Self-Assembled DNA Nanostructures for Molecular Scale Patterning, Computatio and Motors

John H. Rief

Duke University Computer Science Department

2004. 11. 8



Self-assembly is the spontaneous self-ordering of substructures into superstructures driven by the selective affinity of the substructures. DNA provides a molecular scale material for programmable self-assembly, using the selective affinity of pairs of DNA strands to form DNA nanostructures. DNA self-assembly is the most advanced and versatile system that has been experimentally demonstrated for programmable construction of patterned systems on the molecular scale. The methodology of DNA self-assembly begins with the synthesis of single-strand DNA molecules that self-assemble into macromolecular building blocks called DNA tiles. These tiles have sticky ends that match the sticky ends of other DNA tiles, facilitating further assembly into larger structures known as DNA tiling lattices. In principle, DNA tiling assemblies can form any computable two or three-dimensional pattern, however complex, with the appropriate choice of the tiles' component DNA. Two-dimensional DNA tiling lattices composed of hundreds of thousands of tiles have been demonstrated experimentally. These assemblies can be used as scaffolding on which to position molecular electronics and robotics components with precision and specificity. This programmability renders the scaffolding have the patterning required for fabricating complex devices made of these components. We overview the evolution of DNA self-assembly techniques from pure theory, through simulation and design, and then to experimental practice. We will begin with an overview of theoretical models and algorithms for DNA lattice self-assembly. Then we describe our software for the simulation and design of DNA tiling assemblies and DNA nanomechanical devices. As an example, we discuss models and algorithms for the key problem of error control in DNA lattice self-assembly, as well as the computer simulation of these methods for error control. We will then briefly discuss our experimental laboratory demonstrations, including those using the designs derived by our software. These experimental demonstrations of DNA self-assemblies include the assembly of patterned objects at the molecular scale, the execution of molecular computations, and freely running autonomous DNA motors.


- Short Bio

John Reif is Hollis Edens Distinguished Professor in Trinity College of Arts and Sciences at Duke University since 2003 and Professor of Computer Science at Duke University since 1986. Previusly he was Associate Professor, Harvard University. He received a Ph.D. Applied Mathematics in 1977 from Harvard University, an M.S. Applied Mathematics in 1975 from Harvard Univ. and was awarded a magna cum laude B.S. in Applied Math & CS in 1973 from Tufts University. He is Fellow, Association for the Advancement of Science(AAAS) since 2003, Fellow of IEEE since 1993, Fellow of ACM since 1996. Fellow of Inst. of Combinatorics since 1991. Although originally primarily a theoretical computer scientist, he also has made a number of contributions to practical areas of computer science including parallel architectures, data compression, robotics, and optical computing. He has also worked for many years on the development and analysis of parallel algorithms for various fundamental problems including the solution of large sparse systems, sorting, graph problems, data compression, etc. He has developed algorithms and lower bounds for a large variety of robotic motion planning problems, and provided the first known computational complexity results for a robotic motion planning problem. He has also developed a wide range of efficient parallel algorithms, particularly randomized parallel algorithms. Recently Reif has worked on DNA computing and DNA nanostructures. He is the author of over 200 papers and has edited three books on synthesis of parallel and randomized algorithms. Recently Reif has worked on DNA computing and DNA nanostructures.


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Last update: November. 18, 2004