Difference between revisions of "Team:Calgary/Model"
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| + | <h1 class="page-title">Modelling</h1> | ||
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| − | + | <h1>Modelling</h1> | |
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| − | + | <p style="margin-bottom:5vh"><b>Modelling is an extremely powerful aspect of every iGEM project.</b> Models can verify a teams knowledge of their system, and they can also be integral to a projects overall design. We decided early on that for our system to be successful we would have to integrate modelling into the design of every facet of our project. This included the dynamic understanding of our chlorophyll binding protein(6GIX), the informed engineering of 6GIX, the characterization and optimization of our emulsion systems, and the development of the Icarus spacer. | |
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| + | <b>To ensure that we fully utilize the models we generate,</b> a standardized modelling workflow was developed. This workflow was designed to confirm that the models generated actually have a tangible integration into the wetlab portion of the project. Along with the integration into the wetlab, this workflow also allowed for us to take advantage of the wetlabs experience and knowledge at multiple stages. Below is a diagram that illustrates our workflow. | ||
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| + | <img src="https://static.igem.org/mediawiki/2019/7/72/T--Calgary--MODFLOW.svg" width="66%" style="margin-left: auto; margin-right: auto; display: block;"/><br> | ||
| + | <p>This workflow ensured that as we developed models we did not lose sight of the main goal behind these models. Ultimately each modelling component was generated to impact and integrate into the final project. Along with keeping each model focused on integration, the inclusion of our wetlab at multiple steps allowed the team to build familiarity and confidence in the developed model. | ||
| + | </p> | ||
| + | <p style="margin-bottom:5vh">An examplar of this workflow is the <a class="abody" href="https://2019.igem.org/Team:Calgary/Model/EmulsionPrediction" target="_blank">Emulsion Construction Prediction model</a>. Problems in predicting emulsion behaviour in silico led to wet lab data collection to then feed the model. Using the model we could then collect more data in the wet lab, and this cycle was iterated thoroughly. This model was invaluable in <a class="abody" href="https://2019.igem.org/Team:Calgary/Demonstrate" target="_blank">demonstrating</a> our protein's function in emulsions.</p> | ||
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| − | + | <div class="col-lg-6 col-md-6 col-sm-12 col-xs-12 expand-on-hover" > <a target="blank" href="https://2019.igem.org/Team:Calgary/Model/ICARUS"> | |
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| − | + | <img src="https://static.igem.org/mediawiki/2019/9/9b/T--Calgary--newICARUS.png" width="28%" style="margin-left: auto; margin-right: auto; display: block;"/> | |
| − | + | <div class="sources--cms"> | |
| − | + | <h3 style="text-align:center">ICARUS - <i>Pheophorbide Production</i></h3> | |
| − | + | <p style="text-align:center"><b>i</b>GEM <b>C</b>algary <b>A</b>ugmented <b>R</b>epulsion <b>U</b>niversal <b>S</b>pacer: <br/>A spacer to his-purify proteins.</p> | |
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| + | <div class="col-lg-6 col-md-6 col-sm-12 col-xs-12 expand-on-hover" > <a target="blank" href="https://2019.igem.org/Team:Calgary/Model/EmulsionPrediction"> | ||
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| + | <img src="https://static.igem.org/mediawiki/2019/3/31/T--Calgary--ternaryPhaseGIF.gif" width="25%"style="margin-left: auto; margin-right: auto; display: block;"/> | ||
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| + | <h3 style="text-align:center">Emulsion Construction Prediction - <i>Emulsified Proteins</i></h3> | ||
| + | <p style="text-align:center">Using machine learning to predict phase of an emulsion at any composition.</p> | ||
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| + | <div class="col-lg-6 col-md-6 col-sm-12 col-xs-12 expand-on-hover"> <a target="blank" href="https://2019.igem.org/Team:Calgary/Model/DirectedProteinModification"> | ||
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| + | <img src="https://static.igem.org/mediawiki/2019/1/1b/T--Calgary--Tobeyfromtheoffice.png" width="60%"style="margin-left: auto; margin-right: auto; display: block;"/> | ||
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| + | <h3 style="text-align:center">Directed Protein Structure Modification - <i>ModGIX</i></h3> | ||
| + | <p style="text-align:center">Using in silico dynamic simulations to perform informed modifications of amino acids in proteins. </p> | ||
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| + | <img src="https://static.igem.org/mediawiki/2019/d/d9/T--Calgary--WIKIDYNAMICS-Icon.png" width="29%"style="margin-left: auto; margin-right: auto; display: block;"/> | ||
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| + | <h3 style="text-align:center">In Silico Emulsion System Verification - <i>6GIX</i></h3> | ||
| + | <p style="text-align:center">Verifying emulsified protein function and structure stability in antagonistic solvents. </p> | ||
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Latest revision as of 03:02, 22 October 2019
Modelling
Modelling is an extremely powerful aspect of every iGEM project. Models can verify a teams knowledge of their system, and they can also be integral to a projects overall design. We decided early on that for our system to be successful we would have to integrate modelling into the design of every facet of our project. This included the dynamic understanding of our chlorophyll binding protein(6GIX), the informed engineering of 6GIX, the characterization and optimization of our emulsion systems, and the development of the Icarus spacer.
To ensure that we fully utilize the models we generate, a standardized modelling workflow was developed. This workflow was designed to confirm that the models generated actually have a tangible integration into the wetlab portion of the project. Along with the integration into the wetlab, this workflow also allowed for us to take advantage of the wetlabs experience and knowledge at multiple stages. Below is a diagram that illustrates our workflow.
This workflow ensured that as we developed models we did not lose sight of the main goal behind these models. Ultimately each modelling component was generated to impact and integrate into the final project. Along with keeping each model focused on integration, the inclusion of our wetlab at multiple steps allowed the team to build familiarity and confidence in the developed model.
An examplar of this workflow is the Emulsion Construction Prediction model. Problems in predicting emulsion behaviour in silico led to wet lab data collection to then feed the model. Using the model we could then collect more data in the wet lab, and this cycle was iterated thoroughly. This model was invaluable in demonstrating our protein's function in emulsions.
