Team:Oxford/biosensor optimisation

From 2014.igem.org

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<h1>What are these graphs and where did they come from?</h1>
<h1>What are these graphs and where did they come from?</h1>
The 3D plot shown below shows what the model predicts the steady state fluorescence of the bacteria to be when varying amounts of ATC and DCM are added to the system. <u>(which system?)</u>
The 3D plot shown below shows what the model predicts the steady state fluorescence of the bacteria to be when varying amounts of ATC and DCM are added to the system. <u>(which system?)</u>
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+
<br><br>
The two graphs are slices of the overall 3D plot. In these we are analysing how the input added affects the steady state response whilst keeping the other input constant, we plotted this using a system of differential equations that we produced for the characterisation part. <u>(where was this?)</u>  
The two graphs are slices of the overall 3D plot. In these we are analysing how the input added affects the steady state response whilst keeping the other input constant, we plotted this using a system of differential equations that we produced for the characterisation part. <u>(where was this?)</u>  
-
+
<br><br>
To do this, we plotted the final fluorescence value from lots of different possible combinations of the two inputs (ATC and DCM). The top graph shows the variation in final fluorescence when DCM is held constant and ATC is varied, the second graph is vice versa.
To do this, we plotted the final fluorescence value from lots of different possible combinations of the two inputs (ATC and DCM). The top graph shows the variation in final fluorescence when DCM is held constant and ATC is varied, the second graph is vice versa.
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+
<br><br>
It is important to understand that these graphs represent the expected steady state level of fluorescence of thousands of different simulations. From this we can select the DCM and ATC concentrations for a specific fluorescence response.
It is important to understand that these graphs represent the expected steady state level of fluorescence of thousands of different simulations. From this we can select the DCM and ATC concentrations for a specific fluorescence response.
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+
<br><br>
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Revision as of 20:49, 9 September 2014


Biosensor


How can we get the best performance out of our biosensor?
To develop the biosensor to the highest quality that we could reach in the short time period available for the project, it was very important to incorporate mathematical modelling into the design process.

Having already made the mathematical models (see the characterisation section), it was then important to:

• Analyse how varying the amount of each input added affected the response of the system.
• Guide the biochemistry on parameter values to aim for when making the system.

Both of these helped us to save a lot of time and money. It fast tracked the development process because we didn’t then have to run lots of different variations of the tests and more importantly we didn’t have to build lots of different constructs containing different values of the parameters (for example, the degradation and expression rates).
What happens when we change the amount of each input added?

What are these graphs and where did they come from?

The 3D plot shown below shows what the model predicts the steady state fluorescence of the bacteria to be when varying amounts of ATC and DCM are added to the system. (which system?) The two graphs are slices of the overall 3D plot. In these we are analysing how the input added affects the steady state response whilst keeping the other input constant, we plotted this using a system of differential equations that we produced for the characterisation part. (where was this?) To do this, we plotted the final fluorescence value from lots of different possible combinations of the two inputs (ATC and DCM). The top graph shows the variation in final fluorescence when DCM is held constant and ATC is varied, the second graph is vice versa. It is important to understand that these graphs represent the expected steady state level of fluorescence of thousands of different simulations. From this we can select the DCM and ATC concentrations for a specific fluorescence response.

What are these graphs and where did they come from?

The 3D plot shown below shows what the model predicts the steady state fluorescence of the bacteria to be when varying amounts of ATC and DCM are added to the system. (which system?)

The two graphs are slices of the overall 3D plot. In these we are analysing how the input added affects the steady state response whilst keeping the other input constant, we plotted this using a system of differential equations that we produced for the characterisation part. (where was this?)

To do this, we plotted the final fluorescence value from lots of different possible combinations of the two inputs (ATC and DCM). The top graph shows the variation in final fluorescence when DCM is held constant and ATC is varied, the second graph is vice versa.

It is important to understand that these graphs represent the expected steady state level of fluorescence of thousands of different simulations. From this we can select the DCM and ATC concentrations for a specific fluorescence response.