Designing Nucleotide Sequences for Computation: A Survey of Constraints

Sager, Jennifer A.; Stefanović, Darko

doi:10.1007/11753681_22

Cited by 23 publications

(18 citation statements)

References 52 publications

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“…To make a set of DNA sequences effective in DNA computing, they must fulfill a number of combinatorial and thermodynamic constraints. Such a task is not easy to achieve, and it has been shown that designing a set of good DNA sequences is an NP-hard (non-deterministic polynomial-time hard) problem [25,26].…”

Section: Citationmentioning

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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Section: Citationmentioning

confidence: 99%

A membrane evolutionary algorithm for DNA sequence design in DNA computing

Xiao

Zhang

2012

Chin. Sci. Bull.

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“…References [87,98] and others distinguish two elementary subproblems of the encoding sequence design:…”

Section: Dna Encoding: Problem Setting and Notationmentioning

confidence: 99%

“…In contrast, the negative design problem can be solved on a general basis by construction of a library of molecules which do not allow for undesired mutual hybridizations. According to [98], the following conditions must be guaranteed: (1) no strand forms any undesired secondary structure such as hairpin loops ( Figure 9(a)), (2) no string in the library hybridizes with any string in the library, and (3) no string in the library hybridizes with the complement of any string in the library (Figure 9(b) or (c)). Many laboratory techniques stress the importance of a unified framework for the negative design.…”

Section: Dna Encoding: Problem Setting and Notationmentioning

confidence: 99%

DNA Computing — Foundations and Implications

Kari

Seki

Sosík

2012

Handbook of Natural Computing

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“…As customary in formal models of DNA computing [21], each letter represents a domain, i.e., a string over the DNA alphabet {A, C, G, T }. The set of resulting domains must form a set of DNA codewords [11,27,31]. This should always be kept in mind.…”

Section: The Sticker-complex Data Modelmentioning

confidence: 99%

Graph-Theoretic Formalization of Hybridization in DNA Sticker Complexes

Brijder

Gillis

Bussche

2011

Lecture Notes in Computer Science

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Abstract. Sticker complexes are a formal graph-based data model for a restricted class of DNA complexes, motivated by potential applications to databases. This data model allows for a purely declarative definition of hybridization. We introduce the notion of terminating hybridization, and characterize this notion in purely graph-theoretic terms. Terminating hybridization can still produce results of exponential size. We indicate a class of complexes where hybridization is guaranteed to be polynomially bounded.

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Designing Nucleotide Sequences for Computation: A Survey of Constraints

Abstract: We survey the biochemical constraints useful for the design of DNA code words for DNA computation. We define the DNA/RNA Code Constraint problem and cover biochemistry topics relevant to DNA libraries. We examine which biochemical constraints are best suited for DNA word design.

Cited by 23 publications

References 52 publications

A membrane evolutionary algorithm for DNA sequence design in DNA computing

A membrane evolutionary algorithm for DNA sequence design in DNA computing

DNA Computing — Foundations and Implications

Graph-Theoretic Formalization of Hybridization in DNA Sticker Complexes

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