Team:Oxford/biosensor optimisation
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- | <a href=" | + | <a href="https://static.igem.org/mediawiki/2014/3/3d/OxigemLAB_BOOK.pdf" target="_blank"><img src="https://static.igem.org/mediawiki/2014/5/50/OxigemLabbook.png" style="position:absolute;width:6%;margin-left:84%;margin-top:-13%;z-index:10;"></a> |
<a href="https://static.igem.org/mediawiki/2014/1/16/Oxigem_LAB_PROTOCOLS.pdf" target="_blank"><img src="https://static.igem.org/mediawiki/2014/a/a4/OxigemProtocols.png" style="position:absolute;width:6%;margin-left:91%;margin-top:-13%;z-index:10;"></a> | <a href="https://static.igem.org/mediawiki/2014/1/16/Oxigem_LAB_PROTOCOLS.pdf" target="_blank"><img src="https://static.igem.org/mediawiki/2014/a/a4/OxigemProtocols.png" style="position:absolute;width:6%;margin-left:91%;margin-top:-13%;z-index:10;"></a> | ||
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<div style="width:100%;"><font style="font-size:15px;font-weight:500;">Show all:</font></div> | <div style="width:100%;"><font style="font-size:15px;font-weight:500;">Show all:</font></div> | ||
- | <a href="#showmodelling"><div class="orange_news_block1 showmodelling" style="background: #F9A7B0;border-radius:15px;color:black;float:left;height:40%;width:40%;margin-left:6%;padding-top: | + | <a href="#showmodelling"><div class="orange_news_block1 showmodelling" style="background: #F9A7B0;border-radius:15px;color:black;float:left;height:40%;width:40%;margin-left:6%;padding-top:10px;"><center> |
- | <h1white><font style="font-size:15px;font-weight:500;">Modelling</font> | + | <h1white><font style="font-size:15px;font-weight:500;">Modelling</font></h1white></center> |
</div></a> | </div></a> | ||
- | <a href="#showwetlab"><div class="orange_news_block1 showwetlab" style="background: #ADD8E6;border-radius:15px;color:black;float:left;height:40%;width:40 | + | <a href="#showwetlab"><div class="orange_news_block1 showwetlab" style="background: #ADD8E6;border-radius:15px;color:black;float:left;height:40%;width:40%;margin-left:3%;padding-top:10px;"><center> |
- | <h1white><font style="font-size:15px;font-weight:500;">Wetlab</font> | + | <h1white><font style="font-size:15px;font-weight:500;">Wetlab</font></h1white></center> |
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<br> | <br> | ||
- | < | + | <h2>Optimizing the biosensor design</h2> |
An ideal biosensor would fulfil the following performance criteria:<br><br> | An ideal biosensor would fulfil the following performance criteria:<br><br> | ||
- | • Fast response to the presence/absence of DCM.<br> | + | • <strong>Fast response</strong> to the presence/absence of DCM.<br> |
- | • High amplitude of output signal – it must produce enough GFP to generate a distinct signal against background noise.<br> | + | • <strong>High amplitude of output signal</strong> – it must produce enough GFP to generate a distinct signal against background noise.<br> |
- | • | + | • <strong>Sensitive</strong> - 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. <br><br> |
- | + | ||
+ | (regarding modelling): | ||
+ | <br> | ||
+ | • <strong>Robust</strong> - 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. <br><br> | ||
By modelling the effects of parameters we are able to alter in the biological system, we were able to guide our design process to produce a biosensor that is as close to the ideal as possible without sacrificing any one criterion entirely. | By modelling the effects of parameters we are able to alter in the biological system, we were able to guide our design process to produce a biosensor that is as close to the ideal as possible without sacrificing any one criterion entirely. | ||
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<h1></h1> | <h1></h1> | ||
- | Our biosensor will not be able to meet all | + | Our biosensor will not be able to meet all ideal criteria because <strong>1) We are limited by biology as to which parameters we can actually change</strong> and <strong>2) changing a parameter in a cellular system impacts more than one parameter. </strong><br> |
However there are some things we can alter:<br><br> | However there are some things we can alter:<br><br> | ||
- | • The rate of GFP degradation - the cell will degrade GFP, but | + | • <strong>The rate of GFP degradation</strong> - the cell will degrade GFP, but marking the protein with a degradation tag would increase the rate that this occurs.<br> |
- | • The amount of GFP produced per mRNA transcribed – by altering the strength of the ribosome binding site we can alter the efficiency of translation.<br><br> | + | • <strong>The amount of GFP produced per mRNA transcribed</strong> – by altering the strength of the ribosome binding site we can alter the efficiency of translation.<br><br> |
By modelling the effects of these we can answer the following questions:<br><br> | By modelling the effects of these we can answer the following questions:<br><br> | ||
- | • Do we need to include a degradation tag on GFP, or is the turnover of GFP already adequate to give a fast off rate?<br> | + | • <strong>Do we need to include a degradation tag on GFP, or is the turnover of GFP already adequate to give a fast 'off' rate?</strong><br> |
- | • What | + | • <strong>What RBS strength should we use to maximise output amplitude or reach a usable signal output?</strong><br> |
- | • Will altering one of these to optimise one criterion negatively impact any other of our criteria?<br> | + | • <strong>Will altering one of these to optimise one criterion negatively impact any other of our criteria?</strong><br> |
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<br><br><br><br><br> | <br><br><br><br><br> | ||
<h1>What are these graphs and where did they come from?</h1> | <h1>What are these graphs and where did they come from?</h1> | ||
- | 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. | + | 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. |
<br><br> | <br><br> | ||
- | 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. | + | 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 (the amount of either DCM or ATC) while varying the other. |
<br><br> | <br><br> | ||
- | + | The 3-dimensional plot was produced by plotting the final fluorescence value from many 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. | |
<br><br> | <br><br> | ||
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. | 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. | ||
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<div class="white_news_block"> | <div class="white_news_block"> | ||
- | We ran the deterministic model whilst varying the degradation rate (see <a href="https:// | + | We ran the deterministic model whilst varying the degradation rate (see <a href="https://static.igem.org/mediawiki/2014/b/be/Oxford_Equations_explained.png" target="_blank">'where did these equations come from?'</a>) of the sfGFP. The response is shown here: |
</div> | </div> | ||
<div class="white_news_block"> | <div class="white_news_block"> | ||
<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. |
<br><br> | <br><br> | ||
-->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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+ | <div class="row"> | ||
+ | <a href="#show7" class="show wetlab-row" id="show7"><div class="wetlab"> | ||
+ | <h1white>How did this inform our design?</h1white> | ||
+ | <img src="https://static.igem.org/mediawiki/2014/4/4d/Oxford_plus-sign-clip-art.png" style="float:right;position:relative; width:2%;" /> | ||
+ | </div></a> | ||
+ | <a href="#hide7" class="hide" id="hide7"><div class="wetlab"> | ||
+ | <h1white>How did this inform our design?</h1white></div></a> | ||
+ | <div class="list"> | ||
+ | <div class="white_news_block2"> | ||
+ | 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> | ||
+ | <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> | ||
+ | <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. | ||
- | <div class="white_news_block"> | + | </div> |
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+ | </div> | ||
+ | </div> | ||
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+ | <div class="white_news_block" style="background-color:transparent;"> | ||
<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. |
<br><br> | <br><br> | ||
- | 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. |
</div> | </div> | ||
Latest revision as of 02:15, 18 October 2014