Instead of running your model once with some specific parameters (for example rate constants.), you find all the possible parameters in literature and run your model for lots of combinations sampled in a clever way. By doing this you take take into account the uncertainty in the parameters. You will create probabilistic outputs allowing you to make rigorous conclusions.

Following through our flow diagram blueprint should make this concept clear.

We focused on modelling the cell free mechanism, see the experimental section for details. The short version is the AlcoPatch relies on Alcohol, Alcohol Oxidase (AOx), Horseradish Peroxidase (HRP) and ABTS which are mixed together making oxidised ABTS which is colourful. The ensemble modellingprocess will work for any model however and this was chosen as a simpler example to demonstrate the technique, focusing on the process rather than the system itself.

All the modeling and experimental work for mechanism 1 was done using Glucose and GOx instead, this was due to laboratory limitations. This effects the modelling analysis in trying to predict things about alcohol which came from human practices. The equivalent analysis was done for glucose. Rerunning the analysis for alcohol would only require the change of some constants, so the analysis acts as a proof of concept and still shows the integration of human practices.

Firstly we narrowed down the potential reaction schemes and increased our knowledge of our rate constants. Secondly we have created a blueprint for using ensemble modelling instead of simple ODE modelling in the hope that it can become standard practice for iGEM. MATT THIS WAS A PLACE HOLDER YOU ARE TO WRITE THIS AFTER THE RESULTS SECTION AS YOU WILL BE ABLE TO LINK BETWEEN THE TOO.