Difference between revisions of "Team:Manchester/Model"
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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. | 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. | ||
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Following through our flow diagram blueprint should make this concept clear. | Following through our flow diagram blueprint should make this concept clear. | ||
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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. | 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. | ||
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Firstly we narrowed down the potential reaction schemes and increased our knowledge of our rate constants. | 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. | + | 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. --> |
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<p style="border-bottom: 1px black solid ;font-size:25px;text-weight:bold;display:inline-block">What were we modelling?</p> | <p style="border-bottom: 1px black solid ;font-size:25px;text-weight:bold;display:inline-block">What were we modelling?</p> | ||
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Further justification for why these were chosen is given on the respective pages. | Further justification for why these were chosen is given on the respective pages. | ||
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Revision as of 21:38, 16 October 2016
Welcome to our modelling section. We have used a novel ensemble modeling approach, to better aid the synergy between wetalb and dry lab teams. On this page you will find short answers to the questions; What is ensemble modelling? What did we model? what did our model achieve?
For navigating the wiki you need to know that the sections on results and human practice/lab integration can be accessed in the menu bar.
Part of what we hope to achieve with this ensemble methodology is a blueprint for other igem teams. As such each step in creating and using our model is laid out in the below diagram. Clicking on a specific step will take you to a page explaining the theory. Going deeper you can access a discussion of the code. There will be a link to the relevant parts of our github (a website storing our code) Use these codes as you wish.
Link to github homepage
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.
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 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?
PLACEHOLDER
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.
We found great inspiration from our human practices and guidance working both ways to the experiments. Click here to see the detail.
