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.

How much of each input should we use to test the biosensor?

For the biosensor, we need:

• The system to be robust to changes in ATC concentration as we cannot be sure that all of the cells will receive exactly the same amount of ATC in the real system.

The top graph shows that once you get above a certain threshold value of ATC input, the steady state fluorescence of the system doesn’t change. This means that to meet the above requirement, we simply have to use an ATC input value greater than the threshold value.

• The system needs to be very sensitive to changes when there is a low concentration of DCM. This is important because we want the output of our biosensor to change when there is only a very small amount of the DCM left, so that it is safe to be discarded.

We know that when no DCM is added to the system, there will be no fluorescence response aside from the basal rate. However, the model predicts that when even a small amount of DCM is added and the system is left for a while, the system fluoresces with the saturated level of fluorescence. Therefore, we have the potential to develop a very sensitive biosensor that senses the presence of DCM, fluorescing when DCM is present and only switching off when the amount of DCM reaches a very low level.

To summarise, we have established that the inputs to our biosensor should be a constant medium concentration of ATC (it isn’t degraded) and a varying concentration of DCM as it is degraded.

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.