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ABSTRACT: We explore the information processing capabilities and efficiency of DNA computations by giving two different types of implementations of finite-state machines. A ligation-based approach allows input of arbitrary length and can be readily implemented with current biotechnology, but requires sequential input feed and different molecules for different machines. In a second implementation not based on ligation, transitions are represented by reusable molecules, and the input, coded as a molecule, can be introduced at once. We extend the technique for programmable fault-tolerant implementation of nondeterministic finite-state machines by enforcings the basic conditions in the subset constructions that permit efficient computation. All implementations allow optical extraction of the status of the machine.
04/2006: pages 56-74;
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Laura L,
Erik Winfree,
Richard Lipton,
R. Deaton, R C Murphy,
J. A. Rose,
Max H. Garzon,
Donald R. Franceschetti,
S E Stevens,
Jr. A Dna,
A D Ellington,
M P Robertson,
J. Bull In
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ABSTRACT: Computation, DIMACS, Piscataway NJ, January 1999. Available on request from DIMACS. URL: http://dimacs.rutgers.edu/Workshops/Evolution. [13] Laura F. Landweber and Richard J. Lipton, editors. DNA Based Computers II: DIMACS Workshop, June 10-12, 1996, volume 44 of DIMACS series in discrete mathematics and theoretical computer science, Providence, 1998. American Mathematical Society. [14] Richard J. Lipton and Eric B. Baum, editors. DNA Based Computers: Proceedings of a DIMACS Workshop, April 4, 1995, Princeton University, volume 27 of DIMACS series in discrete mathematics and theoretical computer science, Providence, 1996. American Mathematical Society. [15] Carlo C. Maley. DNA computation: Theory, practice, and prospects. Evolutionary Computation, 6(3):201--229, 1998. [16] Robert Pool. Forget silicon, try DNA. New Scientist, 151(2038):26--31, July 13, 1996. [17] Harvey Rubin and David Harlan Wood, editors. Preliminary Proceedings of the Fourth Annual Workshop on DNA Based Compute
06/1999;
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ABSTRACT: . We explore the information processing capabilities and efficiency of DNA computations by giving two different types of implementations of finite-state machines. A ligation-based approach allows input of arbitrary length and can be readily implemented with current biotechnology, but requires sequential input feed and different molecules for different machines. In a second implementation not based on ligation, transitions are represented by reusable molecules, and the input, coded as a molecule, can be introduced at once. We extend the technique for programmable fault-tolerant implementation of nondeterministic finite-state machines by enforcings the basic conditions in the subset constructions that permit efficient computation. All implementations allow optical extraction of the status of the machine. 1 Introduction Biological paradigms are now well known to provide fundamentally new insights to computing. Examples include genetic algorithms, genetic programming, and evolutionary pro...
03/1998;
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ABSTRACT: Artificial immune systems attempt to distinguish self from nonself
through string matching operations. A detector set of strings is
selected by eliminating random strings that match the self strings. DNA
based computers have been proposed to solve complex problems that defy
solution on conventional computers. They are based on (hydrogen bonding
based) matchings (called hybridizations) between Watson-Crick
complementary pairs, Adenine-Thymine or Cytosine-Guanine. Therefore, a
single strand (an oligonucleotide) will bind with other oligonucleotides
that match most closely its sequence under the operation of Watson-Crick
complementation. In this paper, an algorithm for implementing an
artificial immune system for self-nonself discrimination based on DNA is
described. This procedure takes advantage of the inherent pattern
matching capability of DNA hybridization reactions and the notion of
similarity naturally found in DNA hybridization
Systems, Man, and Cybernetics, 1997. Computational Cybernetics and Simulation., 1997 IEEE International Conference on; 11/1997
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ABSTRACT: The fundamental information processing capabilities of DNA based reactions have been explored at the upper end of the computability spectrum using splicing systems and establishing computation universality. We explore the information processing capabilities and efficiency of DNA computations from the other end by giving two different types of implementations of the simplest nontrivial information processing model, the finite-state machine. A ligationbased approach allows input of arbitrary length and can be readily implemented with current biotechnology, but requires sequential input feed and different molecules for different machines. In a second implementation not based on ligation, transitions are represented by reusable molecules, and the input, coded as a molecule, can be introduced at once. Both implementations allow optical extraction of the status of the machine. 1 Introduction Adleman [1] showed how the actual mechanisms underlying DNA recombination and separation carry compu...
05/1997;
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ABSTRACT: A common feature of DNA computing involves the use of molecular
biology techniques to extract molecules representing the solution to a
computation from a reaction mixture. Current applied extraction methods
often employ PCR (polymerase chain reactions) and/or gel
electrophoresis, both of which we believe are too time-consuming and
error-prone to yield a practical DNA-based molecular computing
capability. This paper suggests a new approach to solving the
Hamiltonian graph and similar combinatorial problems that avoids these
traditional techniques in favor of a purely enzymatic methodology
Evolutionary Computation, 1997., IEEE International Conference on; 05/1997
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ABSTRACT: Computation based on manipulation of DNA molecules has the
potential to solve problems with massive parallelism. DNA computation,
however, is implemented with chemical reactions between the nucleotide
bases, and therefore, the results can be error-prone. Application of DNA
based computation to traditional computing paradigms requires error-free
computation, which the DNA chemistry is unable to support. Careful
encoding of the nucleotide sequences can alleviate the production of
errors, but these good encodings are difficult to find. In this paper,
an algorithm for evolutionary computation with DNA is sketched.
Evolutionary computation does not require error-free DNA chemistry, and
in fact, takes advantage of errors to produce change and variation in
the population. An application of the DNA based evolution program to a
search for good DNA encodings is sketched
Evolutionary Computation, 1997., IEEE International Conference on; 05/1997
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Automata Implementation, Second International Workshop on Implementing Automata, WIA '97, London, Ontario, Canada, September 18-20, 1997, Revised Papers; 01/1997
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ABSTRACT: Artiicial immune systems attempt to distinguish self from nonself through string matching operations. A detector set of strings is selected by eliminating random strings that match the self strings. DNA based computers have been proposed to solve c o m p l e x problems (e.g. Traveling Salesman) that defy solution on conventional computers. They are based on (hy-drogen bonding based) matchings (called hybridiza-tions) between Watson-Crick complementary pairs, A-T (Adenine-Thymine) or C-G (Cytosine-Guanine). Therefore, a single strand (an oligonucleotide) will bind with other oligonucleotides that match most closely its sequence under the operation of Watson-Crick complementation. In this paper, an algorithm for implementing an artiicial immune system for self-nonself discrimination based on DNA is described. This procedure takes advantage of the inherent pat-tern matching capability of DNA hybridization reac-tions and the notion of similarity naturally found in DNA hybridization.
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ABSTRACT: It is now known that randomly chosen encod-ings are inadequate to prevent false positives in DNA computations 3, 6, 7]. In fact, the prob-ability of a good encoding in a randomly cho-sen sample goes to zero fairly quickly with the number of errors and the encoding length. We introduce a new measure of hybridization like-lihood, the H-measure, and propose a theory of error-preventing codes for DNA computing. The new measure appears to be a good indica-tor of hybridization resilience and permits the development of criteria for encodings that en-able reliable computations with unreliable hy-bridizations processes. The structure of DNA cubes, the space of all oligos available for encod-ings and their H-distance restrictions, is deter-mined for small sizes of the n;mers. The best codes are given by maximal cliques at maximum distance in the DNA cubes, and nding them appears likely to be a diicult problem in itself. Sample encodings can be precomputed for small encoding lengths and can be used for arbitrary DNA computations because of their H-distance and error-preventing properties.
