What were we modelling?

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 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid) (ABTS) which are mixed together producing oxidised ABTS which is colourful. The ensemble modelling process 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.

Due to time constraints the modelling was based on an analogous system of glucose and glucose oxidase (GOx) rather than alcohol and AOx, this was chosen as the reaction network is the same and it links in with the suggestions given at the Microbiology Society Conference in the human practices that the design need not be limited for alcohol but could be used by diabetics, etc if used to detect other molecules in sweat. While 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 sufficient proof of concept and still shows the integration of human practices.

A schematic diagram of the final scheme is given below. For more information about the steps click on the blue enzyme boxes.

Alternatively you can click on the enzyme name below:

Glucose Oxidase
Horseradish Peroxidase

What is Ensemble Modelling?

We model the reactions in our AlcoPatch system using ordinary differential equations that is dependent on kinetic parameters and have used it to generate a composite of predictions using ensemble modelling.
As multiple and various kinetic values was found for the parameters, each parameter is described by a probability distribution which expresses the associated uncertainty. Sampling from these probability distributions have allowed our AlcoPatch model to simulate its reactions with different sets of plausible kinetic values. This means that instead of running a single prediction from a fixed single set of kinetic parameter values, we could run our AlcoPatch model a number of times to generate multiple predictions that we later analyse as an ensemble to provide probabilistic predictions of the system.
Ensemble modelling has yet to ‘breakthrough’ into the world of biological modelling and systems biology in general. Essentially, we analyse multiple predictions from our model as a composite, which is different from traditional predictive modelling methods.
Instead of running a single prediction from a fixed single set of kinetic parameter values, we run our AlcoPatch model a number of times from different sets of kinetic values by sampling from the probability distributions generated from the previous step. We then analyse the entire set of predictions as an ensemble to draw probabilistic conclusions about the system.
To explore the theory of this process please click the boxes on the diagram below.

Alternatively you can click on the step name below:

Collecting Parameters
Processing Parameters
Generating Probability Density Functions
Simulate the System
Analyse the Results
Update the Model

What did our model achieve?

Regardless how clever, interesting or unique a model is ultimately all that matters is the results at the end. We had 2 main aims for our outputs: improving our understanding of our system and answering key questions that arouse during the Human Practices.

To improve our knowledge of the system we undertook 2 main analyses:
Improving our understanding the reaction network mechanism
Investigating the relationships between the parameters in our system

To answer the questions from the human practices there was 1 main analysis:
Costing the AlcoPatch


Further justification for why these were chosen is given on the respective pages.