Difference between revisions of "Team:Lund/Model"

 
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<h3><em>in silico</em> design of hemoglobin mutants</h3>
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<p>Structure-property modeling of proteins is a notoriously difficult problem. In this model we rationally design human hemoglobin mutants in a semi data-driven manner by combining machine learning and protein engineering. The proteins are expressed in the lab and shown to be functional.  </p>
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      <a class="btn btn-default" href="/Team:Lund/Model/Hemoglobin">Read more</a>
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<h3>Growth Curves</h3>
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<p><em>Vitreoscilla</em> hemoglobin has revealed itself to possess many interesting properties. Here we model the growth curves of cells expressing the hemoglobin under promoters of varying strength. We show that the additional expression of the protein is deemed positive on the growth rate.</p>
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      <a class="btn btn-default" href="/Team:Lund/Model/GrowthCurves">Read more</a>
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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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Convince the judges that your project's design and/or implementation is based on insight you have gained from modeling. This could be either a new model you develop or the implementation of a model from a previous team. You must thoroughly document your model's contribution to your project on your team's wiki, including assumptions, relevant data, model results, and a clear explanation of your model that anyone can understand.
 
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The model should impact your project design in a meaningful way. Modeling may include, but is not limited to, deterministic, exploratory, molecular dynamic, and stochastic models. Teams may also explore the physical modeling of a single component within a system or utilize mathematical modeling for predicting function of a more complex device.
 
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Please see the <a href="https://2018.igem.org/Judging/Medals"> 2018
 
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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://2018.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://2018.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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<h3> Inspiration </h3>
 
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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">2016 Manchester</a></li>
 
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<li><a href="https://2016.igem.org/Team:TU_Delft/Model">2016 TU Delft</li>
 
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<li><a href="https://2014.igem.org/Team:ETH_Zurich/modeling/overview">2014 ETH Zurich</a></li>
 
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<li><a href="https://2014.igem.org/Team:Waterloo/Math_Book">2014 Waterloo</a></li>
 
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Latest revision as of 10:54, 6 November 2018

in silico design of hemoglobin mutants

Structure-property modeling of proteins is a notoriously difficult problem. In this model we rationally design human hemoglobin mutants in a semi data-driven manner by combining machine learning and protein engineering. The proteins are expressed in the lab and shown to be functional.

Read more

Growth Curves

Vitreoscilla hemoglobin has revealed itself to possess many interesting properties. Here we model the growth curves of cells expressing the hemoglobin under promoters of varying strength. We show that the additional expression of the protein is deemed positive on the growth rate.

Read more
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