Team:Calgary/iGAM

Software

iGAM

Inspiration

What needs to be addressed?

The modification of proteins can often be a daunting ordeal. For each single amino acid change there are 23 different possibilities to be considered. iGEM Calgary has generated a toolbox to allow iGEM teams to generate their own genetic algorithms make informed decisions on the modification of their proteins. iGAM, the international genetic algorithm machine was developed to assist iGEM teams in squeezing every ounce of utility out of their proteins.
The iGAM toolbox is an R package with functions, examples and pseudocode to help teams create custom genetic algorithms to meet their specific needs. These tools and examples include those used within the generation of ModGIX an engineered chlorophyll binding protein.


iGAM Functionality

Whats in the toolbox?

Within the iGAM package there is a pseudocode document detailing the basics of generating a genetic algorithm.

There is also a script called GeneticAlg.R that contains a condensed version of the script used in the generation of ModGIX, our modified chlorophyll binding protein. This can be used by other teams for inspiration as they go through the design of their genetic algorithms.

Along with this there is also four functions GenPop, GenLoop, Evaluation, and Mutation. GenPop is a function that generates an initial population of size n. Evaluation is a function that runs through the sequences and returns a list of fitness values. Mutation is a function that can introduce random spot mutations into the sequences. and GenLoop is a script similar to the pseudocode but with more use of the included functions.

Access

How to access iGAM

To access the iGAM tools please visit Download iGAM and download the R package. These are available within an R package project so that they may be easily integrated into an R environment.

References

1. R Core Team (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
2. Davis, L. (1991). editor. Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York,