Structural DNA Nanotechnology: Molecular Construction and Computation View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2005

AUTHORS

Ruojie Sha , Xiaoping Zhang , Shiping Liao , Pamela E. Constantinou , Baoquan Ding , Tong Wang , Alejandra V. Garibotti , Hong Zhong , Lisa B. Israel , Xing Wang , Gang Wu , Banani Chakraborty , Junghuei Chen , Yuwen Zhang , Hao Yan , Zhiyong Shen , Wanqiu Shen , Phiset Sa-Ardyen , Jens Kopatsch , Jiwen Zheng , Philip S. Lukeman , William B. Sherman , Chengde Mao , Natasha Jonoska , Nadrian C. Seeman

ABSTRACT

Structural DNA nanotechnology entails the construction of objects, lattices and devices from branched DNA molecules. Branched DNA molecules open the way for the construction of a variety of N-connected motifs. These motifs can be joined by cohesive interactions to produce larger constructs in a bottom-up approach to nanoconstruction. The first objects produced by this approach were stick polyhedra and topological targets, such as knots and Borromean rings. These were followed by periodic arrays with programmable patterns. It is possible to exploit DNA structural transitions and sequence-specific binding to produce a variety of DNA nanomechanical devices, which include a bipedal walker and a machine that emulates the translational capabilities of the ribosome. Much of the promise of this methodology involves the use of DNA to scaffold other materials, such as biological macromolecules, nanoelectronic components, and polymers. These systems are designed to lead to improvements in crystallography, computation and the production of diverse and exotic materials. Branched DNA can be used to emulate Wang tiles, and it can be used to construct arbitrary irregular graphs and to address their colorability. More... »

PAGES

20-31

Book

TITLE

Unconventional Computation

ISBN

978-3-540-29100-8
978-3-540-32022-7

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/11560319_4

DOI

http://dx.doi.org/10.1007/11560319_4

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1026124652


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