Team:Oxford/biosensor optimisation

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We ran the deterministic model whilst varying the activation rate (see <a href="https://2014.igem.org/Team:Oxford/biosensor_deterministic_equations">'where did these equations come from?'</a>) of the sfGFP. The response is shown here:
We ran the deterministic model whilst varying the activation rate (see <a href="https://2014.igem.org/Team:Oxford/biosensor_deterministic_equations">'where did these equations come from?'</a>) of the sfGFP. The response is shown here:
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<h1>What does this tell us?</h1>
<h1>What does this tell us?</h1>
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.
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.
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<img src="https://static.igem.org/mediawiki/2014/6/67/Oxford_change_degradation_rate.png" style="margin-left:0%; float:right; margin-right:0%; position:relative; width:65%;" />
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We ran the deterministic model whilst varying the degradation rate (see <a href="https://2014.igem.org/Team:Oxford/biosensor_deterministic_equations">'where did these equations come from?'</a>) of the sfGFP. The response is shown here:
We ran the deterministic model whilst varying the degradation rate (see <a href="https://2014.igem.org/Team:Oxford/biosensor_deterministic_equations">'where did these equations come from?'</a>) of the sfGFP. The response is shown here:
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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

Revision as of 10:51, 5 October 2014

#list li { list-style-image: url("https://static.igem.org/mediawiki/2014/6/6f/OxigemTick.png"); } }


Optimisation


Introduction: what are we optimising?

To develop the biosensor to the highest quality that we could reach in the short time period available for the project, it was very important to incorporate mathematical modelling into the design process.

Having already made the mathematical models (see the characterisation section), it was then important to:

• Analyse how varying the amount of each input added affected the response of the system.
• Guide the biochemistry on parameter values to aim for when making the system.

Both of these helped us to save a lot of time and money. It fast tracked the development process because we didn’t then have to run lots of different variations of the tests and more importantly we didn’t have to build lots of different constructs containing different values of the parameters (for example, the degradation and expression rates).
Insert biochem here?
Insert biochem here?

Biochemistry...

Biochem stuff...
What happens when we change the amount of each input added?
What happens when we change the amount of each input added?

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.
Insert biochem here?
Insert biochem here?

Biochemistry...

Biochem stuff...
Modelling the biosensor to optimise ’ON’ and ‘OFF’ response
Modelling the biosensor to optimise ’ON’ and ‘OFF’ response
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 (see above) 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 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 we can realistically change 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 expressing more protein. (HOW?) (CORRECT + DETAIL?)

We can change the degradation rate of the fluorescent protein by adding degradation tags. (CORRECT + DETAIL?)
Insert biochem here?
Insert biochem here?

Biochemistry...

Biochem stuff...
Should we aim for high or low RBS strength?
Should we aim for high or low RBS strength?
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.
Insert biochem here?
Insert biochem here?

Biochemistry...

Biochem stuff...
Should we aim for high or low degradation rate?
Should we aim for high or low degradation rate?
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.
Insert biochem here?
Insert biochem here?

Biochemistry summary

Biochem stuff...



Oxford iGEM 2014