Molecular Computation of Solutions to Combinatorial Problems

Adleman, Leonard M.

doi:10.1126/science.7973651

Cited by 3,909 publications

(2,249 citation statements)

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“…9 In principle, arbitrarily complex objects can be constructed using algorithmic self-assembly, 10 and objects including large finite-sized shapes and some circuit diagrams can be assembled from a small number of components. [11][12][13][14] Aperiodic one- 15,16 and two-dimensional 17,18 structures have been algorithmically self-assembled from programmable crystal monomers constructed from DNA tiles.…”

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confidence: 99%

Reducing Facet Nucleation during Algorithmic Self-Assembly

et al. 2007

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Algorithmic self-assembly, a generalization of crystal growth, has been proposed as a mechanism for bottom-up fabrication of complex nanostructures and autonomous DNA computation. In principle, growth can be programmed by designing a set of molecular tiles with binding interactions that enforce assembly rules. In practice, however, errors during assembly cause undesired products, drastically reducing yields. Here we provide experimental evidence that assembly can be made more robust to errors by adding redundant tiles that "proofread" assembly. We construct DNA tile sets for two methods, uniform and snaked proofreading. While both tile sets are predicted to reduce errors during growth, the snaked proofreading tile set is also designed to reduce nucleation errors on crystal facets. Using atomic force microscopy to image growth of proofreading tiles on ribbon-like crystals presenting long facets, we show that under the physical conditions we studied the rate of facet nucleation is 4-fold smaller for snaked proofreading tile sets than for uniform proofreading tile sets.

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confidence: 99%

Reducing Facet Nucleation during Algorithmic Self-Assembly

et al. 2007

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“…Biological Computing has attracted interest since Adleman's original paper [2] because of its potential for high performance parallel computation. Lipton [12], Adleman [3], and others have proposed that the intrinsic power of processing large numbers (108) molecules in parallel may permit DNA computers to solve previously intractable problems.…”

Section: Chapter 1 Intoductionmentioning

confidence: 99%

Design and implementation of computational systems based on programmed mutagenesis

Khodor

Gifford

1999

Biosystems

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We introduce programmed mutagenesis, a technique for rewriting DNA strands according to programmed rules. We present the Unary Counter-a simple computational system based on programmed mutagenesis.We present the experimental results of the first two cycles of the unary counter which show that string rewriting by programmed mutagenesis is possible. We discuss the two enzyme system which allows to implement programmed mutagenesis by thermocycling a single reaction in a single test tube.We discuss the sources of rewrite rule specificity within the framework of programmed mutagenesis and present experimental results which indicate that it is possible to guarantee the specificity of the rewrite rules, and thus the correctness of the computation.We demonstrate that two oligonucleotides annealing to the template next to each other can ligate and extend to form the correct product. We argue that the ability to have oligonucleotides in close proximity function properly, together with the MIMD nature of the programmed mutagenesis systems provides evidence that parallel and nondeterministic computations are possible with programmed mutagenesis.Finally, we discuss the significance of our results and outline directions for future work.

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“…(Word processors in the modern sense were not yet in common use). The recent interest was triggered by Adleman's recognition and experimental demonstration that the PCR technique allows for a formal model of DNA computing that affords massively powerful fine grained parallelism (Adleman, 1994).…”

Section: Introductionmentioning

confidence: 99%

DNA as a vehicle for the self-assembly model of computing

Conrad

Zauner

1998

Biosystems

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A DNA version of the self-assembly model of computing, feasible using currently available laboratory techniques, is proposed. Input signals are coded into unmethylated and methylated oligonucleotides which then hybridize with a backbone that contains complementary sequences. Different input signal patterns are thus represented as DNA duplexes with distinctly different conformational dynamics, in particular different equilibria of B and Z DNA. The pattern classification activity of the system is mediated by the interactions that lead to the secondary structural organization. Circular dichroism may be used for readout.

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Molecular Computation of Solutions to Combinatorial Problems

Cited by 3,909 publications

References 9 publications

Reducing Facet Nucleation during Algorithmic Self-Assembly

Reducing Facet Nucleation during Algorithmic Self-Assembly

Design and implementation of computational systems based on programmed mutagenesis

DNA as a vehicle for the self-assembly model of computing

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