Team:Oxford/Modelling

From 2014.igem.org

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<h1blue3>Characterising a genetic network</h1blue3>
<h1blue3>Characterising a genetic network</h1blue3>
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<h1orange>Analysing microcompartments mathematically</h1orange>
<h1orange>Analysing microcompartments mathematically</h1orange>
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<h1green>Collaboration</h1green>
<h1green>Collaboration</h1green>
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<h1orange>Bioremediation realisation</h1orange>
<h1orange>Bioremediation realisation</h1orange>

Revision as of 00:45, 21 September 2014

#list li { list-style-image: url("https://static.igem.org/mediawiki/2014/6/6f/OxigemTick.png"); } }


Modelling Homepage


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

This means that, unlike iGEM teams in the past, our modelling is presented interspersed with our biochemistry information to hopefully 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 human 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 of the expected product and the 3D printing and circuit building of the biosensor unit.

Click on the links below to find out more.

Characterising a genetic network
We used stochastic and deterministic genetic circuit modelling helped the wet-lab team develop the ability to characterise a previously unknown genetic circuit. Find out more here...
Optimising a genetic network
We used these genetic circuit models to predict the fluoresence of the system as a response to thousands of different combinations of the inputs. This allowed us to optimise the input levels and advise the biochemists on the construction so that we could develop the best possible system in the amount of time available. See what we found out...
Analysing the native bacteria
We constructed a model based on Michaelis-Menten kinetics that could inform us how much DCM the native bacteria would be able to degrade and also what the pH change of the system would be. This further convinced us to use synthetic biology to solve the problem of chlorinated waste disposal. See how we did it here...
Analysing microcompartments mathematically
We used spacial modelling to determine an estimate of various parameters to do with the microcompartments. We then fed this information to the biochemists for use in inserting the microcompartments into the bacteria.
Analysing the benefit of microcompartments
On this page we explain in detail how our stochastic diffusion models work and how lots of information on how we then used them to provide some very detailed analysis of the benefits of microcompartments for our system.
Collaboration
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 due to the increase in the rate of reaction. To do this we drew on the extensive knowledge that we'd gained of stochastically modelling diffusion driven systems.
Realising the biosensor
On the advice of industry experts, we produced concept designs of our whole system using CAD, we built the biosensor using the latest 3D printing technologies and we designed and built a very cheap circuit that can detect low levels of GFP fluorescence to go inside the biosensor. This part is really exciting...
Bioremediation realisation
Jack's beads and the modelling of them, anything else that you can think of