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Paradigms for Biomolecular Computation
 UNCONVENTIONAL MODELS OF COMPUTATION
, 1998
"... Biomolecular Computation (BMC) is computation done at the molecular scale, using Biotechnological techniques. This paper discusses the underlying biotechnology that BMC may utilize, and surveys a number of distinct paradigms for doing BMC. We also identify a number of key future experimental mile ..."
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Cited by 15 (6 self)
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Biomolecular Computation (BMC) is computation done at the molecular scale, using Biotechnological techniques. This paper discusses the underlying biotechnology that BMC may utilize, and surveys a number of distinct paradigms for doing BMC. We also identify a number of key future experimental milestones for the field of BMC.
Molecular Computing, Bounded Nondeterminism, and Efficient Recursion
 In Proceedings of the 24th International Colloquium on Automata, Languages, and Programming
, 1998
"... The maximum number of strands used is an important measure of a molecular algorithm's complexity. This measure is also called the volume used by the algorithm. Every problem that can be solved by an NP Turing machine with b(n) binary nondeterministic choices can be solved by molecular computation in ..."
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Cited by 14 (5 self)
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The maximum number of strands used is an important measure of a molecular algorithm's complexity. This measure is also called the volume used by the algorithm. Every problem that can be solved by an NP Turing machine with b(n) binary nondeterministic choices can be solved by molecular computation in a polynomial number of steps, with four test tubes, in volume 2 b(n) . We identify a large class of recursive algorithms that can be implemented using bounded nondeterminism. This yields improved molecular algorithms for important problems like 3SAT, independent set, and 3colorability. 1. A model of molecular computing Molecular computation was first studied in [1, 20]. The models we define were inspired as well by the work of [3, 28]. A molecular sequence is a string over an alphabet \Sigma (we can use any alphabet we like, encoding characters of \Sigma by finite sequences of base pairs). A test tube is a multiset of molecular sequences. We describe the allowable operations below. Whe...
DNABased Parallel Computation by "Counting"
, 1997
"... The potential of DNA as a truly parallel computing device is enormous. Solutionphase DNA chemistry, though not unlimited, provides the only currentlyavailable experimental system. Its practical feasibility, however, is controversial. We have sought to extend the feasibility and generality of DNA c ..."
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Cited by 8 (4 self)
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The potential of DNA as a truly parallel computing device is enormous. Solutionphase DNA chemistry, though not unlimited, provides the only currentlyavailable experimental system. Its practical feasibility, however, is controversial. We have sought to extend the feasibility and generality of DNA computing by a novel application of the theory of counting . The biochemically equivalent operation for DNA counting is well known. We propose a DNA algorithm that employs this new operation. We also present an implementation of this algorithm by a novel DNAchemical method. Preliminary computer simulations suggest that the algorithm can significantly reduce the DNA space complexity (i.e., the maximum number of DNA molecules that must be present in the test tube during computation) for solving 3SAT to O(2 0:4n ). If the observation is correct, our algorithm can solve 3SAT instances of size up to or exceeding 120 variables. 1 Introduction 1.1 Two major issues in DNA computing Adleman [Ad...
A Comparison of ResourceBounded Molecular Computation Models
 In Proceedings of the 5th Israel Symposium on Theory of Computing and Systems
, 1997
"... The number of molecular strands used by a molecular algorithm is an important measure of the algorithm's complexity. This measure is also called the volume used by the algorithm. We prove that three important polynomialtime models of molecular computation with bounded volume are equivalent to model ..."
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Cited by 8 (3 self)
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The number of molecular strands used by a molecular algorithm is an important measure of the algorithm's complexity. This measure is also called the volume used by the algorithm. We prove that three important polynomialtime models of molecular computation with bounded volume are equivalent to models of polynomialtime Turing machine computation with bounded nondeterminism. Without any assumption, we show that the Split operation does not increase the power of polynomialtime molecular computation. Assuming a plausible separation between Turing machine complexity classes, the Amplify operation does increase the power of polynomialtime molecular computation. 1. Introduction Molecular computation was first studied in [1, 15], which identified the number of molecular strands used as an important resource. This measure is also Research performed at Yale University and at the University of Maryland. Supported in part by the National Science Foundation under grant CCR8958528, CCR94154...
Executing parallel logical operations with DNA
 In Proceedings of the IEEE Congress on Evolutionary Computation
, 1999
"... DNA computation investigates the potential of DNA as a massively parallel computing device. Research is focused on designing parallel computation models executable by DNAbased chemical processes and on developing algorithms in the models. In 1994 Leonard Adleman initiated this area of research by p ..."
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Cited by 5 (0 self)
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DNA computation investigates the potential of DNA as a massively parallel computing device. Research is focused on designing parallel computation models executable by DNAbased chemical processes and on developing algorithms in the models. In 1994 Leonard Adleman initiated this area of research by presenting a DNAbased method for solving the Hamilton Path Problem. That contribution raised the hope that parallel computation by DNA could be used to tackle NPcomplete problems which are thought of as intractable. The current realization, however, is that NPcomplete problems may not be best suited for DNAbased (more generally, moleculebased) computing. A better subject for DNA computing could be largescale evaluation of parallel computation models. Several proposals have been made in this direction. We overview those methods, discuss technical and theoretical issues involved, and present some possible applications of those methods. 1 Introduction Biomolecular computing is the computi...
On Molecular Approximation Algorithms for NP Optimization Problems
 Problems, 3rd DIMACS Meeting on DNA Based Computers, Univ. of Penns
, 1997
"... We develop a general technique for constructing molecularbased approximation algorithms for NP optimization problems. Our algorithms exhibit a useful volumeaccuracy tradeoff. In particular we solve the Covering problem of Hochbaum and Maass using polynomial time and O i ` 2 (log `)n 2 \Gamma ..."
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Cited by 4 (2 self)
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We develop a general technique for constructing molecularbased approximation algorithms for NP optimization problems. Our algorithms exhibit a useful volumeaccuracy tradeoff. In particular we solve the Covering problem of Hochbaum and Maass using polynomial time and O i ` 2 (log `)n 2 \Gamma n\Delta(n\Gamma1) l 2 =2 \Delta j volume with error ratio (1 + 1 ` ) 2 . We also present the first candidate for a problem that can be solved more efficiently with the Amplify operation than without. 1. Introduction Molecular computers were introduced by Adleman [1, 8], but so far the field lacks a "killer application." It is well known that a DNA computer can solve SAT in linear time [8], but using an exponential number of DNA strands. The number of strands used by an algorithm is called the "volume." Although recent papers [5, 4, 9] solve NP problems using smaller exponential volume, we believe that it is essential to find applications of DNA computers that use subexponential vol...
Length Bounded Molecular Computing
, 1998
"... Length of DNA strands is an important resource in DNA computing. We show how to decrease strand lengths in known molecular algorithms for some NPcomplete problems, such as like 3SAT and Independent Set, without substantially increasing their running time or volume. 1. Introduction Since Adleman's ..."
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Cited by 3 (0 self)
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Length of DNA strands is an important resource in DNA computing. We show how to decrease strand lengths in known molecular algorithms for some NPcomplete problems, such as like 3SAT and Independent Set, without substantially increasing their running time or volume. 1. Introduction Since Adleman's pioneering experiment [1], many researchers have explored efficient molecular algorithms for NPcomplete problems. The running time for a molecular algorithm is to the number of operations on test tubes. The volume is the maximum number of strings in all test tubes at any time, counting multiplicities. The strandlength complexity of a molecular algorithm is the length of the longest DNA strand used in the computation. Although time and volume complexity have been well studied [13, 6, 2, 14, 5, 9, 10, 8], strand length has received less attention. Yet Roweis et al [16], state that 2500base sequences decay at a rate of 10% per hour, and Sambrook [17] states that DNA strands longer than 1000...
Solving Intractable Problems with DNA Computing
 In Proceedings of the 13th Annual IEEE Conference on Computational Complexity
, 1998
"... We survey the theoretical use of DNA computing to solve intractable problems. We also discuss the relationship between problems in DNA computing and questions in complexity theory. 1. Introduction Adleman's pioneering experiment [1] opened the possibility that moderately large instances of NPcomp ..."
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Cited by 2 (0 self)
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We survey the theoretical use of DNA computing to solve intractable problems. We also discuss the relationship between problems in DNA computing and questions in complexity theory. 1. Introduction Adleman's pioneering experiment [1] opened the possibility that moderately large instances of NPcomplete problems might be solved via techniques from molecular biology. Since then numerous papers have explored more efficient molecular algorithms for particular problems in NP [27, 10, 3, 30, 8, 20, 21, 18], molecular solutions to PSPACEcomplete problems [7, 37], and fault tolerant molecular algorithms [12, 25]. Other papers have examined the relationships between molecular complexity classes and classical complexity classes [38, 19]. We will survey some of these advances in this paper. For previous surveys in DNA computing, see [24, 36, 34, 32]. 2. Biological Background DNA is the storage medium for genetic information. It is composed of units called nucleotides, distinguished by the che...
A spacee#cient randomized DNA algorithm for ksat
 Sixth International Workshop on DNAbased Computers, volume 2054 of LNCS
, 2001
"... Abstract. We present a randomized DNA algorithm for kSAT based on the classical algorithm of Paturi et al. [8]. For an nvariable, mclause instance of kSAT (m>n), our algorithm finds a satisfying assignment, assuming one exists, with probability 1 − e −α, in worstcase time O(k 2 mn) and space O( ..."
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Abstract. We present a randomized DNA algorithm for kSAT based on the classical algorithm of Paturi et al. [8]. For an nvariable, mclause instance of kSAT (m>n), our algorithm finds a satisfying assignment, assuming one exists, with probability 1 − e −α, in worstcase time O(k 2 mn) and space O(2 (1 − 1 k)n+log α). This makes it the most spaceefficient DNA kSAT algorithm for k>3andk<n/log α (i.e. the clause size is small compared to the number of variables). In addition, our algorithm is the first DNA algorithm to adapt techniques from the field of randomized classical algorithms. 1
An Õ(2^n) Volume Molecular Algorithm for Hamiltonian Path
, 1998
"... We design volumeefficient molecular algorithms for all problems in #P, using only reasonable biological operations. In particular, we give a polynomialtime O(2 n n 2 log 2 n)volume algorithm to compute the number of Hamiltonian paths in an nnode graph. This improves Adleman's celebrated ..."
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We design volumeefficient molecular algorithms for all problems in #P, using only reasonable biological operations. In particular, we give a polynomialtime O(2 n n 2 log 2 n)volume algorithm to compute the number of Hamiltonian paths in an nnode graph. This improves Adleman's celebrated n!volume algorithm for finding a single Hamiltonian path. 1. Introduction Molecular computation was first proposed by Feynman [10], but his idea was not implemented by experiment for a few decades. In 1994 Adleman [1] succeeded to practically solve an instance of the Hamiltonian path problem in a test tube, just by handling DNA strings. DNA is the storage medium for genetic information. It is composed of units called nucleotides, distinguished by the chemical group (base) attached to them. The four bases are adenine, guanine, cytosine, and thymine, abbreviated as A, G, C, and T. Single nucleotides are linked endtoend to form DNA strands. Each DNA strand has two chemically distinguishable...