Difference between revisions of "Team:Oxford/Model"

 
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<h1> Modeling</h1>
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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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<h3> Gold Medal Criterion #3</h3>
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To complete for the gold medal criterion #3, please describe your work on this page and fill out the description on your <a href="https://2017.igem.org/Judging/Judging_Form">judging form</a>. To achieve this medal criterion, you must convince the judges that your team has gained insight into your project from modeling. You may not convince the judges if your model does not have an effect on your project design or implementation.  
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Please see the <a href="https://2017.igem.org/Judging/Medals"> 2017 Medals Page</a> for more information.
 
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<h3>Best Model Special Prize</h3>
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To compete for the <a href="https://2017.igem.org/Judging/Awards">Best Model prize</a>, please describe your work on this page  and also fill out the description on the <a href="https://2017.igem.org/Judging/Judging_Form">judging form</a>. Please note you can compete for both the gold medal criterion #3 and the best model prize with this page.  
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You must also delete the message box on the top of this page to be eligible for the Best Model Prize.
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<h1>Modelling Overview</h1>
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<center><img class="img-responsive" width="200px;" src="https://static.igem.org/mediawiki/2017/1/1d/T--oxford--drylablogo.png"></center>
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<h2>Why did we model? What did we hope to achieve?</h2>
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<p>We generated mathematical models of our DNA-based system, our protein-based system, and the effects associated with the implementation of our diagnostic device on a human population. The aim of our models was to better understand our systems and improve them to become more feasible in practice.</p>
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<p>We also created a piece of software, which highlights the versatility of our device – by changing some of the DNA sequences (particularly the cleavage sites and output), our device can be used to detect various diseases. Our software aims to make it easier for other groups to use our diagnostic for other diseases.</p>
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<h2>What techniques did we use for modelling?</h2>
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<p> We employed numerous modelling techniques and equations to most accurately assess our systems. These include mass action and Michaelis-Menten kinetics; sensitivity analyses; parameter scans; stochastic modelling; and SIR and vector epidemiological modeling. Please follow the links below to see more detailed explanations of our models and to try out our software!</p><br>
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<h5> Inspiration </h5>
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<h2>How is our model integrated with the rest of our project?</h2>
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<p>Our modelling was closely interlinked with the rest of our project: it greatly informed and was informed by our design, wet lab work, and human practices. Some examples include:</p>
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Here are a few examples from previous teams:
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<li><a href="https://2016.igem.org/Team:Manchester/Model">Manchester 2016</a></li>
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<li>The <a href = "https://2017.igem.org/Team:Oxford/Disease_Model">epidemiological modeling</a> confirmed that focusing our project and device on the diagnosis of congenital Chagas disease can have a profound impact on the levels of the disease in a population. It also informed our <a href = "https://2017.igem.org/Team:Oxford/Applied_Design">applied design</a> and contributed to our decision to make a <a href = "https://2017.igem.org/Team:Oxford/Applied_Design_Implementation#poster">Chagas public health poster</a>.</li>
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<li><a href="https://2016.igem.org/Team:TU_Delft/Model">TU Delft 2016  </li>
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<li>The modelling of our <a href = "https://2017.igem.org/Team:Oxford/DNA_Based_Model#RBS">DNA-based system</a> led us to confirm the need for a strong ribosome binding site (RBS) to minimize false negatives</li>
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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>We communicated with experts in blood clotting, <a href = "https://2017.igem.org/Team:Oxford/HP/Gold_Integrated#SDiamond">Dr Scott Diamond</a> and <a href = "https://2017.igem.org/Team:Oxford/HP/Gold_Integrated#MLaffan">Professor Mike Laffan</a>. They provided incredibly important information that enabled us to <a href = "https://2017.igem.org/Team:Oxford/DNA_Based_Model#Production_of_hirudin">model</a> the output of our device and modify our design accordingly.</li>
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<li><a href="https://2014.igem.org/Team:Waterloo/Math_Book">Waterloo 2014</a></li>
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<li><a href = "https://2017.igem.org/Team:Oxford/DNA_Based_Model#Production_of_hirudin">DNA-based system</a> models with a hirudin output indicated to us that we would need an amplification step to limit false negatives: instead of producing hirudin in response to cruzipain, we decided to produce TEV protease, which can cleave multiple molecules of already-present, inactive hirudin.</li>
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<li>In our efforts to create a complex and accurate epidemiological model, we consulted experts in the field, including <a href = "https://2017.igem.org/Team:Oxford/HP/Gold_Integrated#MBonsall">Professor Mike Bonsall</a>.</li>
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<li>When designing our protein-based system parts, we decided to tag our fusion proteins with sfGFP so future experimental data could better <a href = "https://2017.igem.org/Team:Oxford/Results_Protein#OMV_modelling">inform our modelling</a>, for example with respect to efficiency of protein transport to the outer membrane. Unfortunately, however, we encountered difficulties with the cloning of some of our OMV-targeted proteins and were unable to include experimental parameters in our models.</li>
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<p>For more detail, please look at the specific modelling pages!</p>
  
 
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Latest revision as of 03:06, 2 November 2017

Modelling Overview

Why did we model? What did we hope to achieve?

We generated mathematical models of our DNA-based system, our protein-based system, and the effects associated with the implementation of our diagnostic device on a human population. The aim of our models was to better understand our systems and improve them to become more feasible in practice.

We also created a piece of software, which highlights the versatility of our device – by changing some of the DNA sequences (particularly the cleavage sites and output), our device can be used to detect various diseases. Our software aims to make it easier for other groups to use our diagnostic for other diseases.

What techniques did we use for modelling?

We employed numerous modelling techniques and equations to most accurately assess our systems. These include mass action and Michaelis-Menten kinetics; sensitivity analyses; parameter scans; stochastic modelling; and SIR and vector epidemiological modeling. Please follow the links below to see more detailed explanations of our models and to try out our software!


How is our model integrated with the rest of our project?

Our modelling was closely interlinked with the rest of our project: it greatly informed and was informed by our design, wet lab work, and human practices. Some examples include:

  • The epidemiological modeling confirmed that focusing our project and device on the diagnosis of congenital Chagas disease can have a profound impact on the levels of the disease in a population. It also informed our applied design and contributed to our decision to make a Chagas public health poster.
  • The modelling of our DNA-based system led us to confirm the need for a strong ribosome binding site (RBS) to minimize false negatives
  • We communicated with experts in blood clotting, Dr Scott Diamond and Professor Mike Laffan. They provided incredibly important information that enabled us to model the output of our device and modify our design accordingly.
  • DNA-based system models with a hirudin output indicated to us that we would need an amplification step to limit false negatives: instead of producing hirudin in response to cruzipain, we decided to produce TEV protease, which can cleave multiple molecules of already-present, inactive hirudin.
  • In our efforts to create a complex and accurate epidemiological model, we consulted experts in the field, including Professor Mike Bonsall.
  • When designing our protein-based system parts, we decided to tag our fusion proteins with sfGFP so future experimental data could better inform our modelling, for example with respect to efficiency of protein transport to the outer membrane. Unfortunately, however, we encountered difficulties with the cloning of some of our OMV-targeted proteins and were unable to include experimental parameters in our models.

For more detail, please look at the specific modelling pages!