Team:Oxford/modelling biosensor

From 2014.igem.org

(Difference between revisions)
Line 157: Line 157:
}
}
-
.orange_news_block {
+
.orange_news_block1 {
        
        
         background: #FA6800;
         background: #FA6800;
         border-radius:30px; margin-top:2%; padding-left:2%;padding-right:2%;padding-top:2%;padding-bottom:2%;
         border-radius:30px; margin-top:2%; padding-left:2%;padding-right:2%;padding-top:2%;padding-bottom:2%;
-
width:45%;
+
width:32%;
 +
}
 +
 
 +
.orange_news_block2 {
 +
     
 +
        background: #FA6800;
 +
        border-radius:30px; margin-top:2%; padding-left:2%;padding-right:2%;padding-top:2%;padding-bottom:2%;
 +
width:44%;
}
}
Line 253: Line 260:
</div>
</div>
-
<div class="orange_news_block">
+
<div class="orange_news_block1">
<h1white>Modelling genetic circuits</h1white>
<h1white>Modelling genetic circuits</h1white>
</div>
</div>
Line 313: Line 320:
</div>
</div>
-
<div class="orange_news_block">
+
<div class="orange_news_block2">
<h1white>How can we tell the systems apart?</h1white>
<h1white>How can we tell the systems apart?</h1white>
</div>
</div>

Revision as of 12:37, 9 September 2014


Characterisation


Insert intro here
Insert biochem here
Modelling genetic circuits

Predicting the mCherry fluorescence

To model the first double repression, we took the fact that we won’t need to know the amount of tetR in the system and used the assumption that ATC is effectively activating the expression of dcmR, albeit parameterised by different constants. This assumption should be justified by the fact that we will be able to precisely control the addition of ATC and we will be able to measure the fluorescence of the mCherry.

We modelled this first step using both deterministic and stochastic models.

Deterministic

Deterministic models are very powerful tools in systems biology. They analyse the behaviour of the bacteria on a culture level and use ordinary differential equations (ODEs) to relate each activation and repression. By constructing a cascade of differential equations you can build a very robust model of the average behaviour of the gene circuit.
The differential equation that applies to this first step in the system is:






Where did this equation come from?

Solving this ODE in Matlab (with zero basal rate) gives the response of the system to be:

























While the analysis of this circuit isn’t critical to the successful outcome of this part of the project, it will provide us with a very good practice of both obtaining fluorescence time data and accurately fitting the data to the model. It will also help us develop our methods of predicting future system behaviour. This is because this system is already well documented in literature and so we should be able to test our methods and responses against well documented results from labs across the world. As you can clearly see from the graph, the model predicts a large fluorescence increase as the input is added. This is the expected response from the real response and is the best approximation that is obtainable before we get data from the biochemists. In the graph above, the model is set to have a basal rate of zero. This is why there is a zero fluorescence response before the input has been added. In biochemical terms, this is the same as the tetO promoter not being leaky at all. This basal rate will be calibrated alongside all of the other parameters in the model.
Insert biochem here
How can we tell the systems apart?