Biomod/2011/PSU/BlueGenes/overview

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

Overview 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. Project Goal The goal is to develop a computational algorithm for structural characterization of synthetic DNA. Successful completion of the goal would allow for fluctuation calculations of any synthetic DNA structure so long as it is in PDB format. Additionally, the results should allow for feedback of the structure. Tools of the Trade caDNAno MATLAB NanoEngineer-1 Excel CanDo Why Synthetic DNA? Nature has already figured out how to assemble things that have high complexity. Up to now, nanostructures that people have made are not complex and do not have the level of specificity that nature does. Synthetic DNA comes to play because it allows for this high level of specificity that we seek. Previous research has shown that 3D structures can be created using DNA. Thus synthetic DNA structures can be used to create structures of desired complexity and specificity.