Difference between revisions of "Team:Edinburgh OG/Model"

 
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<h1> Modeling</h1>
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<h1 class="subtitle text-center">Model: Homepage</h1>
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<p>The overall goal of PhagED is to stop infections and deaths caused by antibiotic resistant pathogens. In the strategy that we use, the key step is to re-sensitise antibiotic resistant populations of bacteria using a system of two phages. In order to
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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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improve our understanding of complex interactions between these populations and at the same time to overcome the limitations imposed by the lack resources, such as finance and time available for the research, we developed an <em>in silico</em> model.
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<p>Therefore, the main objective was to develop a mathematical model describing interactions between populations of lysogenic and lytic phages as well as bacteria in one system in continuous and batch processes. This was done by augmenting separate existing
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models of bacteria-phage interactions described as systems of ordinary (ODEs) and delayed differential equations (DDEs). The model was then used to:</p>
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<li>Run simulations of a continuous process in Python using PyDDE package for solving systems of DDEs and compare the change in populations relative to the previous separate models of lytic phage and bacteria (Levin, Stewart and Chao, 1977), and lysogenic
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<h3> Gold Medal Criterion #3</h3>
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phage and bacteria interactions in chemostat (Qiu, 2007).</li>
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<li>Perform a local sensitivity analysis of the model parameters by calculating the elasticity for each parameter in Python to identify the key parameters that are largely responsible for the variation in the model output.</li>
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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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<p>Our model is divided into four parts that you can see below. At the end of each part you will find the menu, similar to the one below, to help you navigate around. </p>
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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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<h4 class="media-heading">Background</h4>
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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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<p>Review of existing models of bacteria-phage interactions with their assumptions and limitations.</p>
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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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<h5> Inspiration </h5>
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Here are a few examples from previous teams:
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<h4 class="media-heading">Methodology</h4>
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<p>Here we present the methodology that was developed and used for the model.</p>
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<li><a href="https://2016.igem.org/Team:Manchester/Model">Manchester 2016</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><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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<h4 class="media-heading">Results</h4>
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<p>Here we present the results of simulations of newly created models, review and analyse together with comparison to other models.</p>
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<h4 class="media-heading">References</h4>
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<p>We gathered almost all up-to-date scientific literature about phage-bacteria interactions.</p>
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<div><a class="btn-result btn-active" style="width:90%;" data-wow-delay=".9s" href="https://2017.igem.org/Team:Edinburgh_OG/Model" style="visibility: visible; animation-delay: 0.9s;">Model Homepage</a></div>
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<div style="clear:both;"><a class="btn-result" data-wow-delay=".9s" href="https://2017.igem.org/Team:Edinburgh_OG/Model:Background" style="visibility: visible; animation-delay: 0.9s;">Background</a>
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<a class="btn-result" data-wow-delay=".9s" href="https://2017.igem.org/Team:Edinburgh_OG/Model:Methodology" style="visibility: visible; animation-delay: 0.9s;">Methodology</a></div>
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<div style="clear:both;" class="btn-row"><a class="btn-result" data-wow-delay=".9s" href="https://2017.igem.org/Team:Edinburgh_OG/Model:Results" style="visibility: visible; animation-delay: 0.9s;">Results</a>
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<a class="btn-result" data-wow-delay=".9s" href="https://2017.igem.org/Team:Edinburgh_OG/Model:References" style="visibility: visible; animation-delay: 0.9s;">References</a></div>
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Latest revision as of 03:33, 2 November 2017

PhagED: a molecular toolkit to re-sensitise ESKAPE pathogens

Model: Homepage

The overall goal of PhagED is to stop infections and deaths caused by antibiotic resistant pathogens. In the strategy that we use, the key step is to re-sensitise antibiotic resistant populations of bacteria using a system of two phages. In order to improve our understanding of complex interactions between these populations and at the same time to overcome the limitations imposed by the lack resources, such as finance and time available for the research, we developed an in silico model.

Therefore, the main objective was to develop a mathematical model describing interactions between populations of lysogenic and lytic phages as well as bacteria in one system in continuous and batch processes. This was done by augmenting separate existing models of bacteria-phage interactions described as systems of ordinary (ODEs) and delayed differential equations (DDEs). The model was then used to:

  • Run simulations of a continuous process in Python using PyDDE package for solving systems of DDEs and compare the change in populations relative to the previous separate models of lytic phage and bacteria (Levin, Stewart and Chao, 1977), and lysogenic phage and bacteria interactions in chemostat (Qiu, 2007).
  • Perform a local sensitivity analysis of the model parameters by calculating the elasticity for each parameter in Python to identify the key parameters that are largely responsible for the variation in the model output.

Our model is divided into four parts that you can see below. At the end of each part you will find the menu, similar to the one below, to help you navigate around.

Background

Review of existing models of bacteria-phage interactions with their assumptions and limitations.

Methodology

Here we present the methodology that was developed and used for the model.

Results

Here we present the results of simulations of newly created models, review and analyse together with comparison to other models.

References

We gathered almost all up-to-date scientific literature about phage-bacteria interactions.

Results References