Difference between revisions of "Team:Waterloo/Model"
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<img class="image-wide" src="https://static.igem.org/mediawiki/2016/9/9f/Cfp_impact_folding.png"/> | <img class="image-wide" src="https://static.igem.org/mediawiki/2016/9/9f/Cfp_impact_folding.png"/> | ||
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<p>We summarized literature knowledge in order to determine the insertion location that would provide the greatest readthrough efficiency. This was then used to inform the design of our construct.</p> | <p>We summarized literature knowledge in order to determine the insertion location that would provide the greatest readthrough efficiency. This was then used to inform the design of our construct.</p> | ||
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<a href="https://2016.igem.org/Team:Waterloo/Aggregation">Modelling Aggregation</a> | <a href="https://2016.igem.org/Team:Waterloo/Aggregation">Modelling Aggregation</a> | ||
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| − | </div> | + | <img class="image-wide" src="https://static.igem.org/mediawiki/2016/4/48/T--Waterloo--Aggregation_Model_Fig2.png"/> |
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| + | <p>We modelled the accumulation of amyloids and the curing of the [PSI+] state as a result of Hsp104 overexpression and knockdown.</p> | ||
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<img class="image-wide" src="https://static.igem.org/mediawiki/2016/6/61/Network_cluster_Medal.png"/> | <img class="image-wide" src="https://static.igem.org/mediawiki/2016/6/61/Network_cluster_Medal.png"/> | ||
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<p>We analysed data from 2015 about collaborations between iGEM teams in order to find patterns. We tried to identify whether there were correlations between variables like team size, success, location, and collaboration in order to make recommendations to future teams.</p> | <p>We analysed data from 2015 about collaborations between iGEM teams in order to find patterns. We tried to identify whether there were correlations between variables like team size, success, location, and collaboration in order to make recommendations to future teams.</p> | ||
</div></div> | </div></div> | ||
Revision as of 22:29, 19 October 2016
Modelling
This page will contain a brief overview for each of the things we modelled this year - it shouldn't be linked from Protein Aggregation on the navbar. We're a good candidate for best model because of the breadth of understanding we accumulated by tackling all of the different aspects of our system, not just because we made a good model of prion aggregation.
We summarized literature knowledge in order to determine the insertion location that would provide the greatest readthrough efficiency. This was then used to inform the design of our construct.
We modelled the accumulation of amyloids and the curing of the [PSI+] state as a result of Hsp104 overexpression and knockdown.
We modelled the loss rates of yeast plasmids, in order to determine whether we should use a high copy or low copy number plasmid in the lab.
We modelled the effectiveness of applying CRISPR-dCas9 to cause knockdown of Hsp104 expression.
We analysed data from 2015 about collaborations between iGEM teams in order to find patterns. We tried to identify whether there were correlations between variables like team size, success, location, and collaboration in order to make recommendations to future teams.
