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<p id="TopTitle" style="border-bottom: 1px black solid ;font-size:25px;text-weight:bold;display:inline-block">Human Practices and Lab Integration</p>
 
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All good model’s should be driven and guided by external influences if the model is to be of any use. In iGEM these factors are <a href="https://2016.igem.org/Team:Manchester/Human_Practices#gohere">Human Practices</a> and <a href="https://2016.igem.org/Team:Manchester/Description/mechanism1">Experimental work</a>.  
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Any good model should not be an end in itself, but should be motivated by real questions and solve real problems. In our project, the questions we addressed were directly derived from our Human Practices work and our models helped to direct the experimental work in several important ways. The questions brought to us guided the construction of the model and the analysis of the results. The major areas of integration between modelling and the rest of the project were:
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They guide the construction of the model and the analysis of the results.
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Construction of the model
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    <th>Question raised</th>
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    <th>Modelling Contribution</th>
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Initially we generated a mass action based model. This accurately modelled the system however we quickly realised that finding parameter values was going to prove particularly difficult.
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<td>"What would be the best ratio of enzymes to obtain a robust response for the lowest possible cost?" (Raised in discussion with the <a href="https://2016.igem.org/Team:Manchester/Human_Practices/Professional_Bodies#gmp">Police</a> and at the <a href="https://2016.igem.org/Team:Manchester/Human_Practices/Professional_Bodies#microbio">Microbiology Society Conference</a> </td>
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We looked into the research and found BRENDA, the enzyme database. This had a great source of kinetic parameters for Michaelis-Menten for the rate equation. So our focus turned to this - we lost the accuracy in the early, transient, stage of the model and this introduced a limitation on the enzyme:substrate ratio to ensure the validity conditions we always met but since this region was of little interest to us and enzyme will be a major cost in the system so should be minimized when at all possible, this was a tradeoff we were willing to make for the more well reported parameters.
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    <td><a href="https://2016.igem.org/Team:Manchester/Model/Costing">Cost Analysis</a>, examining predicted costs for a range  of different system designs with variations in the amount of enzymes based on experimental data</td>
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Off to the lab! Unfortunately the experimental results didn’t fit. Our model was reacting to completion given enough time which was causing problems.
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  <td>"How important are the specific parameters on the system?" (asked by the experimentalists)</td>  
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    <td><a href="https://2016.igem.org/Team:Manchester/Model/ParameterRelationships">Parameter Relationship Analysis</a>, assessing the importance of specific parameters and combinations of parameters on the outcomes of the system</td>
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With the model broken we had a chat with Aliah, a postdoc in the MIB, who suggested we should look into the reversible form of Michaelis-Menten as this would help overcome the problem we had noticed - the inevitable same steady state concentration. This model was implemented and data was generated.
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Then we took it to the lab, comparing what the model suggested with experimental results. Something else was wrong! The model wasn’t accounting for the amount of ABTS used at all. Only experiments where ABTS was in a large excess were being modelled accurately. The patch design relies on having variable limiting amount of ABTS so this was a problem.
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<td>"Is the system we have assembled really functioning in the way we think it does?" (asked by the experimentalists) </td>
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    <td><a href="https://2016.igem.org/Team:Manchester/Model/MechanismUncertainty">Network Mechanism Analysis</a>, comparing model predictions and experimental data for different potential circuit topologies</td>
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Back to the drawing board again. Taking note that the problem was almost certainly in the rate equations we were using we hit the library.  After another session cramming on Enzyme Kinetics and analysis of the results we decided a Bi-Uni rate law would be able to account for the ABTS.
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Analysis of Results
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During our interview with the police it was brought to our attention that our device could be of use to them, if it could improve on the current breathalyser device currently in use.
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This introduced constraints to our analysis since they said it would need to be at least as fast as the current method or ideally faster. This determined a maximum time until expression. The second constraint is from the experimental work, since the police would be using the device out and about the expression of the marker would need to be clear. This determined the minimum marker expression constraint.
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The analysis was therefore decided to find the optimum enzyme ratio to minimize the cost of the alcopatch, ensuring that it reaches a minimum expression in a given time. Since the patch will be of a finite size the total amount of enzyme used was kept constant, only the ratio of the enzymes was varied in the simulation. The results of this analysis should then be verified in the lab and could then be used as evidence for the utility of the AlcoPatch.
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Latest revision as of 12:57, 19 October 2016

Manchester iGEM 2016

Human Practices and Lab Integration

Any good model should not be an end in itself, but should be motivated by real questions and solve real problems. In our project, the questions we addressed were directly derived from our Human Practices work and our models helped to direct the experimental work in several important ways. The questions brought to us guided the construction of the model and the analysis of the results. The major areas of integration between modelling and the rest of the project were:


Question raised Modelling Contribution
"What would be the best ratio of enzymes to obtain a robust response for the lowest possible cost?" (Raised in discussion with the Police and at the Microbiology Society Conference Cost Analysis, examining predicted costs for a range of different system designs with variations in the amount of enzymes based on experimental data
"How important are the specific parameters on the system?" (asked by the experimentalists) Parameter Relationship Analysis, assessing the importance of specific parameters and combinations of parameters on the outcomes of the system
"Is the system we have assembled really functioning in the way we think it does?" (asked by the experimentalists) Network Mechanism Analysis, comparing model predictions and experimental data for different potential circuit topologies

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