http://2010.igem.org/wiki/index.php?title=Special:Contributions&feed=atom&limit=20&target=Eushiu2010.igem.org - User contributions [en]2024-03-29T05:45:15ZFrom 2010.igem.orgMediaWiki 1.16.5http://2010.igem.org/Team:Davidson-MissouriWTeam:Davidson-MissouriW2010-07-29T23:03:29Z<p>Eushiu: </p>
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<div id="orangeBox"><h3>Optimizing Codons</h3><br><br />
<p>Building weighted items for the Knapsack through codon variation of the TetA gene led to Foundational Advances</p><br><br />
<a href="https://2010.igem.org/Team:Davidson-MissouriW/OptimizingCodons">Details</a><br />
</div><br />
<div id="greenBox"><h3>Characterizing Cre/Lox</h3><br><br />
<p>Foundational Advances were made as 11 novel lox sites for Cre recombination were built for randomly choosing Knapsack objects<br />
</p><br />
<a href="https://2010.igem.org/Team:Davidson-MissouriW/CreLox">Details</a><br />
</div><br />
<div id="blueBox"><h3>Measuring Gene Expression</h3><br><br />
<p>Design and construction of a Knapsack biological computer required Foundational Advances in the measurement of gene expression</p><br />
<a href="https://2010.igem.org/Team:Davidson-MissouriW/MeasuringExpression">Details</a><br />
</div><br />
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<div id="mission_box"> <h3> iGEM Davidson – Missouri Western 2010:<br>Foundational Advances in Biology and the Knapsack Problem </h3><br />
<p> The Davidson/Missouri Western multidisciplinary team is using synthetic biology to address a mathematical problem in <i>Escherichia coli</i>. Specifically, we are addressing the Knapsack Problem, an NP-complete problem that asks, “Given a finite number of weighted items, can one find a subset of these items that completely fills a knapsack of fixed capacity?” </p><br />
<br />
<p>In our design, weighted items are represented by versions of <i>TetA</i> genes that confer measurably distinct levels of tetracycline resistance. We have altered the codons of the wild type <i>TetA</i> gene, optimizing and de-optimizing several segments of the coding sequence. Each <i>TetA</i> variant is coupled with a distinctive fluorescent gene, and each pair of genes is flanked by <i>lox</i> sites. In the presence of Cre protein, the <i>lox</i> mechanism either inverts or excises the coding sequence, yielding different combinations of expressed <i>TetA</i> variants. An expressed variant corresponds to an item being placed in the knapsack. Over-expression of <i>TetA</i> results in cell death, which represents exceeding the capacity of the knapsack. Under-expression of <i>TetA</i> causes the cells to stop growing due to tetracycline in the growth medium, which represents not completely filling the knapsack. Surviving cells correspond to cells within a certain range of <i>TetA</i> production and the fluorescence tag allows for comparative measurement within this range.</p><br />
<br />
<p>The team is also working to develop software tools relevant to the specific project and applicable to projects in the wider synthetic biology community.</p><br><br />
</div><br />
<div id="team_box"><center><a href="https://2010.igem.org/Team:Davidson-MissouriW/Team"><img src="https://static.igem.org/mediawiki/2010/8/86/Davidson-MissouriWTeam.png" alt="Team" width="174px" height="36px"/></center><br />
<h3>Team</h3></a><br />
<p>The 2010 iGEM team from Davidson College and Missouri Western State University is composed of approximately 15 multidisciplinary undergraduate students and 4 professors – 2 biologists and 2 mathematicians. The team includes math, biology, computer science, and chemistry majors. The team has traveled back and forth across the country and research was conducted on both campuses. View the Davidson- Missouri Western <a href="https://2010.igem.org/Team:Davidson-MissouriW/Team">team </a>page. </p><br />
</div><br />
<div id="zoo_box"><center><a href="https://2010.igem.org/Team:Davidson-MissouriW/Project"><img src="https://static.igem.org/mediawiki/igem.org/3/3e/Davidson-MissouriW_Project.jpeg" alt="Project"/></center><br />
<h3>Project</h3></a><br />
<p>In an attempt to solve the knapsack problem, we explored a variety of different topics. We optimized the codons for a portion of the TetA gene in order to produce variant genes that confer differing amounts of tetracycline resistance. We also created 11 variant lox sites that have differing recombination frequencies. Finally, we explored gene expression of RFP and the TetA gene. View the <a href="https://2010.igem.org/Team:Davidson-MissouriW/Project">work </a> done by Davidson and Missouri Western undergrads.</p><br />
</div><br />
<div id="notebook_box"><center><a href="https://2010.igem.org/Team:Davidson-MissouriW/Notebook"><img src="https://static.igem.org/mediawiki/2010/0/0b/Davidson-MissouriWNotebook.png" alt="Notebook"/></center><br />
<h3>Notebook</h3></a><br />
<p>Lab notebooks are an integral part of conducting scientific research because the results of a scientific experiment must be reproducible. In an effort to properly document our efforts, each team member kept a detailed record of their daily activities. We have condensed the information from all of these sources so that each entry in this virtual notebook contains the highlights of each day’s work. View the daily progress of our project via the lab <a href="https://2010.igem.org/Team:Davidson-MissouriW/Notebook">Notebook</a>.</p><br />
</div><br />
<div class="clear"><br />
</div><br />
<div id="parts_box"> <center><a href="http://partsregistry.org/cgi/partsdb/pgroup.cgi?pgroup=iGEM2010&group=Davidson-MissouriW"><img src="https://static.igem.org/mediawiki/2010/2/26/Davidson-MissouriWParts.jpg" alt="Parts"/></center><br />
<h3>Parts</h3></a><br />
<p>BioBricks are the foundation of iGEM. We have created more than 40 basic and composite parts that are now available for the entire synthetic biology community to use. Among these parts are 11 new variant lox sites in both forward and reverse versions. Using these variants, we have constructed “modules” consisting of RFP floxed by multiple different combinations. Furthermore, we have assembled new cre recombinase expression cassettes and added them to the RFP modules. View the <a href="http://partsregistry.org/cgi/partsdb/pgroup.cgi?pgroup=iGEM2010&group=Davidson-MissouriW">parts</a> built by our team.</p> <br />
</div><br />
<div id="gallery_box"><center><a href="https://2010.igem.org/Team:Davidson-MissouriW/Tools"><img src="https://static.igem.org/mediawiki/2010/9/90/Davidson-MissouriW_Tools.png" alt="Tools"/></center><br />
<h3>Tools</h3></a><br />
<p> We have designed many programs that will be useful to the public. VeriPart will identify the BioBrick part associated with any DNA sequence thus eliminating the tedious process of manually confirming sequences. The Oligator suggests which oligos are needed to assemble the submitted sequence. The Optimus allows users to choose different equations to optimize a given segment of DNA. The Construct Simulator models how floxed modules behave when exposed to cre. The Knapsack Game is an educational tool intended to explain the problem. View our<a href="https://2010.igem.org/Team:Davidson-MissouriW/Tool"> Tools </a>page.</p><br />
</div><br />
<div id="sponsors_box"> <center><a href="https://2010.igem.org/Team:Davidson-MissouriW/Sponsors"><img src="https://static.igem.org/mediawiki/2010/a/ab/Davidson-MissouriWsponsorship.jpg" alt="Acknowledgements"/></center><br />
<h3>Acknowledgements</h3></a><br />
<p> This project and our participation in iGEM 2010 would not have been possible without help from numerous sources. We have received invaluable assistance from numerous people both at Davidson College and at Missouri Western State University. Furthermore, many organizations have contributed generously to our efforts, and without their help, we could not have come this far. This section is a thank you to our <a href="https://2010.igem.org/Team:Davidson-MissouriW/Sponsors"> sponsors </a> and all of those who have helped us in any way.</p> <br />
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</body></html></div>Eushiuhttp://2010.igem.org/Team:Davidson-MissouriWTeam:Davidson-MissouriW2010-07-29T23:03:13Z<p>Eushiu: </p>
<hr />
<div>{{Template_Wiki}}<br />
<html><br />
<body id="home" onload="setPageSize()"><br />
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<div id="orangeBox"><h3>Optimizing Codons</h3><br><br />
<p>Building weighted items for the Knapsack through codon variation of the TetA gene led to Foundational Advances</p><br><br />
<a href="https://2010.igem.org/Team:Davidson-MissouriW/OptimizingCodons">Details</a><br />
</div><br />
<div id="greenBox"><h3>Characterizing Cre/Lox</h3><br><br />
<p>Foundational Advances were made as 11 novel lox sites for Cre recombination were built for randomly choosing Knapsack objects<br />
</p><br />
<a href="https://2010.igem.org/Team:Davidson-MissouriW/CreLox">Details</a><br />
</div><br />
<div id="blueBox"><h3>Measuring Gene Expression</h3><br><br />
<p>Design and construction of a Knapsack biological computer required Foundational Advances in the measurement of gene expression</p><br />
<a href="https://2010.igem.org/Team:Davidson-MissouriW/MeasuringExpression">Details</a><br />
</div><br />
<div class="clear"></div><br />
<div id="SubWrapper"> <br />
<div id="mission_box"> <h3> iGEM Davidson – Missouri Western 2010:<br>Foundational Advances in Biology and the Knapsack Problem </h3><br />
<p> The Davidson/Missouri Western multidisciplinary team is using synthetic biology to address a mathematical problem in <i>Escherichia coli</i>. Specifically, we are addressing the Knapsack Problem, an NP-complete problem that asks, “Given a finite number of weighted items, can one find a subset of these items that completely fills a knapsack of fixed capacity?” </p><br />
<br />
<p>In our design, weighted items are represented by versions of <i>TetA</i> genes that confer measurably distinct levels of tetracycline resistance. We have altered the codons of the wild type <i>TetA</i> gene, optimizing and de-optimizing several segments of the coding sequence. Each <i>TetA</i> variant is coupled with a distinctive fluorescent gene, and each pair of genes is flanked by <i>lox</i> sites. In the presence of Cre protein, the <i>lox</i> mechanism either inverts or excises the coding sequence, yielding different combinations of expressed <i>TetA</i> variants. An expressed variant corresponds to an item being placed in the knapsack. Over-expression of <i>TetA</i> results in cell death, which represents exceeding the capacity of the knapsack. Under-expression of <i>TetA</i> causes the cells to stop growing due to tetracycline in the growth medium, which represents not completely filling the knapsack. Surviving cells correspond to cells within a certain range of <i>TetA</i> production and the fluorescence tag allows for comparative measurement within this range.</p><br />
<br />
<p>helloThe team is also working to develop software tools relevant to the specific project and applicable to projects in the wider synthetic biology community.</p><br><br />
</div><br />
<div id="team_box"><center><a href="https://2010.igem.org/Team:Davidson-MissouriW/Team"><img src="https://static.igem.org/mediawiki/2010/8/86/Davidson-MissouriWTeam.png" alt="Team" width="174px" height="36px"/></center><br />
<h3>Team</h3></a><br />
<p>The 2010 iGEM team from Davidson College and Missouri Western State University is composed of approximately 15 multidisciplinary undergraduate students and 4 professors – 2 biologists and 2 mathematicians. The team includes math, biology, computer science, and chemistry majors. The team has traveled back and forth across the country and research was conducted on both campuses. View the Davidson- Missouri Western <a href="https://2010.igem.org/Team:Davidson-MissouriW/Team">team </a>page. </p><br />
</div><br />
<div id="zoo_box"><center><a href="https://2010.igem.org/Team:Davidson-MissouriW/Project"><img src="https://static.igem.org/mediawiki/igem.org/3/3e/Davidson-MissouriW_Project.jpeg" alt="Project"/></center><br />
<h3>Project</h3></a><br />
<p>In an attempt to solve the knapsack problem, we explored a variety of different topics. We optimized the codons for a portion of the TetA gene in order to produce variant genes that confer differing amounts of tetracycline resistance. We also created 11 variant lox sites that have differing recombination frequencies. Finally, we explored gene expression of RFP and the TetA gene. View the <a href="https://2010.igem.org/Team:Davidson-MissouriW/Project">work </a> done by Davidson and Missouri Western undergrads.</p><br />
</div><br />
<div id="notebook_box"><center><a href="https://2010.igem.org/Team:Davidson-MissouriW/Notebook"><img src="https://static.igem.org/mediawiki/2010/0/0b/Davidson-MissouriWNotebook.png" alt="Notebook"/></center><br />
<h3>Notebook</h3></a><br />
<p>Lab notebooks are an integral part of conducting scientific research because the results of a scientific experiment must be reproducible. In an effort to properly document our efforts, each team member kept a detailed record of their daily activities. We have condensed the information from all of these sources so that each entry in this virtual notebook contains the highlights of each day’s work. View the daily progress of our project via the lab <a href="https://2010.igem.org/Team:Davidson-MissouriW/Notebook">Notebook</a>.</p><br />
</div><br />
<div class="clear"><br />
</div><br />
<div id="parts_box"> <center><a href="http://partsregistry.org/cgi/partsdb/pgroup.cgi?pgroup=iGEM2010&group=Davidson-MissouriW"><img src="https://static.igem.org/mediawiki/2010/2/26/Davidson-MissouriWParts.jpg" alt="Parts"/></center><br />
<h3>Parts</h3></a><br />
<p>BioBricks are the foundation of iGEM. We have created more than 40 basic and composite parts that are now available for the entire synthetic biology community to use. Among these parts are 11 new variant lox sites in both forward and reverse versions. Using these variants, we have constructed “modules” consisting of RFP floxed by multiple different combinations. Furthermore, we have assembled new cre recombinase expression cassettes and added them to the RFP modules. View the <a href="http://partsregistry.org/cgi/partsdb/pgroup.cgi?pgroup=iGEM2010&group=Davidson-MissouriW">parts</a> built by our team.</p> <br />
</div><br />
<div id="gallery_box"><center><a href="https://2010.igem.org/Team:Davidson-MissouriW/Tools"><img src="https://static.igem.org/mediawiki/2010/9/90/Davidson-MissouriW_Tools.png" alt="Tools"/></center><br />
<h3>Tools</h3></a><br />
<p> We have designed many programs that will be useful to the public. VeriPart will identify the BioBrick part associated with any DNA sequence thus eliminating the tedious process of manually confirming sequences. The Oligator suggests which oligos are needed to assemble the submitted sequence. The Optimus allows users to choose different equations to optimize a given segment of DNA. The Construct Simulator models how floxed modules behave when exposed to cre. The Knapsack Game is an educational tool intended to explain the problem. View our<a href="https://2010.igem.org/Team:Davidson-MissouriW/Tool"> Tools </a>page.</p><br />
</div><br />
<div id="sponsors_box"> <center><a href="https://2010.igem.org/Team:Davidson-MissouriW/Sponsors"><img src="https://static.igem.org/mediawiki/2010/a/ab/Davidson-MissouriWsponsorship.jpg" alt="Acknowledgements"/></center><br />
<h3>Acknowledgements</h3></a><br />
<p> This project and our participation in iGEM 2010 would not have been possible without help from numerous sources. We have received invaluable assistance from numerous people both at Davidson College and at Missouri Western State University. Furthermore, many organizations have contributed generously to our efforts, and without their help, we could not have come this far. This section is a thank you to our <a href="https://2010.igem.org/Team:Davidson-MissouriW/Sponsors"> sponsors </a> and all of those who have helped us in any way.</p> <br />
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</body></html></div>Eushiuhttp://2010.igem.org/Team:Davidson-MissouriW/CreLoxTeam:Davidson-MissouriW/CreLox2010-07-29T18:49:53Z<p>Eushiu: </p>
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<div id="mission_box"> <h1> Characterizing Cre/lox Recombination Method </h1><br />
<br />
<h2> Mechanism behind Cre/lox Recombination <h2><br />
<br />
<p> <br />
The Cre-lox tool is a site-specific recombination system that is widely used in biological research to manipulate DNA. It was discovered in the early 90's through characterization of coliphage P1 recombination system. The Cre recombinase enzyme, a 38kDa protein, catalyzes the recombination of DNA between two lox sites. These lox sites, each 34 bp long, consist of two inverted repeat arms flanking a spacer region of 8bp that is unique to the lox site.</p><br />
<br />
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<img height="210px" src="https://static.igem.org/mediawiki/2010/2/24/Davidson-missouriwCut.png"> <br />
<center>Excision</center><br />
</td><br />
<td><br />
<img height="210px" src="https://static.igem.org/mediawiki/2010/8/8b/Davidson-missouriw2Flip.png"><br />
<center>Inversion</center><br />
</td><br />
</tr><br />
</tbody><br />
</table><br />
<h2>Designing Lox Sites</h2><br />
<p>In order to randomly "select objects" for the knapsack problem, we used the Cre-lox recombination method of excision and inversion. We needed a set of variant lox sites in order to create constructs that would yield different subsets of survival and fluorescence to optimally fill the knapsack. These variant lox sites have mutations specific to the 8bp spacer region and do not recombine. We created 10 new lox sites with mutations in the 8 bp region: loxN Forward and Reverse, loxm2 Forward and Reverse, lox2272 Forward and Reverse, lox5171 Forward and Reverse, and loxBri Forward and Reverse. In addition, we added the wildtype loxP Reverse to the registry. </p><br />
<br />
<center><table cellpadding=0 cellspacing=0><br />
<tbody bgcolor="#ede8e2"><br />
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<img src="https://static.igem.org/mediawiki/2010/d/df/Davidson_MissouriW_Loxsite_7-25-10.png"><br />
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<img src="https://static.igem.org/mediawiki/2010/8/85/Davidson_MissouriW_Lox_site_key.png"><br />
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<p>We floxed red fluorescent protein with these variant lox sites using 16 out of the 21 combinations of the 5 lox forward variants to see immediately if recombination occurred in the presence of Cre. These constructs below follow a moderate promoter, pTet. The key for the constructs is above and colors refer to specific lox sites.</p><br />
<center><img src="https://static.igem.org/mediawiki/2010/c/c3/Davidson-missouriwFish.png"></center><br />
<center><h3>Would you like a bagel with all that lox?</h3></center><br />
<br />
<h1>Knapsack Construct Models</h1><br />
We designed five different construct models that could potentially lead us to a biological solution to the knapsack problem. These constructs include various lox sites, a reporter fluorescent protein, and a TetA gene that has an upper and lower threshold of expression in order to survive, thus providing a means to find "objects" (subset of modules) with the correct "weight" (expression of TetA).<br><br><br />
To determine which model would be best to solve the knapsack problems, we analyzed each version and its theoretical probabilities for different results that could be left after any number cre interactions. Furthermore, we created the construct simulator that can calculate for us the theoretical probabilities of the possible results after any number of cre steps for our construct and any custom construct. From this tool and an analysis of the possible results provided by the tool, we have decided that version D is the model to solve the knapsack problem.</p><br />
<br />
<center><h3>Version A: Allows inversion and excision within and over multiple modules.</h3></center><br />
<center><img width="700px" src="https://static.igem.org/mediawiki/2010/6/63/Davidson-missouriwA-1.png"></center><br />
<center><h3>Version B: Allows only excision within and over multiple modules.</h3></center><br />
<center><img width="700px"src="https://static.igem.org/mediawiki/2010/7/7c/Davidson-missouriwB-1.png"></center><br />
<center><h3>Version C: Only allows flipping within modules. Requires only 3 different Lox sites.</h3></center><br />
<img src="https://static.igem.org/mediawiki/igem.org/d/d7/Davidson-missouriwC.png"><br />
<center><h3>Version D: Only allows inversion within modules. Requires "n" different Lox sites.</h3></center><br />
<center><img width="700px"src="https://static.igem.org/mediawiki/2010/d/d9/Davidson-missouriwD.png"></center><br />
<center><h3>Version E: Initially only allows flipping over multiple modules, then allows cutting and flipping over multiple modules.</h3></center><br />
<img src="https://static.igem.org/mediawiki/igem.org/b/b7/Davidson-missouriwE.png"><br />
<br />
<h1>Troubleshooting with Cre</h1><br />
Clearly, characterizing the activity of Cre recombinase is integral to our project. Initially, we chose to use pBad-RBS-Cre because of its moderate promoter to test the floxed RBS-RFP constructs and analyze their level of recombination. While transforming pBad-RBS-Cre (<a href="http://partsregistry.org/Part:BBa_I718008">I718008</a>) from the 2010 iGEM kit plate, we observed unexpected results when size verifying the band. We digested the part with PvuI (an internal restriction site in the Cre gene sequence) and PstI and expected to see a 600 bp band along with two additional bands. Instead, we saw bands of unexplained size from digestion of the experimental colonies. In addition, the negative control (which contained only pBad-RBS-Cre) yielded only one band at around 1600bp when there should have been at least two bands (because of the internal PvuI site in Cre) and the band was at a size that pertained to none of the parts that were supposed to be involved in the ligation (see below, left). Furthermore, we religated pBAd-RBS-Cre from our own Davidson GCAT-alog from earlier years and digested with the same restriction enzymes. The presence of the 600 bp band confirmed the success of the ligation and this was used in furthur ligations (see below, right). We noted the incorrect part on the pBad-RBS-Cre wiki under experience. </p><br />
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<img src="https://static.igem.org/mediawiki/2010/a/ad/Davidson_MissouriWestern_Slide2.jpg"><br />
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<div id="mission_box"> <h1> Characterizing Cre/lox Recombination Method </h1><br />
<br />
<h2> Mechanism behind Cre/lox Recombination <h2><br />
<br />
<p> <br />
The Cre-lox tool is a site-specific recombination system that is widely used in biological research to manipulate DNA. It was discovered in the early 90's through characterization of coliphage P1 recombination system. The Cre recombinase enzyme, a 38kDa protein, catalyzes the recombination of DNA between two lox sites. These lox sites, each 34 bp long, consist of two inverted repeat arms flanking a spacer region of 8bp that is unique to the lox site.</p><br />
<br />
<table cellpadding=0 cellspacing=0><br />
<tbody bgcolor="#ede8e2"><br />
<tr><br />
<td><br />
<img height="210px" src="https://static.igem.org/mediawiki/2010/2/24/Davidson-missouriwCut.png"> <br />
<center>Excision</center><br />
</td><br />
<td><br />
<img height="210px" src="https://static.igem.org/mediawiki/2010/8/8b/Davidson-missouriw2Flip.png"><br />
<center>Inversion</center><br />
</td><br />
</tr><br />
</tbody><br />
</table><br />
<h2>Designing Lox Sites</h2><br />
<p>In order to randomly "select objects" for the knapsack problem, we used the Cre-lox recombination method of excision and inversion. We needed a set of variant lox sites in order to create constructs that would yield different subsets of survival and fluorescence to optimally fill the knapsack. These variant lox sites have mutations specific to the 8bp spacer region and do not recombine. We created 10 new lox sites with mutations in the 8 bp region: loxN Forward and Reverse, loxm2 Forward and Reverse, lox2272 Forward and Reverse, lox5171 Forward and Reverse, and loxBri Forward and Reverse. In addition, we added the wildtype loxP Reverse to the registry. </p><br />
<br />
<center><table cellpadding=0 cellspacing=0><br />
<tbody bgcolor="#ede8e2"><br />
<td><br />
<img src="https://static.igem.org/mediawiki/2010/d/df/Davidson_MissouriW_Loxsite_7-25-10.png"><br />
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<td><br />
<img src="https://static.igem.org/mediawiki/2010/8/85/Davidson_MissouriW_Lox_site_key.png"><br />
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<br />
<p>We floxed red fluorescent protein with these variant lox sites using 16 out of the 21 combinations of the 5 lox forward variants to see immediately if recombination occurred in the presence of Cre. These constructs below follow a moderate promoter, pTet. The key for the constructs is above and colors refer to specific lox sites.</p><br />
<center><img src="https://static.igem.org/mediawiki/2010/c/c3/Davidson-missouriwFish.png"></center><br />
<center><h3>Would you like a bagel with all that lox?</h3></center><br />
<br />
<h1>Knapsack Construct Models</h1><br />
We designed five different construct models that could potentially lead us to a biological solution to the knapsack problem. These constructs include various lox sites, a reporter fluorescent protein, and a TetA gene that has an upper and lower threshold of expression in order to survive, thus providing a means to find "objects" (subset of modules) with the correct "weight" (expression of TetA).<br />
\To determine which model would be best to solve the knapsack problems, we analyzed each version and its theoretical probabilities for different results that could be left after any number cre interactions. Furthermore, we created the construct simulator that can calculate for us the theoretical probabilities of the possible results after any number of cre steps for our construct and any custom construct. From this tool and an analysis of the possible results provided by the tool, we have decided that version D is the model to solve the knapsack problem.</p><br />
<br />
<center><h3>Version A: Allows inversion and excision within and over multiple modules.</h3></center><br />
<center><img width="700px" src="https://static.igem.org/mediawiki/2010/6/63/Davidson-missouriwA-1.png"></center><br />
<center><h3>Version B: Allows only excision within and over multiple modules.</h3></center><br />
<center><img width="700px"src="https://static.igem.org/mediawiki/2010/7/7c/Davidson-missouriwB-1.png"></center><br />
<center><h3>Version C: Only allows flipping within modules. Requires only 3 different Lox sites.</h3></center><br />
<img src="https://static.igem.org/mediawiki/igem.org/d/d7/Davidson-missouriwC.png"><br />
<center><h3>Version D: Only allows inversion within modules. Requires "n" different Lox sites.</h3></center><br />
<center><img width="700px"src="https://static.igem.org/mediawiki/2010/d/d9/Davidson-missouriwD.png"></center><br />
<center><h3>Version E: Initially only allows flipping over multiple modules, then allows cutting and flipping over multiple modules.</h3></center><br />
<img src="https://static.igem.org/mediawiki/igem.org/b/b7/Davidson-missouriwE.png"><br />
<br />
<h1>Troubleshooting with Cre</h1><br />
Clearly, characterizing the activity of Cre recombinase is integral to our project. Initially, we chose to use pBad-RBS-Cre because of its moderate promoter to test the floxed RBS-RFP constructs and analyze their level of recombination. While transforming pBad-RBS-Cre (<a href="http://partsregistry.org/Part:BBa_I718008">I718008</a>) from the 2010 iGEM kit plate, we observed unexpected results when size verifying the band. We digested the part with PvuI (an internal restriction site in the Cre gene sequence) and PstI and expected to see a 600 bp band along with two additional bands. Instead, we saw bands of unexplained size from digestion of the experimental colonies. In addition, the negative control (which contained only pBad-RBS-Cre) yielded only one band at around 1600bp when there should have been at least two bands (because of the internal PvuI site in Cre) and the band was at a size that pertained to none of the parts that were supposed to be involved in the ligation (see below, left). Furthermore, we religated pBAd-RBS-Cre from our own Davidson GCAT-alog from earlier years and digested with the same restriction enzymes. The presence of the 600 bp band confirmed the success of the ligation and this was used in furthur ligations (see below, right). We noted the incorrect part on the pBad-RBS-Cre wiki under experience. </p><br />
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<div id="mission_box"> <h1> Characterizing Cre/lox Recombination Method </h1><br />
<br />
<h2> Mechanism behind Cre/lox Recombination <h2><br />
<br />
<p> <br />
The Cre-lox tool is a site-specific recombination system that is widely used in biological research to manipulate DNA. It was discovered in the early 90's through characterization of coliphage P1 recombination system. The Cre recombinase enzyme, a 38kDa protein, catalyzes the recombination of DNA between two lox sites. These lox sites, each 34 bp long, consist of two inverted repeat arms flanking a spacer region of 8bp that is unique to the lox site.</p><br />
<br />
<table cellpadding=0 cellspacing=0><br />
<tbody bgcolor="#ede8e2"><br />
<tr><br />
<td><br />
<img height="210px" src="https://static.igem.org/mediawiki/2010/2/24/Davidson-missouriwCut.png"> <br />
<center>Excision</center><br />
</td><br />
<td><br />
<img height="210px" src="https://static.igem.org/mediawiki/2010/8/8b/Davidson-missouriw2Flip.png"><br />
<center>Inversion</center><br />
</td><br />
</tr><br />
</tbody><br />
</table><br />
<h2>Designing Lox Sites</h2><br />
<p>In order to randomly "select objects" for the knapsack problem, we used the Cre-lox recombination method of excision and inversion. We needed a set of variant lox sites in order to create constructs that would yield different subsets of survival and fluorescence to optimally fill the knapsack. These variant lox sites have mutations specific to the 8bp spacer region and do not recombine. We created 10 new lox sites with mutations in the 8 bp region: loxN Forward and Reverse, loxm2 Forward and Reverse, lox2272 Forward and Reverse, lox5171 Forward and Reverse, and loxBri Forward and Reverse. In addition, we added the wildtype loxP Reverse to the registry. </p><br />
<br />
<center><table cellpadding=0 cellspacing=0><br />
<tbody bgcolor="#ede8e2"><br />
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<img src="https://static.igem.org/mediawiki/2010/d/df/Davidson_MissouriW_Loxsite_7-25-10.png"><br />
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<img src="https://static.igem.org/mediawiki/2010/8/85/Davidson_MissouriW_Lox_site_key.png"><br />
</td><br />
</tr><br />
</tbody><br />
</table></center><br />
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<p>We floxed red fluorescent protein with these variant lox sites using 16 out of the 21 combinations of the 5 lox forward variants to see immediately if recombination occurred in the presence of Cre. These constructs below follow a moderate promoter, pTet. The key for the constructs is above and colors refer to specific lox sites.</p><br />
<center><img src="https://static.igem.org/mediawiki/2010/c/c3/Davidson-missouriwFish.png"></center><br />
<center><h3>Would you like a bagel with all that lox?</h3></center><br />
<br />
<h1>Knapsack Construct Models</h1><br />
We designed five different construct models that could potentially lead us to a biological solution to the knapsack problem. These constructs include various lox sites, a reporter fluorescent protein, and a TetA gene that has an upper and lower threshold of expression in order to survive, thus providing a means to find "objects" (subset of modules) with the correct "weight" (expression of TetA).</p><br />
<br />
<p>To determine which model would be best to solve the knapsack problems, we analyzed each version and its theoretical probabilities for different results that could be left after any number cre interactions. Furthermore, we created the construct simulator that can calculate for us the theoretical probabilities of the possible results after any number of cre steps for our construct and any custom construct. From this tool and an analysis of the possible results provided by the tool, we have decided that version D is the model to solve the knapsack problem.</p><br />
<br />
<center><h3>Version A: Allows inversion and excision within and over multiple modules.</h3></center><br />
<center><img width="700px" src="https://static.igem.org/mediawiki/2010/6/63/Davidson-missouriwA-1.png"></center><br />
<center><h3>Version B: Allows only excision within and over multiple modules.</h3></center><br />
<center><img width="700px"src="https://static.igem.org/mediawiki/2010/7/7c/Davidson-missouriwB-1.png"></center><br />
<center><h3>Version C: Only allows flipping within modules. Requires only 3 different Lox sites.</h3></center><br />
<img src="https://static.igem.org/mediawiki/igem.org/d/d7/Davidson-missouriwC.png"><br />
<center><h3>Version D: Only allows inversion within modules. Requires "n" different Lox sites.</h3></center><br />
<center><img width="700px"src="https://static.igem.org/mediawiki/2010/d/d9/Davidson-missouriwD.png"></center><br />
<center><h3>Version E: Initially only allows flipping over multiple modules, then allows cutting and flipping over multiple modules.</h3></center><br />
<img src="https://static.igem.org/mediawiki/igem.org/b/b7/Davidson-missouriwE.png"><br />
<br />
<h1>Troubleshooting with Cre</h1><br />
Clearly, characterizing the activity of Cre recombinase is integral to our project. Initially, we chose to use pBad-RBS-Cre because of its moderate promoter to test the floxed RBS-RFP constructs and analyze their level of recombination. While transforming pBad-RBS-Cre (<a href="http://partsregistry.org/Part:BBa_I718008">I718008</a>) from the 2010 iGEM kit plate, we observed unexpected results when size verifying the band. We digested the part with PvuI (an internal restriction site in the Cre gene sequence) and PstI and expected to see a 600 bp band along with two additional bands. Instead, we saw bands of unexplained size from digestion of the experimental colonies. In addition, the negative control (which contained only pBad-RBS-Cre) yielded only one band at around 1600bp when there should have been at least two bands (because of the internal PvuI site in Cre) and the band was at a size that pertained to none of the parts that were supposed to be involved in the ligation (see below, left). Furthermore, we religated pBAd-RBS-Cre from our own Davidson GCAT-alog from earlier years and digested with the same restriction enzymes. The presence of the 600 bp band confirmed the success of the ligation and this was used in furthur ligations (see below, right). We noted the incorrect part on the pBad-RBS-Cre wiki under experience. </p><br />
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<div id="mission_box" style="padding:10px"> <h2> iGEM Davidson – MWSU 2010: Project </h2><br />
<a Name="abstract"></a> <br />
<h4>Abstract</h4> <br />
<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;The Cre-lox system is a tool that involves the splicing of a specific pair of DNA sequences called lox sites with an enzyme called Cre recombinase. We implicated this system in attempts to manipulate gene expression that mimics randomly "selecting objects" to fill a knapsack in order to solve the knapsack problem. To do this we needed a set of variant lox sites in order to create constructs that would yield different subsets of survival and fluorescence to optimally fill the knapsack. Initially the design to address the knapsack problem had several vital components using modules that consisted of lox sites that floxed the TetA gene and RFP.<br />
In our attempts to solve the knapsack problem, we assembled 10 new lox sites with mutations in the 8 bp region and floxed 16 out of 21 possible lox site combinations with red fluorescent protein with these variant lox sites. We built a construct with a fluorescent protein gene downstream of TetA to test for the presence of a terminator within the TetA gene. Along the way, we built several tools to assist us in the wetlab. In addition, we recorded several observations regarding gene expression and codon optimization as we progressed. Characterizing and attempting to understand these foundational problems became one of the new focuses of this team.<br />
</p><br />
<a Name="Introduction"></a><br />
<h4> Introduction</h4><br />
<br />
<h4> What is the Knapsack problem?</h4><br />
<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;The knapsack problem is an NP-complete problem that asks whether: given a capacity and different weighted items, if there exists a subset of the items for which the sum of its values is equal to that of the capacity.<br><br><br />
<center><img src="https://static.igem.org/mediawiki/2010/d/da/Davidson-MissouriWknapsack.png" alt="" width=300/></center><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;There are several variations of the knapsack problem. The one that we are focusing on involves attempting to fit various items of various weights into a knapsack with a certain capacity. You have solved the knapsack problem if you manage to fill the knapsack to capacity. Other variations include looking for the closest possible value to the capacity that we can fill if the capacity itself cannot actually be reached.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;In order to help people understand the knapsack problem and its complexity, we have built a <a href="http://72.22.219.205/knapsack">knapsack game</a>. In tutorial mode this game will give the user hints to help solve the problem. The challenge mode of the game should give the user an appreciation of the complexity of the knapsack problem and the overall class of NP-complete problems.<br />
</p><br />
<h4> Why Bacterial Computing? </h4><br />
<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;In mathematics, there exists a class of complex mathematical problems, known as NP-complete problems, where there are no efficient algorithms to solve these problems other than exhaustive search of trying all the possible solutions one at a time and determining whether or not they are actually solutions. Bacterial computers offer a more efficient alternative approach to solving these NP-complete problems by using massive parallel computing. By engineering a bacterial computer, the billions of cells can simultaneously try the possible solutions and determine whether they are in fact a solution to the given problem.<br />
<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Besides the much greater efficiency and speed, another advantage of bacterial computers is that it actually becomes more powerful as the number of cells increase from cell division. Despite the complexity of bacterial computing, it is quickly gaining popularity because of its many advantages over the traditional computer. <br />
</p><br />
<h4> Our Biological Design </h4><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Cells of identical genotype can produce different levels of protein. This variation is called noise. While noise is generally treated as a necessary evil in synthetic biology, we chose to use the stochastic nature of gene expression to our advantage.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;In order to control for the random nature of gene expression, we decided to implement a band pass filter. Essentially, this filter kills off cells that express either too much or two little of a chosen protein. Band pass filters harness and focus noise without eliminating it. We chose to use this system in order to help bridge the gap between a digital problem and an analog solution.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;The combination of the tetA gene and an environment containing the antibiotic tetracycline produces a natural band pass filter. The tetA gene provides a level of resistance to tetracycline by creating efflux pumps that help the cell excrete the otherwise fatal tetracycline. However, these pumps come with a cost. They make the cell membrane more porous and permeable. With too many pumps, the cell cannot maintain homeostasis and dies; with too few pumps, the cells cannot remove tetracycline from its cytoplasm and dies from blocked protein synthesis. Therefore, tetA is a band pass filter.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;With this band pass filter in mind, we decided to make each cell a knapsack. Overexpression of tetA would be analogous to exceeding the capacity of the knapsack. However, we still had to solve a fundamental problem: weights. We used a novel tool called codon optimization to assign weights. By rewriting the codons of the tetA gene, we could alter the number of pumps produced by different versions of the same gene in a given time frame.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;We planned to produce a chain of tetA modules with a different fluorescent protein attached to each one to act as visual and biochemical reporters. Each tetA and fluorescent color pair would be attached to a genetic switch in the form of the Cre/lox system. A switch being on would correspond to packing an item of that weight (color and tetA level) into the knapsack. Allowing this system to run and randomly flip switches on and off would ensure that all possible combinations of packed knapsacks would be generated. The tetA band pass filter could give us a narrow subsets of cells that solved the problem. We could then figure out how a cell packed the knapsack based on the fluorescent reporters.</p><br><br><br />
<center><img width=600 src="https://static.igem.org/mediawiki/2010/4/46/Davidson-MissouriWFrontpage.png" /></center><br />
<br><br />
<a Name="Summary and Outlook"></a><br />
<h4> Summary and Outlook</h4><br />
<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Our summer research project culminated with foundational advances in the fields of codon optimization, cre-lox characterization, and gene expression. The team successfully built 6 tetA constructs with varying levels of codon optimization and deoptimization. We also assembled eleven novel lox sites and characterized interactions between several of them in the presence of the cre protein. While choosing and characterizing reporters, we observed variations in gene expression due to environmental factors and inherent variability in bacterial cells. To assist our research in different ways, we designed tools such as the VeriPart, the Oligator, the Optimus, the Construct Simulator, and the Knapsack game.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;With the help of this integral research, we have the tools and knowledge to address the knapsack problem. In the future, we wish to further experiment with varying levels of codon optimization and gene placement. Finally, we wish to build and test different constructs to find the most efficient way to solving the knapsack problem.</p><br><br />
<br />
<div><br />
<a Name="References"></a><br />
<H4>References</H4><br />
<p><br />
Gwang Lee I, Izumu S. 1998. Role of nucleotide sequences of loxP spacer region in Cre-mediated recombination. Gene 216: 55-65.<br />
<br><br><br />
Jean L, Tamily A. W, Hyuno K, Ryan W. D, Ju L, Robyn A. B, Joshua R. S, Jeff W. L. 2007 Transgenic strategies for combinatorial expression of fluorescent proteins in the nervous systems. Nature [need volume and page numbers]<br />
<br><br><br />
Jesse M. Fox, Ivan Erill. 2010 Relative Codon Adaptation: A Generic Codon Bias Index for Prediction of Gene Expression. DNA Research [Need volume] 1-12. Avalaible from http://dnaresearch.oxfordjournals.org<br />
<br><br><br />
Ronald J, Harmen J. B, Mark G. 2003. Revisiting the codon adaptation index from a whole-genome perspective: analyzing the relationship between gene expression and codon occurrence in yeast using a variety of models. Nucleic Acid Research 31(8): 2242-2251. Available online from http://nar.oxfordjournals.org/cgi/content/full/31/8/2242<br />
<br><br><br />
Stuart B. Levy. 1992 April. MINIREVIEW Active Efflux Mechanisms for Antimicrobial Resistance. American Society for Microbiology 36 (4): 695-703.<br />
<br><br><br />
Ui-Jung J, Sun P, Gwang L, Ho-Joon S, Myung-Hee K. [received 22 August 2007, available online 30 August 2007]. Analysis of spacer regions derived from intramolecular recombination between heterologous loxP sites.<br />
<br><br><br />
Uttam R, Shibsankar D, Satyabrata S. 2009. [received 21 March 2008, accepted 16 October 2008, published online 8 January 2009]. Predicting Gene Expression Level from Relative Codon Usage Bias: An Application to Escherichia coli Genome. DNA Research [Internet]: 16, 13-30.<br />
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<div id="mission_box" style="padding:10px"> <h2> iGEM Davidson – MWSU 2010: Project </h2><br />
<a Name="abstract"></a> <br />
<h4>Abstract</h4> <br />
<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;The Cre-lox system is a tool that involves the splicing of a specific pair of DNA sequences called lox sites with an enzyme called Cre recombinase. We implicated this system in attempts to manipulate gene expression that mimics randomly "selecting objects" to fill a knapsack in order to solve the knapsack problem. To do this we needed a set of variant lox sites in order to create constructs that would yield different subsets of survival and fluorescence to optimally fill the knapsack. Initially the design to address the knapsack problem had several vital components using modules that consisted of lox sites that floxed the TetA gene and RFP.<br />
In our attempts to solve the knapsack problem, we assembled 10 new lox sites with mutations in the 8 bp region and floxed 16 out of 21 possible lox site combinations with red fluorescent protein with these variant lox sites. We built a construct with a fluorescent protein gene downstream of TetA to test for the presence of a terminator within the TetA gene. Along the way, we built several tools to assist us in the wetlab. In addition, we recorded several observations regarding gene expression and codon optimization as we progressed. Characterizing and attempting to understand these foundational problems became one of the new focuses of this team.<br />
</p><br />
<a Name="Introduction"></a><br />
<h4> Introduction</h4><br />
<br />
<h4> What is the Knapsack problem?</h4><br />
<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;The knapsack problem is an NP-complete problem that asks whether: given a capacity and different weighted items, if there exists a subset of the items for which the sum of its values is equal to that of the capacity.<br><br><br />
<center><img src="https://static.igem.org/mediawiki/2010/d/da/Davidson-MissouriWknapsack.png" alt="" width=300/></center><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;There are several variations of the knapsack problem. The one that we are focusing on involves attempting to fit various items of various weights into a knapsack with a certain capacity. You have solved the knapsack problem if you manage to fill the knapsack to capacity. Other variations include looking for the closest possible value to the capacity that we can fill if the capacity itself cannot actually be reached.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;In order to help people understand the knapsack problem and its complexity, we have built a <a href="http://72.22.219.205/knapsack">knapsack game</a>. In tutorial mode this game will give the user hints to help solve the problem. The challenge mode of the game should give the user an appreciation of the complexity of the knapsack problem and the overall class of NP-complete problems.<br />
</p><br />
<h4> Why Bacterial Computing? </h4><br />
<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;In mathematics, there exists a class of complex mathematical problems, known as NP-complete problems, where there are no efficient algorithms to solve these problems other than exhaustive search of trying all the possible solutions one at a time and determining whether or not they are actually solutions. Bacterial computers offer a more efficient alternative approach to solving these NP-complete problems by using massive parallel computing. By engineering a Bacterial computer, the billions of cells can simultaneously try the possible solutions and determine whether they are in fact a solution to the given problem.<br />
<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Besides the much greater efficiency and speed, another advantage of bacterial computers is that it actually becomes more powerful as the number of cells increase from cell division. Despite the complexity of bacterial computing, it is quickly gaining popularity because of its many advantages over the traditional computer. <br />
</p><br />
<h4> Our Biological Design </h4><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Cells of identical genotype can produce different levels of protein. This variation is called noise. While noise is generally treated as a necessary evil in synthetic biology, we chose to use the stochastic nature of gene expression to our advantage.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;In order to control for the random nature of gene expression, we decided to implement a band pass filter. Essentially, this filter kills off cells that express either too much or two little of a chosen protein. Band pass filters harness and focus noise without eliminating it. We chose to use this system in order to help bridge the gap between a digital problem and an analog solution.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;The combination of the tetA gene and an environment containing the antibiotic tetracycline produces a natural band pass filter. The tetA gene provides a level of resistance to tetracycline by creating efflux pumps that help the cell excrete the otherwise fatal tetracycline. However, these pumps come with a cost. They make the cell membrane more porous and permeable. With too many pumps, the cell cannot maintain homeostasis and dies; with too few pumps, the cells cannot remove tetracycline from its cytoplasm and dies from blocked protein synthesis. Therefore, tetA is a band pass filter.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;With this band pass filter in mind, we decided to make each cell a knapsack. Overexpression of tetA would be analogous to exceeding the capacity of the knapsack. However, we still had to solve a fundamental problem: weights. We used a novel tool called codon optimization to assign weights. By rewriting the codons of the tetA gene, we could alter the number of pumps produced by different versions of the same gene in a given time frame.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;We planned to produce a chain of tetA modules with a different fluorescent protein attached to each one to act as visual and biochemical reporters. Each tetA and fluorescent color pair would be attached to a genetic switch in the form of the Cre/lox system. A switch being on would correspond to packing an item of that weight (color and tetA level) into the knapsack. Allowing this system to run and randomly flip switches on and off would ensure that all possible combinations of packed knapsacks would be generated. The tetA band pass filter could give us a narrow subsets of cells that solved the problem. We could then figure out how a cell packed the knapsack based on the fluorescent reporters.</p><br><br><br />
<center><img width=600 src="https://static.igem.org/mediawiki/2010/4/46/Davidson-MissouriWFrontpage.png" /></center><br />
<br><br />
<a Name="Summary and Outlook"></a><br />
<h4> Summary and Outlook</h4><br />
<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Our summer research project culminated with foundational advances in the fields of codon optimization, cre-lox characterization, and gene expression. The team successfully built 6 tetA constructs with varying levels of codon optimization and deoptimization. We also assembled eleven novel lox sites and characterized interactions between several of them in the presence of the cre protein. While choosing and characterizing reporters, we observed variations in gene expression due to environmental factors and inherent variability in bacterial cells. To assist our research in different ways, we designed tools such as the VeriPart, the Oligator, the Optimus, the Construct Simulator, and the Knapsack game.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;With the help of this integral research, we have the tools and knowledge to address the knapsack problem. In the future, we wish to further experiment with varying levels of codon optimization and gene placement. Finally, we wish to build and test different constructs to find the most efficient way to solving the knapsack problem.</p><br><br />
<br />
<div><br />
<a Name="References"></a><br />
<H4>References</H4><br />
<p><br />
Gwang Lee I, Izumu S. 1998. Role of nucleotide sequences of loxP spacer region in Cre-mediated recombination. Gene 216: 55-65.<br />
<br><br><br />
Jean L, Tamily A. W, Hyuno K, Ryan W. D, Ju L, Robyn A. B, Joshua R. S, Jeff W. L. 2007 Transgenic strategies for combinatorial expression of fluorescent proteins in the nervous systems. Nature [need volume and page numbers]<br />
<br><br><br />
Jesse M. Fox, Ivan Erill. 2010 Relative Codon Adaptation: A Generic Codon Bias Index for Prediction of Gene Expression. DNA Research [Need volume] 1-12. Avalaible from http://dnaresearch.oxfordjournals.org<br />
<br><br><br />
Ronald J, Harmen J. B, Mark G. 2003. Revisiting the codon adaptation index from a whole-genome perspective: analyzing the relationship between gene expression and codon occurrence in yeast using a variety of models. Nucleic Acid Research 31(8): 2242-2251. Available online from http://nar.oxfordjournals.org/cgi/content/full/31/8/2242<br />
<br><br><br />
Stuart B. Levy. 1992 April. MINIREVIEW Active Efflux Mechanisms for Antimicrobial Resistance. American Society for Microbiology 36 (4): 695-703.<br />
<br><br><br />
Ui-Jung J, Sun P, Gwang L, Ho-Joon S, Myung-Hee K. [received 22 August 2007, available online 30 August 2007]. Analysis of spacer regions derived from intramolecular recombination between heterologous loxP sites.<br />
<br><br><br />
Uttam R, Shibsankar D, Satyabrata S. 2009. [received 21 March 2008, accepted 16 October 2008, published online 8 January 2009]. Predicting Gene Expression Level from Relative Codon Usage Bias: An Application to Escherichia coli Genome. DNA Research [Internet]: 16, 13-30.<br />
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<div id="mission_box" style="padding:10px"> <h2> iGEM Davidson – MWSU 2010: Project </h2><br />
<a Name="abstract"></a> <br />
<h4>Abstract</h4> <br />
<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;The Cre-lox system is a tool that involves the splicing of a specific pair of DNA sequences called lox sites with an enzyme called Cre recombinase. We implicated this system in attempts to manipulate gene expression that mimics randomly "selecting objects" to fill a knapsack in order to solve the knapsack problem. To do this we needed a set of variant lox sites in order to create constructs that would yield different subsets of survival and fluorescence to optimally fill the knapsack. Initially the design to address the knapsack problem had several vital components using modules that consisted of lox sites that floxed the TetA gene and RFP.<br />
In our attempts to solve the knapsack problem, we assembled 10 new lox sites with mutations in the 8 bp region and floxed 16 out of 21 possible lox site combinations with red fluorescent protein with these variant lox sites. We built a construct with a fluorescent protein gene downstream of TetA to test for the presence of a terminator within the TetA gene. Along the way, we built several tools to assist us in the wetlab. In addition, we recorded several observations regarding gene expression and codon optimization as we progressed. Characterizing and attempting to understand these foundational problems became one of the new focuses of this team.<br />
</p><br />
<a Name="Introduction"></a><br />
<h4> Introduction</h4><br />
<br />
<h4> What is the Knapsack problem?</h4><br />
<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;The knapsack problem is an NP-complete problem that asks whether: given a capacity and different weighted items, if there exists a subset of the items for which the sum of its values is equal to that of the capacity.<br><br><br />
<center><img src="https://static.igem.org/mediawiki/2010/d/da/Davidson-MissouriWknapsack.png" alt="" width=300/></center><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;There are several variations of the knapsack problem. The one that we are focusing on involves attempting to fit various items of various weights into a knapsack with a certain capacity. You have solved the knapsack problem if you manage to fill the knapsack to capacity. Other variations include looking for the closest possible value to the capacity that we can fill if the capacity itself cannot actually be reached.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;In order to help people understand the knapsack problem and its complexity, we have built a <a href="http://72.22.219.205/knapsack">knapsack game</a>. In tutorial mode this game will give the user hints to help solve the problem. The challenge mode of the game should give the user an appreciation of the complexity of the knapsack problem and the overall class of NP-complete problems.<br />
</p><br />
<h4> Why Bacterial Computing? </h4><br />
<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;In mathematics, there exists a class of complex mathematical problems, known as NP-complete problems, where there are no efficient algorithms to solve these problems other than exhaustive search of trying all the possible solutions one at a time and determining whether or not they are actually solutions. Bacterial computers offer a more efficient alternative approach to solving these NP-complete problems by using massive parallel computing. By engineering a Bacterial computer, the billions of cells can simultaneously try the possible solutions and determine whether they are in fact a solution to the given problem.<br />
<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Besides the much greater efficiency and speed, another advantage of bacterial computers is that it actually becomes more powerful as the number of cells increase from cell division. Despite the complexity of bacterial computing, it is quickly gaining popularity because of its many advantages over the traditional computer. <br />
</p><br />
<h4> Our Biological Design </h4><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Cells of identical genotype can produce different levels of protein. This variation is called noise. While noise is generally treated as a necessary evil in synthetic biology, we chose to use the stochastic nature of gene expression to our advantage.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;In order to control for the random nature of gene expression, we decided to implement a band pass filter. Essentially, this filter kills off cells that express either too much or two little of a chosen protein. Band pass filters harness and focus noise without eliminating it. We chose to use this system in order to help bridge the gap between a digital problem and an analog solution.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;The combination of the tetA gene and an environment containing the antibiotic tetracycline produces a natural band pass filter. The tetA gene provides a level of resistance to tetracycline by creating efflux pumps that help the cell excrete the otherwise fatal tetracycline. However, these pumps come with a cost. They make the cell membrane more porous and permeable. With too many pumps, the cell cannot maintain homeostasis and dies; with too few pumps, the cells cannot remove tetracycline from its cytoplasm and dies from blocked protein synthesis. Therefore, tetA is a band pass filter.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;With this band pass filter in mind, we decided to make each cell a knapsack. Overexpression of tetA would be analogous to exceeding the capacity of the knapsack. However, we still had to solve a fundamental problem: weights. We used a novel tool called codon optimization to assign weights. By rewriting the codons of the tetA gene, we could alter the number of pumps produced by different versions of the same gene in a given time frame.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;We planned to produce a chain of tetA modules with a different fluorescent protein attached to each one to act as visual and biochemical reporters. Each tetA and fluorescent color pair would be attached to a genetic switch in the form of the Cre/lox system. A switch being on would correspond to packing an item of that weight (color and tetA level) into the knapsack. Allowing this system to run and randomly flip switches on and off would ensure that all possible combinations of packed knapsacks would be generated. The tetA band pass filter could give us a narrow subsets of cells that solved the problem. We could then figure out how a cell packed the knapsack based on the fluorescent reporters.</p><br><br><br />
<center><img width=700 src="https://static.igem.org/mediawiki/2010/4/46/Davidson-MissouriWFrontpage.png" /></center><br />
<br><br />
<a Name="Summary and Outlook"></a><br />
<h4> Summary and Outlook</h4><br />
<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Our summer research project culminated with foundational advances in the fields of codon optimization, cre-lox characterization, and gene expression. The team successfully built 6 tetA constructs with varying levels of codon optimization and deoptimization. We also assembled eleven novel lox sites and characterized interactions between several of them in the presence of the cre protein. While choosing and characterizing reporters, we observed variations in gene expression due to environmental factors and inherent variability in bacterial cells. To assist our research in different ways, we designed tools such as the VeriPart, the Oligator, the Optimus, the Construct Simulator, and the Knapsack game.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;With the help of this integral research, we have the tools and knowledge to address the knapsack problem. In the future, we wish to further experiment with varying levels of codon optimization and gene placement. Finally, we wish to build and test different constructs to find the most efficient way to solving the knapsack problem.</p><br><br />
<br />
<div><br />
<a Name="References"></a><br />
<H4>References</H4><br />
<p><br />
Gwang Lee I, Izumu S. 1998. Role of nucleotide sequences of loxP spacer region in Cre-mediated recombination. Gene 216: 55-65.<br />
<br><br><br />
Jean L, Tamily A. W, Hyuno K, Ryan W. D, Ju L, Robyn A. B, Joshua R. S, Jeff W. L. 2007 Transgenic strategies for combinatorial expression of fluorescent proteins in the nervous systems. Nature [need volume and page numbers]<br />
<br><br><br />
Jesse M. Fox, Ivan Erill. 2010 Relative Codon Adaptation: A Generic Codon Bias Index for Prediction of Gene Expression. DNA Research [Need volume] 1-12. Avalaible from http://dnaresearch.oxfordjournals.org<br />
<br><br><br />
Ronald J, Harmen J. B, Mark G. 2003. Revisiting the codon adaptation index from a whole-genome perspective: analyzing the relationship between gene expression and codon occurrence in yeast using a variety of models. Nucleic Acid Research 31(8): 2242-2251. Available online from http://nar.oxfordjournals.org/cgi/content/full/31/8/2242<br />
<br><br><br />
Stuart B. Levy. 1992 April. MINIREVIEW Active Efflux Mechanisms for Antimicrobial Resistance. American Society for Microbiology 36 (4): 695-703.<br />
<br><br><br />
Ui-Jung J, Sun P, Gwang L, Ho-Joon S, Myung-Hee K. [received 22 August 2007, available online 30 August 2007]. Analysis of spacer regions derived from intramolecular recombination between heterologous loxP sites.<br />
<br><br><br />
Uttam R, Shibsankar D, Satyabrata S. 2009. [received 21 March 2008, accepted 16 October 2008, published online 8 January 2009]. Predicting Gene Expression Level from Relative Codon Usage Bias: An Application to Escherichia coli Genome. DNA Research [Internet]: 16, 13-30.<br />
<br><br><br />
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</body></html></div>Eushiuhttp://2010.igem.org/Team:Davidson-MissouriW/ProjectTeam:Davidson-MissouriW/Project2010-07-29T14:53:05Z<p>Eushiu: </p>
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<div id="mission_box" style="padding:10px"> <h2> iGEM Davidson – MWSU 2010: Project </h2><br />
<a Name="abstract"></a> <br />
<h4>Abstract</h4> <br />
<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;The Cre-lox system is a tool that involves the splicing of a specific pair of DNA sequences called lox sites with an enzyme called Cre recombinase. We implicated this system in attempts to manipulate gene expression that mimics randomly "selecting objects" to fill a knapsack in order to solve the knapsack problem. To do this we needed a set of variant lox sites in order to create constructs that would yield different subsets of survival and fluorescence to optimally fill the knapsack. Initially the design to address the knapsack problem had several vital components using modules that consisted of lox sites that floxed the TetA gene and RFP.<br />
In our attempts to solve the knapsack problem, we assembled 10 new lox sites with mutations in the 8 bp region and floxed 16 out of 21 possible lox site combinations with red fluorescent protein with these variant lox sites. We built a construct with a fluorescent protein gene downstream of TetA to test for the presence of a terminator within the TetA gene. Along the way, we built several tools to assist us in the wetlab. In addition, we recorded several observations regarding gene expression and codon optimization as we progressed. Characterizing and attempting to understand these foundational problems became one of the new focuses of this team.<br />
</p><br />
<a Name="Introduction"></a><br />
<h4> Introduction</h4><br />
<br />
<h4> What is the Knapsack problem?</h4><br />
<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;The knapsack problem is an NP-complete problem that asks whether: given a capacity and different weighted items, if there exists a subset of the items for which the sum of its values is equal to that of the capacity.<br><br><br />
<center><img src="https://static.igem.org/mediawiki/2010/d/da/Davidson-MissouriWknapsack.png" alt="" width=300/></center><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;There are several variations of the knapsack problem. The one that we are focusing on involves attempting to fit various items of various weights into a knapsack with a certain capacity. You have solved the knapsack problem if you manage to fill the knapsack to capacity. Other variations include looking for the closest possible value to the capacity that we can fill if the capacity itself cannot actually be reached.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;In order to help people understand the knapsack problem and its complexity, we have built a <a href="http://72.22.219.205/knapsack">knapsack game</a>. In tutorial mode this game will give the user hints to help solve the problem. The challenge mode of the game should give the user an appreciation of the complexity of the knapsack problem and the overall class of NP-complete problems.<br />
</p><br />
<h4> Why Bacterial Computing? </h4><br />
<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;In mathematics, there exists a class of complex mathematical problems, known as NP-complete problems, where there are no efficient algorithms to solve these problems other than exhaustive search of trying all the possible solutions one at a time and determining whether or not they are actually solutions. Bacterial computers offer a more efficient alternative approach to solving these NP-complete problems by using massive parallel computing. By engineering a Bacterial computer, the billions of cells can simultaneously try the possible solutions and determine whether they are in fact a solution to the given problem.<br />
<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Besides the much greater efficiency and speed, another advantage of bacterial computers is that it actually becomes more powerful as the number of cells increase from cell division. Despite the complexity of bacterial computing, it is quickly gaining popularity because of its many advantages over the traditional computer. <br />
</p><br />
<h4> Our Biological Design </h4><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Cells of identical genotype can produce different levels of protein. This variation is called noise. While noise is generally treated as a necessary evil in synthetic biology, we chose to use the stochastic nature of gene expression to our advantage.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;In order to control for the random nature of gene expression, we decided to implement a band pass filter. Essentially, this filter kills off cells that express either too much or two little of a chosen protein. Band pass filters harness and focus noise without eliminating it. We chose to use this system in order to help bridge the gap between a digital problem and an analog solution.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;The combination of the tetA gene and an environment containing the antibiotic tetracycline produces a natural band pass filter. The tetA gene provides a level of resistance to tetracycline by creating efflux pumps that help the cell excrete the otherwise fatal tetracycline. However, these pumps come with a cost. They make the cell membrane more porous and permeable. With too many pumps, the cell cannot maintain homeostasis and dies; with too few pumps, the cells cannot remove tetracycline from its cytoplasm and dies from blocked protein synthesis. Therefore, tetA is a band pass filter.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;With this band pass filter in mind, we decided to make each cell a knapsack. Overexpression of tetA would be analogous to exceeding the capacity of the knapsack. However, we still had to solve a fundamental problem: weights. We used a novel tool called codon optimization to assign weights. By rewriting the codons of the tetA gene, we could alter the number of pumps produced by different versions of the same gene in a given time frame.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;We planned to produce a chain of tetA modules with a different fluorescent protein attached to each one to act as visual and biochemical reporters. Each tetA and fluorescent color pair would be attached to a genetic switch in the form of the Cre/lox system. A switch being on would correspond to packing an item of that weight (color and tetA level) into the knapsack. Allowing this system to run and randomly flip switches on and off would ensure that all possible combinations of packed knapsacks would be generated. The tetA band pass filter could give us a narrow subsets of cells that solved the problem. We could then figure out how a cell packed the knapsack based on the fluorescent reporters.</p><br><br><br />
<center><img width=200 src="https://static.igem.org/mediawiki/2010/4/46/Davidson-MissouriWFrontpage.png" /></center><br />
<br><br />
<a Name="Summary and Outlook"></a><br />
<h4> Summary and Outlook</h4><br />
<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Our summer research project culminated with foundational advances in the fields of codon optimization, cre-lox characterization, and gene expression. The team successfully built 6 tetA constructs with varying levels of codon optimization and deoptimization. We also assembled eleven novel lox sites and characterized interactions between several of them in the presence of the cre protein. While choosing and characterizing reporters, we observed variations in gene expression due to environmental factors and inherent variability in bacterial cells. To assist our research in different ways, we designed tools such as the VeriPart, the Oligator, the Optimus, the Construct Simulator, and the Knapsack game.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;With the help of this integral research, we have the tools and knowledge to address the knapsack problem. In the future, we wish to further experiment with varying levels of codon optimization and gene placement. Finally, we wish to build and test different constructs to find the most efficient way to solving the knapsack problem.</p><br><br />
<br />
<div><br />
<a Name="References"></a><br />
<H4>References</H4><br />
<p><br />
Gwang Lee I, Izumu S. 1998. Role of nucleotide sequences of loxP spacer region in Cre-mediated recombination. Gene 216: 55-65.<br />
<br><br><br />
Jean L, Tamily A. W, Hyuno K, Ryan W. D, Ju L, Robyn A. B, Joshua R. S, Jeff W. L. 2007 Transgenic strategies for combinatorial expression of fluorescent proteins in the nervous systems. Nature [need volume and page numbers]<br />
<br><br><br />
Jesse M. Fox, Ivan Erill. 2010 Relative Codon Adaptation: A Generic Codon Bias Index for Prediction of Gene Expression. DNA Research [Need volume] 1-12. Avalaible from http://dnaresearch.oxfordjournals.org<br />
<br><br><br />
Ronald J, Harmen J. B, Mark G. 2003. Revisiting the codon adaptation index from a whole-genome perspective: analyzing the relationship between gene expression and codon occurrence in yeast using a variety of models. Nucleic Acid Research 31(8): 2242-2251. Available online from http://nar.oxfordjournals.org/cgi/content/full/31/8/2242<br />
<br><br><br />
Stuart B. Levy. 1992 April. MINIREVIEW Active Efflux Mechanisms for Antimicrobial Resistance. American Society for Microbiology 36 (4): 695-703.<br />
<br><br><br />
Ui-Jung J, Sun P, Gwang L, Ho-Joon S, Myung-Hee K. [received 22 August 2007, available online 30 August 2007]. Analysis of spacer regions derived from intramolecular recombination between heterologous loxP sites.<br />
<br><br><br />
Uttam R, Shibsankar D, Satyabrata S. 2009. [received 21 March 2008, accepted 16 October 2008, published online 8 January 2009]. Predicting Gene Expression Level from Relative Codon Usage Bias: An Application to Escherichia coli Genome. DNA Research [Internet]: 16, 13-30.<br />
<br><br><br />
</div><br />
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</body></html></div>Eushiuhttp://2010.igem.org/Team:Davidson-MissouriW/ProjectTeam:Davidson-MissouriW/Project2010-07-29T14:52:14Z<p>Eushiu: </p>
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<div id="mission_box" style="padding:10px"> <h2> iGEM Davidson – MWSU 2010: Project </h2><br />
<a Name="abstract"></a> <br />
<h4>Abstract</h4> <br />
<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;The Cre-lox system is a tool that involves the splicing of a specific pair of DNA sequences called lox sites with an enzyme called Cre recombinase. We implicated this system in attempts to manipulate gene expression that mimics randomly "selecting objects" to fill a knapsack in order to solve the knapsack problem. To do this we needed a set of variant lox sites in order to create constructs that would yield different subsets of survival and fluorescence to optimally fill the knapsack. Initially the design to address the knapsack problem had several vital components using modules that consisted of lox sites that floxed the TetA gene and RFP.<br />
In our attempts to solve the knapsack problem, we assembled 10 new lox sites with mutations in the 8 bp region and floxed 16 out of 21 possible lox site combinations with red fluorescent protein with these variant lox sites. We built a construct with a fluorescent protein gene downstream of TetA to test for the presence of a terminator within the TetA gene. Along the way, we built several tools to assist us in the wetlab. In addition, we recorded several observations regarding gene expression and codon optimization as we progressed. Characterizing and attempting to understand these foundational problems became one of the new focuses of this team.<br />
</p><br />
<a Name="Introduction"></a><br />
<h4> Introduction</h4><br />
<br />
<h4> What is the Knapsack problem?</h4><br />
<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;The knapsack problem is an NP-complete problem that asks whether: given a capacity and different weighted items, if there exists a subset of the items for which the sum of its values is equal to that of the capacity.<br><br><br />
<center><img src="https://static.igem.org/mediawiki/2010/d/da/Davidson-MissouriWknapsack.png" alt="" width=300/></center><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;There are several variations of the knapsack problem. The one that we are focusing on involves attempting to fit various items of various weights into a knapsack with a certain capacity. You have solved the knapsack problem if you manage to fill the knapsack to capacity. Other variations include looking for the closest possible value to the capacity that we can fill if the capacity itself cannot actually be reached.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;In order to help people understand the knapsack problem and its complexity, we have built a <a href="http://72.22.219.205/knapsack">knapsack game</a>. In tutorial mode this game will give the user hints to help solve the problem. The challenge mode of the game should give the user an appreciation of the complexity of the knapsack problem and the overall class of NP-complete problems.<br />
</p><br />
<h4> Why Bacterial Computing? </h4><br />
<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;In mathematics, there exists a class of complex mathematical problems, known as NP-complete problems, where there are no efficient algorithms to solve these problems other than exhaustive search of trying all the possible solutions one at a time and determining whether or not they are actually solutions. Bacterial computers offer a more efficient alternative approach to solving these NP-complete problems by using massive parallel computing. By engineering a Bacterial computer, the billions of cells can simultaneously try the possible solutions and determine whether they are in fact a solution to the given problem.<br />
<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Besides the much greater efficiency and speed, another advantage of bacterial computers is that it actually becomes more powerful as the number of cells increase from cell division. Despite the complexity of bacterial computing, it is quickly gaining popularity because of its many advantages over the traditional computer. <br />
</p><br />
<h4> Our Biological Design </h4><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Cells of identical genotype can produce different levels of protein. This variation is called noise. While noise is generally treated as a necessary evil in synthetic biology, we chose to use the stochastic nature of gene expression to our advantage.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;In order to control for the random nature of gene expression, we decided to implement a band pass filter. Essentially, this filter kills off cells that express either too much or two little of a chosen protein. Band pass filters harness and focus noise without eliminating it. We chose to use this system in order to help bridge the gap between a digital problem and an analog solution.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;The combination of the tetA gene and an environment containing the antibiotic tetracycline produces a natural band pass filter. The tetA gene provides a level of resistance to tetracycline by creating efflux pumps that help the cell excrete the otherwise fatal tetracycline. However, these pumps come with a cost. They make the cell membrane more porous and permeable. With too many pumps, the cell cannot maintain homeostasis and dies; with too few pumps, the cells cannot remove tetracycline from its cytoplasm and dies from blocked protein synthesis. Therefore, tetA is a band pass filter.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;With this band pass filter in mind, we decided to make each cell a knapsack. Overexpression of tetA would be analogous to exceeding the capacity of the knapsack. However, we still had to solve a fundamental problem: weights. We used a novel tool called codon optimization to assign weights. By rewriting the codons of the tetA gene, we could alter the number of pumps produced by different versions of the same gene in a given time frame.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;We planned to produce a chain of tetA modules with a different fluorescent protein attached to each one to act as visual and biochemical reporters. Each tetA and fluorescent color pair would be attached to a genetic switch in the form of the Cre/lox system. A switch being on would correspond to packing an item of that weight (color and tetA level) into the knapsack. Allowing this system to run and randomly flip switches on and off would ensure that all possible combinations of packed knapsacks would be generated. The tetA band pass filter could give us a narrow subsets of cells that solved the problem. We could then figure out how a cell packed the knapsack based on the fluorescent reporters.</p><br><br><br />
<center><img src="https://static.igem.org/mediawiki/2010/4/46/Davidson-MissouriWFrontpage.png" /></center><br />
<br><br />
<a Name="Summary and Outlook"></a><br />
<h4> Summary and Outlook</h4><br />
<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Our summer research project culminated with foundational advances in the fields of codon optimization, cre-lox characterization, and gene expression. The team successfully built 6 tetA constructs with varying levels of codon optimization and deoptimization. We also assembled eleven novel lox sites and characterized interactions between several of them in the presence of the cre protein. While choosing and characterizing reporters, we observed variations in gene expression due to environmental factors and inherent variability in bacterial cells. To assist our research in different ways, we designed tools such as the VeriPart, the Oligator, the Optimus, the Construct Simulator, and the Knapsack game.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;With the help of this integral research, we have the tools and knowledge to address the knapsack problem. In the future, we wish to further experiment with varying levels of codon optimization and gene placement. Finally, we wish to build and test different constructs to find the most efficient way to solving the knapsack problem.</p><br><br />
<br />
<div><br />
<a Name="References"></a><br />
<H4>References</H4><br />
<p><br />
Gwang Lee I, Izumu S. 1998. Role of nucleotide sequences of loxP spacer region in Cre-mediated recombination. Gene 216: 55-65.<br />
<br><br><br />
Jean L, Tamily A. W, Hyuno K, Ryan W. D, Ju L, Robyn A. B, Joshua R. S, Jeff W. L. 2007 Transgenic strategies for combinatorial expression of fluorescent proteins in the nervous systems. Nature [need volume and page numbers]<br />
<br><br><br />
Jesse M. Fox, Ivan Erill. 2010 Relative Codon Adaptation: A Generic Codon Bias Index for Prediction of Gene Expression. DNA Research [Need volume] 1-12. Avalaible from http://dnaresearch.oxfordjournals.org<br />
<br><br><br />
Ronald J, Harmen J. B, Mark G. 2003. Revisiting the codon adaptation index from a whole-genome perspective: analyzing the relationship between gene expression and codon occurrence in yeast using a variety of models. Nucleic Acid Research 31(8): 2242-2251. Available online from http://nar.oxfordjournals.org/cgi/content/full/31/8/2242<br />
<br><br><br />
Stuart B. Levy. 1992 April. MINIREVIEW Active Efflux Mechanisms for Antimicrobial Resistance. American Society for Microbiology 36 (4): 695-703.<br />
<br><br><br />
Ui-Jung J, Sun P, Gwang L, Ho-Joon S, Myung-Hee K. [received 22 August 2007, available online 30 August 2007]. Analysis of spacer regions derived from intramolecular recombination between heterologous loxP sites.<br />
<br><br><br />
Uttam R, Shibsankar D, Satyabrata S. 2009. [received 21 March 2008, accepted 16 October 2008, published online 8 January 2009]. Predicting Gene Expression Level from Relative Codon Usage Bias: An Application to Escherichia coli Genome. DNA Research [Internet]: 16, 13-30.<br />
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<div id="mission_box" style="padding:10px"> <h2> iGEM Davidson – MWSU 2010: Project </h2><br />
<a Name="abstract"></a> <br />
<h4>Abstract</h4> <br />
<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;The Cre-lox system is a tool that involves the splicing of a specific pair of DNA sequences called lox sites with an enzyme called Cre recombinase. We implicated this system in attempts to manipulate gene expression that mimics randomly "selecting objects" to fill a knapsack in order to solve the knapsack problem. To do this we needed a set of variant lox sites in order to create constructs that would yield different subsets of survival and fluorescence to optimally fill the knapsack. Initially the design to address the knapsack problem had several vital components using modules that consisted of lox sites that floxed the TetA gene and RFP.<br />
In our attempts to solve the knapsack problem, we assembled 10 new lox sites with mutations in the 8 bp region and floxed 16 out of 21 possible lox site combinations with red fluorescent protein with these variant lox sites. We built a construct with a fluorescent protein gene downstream of TetA to test for the presence of a terminator within the TetA gene. Along the way, we built several tools to assist us in the wetlab. In addition, we recorded several observations regarding gene expression and codon optimization as we progressed. Characterizing and attempting to understand these foundational problems became one of the new focuses of this team.<br />
</p><br />
<a Name="Introduction"></a><br />
<h4> Introduction</h4><br />
<br />
<h4> What is the Knapsack problem?</h4><br />
<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;The knapsack problem is an NP-complete problem that asks whether: given a capacity and different weighted items, if there exists a subset of the items for which the sum of its values is equal to that of the capacity.<br><br><br />
<center><img src="https://static.igem.org/mediawiki/2010/d/da/Davidson-MissouriWknapsack.png" alt="" width=300/></center><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;There are several variations of the knapsack problem. The one that we are focusing on involves attempting to fit various items of various weights into a knapsack with a certain capacity. You have solved the knapsack problem if you manage to fill the knapsack to capacity. Other variations include looking for the closest possible value to the capacity that we can fill if the capacity itself cannot actually be reached.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;In order to help people understand the knapsack problem and its complexity, we have built a <a href="http://72.22.219.205/knapsack">knapsack game</a>. In tutorial mode this game will give the user hints to help solve the problem. The challenge mode of the game should give the user an appreciation of the complexity of the knapsack problem and the overall class of NP-complete problems.<br />
</p><br />
<h4> Why Bacterial Computing? </h4><br />
<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;In mathematics, there exists a class of complex mathematical problems, known as NP-complete problems, where there are no efficient algorithms to solve these problems other than exhaustive search of trying all the possible solutions one at a time and determining whether or not they are actually solutions. Bacterial computers offer a more efficient alternative approach to solving these NP-complete problems by using massive parallel computing. By engineering a Bacterial computer, the billions of cells can simultaneously try the possible solutions and determine whether they are in fact a solution to the given problem.<br />
<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Besides the much greater efficiency and speed, another advantage of bacterial computers is that it actually becomes more powerful as the number of cells increase from cell division. Despite the complexity of bacterial computing, it is quickly gaining popularity because of its many advantages over the traditional computer. <br />
</p><br />
<h4> Our Biological Design </h4><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Cells of identical genotype can produce different levels of protein. This variation is called noise. While noise is generally treated as a necessary evil in synthetic biology, we chose to use the stochastic nature of gene expression to our advantage.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;In order to control for the random nature of gene expression, we decided to implement a band pass filter. Essentially, this filter kills off cells that express either too much or two little of a chosen protein. Band pass filters harness and focus noise without eliminating it. We chose to use this system in order to help bridge the gap between a digital problem and an analog solution.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;The combination of the tetA gene and an environment containing the antibiotic tetracycline produces a natural band pass filter. The tetA gene provides a level of resistance to tetracycline by creating efflux pumps that help the cell excrete the otherwise fatal tetracycline. However, these pumps come with a cost. They make the cell membrane more porous and permeable. With too many pumps, the cell cannot maintain homeostasis and dies; with too few pumps, the cells cannot remove tetracycline from its cytoplasm and dies from blocked protein synthesis. Therefore, tetA is a band pass filter.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;With this band pass filter in mind, we decided to make each cell a knapsack. Overexpression of tetA would be analogous to exceeding the capacity of the knapsack. However, we still had to solve a fundamental problem: weights. We used a novel tool called codon optimization to assign weights. By rewriting the codons of the tetA gene, we could alter the number of pumps produced by different versions of the same gene in a given time frame.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;We planned to produce a chain of tetA modules with a different fluorescent protein attached to each one to act as visual and biochemical reporters. Each tetA and fluorescent color pair would be attached to a genetic switch in the form of the Cre/lox system. A switch being on would correspond to packing an item of that weight (color and tetA level) into the knapsack. Allowing this system to run and randomly flip switches on and off would ensure that all possible combinations of packed knapsacks would be generated. The tetA band pass filter could give us a narrow subsets of cells that solved the problem. We could then figure out how a cell packed the knapsack based on the fluorescent reporters.</p><br><br><br />
<center><img src="https://static.igem.org/mediawiki/2010/b/b3/Davidson-MissouriWIntro1.png" /></center><br />
<br><br />
<a Name="Summary and Outlook"></a><br />
<h4> Summary and Outlook</h4><br />
<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Our summer research project culminated with foundational advances in the fields of codon optimization, cre-lox characterization, and gene expression. The team successfully built 6 tetA constructs with varying levels of codon optimization and deoptimization. We also assembled eleven novel lox sites and characterized interactions between several of them in the presence of the cre protein. While choosing and characterizing reporters, we observed variations in gene expression due to environmental factors and inherent variability in bacterial cells. To assist our research in different ways, we designed tools such as the VeriPart, the Oligator, the Optimus, the Construct Simulator, and the Knapsack game.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;With the help of this integral research, we have the tools and knowledge to address the knapsack problem. In the future, we wish to further experiment with varying levels of codon optimization and gene placement. Finally, we wish to build and test different constructs to find the most efficient way to solving the knapsack problem.</p><br><br />
<br />
<div><br />
<a Name="References"></a><br />
<H4>References</H4><br />
<p><br />
Gwang Lee I, Izumu S. 1998. Role of nucleotide sequences of loxP spacer region in Cre-mediated recombination. Gene 216: 55-65.<br />
<br><br><br />
Jean L, Tamily A. W, Hyuno K, Ryan W. D, Ju L, Robyn A. B, Joshua R. S, Jeff W. L. 2007 Transgenic strategies for combinatorial expression of fluorescent proteins in the nervous systems. Nature [need volume and page numbers]<br />
<br><br><br />
Jesse M. Fox, Ivan Erill. 2010 Relative Codon Adaptation: A Generic Codon Bias Index for Prediction of Gene Expression. DNA Research [Need volume] 1-12. Avalaible from http://dnaresearch.oxfordjournals.org<br />
<br><br><br />
Ronald J, Harmen J. B, Mark G. 2003. Revisiting the codon adaptation index from a whole-genome perspective: analyzing the relationship between gene expression and codon occurrence in yeast using a variety of models. Nucleic Acid Research 31(8): 2242-2251. Available online from http://nar.oxfordjournals.org/cgi/content/full/31/8/2242<br />
<br><br><br />
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<div id="mission_box" style="padding:10px"> <h2> iGEM Davidson – MWSU 2010: Project </h2><br />
<a Name="abstract"></a> <br />
<h4>Abstract</h4> <br />
<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;The Cre-lox system is a tool that involves the splicing of a specific pair of DNA sequences called lox sites with an enzyme called Cre recombinase. We implicated this system in attempts to manipulate gene expression that mimics randomly "selecting objects" to fill a knapsack in order to solve the knapsack problem. To do this we needed a set of variant lox sites in order to create constructs that would yield different subsets of survival and fluorescence to optimally fill the knapsack. Initially the design to address the knapsack problem had several vital components using modules that consisted of lox sites that floxed the TetA gene and RFP.<br />
In our attempts to solve the knapsack problem, we assembled 10 new lox sites with mutations in the 8 bp region and floxed 16 out of 21 possible lox site combinations with red fluorescent protein with these variant lox sites. We built a construct with a fluorescent protein gene downstream of TetA to test for the presence of a terminator within the TetA gene. Along the way, we built several tools to assist us in the wetlab. In addition, we recorded several observations regarding gene expression and codon optimization as we progressed. Characterizing and attempting to understand these foundational problems became one of the new focuses of this team.<br />
</p><br />
<a Name="Introduction"></a><br />
<h4> Introduction</h4><br />
<br />
<h4> What is the Knapsack problem?</h4><br />
<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;The knapsack problem is an NP-complete problem that asks whether: given a capacity and different weighted items, if there exists a subset of the items for which the sum of its values is equal to that of the capacity.<br><br><br />
<center><img src="https://static.igem.org/mediawiki/2010/d/da/Davidson-MissouriWknapsack.png" alt="" width=300/></center><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;There are several variations of the knapsack problem. The one that we are focusing on involves attempting to fit various items of various weights into a knapsack with a certain capacity. You have solved the knapsack problem if you manage to fill the knapsack to capacity. Other variations include looking for the closest possible value to the capacity that we can fill if the capacity itself cannot actually be reached.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;In order to help people understand the knapsack problem and its complexity, we have built a <a href="http://72.22.219.205/knapsack">knapsack game</a>. In tutorial mode this game will give the user hints to help solve the problem. The challenge mode of the game should give the user an appreciation of the complexity of the knapsack problem and the overall class of NP-complete problems.<br />
</p><br />
<h4> Why Bacterial Computing? </h4><br />
<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;In mathematics, there exists a class of complex mathematical problems, known as NP-complete problems, where there are no efficient algorithms to solve these problems other than exhaustive search of trying all the possible solutions one at a time and determining whether or not they are actually solutions. Bacterial computers offer a more efficient alternative approach to solving these NP-complete problems by using massive parallel computing. By engineering a Bacterial computer, the billions of cells can simultaneously try the possible solutions and determine whether they are in fact a solution to the given problem.<br />
<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Besides the much greater efficiency and speed, another advantage of bacterial computers is that it actually becomes more powerful as the number of cells increase from cell division. Despite the complexity of bacterial computing, it is quickly gaining popularity because of its many advantages over the traditional computer. <br />
</p><br />
<h4> Our Biological Design </h4><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Cells of identical genotype can produce different levels of protein. This variation is called noise. While noise is generally treated as a necessary evil in synthetic biology, we chose to use the stochastic nature of gene expression to our advantage.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;In order to control for the random nature of gene expression, we decided to implement a band pass filter. Essentially, this filter kills off cells that express either too much or two little of a chosen protein. Band pass filters harness and focus noise without eliminating it. We chose to use this system in order to help bridge the gap between a digital problem and an analog solution.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;The combination of the tetA gene and an environment containing the antibiotic tetracycline produces a natural band pass filter. The tetA gene provides a level of resistance to tetracycline by creating efflux pumps that help the cell excrete the otherwise fatal tetracycline. However, these pumps come with a cost. They make the cell membrane more porous and permeable. With too many pumps, the cell cannot maintain homeostasis and dies; with too few pumps, the cells cannot remove tetracycline from its cytoplasm and dies from blocked protein synthesis. Therefore, tetA is a band pass filter.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;With this band pass filter in mind, we decided to make each cell a knapsack. Overexpression of tetA would be analogous to exceeding the capacity of the knapsack. However, we still had to solve a fundamental problem: weights. We used a novel tool called codon optimization to assign weights. By rewriting the codons of the tetA gene, we could alter the number of pumps produced by different versions of the same gene in a given time frame.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;We planned to produce a chain of tetA modules with a different fluorescent protein attached to each one to act as visual and biochemical reporters. Each tetA and fluorescent color pair would be attached to a genetic switch in the form of the Cre/lox system. A switch being on would correspond to packing an item of that weight (color and tetA level) into the knapsack. Allowing this system to run and randomly flip switches on and off would ensure that all possible combinations of packed knapsacks would be generated. The tetA band pass filter could give us a narrow subsets of cells that solved the problem. We could then figure out how a cell packed the knapsack based on the fluorescent reporters.</p><br />
<br><br />
<center><img src="https://static.igem.org/mediawiki/2010/b/b3/Davidson-MissouriWIntro1.png" /></center><br />
<br><br />
<a Name="Summary and Outlook"></a><br />
<h4> Summary and Outlook</h4><br />
<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Our summer research project culminated with foundational advances in the fields of codon optimization, cre-lox characterization, and gene expression. The team successfully built 6 tetA constructs with varying levels of codon optimization and deoptimization. We also assembled eleven novel lox sites and characterized interactions between several of them in the presence of the cre protein. While choosing and characterizing reporters, we observed variations in gene expression due to environmental factors and inherent variability in bacterial cells. To assist our research in different ways, we designed tools such as the VeriPart, the Oligator, the Optimus, the Construct Simulator, and the Knapsack game.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;With the help of this integral research, we have the tools and knowledge to address the knapsack problem. In the future, we wish to further experiment with varying levels of codon optimization and gene placement. Finally, we wish to build and test different constructs to find the most efficient way to solving the knapsack problem.</p><br><br />
<br />
<div><br />
<a Name="References"></a><br />
<H4>References</H4><br />
<p><br />
Gwang Lee I, Izumu S. 1998. Role of nucleotide sequences of loxP spacer region in Cre-mediated recombination. Gene 216: 55-65.<br />
<br><br><br />
Jean L, Tamily A. W, Hyuno K, Ryan W. D, Ju L, Robyn A. B, Joshua R. S, Jeff W. L. 2007 Transgenic strategies for combinatorial expression of fluorescent proteins in the nervous systems. Nature [need volume and page numbers]<br />
<br><br><br />
Jesse M. Fox, Ivan Erill. 2010 Relative Codon Adaptation: A Generic Codon Bias Index for Prediction of Gene Expression. DNA Research [Need volume] 1-12. Avalaible from http://dnaresearch.oxfordjournals.org<br />
<br><br><br />
Ronald J, Harmen J. B, Mark G. 2003. Revisiting the codon adaptation index from a whole-genome perspective: analyzing the relationship between gene expression and codon occurrence in yeast using a variety of models. Nucleic Acid Research 31(8): 2242-2251. Available online from http://nar.oxfordjournals.org/cgi/content/full/31/8/2242<br />
<br><br><br />
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</body></html></div>Eushiuhttp://2010.igem.org/Team:Davidson-MissouriW/ProjectTeam:Davidson-MissouriW/Project2010-07-29T14:42:05Z<p>Eushiu: </p>
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<div id="mission_box" style="padding:10px"> <h2> iGEM Davidson – MWSU 2010: Project </h2><br />
<a Name="abstract"></a> <br />
<h4>Abstract</h4> <br />
<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;The Cre-lox system is a tool that involves the splicing of a specific pair of DNA sequences called lox sites with an enzyme called Cre recombinase. We implicated this system in attempts to manipulate gene expression that mimics randomly "selecting objects" to fill a knapsack in order to solve the knapsack problem. To do this we needed a set of variant lox sites in order to create constructs that would yield different subsets of survival and fluorescence to optimally fill the knapsack. Initially the design to address the knapsack problem had several vital components using modules that consisted of lox sites that floxed the TetA gene and RFP.<br />
In our attempts to solve the knapsack problem, we assembled 10 new lox sites with mutations in the 8 bp region and floxed 16 out of 21 possible lox site combinations with red fluorescent protein with these variant lox sites. We built a construct with a fluorescent protein gene downstream of TetA to test for the presence of a terminator within the TetA gene. Along the way, we built several tools to assist us in the wetlab. In addition, we recorded several observations regarding gene expression and codon optimization as we progressed. Characterizing and attempting to understand these foundational problems became one of the new focuses of this team.<br />
</p><br />
<a Name="Introduction"></a><br />
<h4> Introduction</h4><br />
<br />
<h4> What is the Knapsack problem?</h4><br />
<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;The knapsack problem is an NP-complete problem that asks whether: given a capacity and different weighted items, if there exists a subset of the items for which the sum of its values is equal to that of the capacity.<br><br><br />
<center><img src="https://static.igem.org/mediawiki/2010/d/da/Davidson-MissouriWknapsack.png" alt="" width=300/></center><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;There are several variations of the knapsack problem. The one that we are focusing on involves attempting to fit various items of various weights into a knapsack with a certain capacity. You have solved the knapsack problem if you manage to fill the knapsack to capacity. Other variations include looking for the closest possible value to the capacity that we can fill if the capacity itself cannot actually be reached.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;In order to help people understand the knapsack problem and its complexity, we have built a <a href="http://72.22.219.205/knapsack">knapsack game</a>. In tutorial mode this game will give the user hints to help solve the problem. The challenge mode of the game should give the user an appreciation of the complexity of the knapsack problem and the overall class of NP-complete problems.<br />
</p><br />
<h4> Why Bacterial Computing? </h4><br />
<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;In mathematics, there exists a class of complex mathematical problems, known as NP-complete problems, where there are no efficient algorithms to solve these problems other than exhaustive search of trying all the possible solutions one at a time and determining whether or not they are actually solutions. Bacterial computers offer a more efficient alternative approach to solving these NP-complete problems by using massive parallel computing. By engineering a Bacterial computer, the billions of cells can simultaneously try the possible solutions and determine whether they are in fact a solution to the given problem.<br />
<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Besides the much greater efficiency and speed, another advantage of bacterial computers is that it actually becomes more powerful as the number of cells increase from cell division. Despite the complexity of bacterial computing, it is quickly gaining popularity because of its many advantages over the traditional computer. <br />
</p><br />
<h4> Our Biological Design </h4><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Cells of identical genotype can produce different levels of protein. This variation is called noise. While noise is generally treated as a necessary evil in synthetic biology, we chose to use the stochastic nature of gene expression to our advantage.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;In order to control for the random nature of gene expression, we decided to implement a band pass filter. Essentially, this filter kills off cells that express either too much or two little of a chosen protein. Band pass filters harness and focus noise without eliminating it. We chose to use this system in order to help bridge the gap between a digital problem and an analog solution.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;The combination of the tetA gene and an environment containing the antibiotic tetracycline produces a natural band pass filter. The tetA gene provides a level of resistance to tetracycline by creating efflux pumps that help the cell excrete the otherwise fatal tetracycline. However, these pumps come with a cost. They make the cell membrane more porous and permeable. With too many pumps, the cell cannot maintain homeostasis and dies; with too few pumps, the cells cannot remove tetracycline from its cytoplasm and dies from blocked protein synthesis. Therefore, tetA is a band pass filter.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;With this band pass filter in mind, we decided to make each cell a knapsack. Overexpression of tetA would be analogous to exceeding the capacity of the knapsack. However, we still had to solve a fundamental problem: weights. We used a novel tool called codon optimization to assign weights. By rewriting the codons of the tetA gene, we could alter the number of pumps produced by different versions of the same gene in a given time frame.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;We planned to produce a chain of tetA modules with a different fluorescent protein attached to each one to act as visual and biochemical reporters. Each tetA and fluorescent color pair would be attached to a genetic switch in the form of the Cre/lox system. A switch being on would correspond to packing an item of that weight (color and tetA level) into the knapsack. Allowing this system to run and randomly flip switches on and off would ensure that all possible combinations of packed knapsacks would be generated. The tetA band pass filter could give us a narrow subsets of cells that solved the problem. We could then figure out how a cell packed the knapsack based on the fluorescent reporters.</p><br />
<br />
<img src="https://static.igem.org/mediawiki/2010/b/b3/Davidson-MissouriWIntro1.png" /><br />
<br />
<a Name="Summary and Outlook"></a><br />
<h4> Summary and Outlook</h4><br />
<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Our summer research project culminated with foundational advances in the fields of codon optimization, cre-lox characterization, and gene expression. The team successfully built 6 tetA constructs with varying levels of codon optimization and deoptimization. We also assembled eleven novel lox sites and characterized interactions between several of them in the presence of the cre protein. While choosing and characterizing reporters, we observed variations in gene expression due to environmental factors and inherent variability in bacterial cells. To assist our research in different ways, we designed tools such as the VeriPart, the Oligator, the Optimus, the Construct Simulator, and the Knapsack game.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;With the help of this integral research, we have the tools and knowledge to address the knapsack problem. In the future, we wish to further experiment with varying levels of codon optimization and gene placement. Finally, we wish to build and test different constructs to find the most efficient way to solving the knapsack problem.</p><br><br />
<br />
<div><br />
<a Name="References"></a><br />
<H4>References</H4><br />
<p><br />
Gwang Lee I, Izumu S. 1998. Role of nucleotide sequences of loxP spacer region in Cre-mediated recombination. Gene 216: 55-65.<br />
<br><br><br />
Jean L, Tamily A. W, Hyuno K, Ryan W. D, Ju L, Robyn A. B, Joshua R. S, Jeff W. L. 2007 Transgenic strategies for combinatorial expression of fluorescent proteins in the nervous systems. Nature [need volume and page numbers]<br />
<br><br><br />
Jesse M. Fox, Ivan Erill. 2010 Relative Codon Adaptation: A Generic Codon Bias Index for Prediction of Gene Expression. DNA Research [Need volume] 1-12. Avalaible from http://dnaresearch.oxfordjournals.org<br />
<br><br><br />
Ronald J, Harmen J. B, Mark G. 2003. Revisiting the codon adaptation index from a whole-genome perspective: analyzing the relationship between gene expression and codon occurrence in yeast using a variety of models. Nucleic Acid Research 31(8): 2242-2251. Available online from http://nar.oxfordjournals.org/cgi/content/full/31/8/2242<br />
<br><br><br />
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<div id="mission_box" style="padding:10px"> <h2> iGEM Davidson – MWSU 2010: Project </h2><br />
<a Name="abstract"></a> <br />
<h4>Abstract</h4> <br />
<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;The Cre-lox system is a tool that involves the splicing of a specific pair of DNA sequences called lox sites with an enzyme called Cre recombinase. We implicated this system in attempts to manipulate gene expression that mimics randomly "selecting objects" to fill a knapsack in order to solve the knapsack problem. To do this we needed a set of variant lox sites in order to create constructs that would yield different subsets of survival and fluorescence to optimally fill the knapsack. Initially the design to address the knapsack problem had several vital components using modules that consisted of lox sites that floxed the TetA gene and RFP.<br />
In our attempts to solve the knapsack problem, we assembled 10 new lox sites with mutations in the 8 bp region and floxed 16 out of 21 possible lox site combinations with red fluorescent protein with these variant lox sites. We built a construct with a fluorescent protein gene downstream of TetA to test for the presence of a terminator within the TetA gene. Along the way, we built several tools to assist us in the wetlab. In addition, we recorded several observations regarding gene expression and codon optimization as we progressed. Characterizing and attempting to understand these foundational problems became one of the new focuses of this team.<br />
</p><br />
<a Name="Introduction"></a><br />
<h4> Introduction</h4><br />
<br />
<h4> What is the Knapsack problem?</h4><br />
<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;The knapsack problem is an NP-complete problem that asks whether: given a capacity and different weighted items, if there exists a subset of the items for which the sum of its values is equal to that of the capacity.<br><br><br />
<center><img src="https://static.igem.org/mediawiki/2010/d/da/Davidson-MissouriWknapsack.png" alt="" width=300/></center><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;There are several variations of the knapsack problem. The one that we are focusing on involves attempting to fit various items of various weights into a knapsack with a certain capacity. You have solved the knapsack problem if you manage to fill the knapsack to capacity. Other variations include looking for the closest possible value to the capacity that we can fill if the capacity itself cannot actually be reached.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;In order to help people understand the knapsack problem and its complexity, we have built a <a href="http://72.22.219.205/knapsack">knapsack game</a>. In tutorial mode this game will give the user hints to help solve the problem. The challenge mode of the game should give the user an appreciation of the complexity of the knapsack problem and the overall class of NP-complete problems.<br />
</p><br />
<h4> Why Bacterial Computing? </h4><br />
<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;In mathematics, there exists a class of complex mathematical problems, known as NP-complete problems, where there are no efficient algorithms to solve these problems other than exhaustive search of trying all the possible solutions one at a time and determining whether or not they are actually solutions. Bacterial computers offer a more efficient alternative approach to solving these NP-complete problems by using massive parallel computing. By engineering a Bacterial computer, the billions of cells can simultaneously try the possible solutions and determine whether they are in fact a solution to the given problem.<br />
<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Besides the much greater efficiency and speed, another advantage of bacterial computers is that it actually becomes more powerful as the number of cells increase from cell division. Despite the complexity of bacterial computing, it is quickly gaining popularity because of its many advantages over the traditional computer. <br />
</p><br />
<h4> Our Biological Design </h4><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Cells of identical genotype can produce different levels of protein. This variation is called noise. While noise is generally treated as a necessary evil in synthetic biology, we chose to use the stochastic nature of gene expression to our advantage.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;In order to control for the random nature of gene expression, we decided to implement a band pass filter. Essentially, this filter kills off cells that express either too much or two little of a chosen protein. Band pass filters harness and focus noise without eliminating it. We chose to use this system in order to help bridge the gap between a digital problem and an analog solution.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;The combination of the tetA gene and an environment containing the antibiotic tetracycline produces a natural band pass filter. The tetA gene provides a level of resistance to tetracycline by creating efflux pumps that help the cell excrete the otherwise fatal tetracycline. However, these pumps come with a cost. They make the cell membrane more porous and permeable. With too many pumps, the cell cannot maintain homeostasis and dies; with too few pumps, the cells cannot remove tetracycline from its cytoplasm and dies from blocked protein synthesis. Therefore, tetA is a band pass filter.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;With this band pass filter in mind, we decided to make each cell a knapsack. Overexpression of tetA would be analogous to exceeding the capacity of the knapsack. However, we still had to solve a fundamental problem: weights. We used a novel tool called codon optimization to assign weights. By rewriting the codons of the tetA gene, we could alter the number of pumps produced by different versions of the same gene in a given time frame.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;We planned to produce a chain of tetA modules with a different fluorescent protein attached to each one to act as visual and biochemical reporters. Each tetA and fluorescent color pair would be attached to a genetic switch in the form of the Cre/lox system. A switch being on would correspond to packing an item of that weight (color and tetA level) into the knapsack. Allowing this system to run and randomly flip switches on and off would ensure that all possible combinations of packed knapsacks would be generated. The tetA band pass filter could give us a narrow subsets of cells that solved the problem. We could then figure out how a cell packed the knapsack based on the fluorescent reporters.</p><br />
<br />
<a Name="Summary and Outlook"></a><br />
<h4> Summary and Outlook</h4><br />
<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Our summer research project culminated with foundational advances in the fields of codon optimization, cre-lox characterization, and gene expression. The team successfully built 6 tetA constructs with varying levels of codon optimization and deoptimization. We also assembled eleven novel lox sites and characterized interactions between several of them in the presence of the cre protein. While choosing and characterizing reporters, we observed variations in gene expression due to environmental factors and inherent variability in bacterial cells. To assist our research in different ways, we designed tools such as the VeriPart, the Oligator, the Optimus, the Construct Simulator, and the Knapsack game.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;With the help of this integral research, we have the tools and knowledge to address the knapsack problem. In the future, we wish to further experiment with varying levels of codon optimization and gene placement. Finally, we wish to build and test different constructs to find the most efficient way to solving the knapsack problem.</p><br><br />
<br />
<img src="https://static.igem.org/mediawiki/2010/b/b3/Davidson-MissouriWIntro1.png" /><br />
<div><br />
<a Name="References"></a><br />
<H4>References</H4><br />
<p><br />
Gwang Lee I, Izumu S. 1998. Role of nucleotide sequences of loxP spacer region in Cre-mediated recombination. Gene 216: 55-65.<br />
<br><br><br />
Jean L, Tamily A. W, Hyuno K, Ryan W. D, Ju L, Robyn A. B, Joshua R. S, Jeff W. L. 2007 Transgenic strategies for combinatorial expression of fluorescent proteins in the nervous systems. Nature [need volume and page numbers]<br />
<br><br><br />
Jesse M. Fox, Ivan Erill. 2010 Relative Codon Adaptation: A Generic Codon Bias Index for Prediction of Gene Expression. DNA Research [Need volume] 1-12. Avalaible from http://dnaresearch.oxfordjournals.org<br />
<br><br><br />
Ronald J, Harmen J. B, Mark G. 2003. Revisiting the codon adaptation index from a whole-genome perspective: analyzing the relationship between gene expression and codon occurrence in yeast using a variety of models. Nucleic Acid Research 31(8): 2242-2251. Available online from http://nar.oxfordjournals.org/cgi/content/full/31/8/2242<br />
<br><br><br />
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<div></div>Eushiuhttp://2010.igem.org/Team:Davidson-MissouriW/ProjectTeam:Davidson-MissouriW/Project2010-07-29T14:24:20Z<p>Eushiu: </p>
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<div id="mission_box" style="padding:10px"> <h2> iGEM Davidson – MWSU 2010: Project </h2><br />
<a Name="abstract"></a> <br />
<h3>Abstract</h3> <br />
<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;The Cre-lox system is a tool that involves the splicing of a specific pair of DNA sequences called lox sites with an enzyme called Cre recombinase. We implicated this system in attempts to manipulate gene expression that mimics randomly "selecting objects" to fill a knapsack in order to solve the knapsack problem. To do this we needed a set of variant lox sites in order to create constructs that would yield different subsets of survival and fluorescence to optimally fill the knapsack. Initially the design to address the knapsack problem had several vital components using modules that consisted of lox sites that floxed the TetA gene and RFP.<br />
In our attempts to solve the knapsack problem, we assembled 10 new lox sites with mutations in the 8 bp region and floxed 16 out of 21 possible lox site combinations with red fluorescent protein with these variant lox sites. We built a construct with a fluorescent protein gene downstream of TetA to test for the presence of a terminator within the TetA gene. Along the way, we built several tools to assist us in the wetlab. In addition, we recorded several observations regarding gene expression and codon optimization as we progressed. Characterizing and attempting to understand these foundational problems became one of the new focuses of this team.<br />
</p><br />
<a Name="Introduction"></a><br />
<h3> Introduction</h3><br />
<br />
<h4> What is the Knapsack problem?</h4><br />
<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;The knapsack problem is an NP-complete problem that asks whether: given a capacity and different weighted items, if there exists a subset of the items for which the sum of its values is equal to that of the capacity.<br><br><br />
<center><img src="https://static.igem.org/mediawiki/2010/d/da/Davidson-MissouriWknapsack.png" alt="" width=300/></center><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;There are several variations of the knapsack problem. The one that we are focusing on involves attempting to fit various items of various weights into a knapsack with a certain capacity. You have solved the knapsack problem if you manage to fill the knapsack to capacity. Other variations include looking for the closest possible value to the capacity that we can fill if the capacity itself cannot actually be reached.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;In order to help people understand the knapsack problem and its complexity, we have built a <a href="http://72.22.219.205/knapsack">knapsack game</a>. In tutorial mode this game will give the user hints to help solve the problem. The challenge mode of the game should give the user an appreciation of the complexity of the knapsack problem and the overall class of NP-complete problems.<br />
</p><br />
<h4> Why Bacterial Computing? </h4><br />
<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;In mathematics, there exists a class of complex mathematical problems, known as NP-complete problems, where there are no efficient algorithms to solve these problems other than exhaustive search of trying all the possible solutions one at a time and determining whether or not they are actually solutions. Bacterial computers offer a more efficient alternative approach to solving these NP-complete problems by using massive parallel computing. By engineering a Bacterial computer, the billions of cells can simultaneously try the possible solutions and determine whether they are in fact a solution to the given problem.<br />
<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Besides the much greater efficiency and speed, another advantage of bacterial computers is that it actually becomes more powerful as the number of cells increase from cell division. Despite the complexity of bacterial computing, it is quickly gaining popularity because of its many advantages over the traditional computer. <br />
</p><br />
<h4> Our Biological Design </h4><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Cells of identical genotype can produce different levels of protein. This variation is called noise. While noise is generally treated as a necessary evil in synthetic biology, we chose to use the stochastic nature of gene expression to our advantage.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;In order to control for the random nature of gene expression, we decided to implement a band pass filter. Essentially, this filter kills off cells that express either too much or two little of a chosen protein. Band pass filters harness and focus noise without eliminating it. We chose to use this system in order to help bridge the gap between a digital problem and an analog solution.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;The combination of the tetA gene and an environment containing the antibiotic tetracycline produces a natural band pass filter. The tetA gene provides a level of resistance to tetracycline by creating efflux pumps that help the cell excrete the otherwise fatal tetracycline. However, these pumps come with a cost. They make the cell membrane more porous and permeable. With too many pumps, the cell cannot maintain homeostasis and dies; with too few pumps, the cells cannot remove tetracycline from its cytoplasm and dies from blocked protein synthesis. Therefore, tetA is a band pass filter.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;With this band pass filter in mind, we decided to make each cell a knapsack. Overexpression of tetA would be analogous to exceeding the capacity of the knapsack. However, we still had to solve a fundamental problem: weights. We used a novel tool called codon optimization to assign weights. By rewriting the codons of the tetA gene, we could alter the number of pumps produced by different versions of the same gene in a given time frame.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;We planned to produce a chain of tetA modules with a different fluorescent protein attached to each one to act as visual and biochemical reporters. Each tetA and fluorescent color pair would be attached to a genetic switch in the form of the Cre/lox system. A switch being on would correspond to packing an item of that weight (color and tetA level) into the knapsack. Allowing this system to run and randomly flip switches on and off would ensure that all possible combinations of packed knapsacks would be generated. The tetA band pass filter could give us a narrow subsets of cells that solved the problem. We could then figure out how a cell packed the knapsack based on the fluorescent reporters.</p><br />
<br />
<a Name="Summary and Outlook"></a><br />
<h4> Summary and Outlook</h4><br />
<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Our summer research project culminated with foundational advances in the fields of codon optimization, cre-lox characterization, and gene expression. The team successfully built 6 tetA constructs with varying levels of codon optimization and deoptimization. We also assembled eleven novel lox sites and characterized interactions between several of them in the presence of the cre protein. While choosing and characterizing reporters, we observed variations in gene expression due to environmental factors and inherent variability in bacterial cells. To assist our research in different ways, we designed tools such as the VeriPart, the Oligator, the Optimus, the Construct Simulator, and the Knapsack game.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;With the help of this integral research, we have the tools and knowledge to address the knapsack problem. In the future, we wish to further experiment with varying levels of codon optimization and gene placement. Finally, we wish to build and test different constructs to find the most efficient way to solving the knapsack problem.</p><br><br />
<br />
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<div id="mission_box" style="padding:10px"> <h2> iGEM Davidson – MWSU 2010: Project </h2><br />
<a Name="abstract"></a> <br />
<h3>Abstract</h3> <br />
<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;The Cre-lox system is a tool that involves the splicing of a specific pair of DNA sequences called lox sites with an enzyme called Cre recombinase. We implicated this system in attempts to manipulate gene expression that mimics randomly "selecting objects" to fill a knapsack in order to solve the knapsack problem. To do this we needed a set of variant lox sites in order to create constructs that would yield different subsets of survival and fluorescence to optimally fill the knapsack. Initially the design to address the knapsack problem had several vital components using modules that consisted of lox sites that floxed the TetA gene and RFP.<br />
In our attempts to solve the knapsack problem, we assembled 10 new lox sites with mutations in the 8 bp region and floxed 16 out of 21 possible lox site combinations with red fluorescent protein with these variant lox sites. We built a construct with a fluorescent protein gene downstream of TetA to test for the presence of a terminator within the TetA gene. Along the way, we built several tools to assist us in the wetlab. In addition, we recorded several observations regarding gene expression and codon optimization as we progressed. Characterizing and attempting to understand these foundational problems became one of the new focuses of this team.<br />
</p><br />
<a Name="Introduction"></a><br />
<h3> Introduction</h3><br />
<br />
<h4> What is the Knapsack problem?</h4><br />
<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;The knapsack problem is an NP-complete problem that asks whether: given a capacity and different weighted items, if there exists a subset of the items for which the sum of its values is equal to that of the capacity.<br><br><br />
<center><img src="https://static.igem.org/mediawiki/2010/d/da/Davidson-MissouriWknapsack.png" alt="" width=300/></center><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;There are several variations of the knapsack problem. The one that we are focusing on involves attempting to fit various items of various weights into a knapsack with a certain capacity. You have solved the knapsack problem if you manage to fill the knapsack to capacity. Other variations include looking for the closest possible value to the capacity that we can fill if the capacity itself cannot actually be reached.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;In order to help people understand the knapsack problem and its complexity, we have built a <a href="http://72.22.219.205/knapsack">knapsack game</a>. In tutorial mode this game will give the user hints to help solve the problem. The challenge mode of the game should give the user an appreciation of the complexity of the knapsack problem and the overall class of NP-complete problems.<br />
</p><br />
<h4> Why Bacterial Computing? </h4><br />
<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;In mathematics, there exists a class of complex mathematical problems, known as NP-complete problems, where there are no efficient algorithms to solve these problems other than exhaustive search of trying all the possible solutions one at a time and determining whether or not they are actually solutions. Bacterial computers offer a more efficient alternative approach to solving these NP-complete problems by using massive parallel computing. By engineering a Bacterial computer, the billions of cells can simultaneously try the possible solutions and determine whether they are in fact a solution to the given problem.<br />
<br><br />
<center><img src="https://static.igem.org/mediawiki/2010/a/a9/Davidson-MissouriWE-Coli_Bacteria_pic11.jpg"></center><br />
<br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Besides the much greater efficiency and speed, another advantage of bacterial computers is that it actually becomes more powerful as the number of cells increase from cell division. Despite the complexity of bacterial computing, it is quickly gaining popularity because of its many advantages over the traditional computer. <br />
</p><br />
<h4> Our Biological Design </h4><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Cells of identical genotype can produce different levels of protein. This variation is called noise. While noise is generally treated as a necessary evil in synthetic biology, we chose to use the stochastic nature of gene expression to our advantage.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;In order to control for the random nature of gene expression, we decided to implement a band pass filter. Essentially, this filter kills off cells that express either too much or two little of a chosen protein. Band pass filters harness and focus noise without eliminating it. We chose to use this system in order to help bridge the gap between a digital problem and an analog solution.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;The combination of the tetA gene and an environment containing the antibiotic tetracycline produces a natural band pass filter. The tetA gene provides a level of resistance to tetracycline by creating efflux pumps that help the cell excrete the otherwise fatal tetracycline. However, these pumps come with a cost. They make the cell membrane more porous and permeable. With too many pumps, the cell cannot maintain homeostasis and dies; with too few pumps, the cells cannot remove tetracycline from its cytoplasm and dies from blocked protein synthesis. Therefore, tetA is a band pass filter.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;With this band pass filter in mind, we decided to make each cell a knapsack. Overexpression of tetA would be analogous to exceeding the capacity of the knapsack. However, we still had to solve a fundamental problem: weights. We used a novel tool called codon optimization to assign weights. By rewriting the codons of the tetA gene, we could alter the number of pumps produced by different versions of the same gene in a given time frame.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;We planned to produce a chain of tetA modules with a different fluorescent protein attached to each one to act as visual and biochemical reporters. Each tetA and fluorescent color pair would be attached to a genetic switch in the form of the Cre/lox system. A switch being on would correspond to packing an item of that weight (color and tetA level) into the knapsack. Allowing this system to run and randomly flip switches on and off would ensure that all possible combinations of packed knapsacks would be generated. The tetA band pass filter could give us a narrow subsets of cells that solved the problem. We could then figure out how a cell packed the knapsack based on the fluorescent reporters.</p><br />
<br />
<a Name="Summary and Outlook"></a><br />
<h4> Summary and Outlook</h4><br />
<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Our summer research project culminated with foundational advances in the fields of codon optimization, cre-lox characterization, and gene expression. The team successfully built 6 tetA constructs with varying levels of codon optimization and deoptimization. We also assembled eleven novel lox sites and characterized interactions between several of them in the presence of the cre protein. While choosing and characterizing reporters, we observed variations in gene expression due to environmental factors and inherent variability in bacterial cells. To assist our research in different ways, we designed tools such as the VeriPart, the Oligator, the Optimus, the Construct Simulator, and the Knapsack game.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;With the help of this integral research, we have the tools and knowledge to address the knapsack problem. In the future, we wish to further experiment with varying levels of codon optimization and gene placement. Finally, we wish to build and test different constructs to find the most efficient way to solving the knapsack problem.</p><br><br />
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</body></html></div>Eushiuhttp://2010.igem.org/Team:Davidson-MissouriW/ProjectTeam:Davidson-MissouriW/Project2010-07-29T14:15:05Z<p>Eushiu: </p>
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<div id="mission_box" style="padding:10px"> <h2> iGEM Davidson – MWSU 2010: Project </h2><br />
<a Name="abstract"></a> <br />
<h3>Abstract</h3> <br />
<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;The Cre-lox system is a tool that involves the splicing of a specific pair of DNA sequences called lox sites with an enzyme called Cre recombinase. We implicated this system in attempts to manipulate gene expression that mimics randomly "selecting objects" to fill a knapsack in order to solve the knapsack problem. To do this we needed a set of variant lox sites in order to create constructs that would yield different subsets of survival and fluorescence to optimally fill the knapsack. Initially the design to address the knapsack problem had several vital components using modules that consisted of lox sites that floxed the TetA gene and RFP.<br />
In our attempts to solve the knapsack problem, we assembled 10 new lox sites with mutations in the 8 bp region and floxed 16 out of 21 possible lox site combinations with red fluorescent protein with these variant lox sites. We built a construct with a fluorescent protein gene downstream of TetA to test for the presence of a terminator within the TetA gene. Along the way, we built several tools to assist us in the wetlab. In addition, we recorded several observations regarding gene expression and codon optimization as we progressed. Characterizing and attempting to understand these foundational problems became one of the new focuses of this team.<br />
</p><br />
<a Name="Introduction"></a><br />
<h3> Introduction</h3><br />
<br />
<h4> What is the Knapsack problem?</h4><br />
<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;The knapsack problem is an NP-complete problem that asks whether: given a capacity and different weighted items, if there exists a subset of the items for which the sum of its values is equal to that of the capacity.<br><br><br />
<center><img src="https://static.igem.org/mediawiki/2010/d/da/Davidson-MissouriWknapsack.png" alt="" width=300/></center><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;There are several variations of the knapsack problem. The one that we are focusing on involves attempting to fit various items of various weights into a knapsack with a certain capacity. You have solved the knapsack problem if you manage to fill the knapsack to capacity. Other variations include looking for the closest possible value to the capacity that we can fill if the capacity itself cannot actually be reached.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;In order to help people understand the knapsack problem and its complexity, we have built a <a href="http://72.22.219.205/knapsack">knapsack game</a>. In tutorial mode this game will give the user hints to help solve the problem. The challenge mode of the game should give the user an appreciation of the complexity of the knapsack problem and the overall class of NP-complete problems.<br />
</p><br />
<h4> Why Bacterial Computing? </h4><br />
<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;In mathematics, there exists a class of complex mathematical problems, known as NP-complete problems, where there are no efficient algorithms to solve these problems other than exhaustive search of trying all the possible solutions one at a time and determining whether or not they are actually solutions. Bacterial computers offer a more efficient alternative approach to solving these NP-complete problems by using massive parallel computing. By engineering a Bacterial computer, the billions of cells can simultaneously try the possible solutions and determine whether they are in fact a solution to the given problem.<br />
<br><br />
<img src="https://static.igem.org/mediawiki/2010/a/a9/Davidson-MissouriWE-Coli_Bacteria_pic11.jpg"><br />
<br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Besides the much greater efficiency and speed, another advantage of bacterial computers is that it actually becomes more powerful as the number of cells increase from cell division. Despite the complexity of bacterial computing, it is quickly gaining popularity because of its many advantages over the traditional computer. <br />
</p><br />
<h4> Our Biological Design </h4><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Cells of identical genotype can produce different levels of protein. This variation is called noise. While noise is generally treated as a necessary evil in synthetic biology, we chose to use the stochastic nature of gene expression to our advantage.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;In order to control for the random nature of gene expression, we decided to implement a band pass filter. Essentially, this filter kills off cells that express either too much or two little of a chosen protein. Band pass filters harness and focus noise without eliminating it. We chose to use this system in order to help bridge the gap between a digital problem and an analog solution.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;The combination of the tetA gene and an environment containing the antibiotic tetracycline produces a natural band pass filter. The tetA gene provides a level of resistance to tetracycline by creating efflux pumps that help the cell excrete the otherwise fatal tetracycline. However, these pumps come with a cost. They make the cell membrane more porous and permeable. With too many pumps, the cell cannot maintain homeostasis and dies; with too few pumps, the cells cannot remove tetracycline from its cytoplasm and dies from blocked protein synthesis. Therefore, tetA is a band pass filter.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;With this band pass filter in mind, we decided to make each cell a knapsack. Overexpression of tetA would be analogous to exceeding the capacity of the knapsack. However, we still had to solve a fundamental problem: weights. We used a novel tool called codon optimization to assign weights. By rewriting the codons of the tetA gene, we could alter the number of pumps produced by different versions of the same gene in a given time frame.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;We planned to produce a chain of tetA modules with a different fluorescent protein attached to each one to act as visual and biochemical reporters. Each tetA and fluorescent color pair would be attached to a genetic switch in the form of the Cre/lox system. A switch being on would correspond to packing an item of that weight (color and tetA level) into the knapsack. Allowing this system to run and randomly flip switches on and off would ensure that all possible combinations of packed knapsacks would be generated. The tetA band pass filter could give us a narrow subsets of cells that solved the problem. We could then figure out how a cell packed the knapsack based on the fluorescent reporters.</p><br />
<br />
<a Name="Summary and Outlook"></a><br />
<h4> Summary and Outlook</h4><br />
<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Our summer research project culminated with foundational advances in the fields of codon optimization, cre-lox characterization, and gene expression. The team successfully built 6 tetA constructs with varying levels of codon optimization and deoptimization. We also assembled eleven novel lox sites and characterized interactions between several of them in the presence of the cre protein. While choosing and characterizing reporters, we observed variations in gene expression due to environmental factors and inherent variability in bacterial cells. To assist our research in different ways, we designed tools such as the VeriPart, the Oligator, the Optimus, the Construct Simulator, and the Knapsack game.<br><br><br />
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;With the help of this integral research, we have the tools and knowledge to address the knapsack problem. In the future, we wish to further experiment with varying levels of codon optimization and gene placement. Finally, we wish to build and test different constructs to find the most efficient way to solving the knapsack problem.</p><br><br />
<br />
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</body></html></div>Eushiuhttp://2010.igem.org/File:Davidson-MissouriWE-Coli_Bacteria_pic11.jpgFile:Davidson-MissouriWE-Coli Bacteria pic11.jpg2010-07-29T14:13:43Z<p>Eushiu: </p>
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