During the design and With a strong dry lab team, making informed decisions based on in silico data was easier, but modelling became a necessity for the iGEM Calgary team as many results would not have been possible were it not for successful modelling of wet lab components.

The workflow of iGEM's Calgary modelling team followed a natural workflow towards making informed decisions. Whenever problems were found, modelling tried to find a way to help. Each created model was vetted by the experienced members of wetlab for its validity. These could then be reworked, or implemented in the lab. In the latter, the data could then be sent to reinforce the model.

An examplar of this model is the Emulsion Construction Prediction modelwhich was developed using wet lab data, using the data to predict good constructions, then further data was collected in those zones, and so on, until we had a model that could predict, with extremely high confidence, the phase constructed with given parameters. This model was invaluable in Demonstrating our proteinsfunction in emulsions as many emulsion phases would denature our proteins.

ICARUS - Pheophorbide Production

iGEM Calgary Augmented Repulsion Universal Spacer:
A spacer to his-purify proteins.

Emulsion Construction Prediction - Emulsified Proteins

Using machine learning to predict phase of an emulsion at any composition.

Directed Protein Structure Modification - modGIX

Using in silico dynamic simulations to perform informed modifications of amino acids in proteins.

In Silico Emulsion System Verification - 6GIX

Verifying emulsified protein function and structure stability in antagonistic solvents.