Difference between revisions of "Team:Calgary/Software"

 
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                    <h1 class="page-title">Software</h1>
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                <p>iGAM</p>
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                 </div>
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            </div><div class="col-lg-6 col-md-6 col-sm-12 col-xs-12"><a href="https://2019.igem.org/Team:Calgary/BOT">
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                <p>BioBrick Optimization Tool - synthesis</p>
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<p>We built a tool that could easily and quickly optimize sequences so synthetic biologists can focus on their experiments not ordering.</p></a>
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            <h1>Inspiration</h1>
 
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            <h2>iGAM</h2>
 
  
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                    <h1>Software Tools</h1>
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                </div>
  
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                <p>
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                    To help the iGEM community, and synthetic biology communit as a whole, iGEM Calgarybuilt two software tools for the iGEM competition. One to optimize BioBricks for synthesis,
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                    and the other to help develop genetic algorithms.
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                </p>
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                <p>
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                    BOTs was shown to be compatible with synthesis checking software such as IDT Gene Fragment Synthesis, can be easily used with its website to optimize BioBricks.
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                </p>
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                <p style="margin-bottom:5vh">
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                    iGAM is built within an R package, iGAM is flexible enough to allow teams to build their own genetic algorithms, iGAM is a toolbox.
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                        <a target="blank" href="https://2019.igem.org/Team:Calgary/BOT">
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                                <img src="https://static.igem.org/mediawiki/2019/7/78/T--Calgary--BOTs-logo.png" width="50%" style="margin-left: auto; margin-right: auto; display: block;" />
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                                <div class="sources--cms">
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                                    <h3 style="text-align:center">BOTs</h3>
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                                    <p style="text-align:center">BioBrick Optimization Tool for synthesis</p>
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                                    <p>A tool created to help synthetic biologists synthesize BioBricks by removing repeats, optimize gc content, and optimize for expression.</p>
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            <b>Proteins are widely used in the iGEM community,</b>  but there is very little iGEM teams can do to understand their protein’s atomic behaviour. At iGEM Calgary we wanted to generate a quantitative way to allow other teams to characterize each amino acid of their proteins.
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            <h1>Measurement</h1>
 
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            <h2>WHAT DID WE QUANTIFY?</h2>
 
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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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            <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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                <h2>WHAT CAN RMSF ACCOMPLISH</h2>
 
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                        <a target="blank" href="https://2019.igem.org/Team:Calgary/iGAM">
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                                <img src="https://static.igem.org/mediawiki/2019/a/ac/T--Calgary--iGAM.png" width="50%" style="margin-left: auto; margin-right: auto; display: block;" />
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                                    <h3 style="text-align:center">iGAM</h3>
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                                    <p style="text-align:center">international Genetic Algorithm Machine</p>
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                                    <p>Using Genetic Algorithms to understand and modify proteins for chosen parameters. This software tool was critical in creating modGIX a potentially more emulsion-resistant version of 6GIX.</p>
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                <h1>Appendix A</h1>
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                <div class="row">
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                <h2>Procedure of data collected <i>in vitro</i></h2>
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                    <h4> <a href="https://2019.igem.org/Team:Calgary/Model">Software in Modelling</a></h4>
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                    <p>
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                        Not only creating software tools for the iGEM community, we developed multiple algorithms
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                        to help us understand our systems to both save time and optimize the processes.
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                        Notably we developed a
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                        <a href="https://2019.igem.org/Team:Calgary/Model/EmulsionPrediction">machine-learning algorithm to help us understand emulsions</a>
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                        , a core component of our project.
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                    </p>
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                    <h4> <a href="https://2019.igem.org/Team:Calgary/Human_Practices">Software to Help an Industry</a></h4>
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                    <p>
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                        Furthermore, our team's devotion to helping the canola industry led us to develop
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                        a <a href="https://2019.igem.org/Team:Calgary/Modelling/StandardizedSeedGrading">computer vision algorithm to help standardize canola seed grading</a>.
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                    </p>
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                    <p>
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                        We also developed a <a href="https://2019.igem.org/Team:Calgary/SunnyDays">long-term weather forecasting system using machine-learning</a>.
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Latest revision as of 04:38, 14 December 2019

Software

Software Tools

To help the iGEM community, and synthetic biology communit as a whole, iGEM Calgarybuilt two software tools for the iGEM competition. One to optimize BioBricks for synthesis, and the other to help develop genetic algorithms.

BOTs was shown to be compatible with synthesis checking software such as IDT Gene Fragment Synthesis, can be easily used with its website to optimize BioBricks.

iGAM is built within an R package, iGAM is flexible enough to allow teams to build their own genetic algorithms, iGAM is a toolbox.

BOTs

BioBrick Optimization Tool for synthesis

A tool created to help synthetic biologists synthesize BioBricks by removing repeats, optimize gc content, and optimize for expression.

iGAM

international Genetic Algorithm Machine

Using Genetic Algorithms to understand and modify proteins for chosen parameters. This software tool was critical in creating modGIX a potentially more emulsion-resistant version of 6GIX.

Software in Modelling

Not only creating software tools for the iGEM community, we developed multiple algorithms to help us understand our systems to both save time and optimize the processes. Notably we developed a machine-learning algorithm to help us understand emulsions , a core component of our project.

Software to Help an Industry

Furthermore, our team's devotion to helping the canola industry led us to develop a computer vision algorithm to help standardize canola seed grading.

We also developed a long-term weather forecasting system using machine-learning.

Project
Human Centered Design
Modelling
Parts
Journal
Team