Difference between revisions of "Team:Manchester/Model"

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<a href="https://2016.igem.org/Team:Manchester/Model/PDF">Generating Probability Density Functions</a> </br>
 
<a href="https://2016.igem.org/Team:Manchester/Model/PDF">Generating Probability Density Functions</a> </br>
 
<a href="https://2016.igem.org/Team:Manchester/Model/Simulate">Simulate the System</a> </br>
 
<a href="https://2016.igem.org/Team:Manchester/Model/Simulate">Simulate the System</a> </br>
<a href="https://2016.igem.org/Team:Manchester/Model/Analyse">Analyse the Results</a> </br>
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<a href="https://2016.igem.org/Team:Manchester/Model/result">Analyse the Results</a> </br>
 
<a href="https://2016.igem.org/Team:Manchester/Model/Story">Story of the Model</a> </br>
 
<a href="https://2016.igem.org/Team:Manchester/Model/Story">Story of the Model</a> </br>
 
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Revision as of 15:00, 19 October 2016

Manchester iGEM 2016
Modelling Banner
What were we modelling?
What is esemble modelling?
What does our modelling achieve?

What were we modelling?

We focused on modelling the Cell-free Mechanism. 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) to detect and quantify alcohol levels. The oxidised ABTS produced in the prescence of alcohol is dark green and can be detected spectophotometrically or visually.

We focused on this small system because it was possible to obtain a large amount of experimental data for model validation, and because it allowed us to establish and illustrate theensemble modelling process.

The majority of our experimental data came from the proof-of-concept study of the analogous system of glucose and glucose oxidase (GOx) rather than alcohol and AOx. The reaction network of the two sytems is the same and only some kinetic parameters differ.

A schematic diagram of the final circuit of our detection system is given below. For more information about the individual reactions click on the blue enzyme boxes.

Reaction Network Diagram used in the modelling

Alternatively you can click on the enzyme name below:

Glucose Oxidase
Horseradish Peroxidase

What is Ensemble Modelling?

Incomplete and uncertain knowledge of kinetic parameters is a common problem when building models for synthetic biology. Ensemble modelling is one strategy to deal with this problem. Instead of running our model with a single set of specific parameters (for example rate constants), we run our model multiple times using different sets of plausible parameter values and analysed the predictions as an ensemble. We collected all the available parameter values from published literature and took into account the uncertainties that are associated with them. The resulting confidence in our parameter values was then described byprobability density functions .
This has created probabilistic outputs allowing us to make rigorous conclusions about our reaction mechanism – and to assess which predictions are reliable, and where we are lacking information.

To explore the theory of this process please click the boxes on the diagram below.

Overview flowchart of ensemble modelling

Alternatively you can click on the step name below:

Collecting and Processing Data
Generating Probability Density Functions
Simulate the System
Analyse the Results
Story of the Model

What did our model achieve?

We achieved 3 main aims in our modelling work:



All of our models are available on our Github page

Network Mechanism Analysis Parameter Relationship Analysis Cost Analysis
Comparing model predictions with experimental data for different potential circuit topologies. a href="https://2016.igem.org/Team:Manchester/Model/MechanismUncertainty">read more Assessing the interlinking nature of specific parameter pairings on the outcomes of the system. read more Predicting the costs for a range of different system specifications by varying the amount of enzymes based on experimental data. read more

We found great inspiration from our human practices and guidance working both ways with the experiments. Click here to see a summary.