Biomod/2011/PSU/BlueGenes: Difference between revisions

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(New page: *Institution: Pennsylvania State University, University Park, PA, USA *Faculty mentor: Prof. Melik Demirel *Team Members ** Jiayi Liang ** Steven Hahn ** William Schuppert == Project == *...)
 
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*Institution: Pennsylvania State University, University Park, PA, USA
{{Template:Biomod2011_PSU}}
*Faculty mentor: Prof. Melik Demirel
*Team Members
** Jiayi Liang
** Steven Hahn
** William Schuppert


== Project ==
<center>
*Our goal is to develop a computational algorithm for structural characterization of synthetic DNA.  If our process is successful, we would like to apply it to biomimetic transport.  
Welcome to our wiki page! Here you will find lots of information regarding our project! Please use the navigation bar above.
</center>


===Abstract===
''Gaussian Network Modeling for Synthetic DNA''


== Papers and Theory ==
Elastic Network Modeling (ENM) has been used to determine the flexibility of proteins and other macromolecules, but little has been done to advance this technique to synthetic DNA. When working on the nanoscale where thermal fluctuations are much more prominent, a better method of predicting the flexibility must be used to create realistic models. ENM, specifically Gaussian Network Modeling (GNM), have thus been applied to studying the flexibility of synthetic DNA. We have accurately predicted the flexibility of these structures using GNM and have shown that it allows for much greater control of the design and thus functionality. We then propose a synthetic DNA surface in which nanoliter droplet transportation may be possible.
*Link to Dr.Demirel's Research Page -- [http://www.personal.psu.edu/mcd18/]




== Programs ==


===Video===
<html><iframe width="560" height="315" src="http://openwetware.org/images/7/75/TeamBlueGenesMovie.swf" frameborder="0" allowfullscreen></iframe></iframe></html>


== Abstract ==
YouTube version: http://www.youtube.com/watch?v=ySqqAUN_avk
Title: Gaussian Network Modeling for Synthetic DNA
 
Elastic Network Modeling (ENM) has been used to determine the flexibility of proteins and other macromolecules, but little has been done to advance this technique to synthetic DNA. When working on the nanoscale where thermal fluctuations are much more prominent, a better method of predicting the flexibility must be used to create realistic models.  ENM, specifically Gaussian Network Modeling (GNM), have thus been applied to studying the flexibility of synthetic DNA.  We have accurately predicted the flexibility of these structures using GNM and have shown that it allows for much greater control of the design and thus functionality. We then propose a synthetic DNA surface in which nanoliter droplet transportation may be possible.

Latest revision as of 19:42, 3 November 2011


Home     ::: Overview     :::     Methods     :::     Results     :::     Application     :::     Literature     :::     Team

Welcome to our wiki page! Here you will find lots of information regarding our project! Please use the navigation bar above.

Abstract

Gaussian Network Modeling for Synthetic DNA

Elastic Network Modeling (ENM) has been used to determine the flexibility of proteins and other macromolecules, but little has been done to advance this technique to synthetic DNA. When working on the nanoscale where thermal fluctuations are much more prominent, a better method of predicting the flexibility must be used to create realistic models. ENM, specifically Gaussian Network Modeling (GNM), have thus been applied to studying the flexibility of synthetic DNA. We have accurately predicted the flexibility of these structures using GNM and have shown that it allows for much greater control of the design and thus functionality. We then propose a synthetic DNA surface in which nanoliter droplet transportation may be possible.


Video

<html><iframe width="560" height="315" src="http://openwetware.org/images/7/75/TeamBlueGenesMovie.swf" frameborder="0" allowfullscreen></iframe></iframe></html>

YouTube version: http://www.youtube.com/watch?v=ySqqAUN_avk

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