Difference between revisions of "Team:Groningen/Model"

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<article>
 
<article>
<section><ul id="tocgerm"></ul></section>
 
<section>
 
<h2>Modelling</h2>
 
 
<p>We modelled several aspects of the CryptoGErM system. These
 
models are described and explained in detail in the following
 
articles:</p>
 
                </section>
 
  
<h2>Belief-Desire-Intention</h2>
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 +
<h2>Belief-Desire-Intention Model</h2>
 
<section>
 
<section>
 
<p>
 
<p>
  
<div class="split"><div class="left fltwo"> The AI model explores the behaviour of the people who use, or try to abuse,
+
<div class="split"><div class="left fltwo">We asked ourselves what happens if someone tries to hack our system (see photo). How would this person behave? Using an artificial intelligence (AI) model, we explored 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 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.
+
  
 
<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.
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<p>
 
<p>
  
<div class="split"><div class="right fltwo">The data stored in the CryptoGErM system
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<div class="split"><div class="right fltwo">We realized a random mutation would be unpleasant (see photo) in our CryptoGErM project. 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 <em>B. subtilis</em> under the special conditions and treatments that will be applied.
might be endangered by random mutations in the DNA.
+
This article contains research of the literature on known mutation rates of <em>B. subtilis</em>  
+
under the special conditions and treatments that will be applied.  
+
  
 
<p><ul><li><a href="/Team:Groningen/MutationRates">Read more about the <em>Bacillus subtilis</em> mutation rates.
 
<p><ul><li><a href="/Team:Groningen/MutationRates">Read more about the <em>Bacillus subtilis</em> mutation rates.
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<p>
 
<p>
  
<div class="split"><div class="left fltwo">In this section we computationally modelled
+
<div class="split"><div class="left fltwo">After exploring the mutation rates in <em>B. subtilis</em>, we asked how will they affected our encrypted message and key in the genome. Therefore in this section, we computationally modelled the probability 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.
+
 
  
  
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<p>
 
<p>
  
<div class="split"><div class="right fltwo">In the decoding fidelity section we explore how accurate the current sequencing techniques  
+
<div class="split"><div class="right fltwo">Our sequence of the encrypted message is integrated in the genome of <em>B. subtilis</em>, but how sure can we be that we get the same sequence back using common techniques. 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.
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<div class="split"><div class="left fltwo">
 
<div class="split"><div class="left fltwo">
We modelled the protein-DNA interactions in oder to search for an optimal strain  
+
We plan to hide our key sequence in one <em>B. subtilis</em> strain, which is mixed with decoy <em>B. subtilis</em> spores. To select the key strain we found the photoswitchable antibiotic spirofloxacin as an interesting option. We modelled the protein-DNA interactions in order to search for an optimal strain for our photoswitchable antibiotic experiments.
for our photoswitchable antibiotic experiments.
+
  
  

Revision as of 13:40, 19 October 2016

CryptoGE®M
Team
Project
Biology
Computing
Human Practice
Acknowledgements

Belief-Desire-Intention Model

We asked ourselves what happens if someone tries to hack our system (see photo). How would this person behave? Using an artificial intelligence (AI) model, we explored 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.

Random mutations in Bacillus subtilis

We realized a random mutation would be unpleasant (see photo) in our CryptoGErM project. 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 B. subtilis under the special conditions and treatments that will be applied.

Random mutations modelling

After exploring the mutation rates in B. subtilis, we asked how will they affected our encrypted message and key in the genome. Therefore in this section, we computationally modelled the probability of random mutation causing our message in the DNA to be corrupted.

Decoding fidelity

Our sequence of the encrypted message is integrated in the genome of B. subtilis, but how sure can we be that we get the same sequence back using common techniques. In the decoding fidelity section we explore how accurate the current sequencing techniques are and how strong our decoy encryption security layer is.

Searching for an optimal strain

We plan to hide our key sequence in one B. subtilis strain, which is mixed with decoy B. subtilis spores. To select the key strain we found the photoswitchable antibiotic spirofloxacin as an interesting option. We modelled the protein-DNA interactions in order to search for an optimal strain for our photoswitchable antibiotic experiments.


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