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Team:Duke
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| - | <li class="first"><a href="https:// | + | <li class="first"><a href="https://2012.igem.org/Team:Duke" accesskey="1" |
| - | <li><a href="https:// | + | title="">Home</a></li> |
| - | <li><a href="https:// | + | <li><a href="https://2012.igem.org/Team:Duke/Team" accesskey="2" |
| - | <li><a href="https:// | + | title="">Meet the Team</a></li> |
| - | <li><a href="https:// | + | <li><a href="https://2012.igem.org/Team:Duke/Project" accesskey="3" |
| - | <li><a href="https:// | + | title="">Project Description</a></li> |
| - | <li><a href="https:// | + | <li><a href="https://2012.igem.org/Team:Duke/Safety" accesskey="4" |
| + | title="">Safety</a></li> | ||
| + | <li><a href="https://2012.igem.org/Team:Duke/Notebook" accesskey="5" | ||
| + | title="">Lab Notebook</a></li> | ||
| + | <li><a href="https://2012.igem.org/Team:Duke/Sponsors" accesskey="5" | ||
| + | title="">Sponsors</a></li> | ||
| + | <li><a href="https://2012.igem.org/Team:Duke/Human Practices" accesskey="6" | ||
| + | title="">Human Practices</a></li> | ||
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| - | <h2 class="title"><a href="#">Welcome to the Duke iGEM Wiki</a></h2> | + | <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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| - | === | + | |
| - | + | ===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. | ||
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.
