Revision as of 23:34, 17 October 2014

Modelling Homepage

Our team believes that the key to synthetic biology is to use engineering-based models and designs to improve the development of biochemical systems. Therefore, the engineers in our team have worked with the biochemists every step of the way to design initial project ideas, analyse in detail the expected response of the system, and interpret the results of the various types of experiments that we have run.

This means that, unlike some teams in the past, our modelling is interspersed with our biochemistry information to give a feel of the real interactions that have taken place between the specialities in our team. To aid the viewer, all modelling sections have pink header bubbles, all of the biochemistry sections have light blue header bubbles.

We have also worked very closely with the policy and practices team and influential figures in industry to look at exactly how this project could be implemented in the real world. Included in this has been the 3D CAD (computer aided design) of the expected product and the 3D printing and circuit building of the biosensor unit.

Click on the links below to find out more!

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 <img src="https://static.igem.org/mediawiki/2014/2/20/Oxford_shake.png" style="float:right;position:relative; width:10%;margin-right:3%;" />
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-<h1green>Collaboration</h1green>
+<h1green>Creating models on iGEM Melbourne's star peptide </h1green>
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-We embarked on a major collaboration project with Melbourne iGEM team. Part of this collaboration involved extensively modelling the benefit of using their star peptide system in a bacterium and how that could control reaction rates. To do this we drew on the extensive knowledge that we've gained of stochastically modelling diffusion-driven systems.
+We embarked on a major collaboration project with Melbourne iGEM team. Part of this collaboration involved helping this team by modelling the benefit of using their star peptide system in a bacterium and how that could control reaction rates. To do this we drew on the extensive knowledge that we've gained of stochastically modelling diffusion-driven systems.
 </h2green>