters and carried out rigorous uncertainty analysis to assess the reliability of the fitted parameters. Read More

Kill Switch

During the planning stage of our project, we had several different ideas on how to efficiently implement a kill-switch in our moss. In this section of the wiki we documented our mathematical train of thought that eventually led us to our final design.
Read More

Filter Model

The filter model is designed to simulate different remediation scenarios. It should be used to calculate the necessary amount of PhyscoFilters, referring to the environmental parameters.
Read More

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