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Team:Dundee/Project/MathOverview
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| - | <p> 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. <a href="https://docs.google.com/presentation/d/1isJ1295-ruLBpLxtkStChEIzYYgbvujTuY-Xr2Cc3wQ/pub?start=false&loop=false&delayms=3000">Click here</a> | + | <p> 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 the Google Presentation : <a href="https://docs.google.com/presentation/d/1isJ1295-ruLBpLxtkStChEIzYYgbvujTuY-Xr2Cc3wQ/pub?start=false&loop=false&delayms=3000">Click here</a> |
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Revision as of 18:52, 4 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 packing estimates for E. coli and B. subtilis determine the former is used as the chassis of choice; efficiency measure of the Tat pathway as a transporter of PP1 to the periplasm in E. coli ; export bottle-necks identified and augmentation of TatB-C complexes targeted as the most efficient method to enhance PP1 export.
This 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. 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 the Google Presentation : 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.
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.
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.
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.
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.
