Difference between revisions of "Team:Groningen/Model"

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the CryptoGErM system. The people are modelled in a multi-agent simulation using genetic algorithms.  
 
the CryptoGErM system. The people are modelled in a multi-agent simulation using genetic algorithms.  
 
This model helps us to predict the  
 
This model helps us to predict the  
and intentions of the users and intruders of our system.</div>
+
and intentions of the users and intruders of our system.
  
 
<p><ul><li><a href="/Team:Groningen/AIModel">Read more about the AI model.
 
<p><ul><li><a href="/Team:Groningen/AIModel">Read more about the AI model.
</a></li></ul></p>
+
</a></li></ul></p></div>
  
 
<div class="right flone"><img src="https://static.igem.org/mediawiki/2016/4/40/T--Groningen--layersguy.png" /></div></div>
 
<div class="right flone"><img src="https://static.igem.org/mediawiki/2016/4/40/T--Groningen--layersguy.png" /></div></div>
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This article contains research of the literature on known mutation rates of Bacillus  
 
This article contains research of the literature on known mutation rates of Bacillus  
 
under the special conditions and treatments that will be applied.  
 
under the special conditions and treatments that will be applied.  
</div>
+
 
  
 
<p><ul><li><a href="/Team:Groningen/MutationRates">Read more about the Bacillus subtilis mutation rates.
 
<p><ul><li><a href="/Team:Groningen/MutationRates">Read more about the Bacillus subtilis mutation rates.
</a></li></ul></p>
+
</a></li></ul></p></div>
  
 
<div class="left flone"><img src="https://static.igem.org/mediawiki/2016/4/40/T--Groningen--layersguy.png" /></div></div>
 
<div class="left flone"><img src="https://static.igem.org/mediawiki/2016/4/40/T--Groningen--layersguy.png" /></div></div>
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<div class="split"><div class="left flone">In this section we computationally modelled
 
<div class="split"><div class="left flone">In this section we computationally modelled
 
the probaility of random mutation causing our message in the DNA to be corrupted.
 
the probaility of random mutation causing our message in the DNA to be corrupted.
</div>
+
 
  
 
<p><ul><li><a href="/Team:Groningen/RandomMutation">Read more about the random mutations modelling.
 
<p><ul><li><a href="/Team:Groningen/RandomMutation">Read more about the random mutations modelling.
</a></li></ul></p>
+
</a></li></ul></p></div>
  
 
<div class="right flone"><img src="https://static.igem.org/mediawiki/2016/4/40/T--Groningen--layersguy.png" /></div></div>
 
<div class="right flone"><img src="https://static.igem.org/mediawiki/2016/4/40/T--Groningen--layersguy.png" /></div></div>
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<div class="split"><div class="right flone">In the decoding fidelity section we explore how accurate the current sequencing techniques  
 
<div class="split"><div class="right flone">In the decoding fidelity section we explore how accurate the current sequencing techniques  
 
are and how strong our decoy encryption security layer is.
 
are and how strong our decoy encryption security layer is.
</div>
+
 
  
 
<p><ul><li><a href="/Team:Groningen/DecodingFidelity">Read more about decoding fidelity.
 
<p><ul><li><a href="/Team:Groningen/DecodingFidelity">Read more about decoding fidelity.
</a></li></ul></p>
+
</a></li></ul></p></div>
  
 
<div class="left flone"><img src="https://static.igem.org/mediawiki/2016/4/40/T--Groningen--layersguy.png" /></div></div>
 
<div class="left flone"><img src="https://static.igem.org/mediawiki/2016/4/40/T--Groningen--layersguy.png" /></div></div>
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We modelled the protein-DNA interactions in oder to search for an optimal strain  
 
We modelled the protein-DNA interactions in oder to search for an optimal strain  
 
for our photoswitchable antibiotic experiments.
 
for our photoswitchable antibiotic experiments.
</div>
+
 
  
 
<p><ul><li><a href="/Team:Groningen/SpiroModel">Read more about the spirofloxacin modelling.
 
<p><ul><li><a href="/Team:Groningen/SpiroModel">Read more about the spirofloxacin modelling.
</a></li></ul></p>
+
</a></li></ul></p></div>
  
 
<div class="right flone"><img src="https://static.igem.org/mediawiki/2016/4/40/T--Groningen--layersguy.png" /></div></div>
 
<div class="right flone"><img src="https://static.igem.org/mediawiki/2016/4/40/T--Groningen--layersguy.png" /></div></div>

Revision as of 11:50, 19 October 2016

CryptoGE®M
Team
Project
Biology
Computing
Human Practice
Acknowledgements

Modelling

We modelled several aspects of the CryptoGErM system. These models are described and explained in detail in the following articles:

The AI model explores the behaviour of the people who use, or try to abuse, the CryptoGErM system. The people are modelled in a multi-agent simulation using genetic algorithms. This model helps us to predict the and intentions of the users and intruders of our system.

The data stored in the CryptoGErM system might be endangered by random mutations in the DNA. This article contains research of the literature on known mutation rates of Bacillus under the special conditions and treatments that will be applied.

In this section we computationally modelled the probaility of random mutation causing our message in the DNA to be corrupted.

In the decoding fidelity section we explore how accurate the current sequencing techniques are and how strong our decoy encryption security layer is.

We modelled the protein-DNA interactions in oder to search for an optimal strain for our photoswitchable antibiotic experiments.


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