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Team:Oxford/biosensor optimisation
From 2014.igem.org
Optimizing the biosensor
Having made the first steps towards understanding the fluorescence response of our bacteria, we turned our attention to realizing our biosensor. This involved both predicting an ideal response for our biosensor and engineering our fluorescing bacteria to behave in a way most similar to this response. The challenge of optimising of our bacteria illustrated perfectly the power and indispensability of computer modelling throughout the synthetic biology process. Using the models developed during the characterization process, we were then able to predict the effect of varying the parameters that control bacterial behaviour thereby calculating the ideal parameter value set for our system.
What are these graphs and where did they come from?
Using the bacterial fluorescence models we have built, we predicted the steady-state fluorescence levels of the system in varying levels of DCM and ATC by solving the system of differential equations we produced during the characterization section. The results are illustrated in the 3-dimensional surface plot below. (which system?)The two 2-dimensional graphs are slices taken from the 3-D plot. In each of these 'slices' we are effectively holding one variable constant (either DCM or ATC) while varying the other.
Producing the 3-dimensional plot was produced by plotting 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.
How much of each input should we use to test the biosensor?
An ideal biosensor must be:• Robust- it must be able to cope with variations in ATC concentration without radically altering the behaviour of the system. This is crucial because we cannot ensure that ATC concentrations throughout all the cells will be uniform in the real system.
The top graph demonstrates this nicely. Beyond a certain threshold value of ATC, there is little change in the fluorescence response predicted - it saturates and maintains a constant level. Practically, this means we have to ensure that the ATC concentrations present in our final system must comfortably exceed this threshold ATC value.
• Sensitive- it must change significantly in low concentrations of DCM. This is vital in order to achieve a response that is as close to binary as possible. The ideal system will have a very sharp decline in fluorescence at a predefined, very low value of DCM. This will ensure that the sensor will clearly indicate when the DCM mixture can be safely disposed of.
From our initial system characterization, we have established that when DCM is not present in the system, there will be no fluorescence response aside from that due to the basal transcription rate. However, the model predicts that when even a small amount of DCM is added and the transient behaviour has stabilized, the fluorescence expressed in the system quickly reaches its saturation value. This corresponds to a highly sensitive biosensor which can effectively only express two fluorescence levels- zero or a predefined maximum. The transition from zero to the maximum saturation value occurs at very low concentrations of DCM.
To summarise, we have established that the inputs to our biosensor should be a constant medium concentration of ATC and a varying concentration of DCM as it is degraded. We should note that the ATC concentration will not value without external influence because the system does not consume ATC and its rate of degradation is negligible.
As described above, the ideal biosensor is binary and its fluorescence response can only take two values. This relies on the system having two feature- a fast response time to concentration changes and a large amplitude of response. Having previously established the ideal concentrations of DCM and ATC (see above) 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 spinning the cells(?). In the real system, the DCM input would be a step in and then a gradual negative ramp as the DCM was degraded.
The two parameters that are most easily changed in the initial production of the bacteria are the RBS strength and the degradation rate.
Increasing the Ribosome Binding Site (RBS) strength can greatly increase the translation initiation rate, hence resulting in amplified fluorescence. (HOW?) (CORRECT + DETAIL?)
The degradation rate of the fluorescent protein can also be changed by adding degradation tags. (CORRECT + DETAIL?)
The two parameters that are most easily changed in the initial production of the bacteria are the RBS strength and the degradation rate.
Increasing the Ribosome Binding Site (RBS) strength can greatly increase the translation initiation rate, hence resulting in amplified fluorescence. (HOW?) (CORRECT + DETAIL?)
The degradation rate of the fluorescent protein can also be changed by adding degradation tags. (CORRECT + DETAIL?)
We ran the deterministic model whilst varying the activation rate (see 'where did these equations come from?') of the sfGFP. The response is shown here:
What does this tell us?
As you can see from this graph, increasing the RBS strength only changes the amplitude of the systems response without affecting the response time of the system. This is highly beneficial for the system.-->Therefore we will aim for as high an RBS strength as possible in our initial design.
We ran the deterministic model whilst varying the degradation rate (see 'where did these equations come from?') of the sfGFP. The response is shown here:
What does this tell us?
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-->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.
Modelling Summary
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 ever, there are limitations, especially in biological systems.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.
Oxford iGEM 2014
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