Team:Dundee/Project/MathOverview

From 2013.igem.org

(Difference between revisions)
Line 52: Line 52:
         <div class="span6" style="text-align:justify">
         <div class="span6" style="text-align:justify">
-
           <h2 style="margin-top:-10px;"> PP1 Packing </h2>
+
           <h2 style="margin-top:-10px;">1.  Detection Time</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>
 +
        </div>
 +
      </div>
 +
 
 +
  <div class="span6">
 +
          <img id="image-6" src="https://static.igem.org/mediawiki/2013/thumb/c/c8/MopEquations.png/731px-MopEquations.png">
 +
          </div>
 +
 
 +
 
 +
        <div class="span6" style="text-align:justify">
 +
          <h2 style="margin-top:-10px;">2.  PP1 Packing </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 host 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 host 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>
         </div>
         </div>
         <div class="span6" style="text-align:justify">
         <div class="span6" style="text-align:justify">
-
           <h2 style="margin-top:-10px;"> Production & Export</h2>
+
           <h2 style="margin-top:-10px;">4. Production & Export</h2>
           <p>We developed a Production & Export model to help us predict the number of PP1 that could be transported into the periplasm of our ToxiMop cells. The model allowed us to optimise the construction of our prototype ToxiMop.</p>
           <p>We developed a Production & Export model to help us predict the number of PP1 that could be transported into the periplasm of our ToxiMop cells. The model allowed us to optimise the construction of our prototype ToxiMop.</p>
         </div>
         </div>
Line 78: Line 89:
         <div class="span6" style="text-align:justify">
         <div class="span6" style="text-align:justify">
-
           <h2 style="margin-top:-10px;"> Mop Simulation </h2>
+
           <h2 style="margin-top:-10px;">3. Mop Simulation </h2>
           <p>We developed  models and visualisation tools allowing the biological processes which take place in the ToxiMop bacteria to be investigated by  a user. Dynamic alteration of key properties of the transport mechanisms provides instant feedback allowing analysis of the concomitant effects.
           <p>We developed  models and visualisation tools allowing the biological processes which take place in the ToxiMop bacteria to be investigated by  a user. Dynamic alteration of key properties of the transport mechanisms provides instant feedback allowing analysis of the concomitant effects.
           </p>
           </p>
       </div>
       </div>
-
 
-
        <div class="span6" style="text-align:justify">
 
-
          <h2 style="margin-top:-10px;"> Detection Time</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>
 
-
        </div>
 
-
      </div>
 
Line 97: Line 102:
           </div>
           </div>
-
        <div class="span6">
+
     
-
          <img id="image-6" src="https://static.igem.org/mediawiki/2013/thumb/c/c8/MopEquations.png/731px-MopEquations.png">
+
-
          </div>
+
       </div><!-- Row End -->
       </div><!-- Row End -->

Revision as of 15:08, 23 October 2013

iGEM Dundee 2013 · ToxiMop

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 their's 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 host 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.

4. Production & Export

We developed a Production & Export model to help us predict the number of PP1 that could be transported into the periplasm of our ToxiMop cells. The model allowed us to optimise the construction of our prototype ToxiMop.

3. Mop Simulation

We developed models and visualisation tools allowing the biological processes which take place in the ToxiMop bacteria to be investigated by a user. Dynamic alteration of key properties of the transport mechanisms provides instant feedback allowing analysis of the concomitant effects.