Team:Duke

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<li class="first"><a href="https://2012hs.igem.org/Team:NC_School_of_Sci_Math" accesskey="1" title="">Home</a></li>
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title="">Home</a></li>
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<h2 class="title"><a href="#">Welcome to the Duke iGEM Wiki</a></h2>
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<h2>Duke iGEM 2012</h2>
<h2>Duke iGEM 2012</h2>
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<p>Our team is fully aware of it's disadvantages, being smaller than the rest, and holding less funding than the competition. However, these notions motivate each individual on the team rather than discourage us. We know that we will get out of this project, exactly what we put into it. Understanding this notion is why it is not unusual to find our team working 12 hour shifts daily. We are inspired and determined to contribute to the scientific community in a substantial way, utilizing our resources, and setting new standards.</p>
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<h2>Useful Links</h2>
<h2>Useful Links</h2>
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===Abstract===
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Medical genetic therapy has shown promise for improved accuracy in personalized genetic therapy of conditions such as Alzheimer’s disease and cancer. However, the speed of current medical genetic screening methods is limited by time-consuming rates of cell growth and gene expression. The goal of this work, therefore, was to develop a comprehensive platform for other researchers to use in further medical genetic studies. In yeast, an orthologous model of human gene function, we developed a system of two dimerizing fusion proteins to control two-hybrid mediated transcriptional activation in response to a 450 nm blue light (optogenetic) stimulus. After extensive characterization and optimization of our system, we compiled our methodologies into a physical toolkit, which contains custom yeast strains frozen in glycerol stocks, standardized plasmids, a stochastic network model, the design of a light pulse generator to induce gene expression, and a custom software package for rapid analysis of data. In the coming weeks, we will begin testing an application of our system by screening for orthologous suppressors of beta-amyloid that may be used in genetic therapy of Alzheimer’s disease. Our comprehensive toolkit streamlines identification of genetic therapeutic targets, and will speed progress toward personalized therapy of a variety of diseases.
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===Executive Summary===
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One of the most promising fields of medical genetic research is gene therapy, which seeks to deliver genes to patients to treat medical conditions. Identifying genes that can be used as therapeutics is critical for the progression of gene therapy to clinical trials. However, current methods for identifying gene therapeutic targets are slow and expensive: the cost of analyzing a gene is $2146.75 and can take three weeks before analysis. The goal of our work was to develop a toolkit for researchers to use for more rapid and cost-efficient identification of gene therapeutic targets. In yeast, a model of human genes, we used an emerging topic known as optogenetics to create a light switch for gene activation: we can activate specific genes in our samples with the addition of blue light. After verifying the functionality of our system, we compiled our utilities into a physical toolkit, which contains custom yeast strains, standardized DNA parts, a computational network model, and a custom software package for rapid analysis of data. When we evaluated the performance of our toolkit, we found that we reduced waiting time during experimentation from 36 hours to 22.0 seconds (a 5900-fold speed increase), and our software reduced data analysis time from 7.5 hours to 3.45 seconds (a 7800-fold speed increase). We reduced the cost of experimental materials by 85.2% while broadening the spectrum of discovery in gene therapy. Our comprehensive toolkit streamlines identification of genetic therapeutic targets, and will speed progress toward personalized therapy of a variety of diseases.

Latest revision as of 21:13, 15 October 2012

Executive Summary

One of the most promising fields of medical genetic research is gene therapy, which seeks to deliver genes to patients to treat medical conditions. Identifying genes that can be used as therapeutics is critical for the progression of gene therapy to clinical trials. However, current methods for identifying gene therapeutic targets are slow and expensive: the cost of analyzing a gene is $2146.75 and can take three weeks before analysis. The goal of our work was to develop a toolkit for researchers to use for more rapid and cost-efficient identification of gene therapeutic targets. In yeast, a model of human genes, we used an emerging topic known as optogenetics to create a light switch for gene activation: we can activate specific genes in our samples with the addition of blue light. After verifying the functionality of our system, we compiled our utilities into a physical toolkit, which contains custom yeast strains, standardized DNA parts, a computational network model, and a custom software package for rapid analysis of data. When we evaluated the performance of our toolkit, we found that we reduced waiting time during experimentation from 36 hours to 22.0 seconds (a 5900-fold speed increase), and our software reduced data analysis time from 7.5 hours to 3.45 seconds (a 7800-fold speed increase). We reduced the cost of experimental materials by 85.2% while broadening the spectrum of discovery in gene therapy. Our comprehensive toolkit streamlines identification of genetic therapeutic targets, and will speed progress toward personalized therapy of a variety of diseases.