Difference between revisions of "Team:Manchester/Model"

 
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<h3>★  ALERT! </h3>
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<p>This page is used by the judges to evaluate your team for the <a href="https://2016.igem.org/Judging/Awards#SpecialPrizes">Best Model award</a>. </p>
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<p> Delete this box in order to be evaluated for this medal. See more information at <a href="https://2016.igem.org/Judging/Pages_for_Awards/Instructions"> Instructions for Pages for awards</a>.</p>
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<h2> Modeling</h2>
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<p>Mathematical models and computer simulations provide a great way to describe the function and operation of BioBrick Parts and Devices. Synthetic Biology is an engineering discipline, and part of engineering is simulation and modeling to determine the behavior of your design before you build it. Designing and simulating can be iterated many times in a computer before moving to the lab. This award is for teams who build a model of their system and use it to inform system design or simulate expected behavior in conjunction with experiments in the wetlab.</p>
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<h5> Inspiration </h5>
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Here are a few examples from previous teams:
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<ul>
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<li><a href="https://2014.igem.org/Team:ETH_Zurich/modeling/overview">ETH Zurich 2014</a></li>
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<li><a href="https://2014.igem.org/Team:Waterloo/Math_Book">Waterloo 2014</a></li>
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Welcome to our modelling section, we have used a novel ensemble modelling approach, to better aid the synergy between wetlab and dry lab teams. You will find on this page an overview of ensemble modelling and our model and what it achieve. Below there is a flowchart showing how the model worked and the different parts, click on them to find out about the theory and in these sections there are links to code analysis of why we did things these ways and how to do them. There is also a section on the story of what we did and how experiment and human practices fed into the model and vica versa.
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Follow this github link to see the full code, feel free to use it. It’s meant to be easy to use. If you can make a simple model you can ensemble model. If you use please attribute us .
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  <img src="https://static.igem.org/mediawiki/2016/f/f1/T--Manchester--modellingbanner.png" alt="Modelling Banner" />
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<br />
 
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<h1 class="teamtitle" style="color:purple;"> Questions you may have (click for more info) </h1>
 
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<div class="column onethird_size">
 
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<a href="#ross"><img class="ross" style="width: 250px;" id="ross" src="https://static.igem.org/mediawiki/2016/f/ff/T--Manchester--whatisensemble.png" alt="Ross photo"></img></a>
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<a href="#model1"><img class="model1" src="https://static.igem.org/mediawiki/2016/c/c1/T--Manchester--modelling1_pic.png" alt="What were we modelling?"></img></a>
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<h3 class="teammatename" style="text-align:center"> </h3>
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<a href="#model2"><img class="model1" src="https://static.igem.org/mediawiki/2016/a/a3/T--Manchester--modelling2_pic.png" alt="What is esemble modelling?"></img></a>
 
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<h5 id="rossbio">
 
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The short version…….
 
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Instead of running your model once with some specific parameters, you find all the possible parameters in literature and run your model for lots of combinations in a clever way. By doing this you take into account the uncertainty in your parameters and as such make meaningful conclusions. An example of this is what is your exact reaction scheme, you can run these simulations for different models and see which can be consistent with experimental data. Furthermore by testing your specific parameters set fit to data you can find out what values work and relationships between your parameters, better understanding them and providing physical insight.
 
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<div  class="column onethird_size" >
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<a href="#nick"><img style="width: 250px;" class="nick" id="nick" src="https://static.igem.org/mediawiki/2016/d/d3/T--Manchester--whatwerewemodelling.png" alt="Nick photo"></img></a>
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<a href="#model3"><img class="model1" src="https://static.igem.org/mediawiki/2016/4/46/T--Manchester--modelling3_pic.png" alt="What does our modelling achieve?"></img></a>
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<h3 class="teammatename" style="text-align:center"> </h3>
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</br></br>
 
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</div>
  
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<h5 id="nickbio">
 
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We focused on modelling the cell free mechanism. See the experimental section for details, but essentially, Alcohol, alcohol oxidase, horseradish peroxidase  and abts are mixed together making oxidised abts which is colourful, click here to see the potential reaction schemes. Click here to be taken to a detailed explanation in the experimental section.
 
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The process will work for any model however and this was chosen as a simpler example to demonstrate the technique.
 
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</h5>
 
  
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<div  class="column onethird_size" >
 
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<a href="#marc"> <img style="width: 200px;" class="marc" id="marc" src="https://static.igem.org/mediawiki/2016/3/39/T--Manchester--whatdidourmodelachieve.png" alt="Marc photo"></img></a>
 
  
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<div id="model3">
 
</div>
 
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<h5 id="marcbio">
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<div  class="modelling_info1">
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Firstly we narrowed down the potential reaction schemes and increased our knowledge of our rate constants.
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<p style="border-bottom: 1px black solid ;font-size:25px;text-weight:bold;display:inline-block">What did our model achieve?</p>
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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.
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<p style="font-size:1.2em;">
 +
We achieved 3 main aims in our modelling work:
  
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</h5
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 +
</br> We introduced a novel <a href="https://2016.igem.org/Team:Manchester/Model/ModelExplorer">ensemble modelling</a> approach to iGEM and made this approach accessible to other iGEM teams by sharing <a target="_Blank" href="https://github.com/Manchester-iGem-2016/Ensemble-Modelling">our code.</a>
 +
<br />
 +
We improved our understanding of our system and used real experimental data to improve our model, using <a href="https://2016.igem.org/Team:Manchester/Model/MechanismUncertainty">network mechanism analysis </a> and <a href="https://2016.igem.org/Team:Manchester/Model/ParameterRelationships">parameter relationship analysis</a>. <br />
 +
We answered key questions that arose during our <a href="https://2016.igem.org/Team:Manchester/Model/hp"> integrated human practices</a> work, helping to improve the design of our system using <a href="https://2016.igem.org/Team:Manchester/Model/Costing">cost analysis</a>.
 +
 
 +
 
 +
</br></br>
 +
All of our models are available on <a target="_Blank" href="https://github.com/Manchester-iGem-2016/Ensemble-Modelling">our Github page</a>
 +
 
 +
</p>
 +
 
 +
<table>
 +
      <th><a href="https://2016.igem.org/Team:Manchester/Model/MechanismUncertainty">Network Mechanism Analysis </a></th>
 +
      <th><a href="https://2016.igem.org/Team:Manchester/Model/ParameterRelationships">Parameter Relationship Analysis</a></th>
 +
      <th><a href="https://2016.igem.org/Team:Manchester/Model/Costing">Cost Analysis</a></th>
 +
<tr>
 +
    <td>Comparing model predictions with experimental data for different potential circuit topologies. <a href="https://2016.igem.org/Team:Manchester/Model/MechanismUncertainty">read more</a></td>
 +
    <td>Assessing the interlinking nature of specific parameter pairings on the outcomes of the system. <a href="https://2016.igem.org/Team:Manchester/Model/ParameterRelationships"> read more </a></td>
 +
    <td>Predicting the costs for a range of different system specifications by varying the amount of enzymes based on experimental data. <a href="https://2016.igem.org/Team:Manchester/Model/Costing">read more</a></td>
 +
</tr>
 +
</table>
 +
 
 +
 
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<div class="modelling_info1">
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<center>
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<p style="font-size:1.2em;">
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We found great inspiration from our <a href="https://2016.igem.org/Team:Manchester/Human_Practices/Industries">human practices</a> and guidance working both ways with the experiments. Click <a href="https://2016.igem.org/Team:Manchester/Model/hp"> here</a> to see a summary.
 +
</p>
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</center>
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</div>
  
 
</div>
 
</div>
 
</div>
 
</div>
  
 +
<div id="model1">
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</div>
  
−
<span class="box1">
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<div class="modelling_info1">
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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="font-size:1.2em;">
 +
We focused on modelling the <a href="https://2016.igem.org/Team:Manchester/Description/mechanism1">Cell-free Mechanism</a>. 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 ABTS<sub>Oxidised</sub> produced in the prescence of alcohol is dark green and can be detected spectophotometrically or visually.
 +
</br></br>
 +
We focused on this small system because it was possible to obtain a large amount of <a href="https://2016.igem.org/Team:Manchester/Notebook">experimental data</a> for model validation, and because it allowed us to establish and illustrate the <a href="https://2016.igem.org/Team:Manchester/Model/ModelExplorer">ensemble modelling</a> process.
 +
</p>
 +
<p style="font-size:1.2em;">
 +
The majority of our experimental data came from the <a href="https://2016.igem.org/Team:Manchester/Proof">proof-of-concept</a> 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.
 +
</p>
 +
<p style="font-size:1.2em;">
 +
A schematic diagram of the final circuit of our detection system is given below. </br>
 +
<b> For more information about the individual reactions click on the blue enzyme boxes. </b>
 +
</p>
 +
<img class="full" src="https://static.igem.org/mediawiki/2016/0/04/T--Manchester--ModellingNetworkDiagram.png" alt="Reaction Network Diagram used in the modelling" usemap="#diagramclick" />
 +
 
 +
<map name="diagramclick">
 +
<area shape="rect" coords="195,120,255,175" href="https://2016.igem.org/Team:Manchester/Model/GlucoseOxidaseReaction" title="Glucose Oxidase Reaction">
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<area shape="rect" coords="505,155,566,210" href="https://2016.igem.org/Team:Manchester/Model/HorseRadishPeroxidaseReaction" title="HorseRadish Peroxidase Reaction">
 +
</map>
 +
 
 +
</br>
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<p style="font-size:1.2em;">
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Alternatively you can click on the enzyme name below:
 +
</p>
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<p style="font-size:1.2em;">
 +
<a href="https://2016.igem.org/Team:Manchester/Model/GlucoseOxidaseReaction">Glucose Oxidase</a>
 +
</br>
 +
<a href="https://2016.igem.org/Team:Manchester/Model/HorseRadishPeroxidaseReaction">Horseradish Peroxidase</a>
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</p>
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</div>
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<div id="model2">
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</div>
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<div  class="modelling_info1">
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<p style="border-bottom: 1px black solid ;font-size:25px;text-weight:bold;display:inline-block">What is Ensemble Modelling?</p>
 +
<p style="font-size:1.2em;">
 +
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 analyse 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 by <a href="https://2016.igem.org/Team:Manchester/Model/PDF">probability density functions</a>. </br>
 +
 
 +
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. </br> </br>
 +
 
 +
<b>To explore the theory of this process please click the boxes on the diagram below.</b>
 +
 
 +
</p>
 +
 
 +
<img class="full" src="https://static.igem.org/mediawiki/2016/a/a9/T--Manchester--ModelFlowchart.jpg" alt="Overview flowchart of ensemble modelling" / usemap="#diagram1click">
 +
 
 +
 
 +
 
 +
<map name="diagram1click">
 +
<area shape="rect" coords="70,82,413,190" href="https://2016.igem.org/Team:Manchester/Model/ParameterSelection" title="Parameter Selection">
 +
<area shape="rect" coords="505,83,861,188" href="https://2016.igem.org/Team:Manchester/Model/PDF" title="PDF">
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<area shape="rect" coords="635,305,850,411" href="https://2016.igem.org/Team:Manchester/Model/Simulate" title="system Simulate">
 +
<area shape="rect" coords="338,307,553,412" href="https://2016.igem.org/Team:Manchester/Model/result" title="Result Analysis">
 +
<area shape="rect" coords="50,298,265,400" href="https://2016.igem.org/Team:Manchester/Model/Story" title="Update Model">
 +
</map>
 +
 
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<p style="font-size:1.2em;">
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Alternatively you can click on the step name below:
 +
</p>
 +
<p  style="font-size:1.2em;">
 +
 
 +
<a href="https://2016.igem.org/Team:Manchester/Model/ParameterSelection">Collecting and Processing Data</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/result">Analyse the Results</a> </br>
 +
<a href="https://2016.igem.org/Team:Manchester/Model/Story">Story of the Model</a> </br>
 +
</p>
 +
</div>
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</centre>
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<span class="box">
  
 
</span>
 
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</body>
 
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<div class="floatleft1 project1">
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<a class="projectlink" href="https://2016.igem.org/Team:Manchester"><< Main Page</a>
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Latest revision as of 14:50, 24 November 2016

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

What did our model achieve?

We achieved 3 main aims in our modelling work:
We introduced a novel ensemble modelling approach to iGEM and made this approach accessible to other iGEM teams by sharing our code.
We improved our understanding of our system and used real experimental data to improve our model, using network mechanism analysis and parameter relationship analysis.
We answered key questions that arose during our integrated human practices work, helping to improve the design of our system using cost analysis.

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. 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.

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 ABTSOxidised 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 the ensemble 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 analyse 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 by probability 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

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