Developing Support System for Sequence Design in DNA Computing

Tomita, Fumiaki; Nakatsugawa, Masashi; Yamamoto, Masahito; Shiba, Toshikazu; Ohuchi, Azuma

doi:10.1007/3-540-48017-x_12

Cited by 33 publications

(16 citation statements)

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“…The use of an appropriate ligase under stringent conditions will prevent mis-ligations (Nilsson et al, 1994). In order to avoid these errors, we should prevent mis-hybridization and undesired secondary structure and also keep uniform chemical characteristics (Braich et al, 2002;Tanaka et al, 2001). Such unintended interactions among DNA strands can be minimized by a careful sequence design.…”

Section: Error-resistance Dna Sequence Generationmentioning

confidence: 97%

“…Crossover and mutation are applied with probability p c and p m , respectively, in offspring that are selected by roulette wheel selection process. In this algorithm, we have employed a multi-objective fitness function with similar criteria given in Tanaka et al (2001). Three measures: H-Measure, Similarity, and Completely complementary at 3 -end, are considered for avoiding mis-hybridization error.…”

Section: Error-resistance Dna Sequence Generationmentioning

confidence: 99%

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DNA algorithm for an unbounded fan-in Boolean circuit

2005

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Section: Error-resistance Dna Sequence Generationmentioning

confidence: 97%

Section: Error-resistance Dna Sequence Generationmentioning

confidence: 99%

DNA algorithm for an unbounded fan-in Boolean circuit

2005

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“…This approach also can find a set of orthogonal DNA sequences within a predefined error rate quickly. Tanaka et al [14] offered some sequence fitness criteria, and generated the sequence using simulated annealing. They also tried to find proper combinations of the proposed fitness functions to find more promising solutions.…”

Section: A Previous Workmentioning

confidence: 99%

“…As shown in Table II, the objectives of NACST/Seq is very similar to [14]. That is why we try to accumulate the objectives as many as possible.…”

Section: A Fitness Criteria For Dna Sequence Designmentioning

confidence: 99%

Multiobjective Evolutionary Optimization of DNA Sequences for Reliable DNA Computing

Shin

Lee

Kim

et al. 2005

IEEE Trans. Evol. Computat.

164

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Abstract-DNA computing relies on biochemical reactions of DNA molecules and may result in incorrect or undesirable computations. Therefore, much work has focused on designing the DNA sequences to make the molecular computation more reliable. Sequence design involves with a number of heterogeneous and conflicting design criteria and traditional optimization methods may face difficulties. In this paper, we formulate the DNA sequence design as a multiobjective optimization problem and solve it using a constrained multiobjective evolutionary algorithm (EA). The method is implemented into the DNA sequence design system, NACST/Seq, with a suite of sequence-analysis tools to help choose the best solutions among many alternatives. The performance of NACST/Seq is compared with other sequence design methods, and analyzed on a traveling salesman problem solved by bio-lab experiments. Our experimental results show that the evolutionary sequence design by NACST/Seq outperforms in its reliability the existing sequence design techniques such as conventional EAs, simulated annealing, and specialized heuristic methods.Index Terms-DNA computing, DNA sequence design, multiobjective evolutionary algorithm (MOEA), nucleic acid computing simulation toolkit/sequence generator (NACST/Seq).

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“…Arita et al [29] introduced a genetic algorithm into the DNA sequence design system and proposed a random generate-and-test algorithm. Tanaka et al [30] applied simulated annealing to optimize the set of DNA sequences. Cui et al [31] proposed DNA sequence design algorithm based on the PSO optimization.…”

mentioning

confidence: 99%

A membrane evolutionary algorithm for DNA sequence design in DNA computing

Xiao

Zhang

2012

Chin. Sci. Bull.

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DNA sequence design has a crucial role in successful DNA computation, which has been proved to be an NP-hard (non-deterministic polynomial-time hard) problem. In this paper, a membrane evolutionary algorithm is proposed for the DNA sequence design problem. The results of computer experiments are reported, in which the new algorithm is validated and out-performs certain known evolutionary algorithms for the DNA sequence design problem.DNA computing, membrane computing, P system, DNA sequence design Citation:Xiao J H, Zhang X Y, Xu J. A membrane evolutionary algorithm for DNA sequence design in DNA computing. Chin Sci Bull, 2012Bull, , 57: 698706, doi: 10.1007 Membrane computing is an emergent branch of natural computing, first introduced by Paun [1]. This unconventional model of computation is a type of distributed parallel system, which is inspired by the structure and function of living cells. The devices of this model are called P systems.Roughly speaking, a P system consists of a cell-like membrane structure, in the compartments of which there are multisets of objects that evolve according to given rules in a synchronous, non-deterministic maximally parallel manner. Many different classes of such computing devices have already been investigated. Most of them are computationally universal, i.e., able to compute whatever a Turing machine can do [2][3][4], and are computationally efficient, i.e., able to trade space for time and in this way solve presumably intractable problems in a feasible time (e.g., [5][6][7][8][9]). Membrane computing is very attractive from a computational point of view because of its hierarchical structure and intrinsic parallelism. Evolutionary algorithms are robust optimization and search methods inspired by evolutionary processes occurring in natural selection and molecular genetics. They are approximation algorithms that seek a trade-off between solution quality and computational costs. The main features of evolutionary algorithms are the representation and evaluation of individuals, population dynamics, and evolutionary operators, such as selection, crossover, and mutation [10]. Evolutionary algorithms exhibit an intrinsic parallelism derived from dealing with multiple individuals, show remarkable adaptability and flexibility to various applications, and provide good search capabilities and robust results [11].Both P systems and evolutionary algorithms are natureinspired models and are used to solve complex problems; however, they are different with respect to the objects and rules used and in the computational strategies employed. While P systems represent a suitable formal framework for parallel-distributed computation, evolutionary algorithms are very effective for implementing different algorithms to solve numerous problems [11]. Thus, the possible interaction between P systems and evolutionary algorithms represents a fertile research field, as suggested by the list of 26 open problems in membrane computing [12]. The first attempt in this direction was by Nishida [13,14], who ...

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Developing Support System for Sequence Design in DNA Computing

Cited by 33 publications

References 1 publication

DNA algorithm for an unbounded fan-in Boolean circuit

DNA algorithm for an unbounded fan-in Boolean circuit

Multiobjective Evolutionary Optimization of DNA Sequences for Reliable DNA Computing

A membrane evolutionary algorithm for DNA sequence design in DNA computing

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