Team:TU-Munich/Modeling/Overview

From 2013.igem.org

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== Modeling Overview ==
== Modeling Overview ==
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In our modeling efforts, we tried to cover a very wide range of different methods, ranging from simple ordinary differential equations, over partial differential equations to stochastic differential equations as well as bioinformatic methods. To gain the largest possible gain we stayed in close contact to the wetlab team and answered design question and fitted parameters that could then be used for implementation aspects.
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In our modeling efforts, we tried to cover a very wide range of different methods, reaching from simple 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>
<div class="box-left overview"><html><img src="https://static.igem.org/mediawiki/2013/9/91/TUM13_modeling-1.jpg" /></html>
===Protein Predictions===
===Protein Predictions===
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For the immobilisation of effectors on the cell membrane, we needed to design a transmembrane domain. Using several bioinformatic 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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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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Revision as of 18:39, 28 October 2013


Modeling Overview

In our modeling efforts, we tried to cover a very wide range of different methods, reaching from simple 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 the effective implementation of our filter system it is essential to analyse 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 aimed to simulate different remediation scenarios and should be used to calculate the perfectly fitting conditions of our Physco filter, referring to the needs of the environment.
Read More

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