Team:Oxford/biosensor characterisation
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<h1>Predicting the sfGFP fluorescence</h1> | <h1>Predicting the sfGFP fluorescence</h1> | ||
<h1>Introduction</h1> | <h1>Introduction</h1> | ||
- | To allow us to characterize the second half of the genetic circuit, we needed to be able to predict the difference in response. To do this, we constructed models by cascading the differential equations according to the respective circuit structures thereby producing two different potential system responses. | + | To allow us to characterize the second half of the genetic circuit (DcmR regulating sfGFP), we needed to be able to predict the difference in response. To do this, we constructed models by cascading the differential equations according to the respective circuit structures thereby producing two different potential system responses. |
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We then set up the differential equations necessary to solve this problem in Matlab. The method and results are as detailed below: | We then set up the differential equations necessary to solve this problem in Matlab. The method and results are as detailed below: | ||
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<h1>Introduction</h1> | <h1>Introduction</h1> | ||
- | + | Our team were able to obtain good data for both the mCherry response of the system and the overall sfGFP response. This bubble shows how we adapted our model to make the most of the mCherry fluorescence data. | |
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The original data is shown on the right with error bars showing the standard error of the measurements. | The original data is shown on the right with error bars showing the standard error of the measurements. | ||
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<h1>Calculating the parameters</h1> | <h1>Calculating the parameters</h1> | ||
- | To calculate the parameters for behaviour of the second half of the genetic circuit, we used a similar approach to the method we used to find the parameters for the top half of the circuit. This involves taking key bits of the data and analysing the corresponding equation. | + | To calculate the parameters for behaviour of the second half of the genetic circuit (DcmR regulating sfGFP), we used a similar approach to the method we used to find the parameters for the top half of the circuit. This involves taking key bits of the data and analysing the corresponding equation. |
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<h1>Degradation rate constant (δ2)</h1> | <h1>Degradation rate constant (δ2)</h1> | ||
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<h1>Basal transcription rate constant (β3)</h1> | <h1>Basal transcription rate constant (β3)</h1> | ||
- | To find the basal transcription rate constant of the second half of the system, we analysed the data of the fluorescence of just the Pdcma and the sfGFP gene. | + | To find the basal transcription rate constant of the second half of the system (DcmR regulating sfGFP), we analysed the data of the fluorescence of just the Pdcma and the sfGFP gene. |
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- | The data shown on the graphs below shows clearly that the fluorescence stops | + | The data shown on the graphs below shows clearly that the fluorescence stops increasing when the bacteria stop growing in the log phase. This means that we can’t reliably use the data from the stationary phase to provide parameters. We have taken this point to be 500 minutes into the data measurement. |
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<h1>Expression rate constant (α3) and Michaelis - Menten constant (k3)</h1> | <h1>Expression rate constant (α3) and Michaelis - Menten constant (k3)</h1> | ||
- | Now that we know two of the four parameters for the second half of our synthetic circuit, we needed to calculate the final two parameters to complete our model. To do this, we analysed wet-lab data that showed the system in the presence of DcmR. This now means that the non linear term in our ODE is non zero and the analysis becomes | + | Now that we know two of the four parameters for the second half of our synthetic circuit (DcmR regulating sfGFP), we needed to calculate the final two parameters to complete our model. To do this, we analysed wet-lab data that showed the system in the presence of DcmR. This now means that the non linear term in our ODE is non zero and the analysis becomes |
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This data is shown here. Note the difference in the right hand plot reaffirming that DcmR acts as a repressor on PdcmA. | This data is shown here. Note the difference in the right hand plot reaffirming that DcmR acts as a repressor on PdcmA. |
Revision as of 03:05, 18 October 2014