Team:Oxford/biosensor optimisation
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- | As described above, the ideal biosensor is binary and its fluorescence response can only take two values. This relies on the system having two features- a fast response time to concentration changes and a large amplitude of response. Having previously established the ideal concentrations of DCM and ATC <u>(see above)</u> for the biosensor, our next task was to predict what combination of controllable variables would result in the ideal binary behaviour. This is a very important step in synthetic biology because it allows us to crudely optimise the design before construction even begins. To test the response of our biosensor, we used a step function of DCM the initial and sudden contact of DCM with our bacteria and then removing DCM through <u>spinning the cells(?)</u>. In the real system, the DCM input would be a step in and then a gradual negative ramp as the DCM was degraded. | + | As described above, the ideal biosensor is binary and its fluorescence response can only take two values. This relies on the system having two features- a fast response time to concentration changes and a large amplitude of response. Having previously established the ideal concentrations of DCM and ATC <u>(see above)</u> for the biosensor, our next task was to predict what combination of controllable variables would result in the ideal binary behaviour. This is a very important step in synthetic biology because it allows us to crudely optimise the design before construction even begins. To test the response of our biosensor, we used a step function of DCM the initial and sudden contact of DCM with our bacteria and then removing DCM through <u>spinning the cells(?)</u>. In the real system, the DCM input would be a step in the curve and then a gradual negative ramp as the DCM was degraded. |
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The two parameters that are most easily changed in the initial production of the bacteria are the RBS strength and the degradation rate. | The two parameters that are most easily changed in the initial production of the bacteria are the RBS strength and the degradation rate. | ||
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<h1>What does this tell us?</h1> | <h1>What does this tell us?</h1> | ||
- | Changing the degradation rate of the protein is more of a trade-off. As you can see, a higher degradation rate gives a faster response but with a much lower steady state responses | + | Changing the degradation rate of the protein is more of a trade-off. As you can see, a higher degradation rate gives a faster response but with a much lower steady state responses. |
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-->We should aim for a low degradation rate to begin with so that we can ensure a detectable level of fluorescence, and then gradually increase the degradation rate to get a faster response. | -->We should aim for a low degradation rate to begin with so that we can ensure a detectable level of fluorescence, and then gradually increase the degradation rate to get a faster response. | ||
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- | Based on the modelling we could optimise each performance characteristic individually, but to create the best overall biosensor we needed to compromise with what we chose to implement:<br><br> | + | Based on the modelling, we could optimise each performance characteristic individually, but to create the best overall biosensor we needed to compromise with what we chose to implement:<br><br> |
<h1>RBS strength</h1> | <h1>RBS strength</h1> | ||
- | <strong>Medium RBS strength</strong> – our modelling suggested we should use as high an RBS strength as possible. We have used a relatively high strength RBS to try and optimise our signal amplitude without | + | <strong>Medium RBS strength</strong> – our modelling suggested we should use as high an RBS strength as possible. We have used a relatively high strength RBS to try and optimise our signal amplitude without stressing cellular metabolism too much.<br><br> |
<h1>GFP degradation</h1> | <h1>GFP degradation</h1> | ||
- | <strong>No degradation tag</strong> - in this instance the model showed that increasing degradation efficiency of GFP(and thus the speed of response) by utilising a degradation tag would also decrease the signal amplitude. In our first attempt at making a biosensor we decided it was more important to increase the chance of generating a usable signal than to have a fast off rate. In the future, once our biosensor is made and if we have found it to have very high amplitude we could add a degradation tag to improve the on/off dynamics at the | + | <strong>No degradation tag</strong> - in this instance the model showed that increasing degradation efficiency of GFP (and thus the speed of response) by utilising a degradation tag would also decrease the signal amplitude. In our first attempt at making a biosensor, we decided it was more important to increase the chance of generating a usable signal than to have a fast off rate. In the future, once our biosensor is made and if we have found it to have very high amplitude, we could add a degradation tag to improve the on/off dynamics at the expense of that excessive signal. |
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<h1>Modelling Summary</h1> | <h1>Modelling Summary</h1> | ||
- | The above results demonstrate well the power of modelling genetic circuits. This approach has allowed us to develop our first construct intelligently and to have some trustworthy predictions on which to develop the rest of our system around. However, as | + | The above results demonstrate well the power of modelling genetic circuits. This approach has allowed us to develop our first construct intelligently and to have some trustworthy predictions on which to develop the rest of our system around. However, as always, there are limitations, especially in biological systems. |
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- | In an ideal world, we would like to have a very high expression rate (for a high steady state amplitude of fluorescence), a high degradation rate (for a fast responding biosensor) and a high copy number of the plasmid in each cell. Conversely though, optimising these parameters puts stress on the cells. This leads to the system not actually being as optimal as the model might have predicted. Here we identify the weakness in preliminary models. We will have to actually develop the bacteria and run the experiments in the lab before we will know if our biosensor will respond this well to the DCM. After this, we will work at creating secondary models which should be able to give more reliable predictions. Ideally we would be able to then make more bacteria and the Engineering-Biochemistry cycle would continue. | + | In an ideal world, we would like to have a very high expression rate (for a high steady state amplitude of fluorescence), a high degradation rate (for a fast responding biosensor) and a high copy number of the plasmid in each cell. Conversely though, optimising these parameters puts metabolic stress on the cells. This leads to the system not actually being as optimal as the model might have predicted. Here we identify the weakness in preliminary models. We will have to actually develop the bacteria and run the experiments in the lab before we will know if our biosensor will respond this well to the DCM. After this, we will work at creating secondary models which should be able to give more reliable predictions. Ideally we would be able to then make more bacteria and the Engineering-Biochemistry cycle would continue. |
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Revision as of 19:02, 17 October 2014