Latest revision as of 03:44, 18 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!

Characterising a genetic network
We used stochastic and deterministic genetic circuit modelling to help the wet-lab team in characterising 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 inputs. This allowed us to optimise the input levels and advise the biochemists on the construction of the system so that we could develop the best possible system in the amount of time available. See what we found out...

Analysing the native bacterium
We constructed a model based on Michaelis-Menten kinetics that could inform us how much DCM the native bacterium 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 gave this information to the biochemists to help them analyse their results with expressing microcompartments in E. coli and P. putida.

Analysing the benefits of microcompartments
On this page we explain in detail how our stochastic diffusion models work and then provide in-depth information on how we then used these carefully analyse of the benefits of microcompartments for our system.

Creating models of iGEM Melbourne's star peptide
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 have 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
Discover how we envisage our project becoming a real world product and see the CAD models that have allowed us to demonstrate our idea to industry experts.

Biopolymer containment
Find out how we modelled the processes that control the diffusion of DCM and reaction products through the biopolymer containment beads, and how this modelling played an integral part in calculating the optimum bead thickness for our system.

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+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 have gained of stochastically modelling diffusion-driven systems.
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Team:Oxford/Modelling

From 2014.igem.org

Latest revision as of 03:44, 18 October 2014