"
Team:Dundee/Project/MathOverview
From 2013.igem.org
| (3 intermediate revisions not shown) | |||
| Line 52: | Line 52: | ||
<div class="span6" style="text-align:justify"> | <div class="span6" style="text-align:justify"> | ||
| - | <h2 style="margin-top:-10px;"><a href=" | + | <h2 style="margin-top:-10px;"><a href="/Team:Dundee/Project/DetectionComparison">1. Detection Time</a></h2> |
<p>The problem with current detection methods is the processing time between sampling and availability of results. Potentially, this could lead to significant increases in the microcystin concentrations before action is taken. Therefore, an effective biological detector must reduce this detection time. </p> | <p>The problem with current detection methods is the processing time between sampling and availability of results. Potentially, this could lead to significant increases in the microcystin concentrations before action is taken. Therefore, an effective biological detector must reduce this detection time. </p> | ||
| - | <a href=" | + | <a href="/Team:Dundee/Project/DetectionComparison"><img id="image-6" src="https://static.igem.org/mediawiki/2013/f/fe/Detectiontime.jpg" style="height:300px;width:100%"></a> |
</div> | </div> | ||
<div class="span6" style="text-align:justify"> | <div class="span6" style="text-align:justify"> | ||
| - | <h2 style="margin-top:-10px;">2. PP1 Packing </h2> | + | <h2 style="margin-top:-10px;"><a href="/Team:Dundee/Project/PP1Capacities">2. PP1 Packing </a></h2> |
<p> The capacity of <i>E. coli</i> and <i>B. subtilis</i> to pack PP1 was investigated in order to determine which chassis could maximally hold the greatest number of PP1 molecules. This analysis indicated that <i>E. coli</i> has the greater potential to be a more efficient mop than <i>B. subtilis</i>. </p> | <p> The capacity of <i>E. coli</i> and <i>B. subtilis</i> to pack PP1 was investigated in order to determine which chassis could maximally hold the greatest number of PP1 molecules. This analysis indicated that <i>E. coli</i> has the greater potential to be a more efficient mop than <i>B. subtilis</i>. </p> | ||
| - | <img id="image-6" src="https://static.igem.org/mediawiki/2013/a/aa/Capacities.jpg"> | + | <a href="/Team:Dundee/Project/PP1Capacities"><img id="image-6" src="https://static.igem.org/mediawiki/2013/a/aa/Capacities.jpg"></a> |
</div> | </div> | ||
</div><!-- Row End --> | </div><!-- Row End --> | ||
| Line 69: | Line 69: | ||
<div class="row"> | <div class="row"> | ||
<div class="span6" style="text-align:justify"> | <div class="span6" style="text-align:justify"> | ||
| - | <h2 style="margin-top:-10px;">3. Mop Simulation </h2> | + | <h2 style="margin-top:-10px;"><a href="/Team:Dundee/Project/NetlogoDoc">3. Mop Simulation </a></h2> |
<p>We developed models and visualisation tools allowing the biological processes that take place in the ToxiMop bacteria to be investigated by other users. Dynamic alteration of key properties of the transport mechanisms provides instant feedback, allowing analysis of the concomitant effects and predictions to be tested. | <p>We developed models and visualisation tools allowing the biological processes that take place in the ToxiMop bacteria to be investigated by other users. Dynamic alteration of key properties of the transport mechanisms provides instant feedback, allowing analysis of the concomitant effects and predictions to be tested. | ||
</p> | </p> | ||
| - | <img id="image-6" src="https://static.igem.org/mediawiki/2013/3/34/NetL.jpg" style="height:300px;width:100%"> | + | <a href="/Team:Dundee/Project/NetlogoDoc"><img id="image-6" src="https://static.igem.org/mediawiki/2013/3/34/NetL.jpg" style="height:300px;width:100%"></a> |
</div> | </div> | ||
<div class="span6" style="text-align:justify"> | <div class="span6" style="text-align:justify"> | ||
| - | <h2 style="margin-top:-10px;">4. Production & Export</h2> | + | <h2 style="margin-top:-10px;"><a href="/Team:Dundee/Project/ProductionExport">4. Production & Export</a></h2> |
<p>We developed a model to help us predict the number of PP1 that could be produced and then transported into the periplasm of our ToxiMop cells. The model pinpointed the cause of inefficiencies in our prototype ToxiMop and identified how its functionality could be improved.</p> | <p>We developed a model to help us predict the number of PP1 that could be produced and then transported into the periplasm of our ToxiMop cells. The model pinpointed the cause of inefficiencies in our prototype ToxiMop and identified how its functionality could be improved.</p> | ||
| - | <img id="image-6" src="https://static.igem.org/mediawiki/2013/thumb/4/4c/TatBC-A-Overview.jpg/800px-TatBC-A-Overview.jpg"> | + | <a href="/Team:Dundee/Project/ProductionExport"><img id="image-6" src="https://static.igem.org/mediawiki/2013/thumb/4/4c/TatBC-A-Overview.jpg/800px-TatBC-A-Overview.jpg"></a> |
</div> | </div> | ||
</div><!-- Row End --> | </div><!-- Row End --> | ||
Latest revision as of 02:06, 29 October 2013
Modelling Overview
The central aims of the Dundee iGEM Dry Team were to (i) underpin and help direct the experimental programme and (ii) design tools to allow the general public to interact with the project. Modelling tools included population dynamics, geometric arguments, ordinary differential equations and stochastic simulation algorithms. Using these tools, we covered aspects of the project across multiple spatial scales, ranging from the determination of population growth within current detection window, through packing estimates for proteins on the membrane/in the periplasm to deterministic and stochastic models for PP1 production and export via the Tat pathway.
Key outputs used directly by the wet team were: identification of the huge fold increase in microsystin level between sample time and final result using current technology; PP1 maximum packing estimates for E. coli and B. subtilis supports the former as the team chassis of choice; efficiency measure of the Tat pathway as a transporter of PP1 to the periplasm in E. coli; PP1 export bottle-necks identified as key limiting factor and augmentation of TatB-C complexes targeted as the most efficient method to enhance mop efficacy.
The targeted modelling work was essential for the development of our project as a whole, but the team also wanted to allow others to investigate, develop and test ideas related to our project and theirs too. We developed an interactive modelling tool based on NetLogo that allows the user to test a large variety of hypotheses connected to the production and export of PP1 and its function as a ToxiMop. This easy-to-use online programme gives instant visual as well as quantitative feedback to the user.
If the presentation does not appear, refreshing the page will fix this. In compliance with the wiki freeze, Google Drive places an unalterable timestamp on the document. Access granted upon request as this requires a specific email address. To access : Click here
Presentation too small to read, Want it bigger? Simply click the button between the slide number and cog to make the presentation full screen.
1. Detection Time
The problem with current detection methods is the processing time between sampling and availability of results. Potentially, this could lead to significant increases in the microcystin concentrations before action is taken. Therefore, an effective biological detector must reduce this detection time.
2. PP1 Packing
The capacity of E. coli and B. subtilis to pack PP1 was investigated in order to determine which chassis could maximally hold the greatest number of PP1 molecules. This analysis indicated that E. coli has the greater potential to be a more efficient mop than B. subtilis.
3. Mop Simulation
We developed models and visualisation tools allowing the biological processes that take place in the ToxiMop bacteria to be investigated by other users. Dynamic alteration of key properties of the transport mechanisms provides instant feedback, allowing analysis of the concomitant effects and predictions to be tested.
4. Production & Export
We developed a model to help us predict the number of PP1 that could be produced and then transported into the periplasm of our ToxiMop cells. The model pinpointed the cause of inefficiencies in our prototype ToxiMop and identified how its functionality could be improved.
