Team:Oxford/biosensor optimisation
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To be a good biosensor, we need to optimise the ‘ON’ and ‘OFF’ response. This relies on the system having two features; namely a fast response time to concentration changes and a large amplitude of response. Having previously established what inputs we need <u>(see above)</u> for the biosensor, we were then asked by the biochemists to analyse the effects of varying some of the parameters that we have control over were. This is a very important step in synthetic biology because it allows us to crudely optimise the design before construction even begins. This saves a lot of time and money to allow us to develop a useful system much faster. To test the response of our biosensor, we shall use a step function of DCM to simulate pouring DCM in 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. | To be a good biosensor, we need to optimise the ‘ON’ and ‘OFF’ response. This relies on the system having two features; namely a fast response time to concentration changes and a large amplitude of response. Having previously established what inputs we need <u>(see above)</u> for the biosensor, we were then asked by the biochemists to analyse the effects of varying some of the parameters that we have control over were. This is a very important step in synthetic biology because it allows us to crudely optimise the design before construction even begins. This saves a lot of time and money to allow us to develop a useful system much faster. To test the response of our biosensor, we shall use a step function of DCM to simulate pouring DCM in 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. | ||
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Revision as of 10:47, 5 October 2014
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