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Published 1998 | Published + Submitted
Journal Article Open

A sticker-based model for DNA computation


We introduce a new model of molecular computation that we call the sticker model. Like many previous proposals it makes use of DNA strands as the physical substrate in which information is represented and of separation by hybridization as a central mechanism. However, unlike previous models, the stickers model has a random access memory that requires no strand extension and uses no enzymes; also (at least in theory), its materials are reusable. The paper describes computation under the stickers model and discusses possible means for physically implementing each operation. Finally, we go on to propose a specific machine architecture for implementing the stickers model as a microprocessor-controlled parallel robotic workstation. In the course of this development a number of previous general concerns about molecular computation (Smith, 1996; Hartmanis, 1995; Linial ct al., 1995) are addressed. First, it is clear that general-purpose algorithms can be implemented by DNA-based computers, potentially solving a wide class of search problems. Second, we Rnd that there are challenging problems, for which only modest volumes of DNA should suffice. Third, we demonstrate that the formation and breaking of covalent bonds is not intrinsic to DNA-based computation. Fourth, we show that a single essential biotechnology, sequence-specific separation, suffices for constructing a general-purpose molecular computer. Concerns about errors in this separation operation and means to reduce them are addressed elsewhere (Karp ct at, 1995; Rowels and Winfree, 1999). Despite these encouraging theoretical advances, we emphasize that substantial engineering challenges remain at almost all stages and that the ultimate success or failure of DNA computing will certainly depend on whether these challenges can be met in laboratory investigations.

Additional Information

© 1998 Mary Ann Leibert, Inc. Received for publication November 9, 1997; accepted as revised June 6, 1998. We would like to express their appreciation to Professor John Baldeschwieler for his contributions to this paper through early discussions of this work. S.R. and E.W. are also grateful to their advisor, Professor John Hopfield, for his perpetual wisdom and long-term advice. Thanks to Kazuyoshi Harada for some corrections. S .R. is supported in part by the Center for Neuromorphic Systems Engineering as a part of the National Science Foundation Engineering Research Center Program under grant EEC-9402726 and by the Natural Sciences and Engineering Research Council of Canada. E.W. is supported in part by National Institute for Mental Health (NIMH) Training Grant no. 5 T32 MH 19138-06; also by General Motors' Technology Research Partnerships program. L.M.A., N.V.C., and P.W.K.R. are supported in part by grants from the National Science Foundation (CCR-9403662) and Sloan Foundation.

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Submitted - stickers_1_.pdf


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