Difference between revisions of "Team:Calgary/iGAM"

 
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            <h2 class="page-subtitle">Software</h2>
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            <h1 class="page-title">iGAM</h1>
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                                <img src="https://static.igem.org/mediawiki/2019/a/ac/T--Calgary--iGAM.png" width="75%" />
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             <h1>Inspiration</h1>
 
             <h1>Inspiration</h1>
 
             <h2>What needs to be addressed?</h2>
 
             <h2>What needs to be addressed?</h2>
  
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            <b>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 tool to allow iGEM teams make informed decisions on the modification of their proteins.  
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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.
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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.
  
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             <h1>iGAM Functionality</h1>
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             <h2>Whats in the toolbox?</h2>
             <h1>iGAM</h1>
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             <h2>the international genetic algorithm machine</h2>
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Within the iGAM package there is a pseudocode document detailing the basics of generating a genetic algorithm.
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            <b>To assist in the dynamic characterization by other teams</b> we looked to develop a methodology that allows for the calculation and aggregation of Brownian motion measurements for each amino acid in a sequence. The Brownian motion measurement chosen was the Root Mean Square Fluctuation(RMSF) calculated for every atom of a protein in ten picosecond intervals.
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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.
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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.
 
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            <b>The RMSF data</b> was calculated from a nanosecond Molecular Dynamic Simulation(MDS) completed within GROMACS, an industrial MDS software.  These values were then averaged over each amino acid, this ensured that the unit of measurement was observed on a scale that was modifiable by teams. This resulted in a series of curves that quantitatively expressed the dynamics for each amino acid.
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            Above is a complete view of the movement attributed to every amino acid of a protein. Having all of the curves present at the same time can be quite confusing. The true power of this measurement can be harnessed when amino acids are displayed in smaller clusters. Below is the dynamics of the 25th, 80th, and 90th amino acid of the 6GIX protein.
 
 
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                <h1>Functionality</h1>
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              <h1>Access</h1>
                <h2>WHAT CAN RMSF ACCOMPLISH</h2>
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              <h2>How to access iGAM</h2>
 
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<p>To access the iGAM tools please visit <a class="abody" href="https://github.com/iGEMCalgary/iGAM" target = "Download iGAM">Download iGAM</a> and download the R package.
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These are available within an R package project so that they may be easily integrated into an R environment.
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                <h1>Appendix A</h1>
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              <h1>References</h1>
                <h2>Procedure of data collected <i>in vitro</i></h2>
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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/.<br>
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2. Davis, L. (1991). editor. Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York,
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Latest revision as of 04:41, 14 December 2019

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,