Difference between revisions of "Team:Waterloo/Model"

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    <a href="https://2016.igem.org/Team:Waterloo/Integrated_Practices/Networks#Analysis">Modelling Networks</a>
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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>
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Revision as of 23:05, 16 October 2016

Notes

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.

Modeling

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.

Inspiration

Here are a few examples from previous teams:

Premature Stop Codon Insertion

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.

Modelling Aggregation

We modelled the accumulation of amyloids and the curing of the [PSI+] state as a result of Hsp104 overexpression and knockdown.

Modelling Plasmid Loss

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.

Modelling CRISPRi

We modelled the effectiveness of applying CRISPR-dCas9 to cause knockdown of Hsp104 expression.

Modelling Networks

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.