Improving the design of sequences for DNA computing: A multiobjective evolutionary approach

Designing oligonucleotide strands that selectively hybridize to reduce undesired reactions is a critical step for successful DNA computing. To accomplish this, DNA molecules must be restricted to a wide window of thermodynamical and logical conditions, which in turn facilitate and control the algorithmic processes implemented by chemical reactions. In this paper, we propose a multiobjective evolutionary algorithm for DNA sequence design that, unlike preceding evolutionary approaches, uses a matrix-based chromosome as encoding strategy. Computational results show that a matrix-based GA along with its specific genetic operators may improve the performance for DNA sequence optimization compared to previous methods.     

A hybrid quantum chaotic swarm evolutionary algorithm for DNA encoding

DNA encoding is crucial to successful DNA computation, which has been extensively researched in recent years. It is difficult to solve by the traditional optimization methods for DNA encoding as it has to meet simultaneously several constraints, such as physical, chemical and logical constraints. In this paper, a novel quantum chaotic swarm evolutionary algorithm (QCSEA) is presented, and is first used to solve the DNA sequence optimization problem. By merging the particle swarm optimization and the chaotic search, the hybrid algorithm cannot only avoid the disadvantage of easily getting to the local optional solution in the later evolution period, but also keeps the rapid convergence performance. The simulation results demonstrate that the proposed quantum chaotic swarm evolutionary algorithm is valid and outperforms the genetic algorithm and conventional evolutionary algorithm for DNA encoding.     

基于文化微粒群优化算法的DNA编码研究

对DNA编码约束进行研究,选择汉明测量以及相似度作为DNA序列集设计的主要约束,并结合连续性约束与GC Content约束,将序列集设计问题抽象为带有强约束的多目标优化问题,采用文化微粒群算法解决该多目标优化问题。仿真结果表明,该混合算法针对DNA编码序列设计问题,在求解最优值能力、解的稳定性方面都能取得较好的效果。     

基于遗传粒子群算法的DNA编码优化

DNA编码序列的设计是影响DNA计算可靠性的重要手段,该文从DNA编码设计应满足的多约束条件中选取适当的约束条件,针对这些约束条件提出每个DNA个体应满足的评估公式,采用遗传粒子群算法解决该多目标优化问题,并在不同的约束准则下将计算得到的序列与已有的DNA序列进行了对比。仿真结果证明了该方法的有效性。     

基于h-距离的DNA编码序列设计

针对DNA编码序列设计问题,将其转换为带约束的多目标优化问题,在单链DNA集合中引入h-距离,构造了DNA序列间的共享函数,应用小种群遗传算法,对DNA编码序列设计问题进行求解。与已有结果比较,算法可以得到更好的DNA序列且计算效率较高。算法可用于DNA计算中编码序列的具体设计。     

基于改进的粒子群遗传算法的DNA编码序列优化

在DNA计算中,DNA编码序列的设计是影响DNA计算可靠性的重要手段.在不同的DNA序列设计中,应该选择适当的约束条件,并且根据相应的约束条件提出每个DNA应该相应满足的评估公式.文中从DNA编码设计应满足的多约束条件中选取适当的约束条件,提出评估公式,并采用改进的粒子群遗传算法来解决多目标优化问题.同时根据得到的序列与已有序列在综合适应度函数结果上进行对比,结果证明了该方法的有效性.     

基于文化进化粒子群算法的DNA序列设计

DNA编码序列的设计是影响DNA计算可靠性的重要途径,从DNA编码应满足的多约束条件中选取适当的约束条件,针对这些约束条件提出每个DNA个体应满足的评估公式以及目标序列集合的评价函数,采用文化进化粒子群算法解决DNA序列设计的多目标优化问题,仿真结果表明该混合算法针对DNA序列设计问题,在求解最优值能力,解的稳定性方面都取得了不错的效果。     

A multiobjective swarm intelligence approach based on artificial bee colony for reliable DNA sequence design

The design of reliable DNA sequences is crucial in many engineering applications which depend on DNA-based technologies, such as nanotechnology or DNA computing. In these cases, two of the most important properties that must be controlled to obtain reliable sequences are self-assembly and self-complementary hybridization. These processes have to be restricted to avoid undesirable reactions, because in the specific case of DNA computing, undesirable reactions usually lead to incorrect computations. Therefore, it is important to design robust sets of sequences which provide efficient and reliable computations. The design of reliable DNA sequences involves heterogeneous and conflicting design criteria that do not fit traditional optimization methods. In this paper, DNA sequence design has been formulated as a multiobjective optimization problem and a novel multiobjective approach based on swarm intelligence has been proposed to solve it. Specifically, a multiobjective version of the Artificial Bee Colony metaheuristics (MO-ABC) is developed to tackle the problem. MO-ABC takes in consideration six different conflicting design criteria to generate reliable DNA sequences that can be used for bio-molecular computing. Moreover, in order to verify the effectiveness of the novel multiobjective proposal, formal comparisons with the well-known multiobjective standard NSGA-II (fast non-dominated sorting genetic algorithm) were performed. After a detailed study, results indicate that our artificial swarm intelligence approach obtains satisfactory reliable DNA sequences. Two multiobjective indicators were used in order to compare the developed algorithms: hypervolume and set coverage. Finally, other relevant works published in the literature were also studied to validate our results. To this respect the conclusion that can be drawn is that the novel approach proposed in this paper obtains very promising DNA sequences that significantly surpass other results previously published.     

A new look at ESO and BESO optimization methods

The “hard-kill” optimization methods such as evolutionary structural optimization (ESO) and bidirectional evolutionary structural optimization (BESO) may result in a nonoptimal design (Zhou and Rozvany in Struct Multidisc Optim 21:80–83, 2001) when these methods are implemented and used inadequately. This note further examines this important problem and shows that failure of ESO may occur when a prescribed boundary support is broken for a statically indeterminate structure. When a boundary support is broken, the structural system could be completely changed from the one originally defined in the initial design and even BESO would not be able to rectify the nonoptimal design. To avoid this problem, it is imperative that the prescribed boundary conditions for the structure be checked and maintained at each iteration during the optimization process. Several simple procedures for solving this problem are suggested. The benchmark problem proposed by Zhou and Rozvany (Struct Multidisc Optim 21:80–83, 2001) is revisited, and it is shown that the highly nonoptimal design can be easily avoided.     

Implementation of an ant colony system for DNA sequence optimization

DNA computation exploits the computational power inherent in molecules for information processing. However, in order to perform the computation correctly, a set of good DNA sequences is crucial. A lot of work has been carried out on designing good DNA sequences to archive a reliable molecular computation. In this article, the ant colony system (ACS) is introduced as a new tool for DNA sequence design. In this approach, the DNA sequence design is modeled as a path-finding problem, which consists of four nodes, to enable the implementation of the ACS. The results of the proposed approach are compared with other methods such as the genetic algorithm.     

An ant colony optimization algorithm for DNA sequencing by hybridization

The reconstruction of DNA sequences from DNA fragments is one of the most challenging problems in computational biology. In recent years the specific problem of DNA sequencing by hybridization has attracted quite a lot of interest in the optimization community. Several metaheuristics such as tabu search and evolutionary algorithms have been applied to this problem. However, the performance of existing metaheuristics is often inferior to the performance of recently proposed constructive heuristics. On the basis of these new heuristics we develop an ant colony optimization algorithm for DNA sequencing by hybridization. An important feature of this algorithm is the implementation in a so-called multi-level framework. The computational results show that our algorithm is currently a state-of-the-art method for the tackled problem.     

Evolutionary Algorithms for Minimax Problems in Robust Design

Many robust design problems can be described by minimax optimization problems. Classical techniques for solving these problems have typically been limited to a discrete form of the problem. More recently, evolutionary algorithms, particularly coevolutionary optimization techniques, have been applied to minimax problems. A new method of solving minimax optimization problems using evolutionary algorithms is proposed. The performance of this algorithm is shown to compare favorably with the existing methods on test problems. The performance of the algorithm is demonstrated on a robust pole placement problem and a ship engineering plant design problem.     

A design for DNA computation of the OneMax problem

 Elements of evolutionary computation and molecular biology are combined to design a DNA evolutionary computation. The traditional test problem for evolutionary computation, OneMax problem is addressed. The key feature is the physical separation of DNA strands consistent with OneMax “fitness.”     

多目标优化机制下DNA编码序列模型

针对现有DNA计算中存在的编码序列设计稳定性、可靠性不完善等问题,充分考虑基本编码问题,设计出一种基于多目标优化机制的DNA编码序列设计算法。在一定的约束条件下,该算法利用了多目标优化机制以及采取小种蚁群算法,将h-distance因子添加到单链DNA架构中,建立一种DNA序列公用方法。通过模拟实验表明,该算法与同类型算法相比,在计算效率、优化性方面具有一定优势。     

Simulations of DNA Computing with In Vitro Selection

An attractive feature of DNA-based computers is the large number of possible sequences (4 ⁿ ) of a given length n with which to represent information. The problem, however, is that any given sequence is not necessarily independent of the other sequences, and thus, reactions among them can interfere with the reliability and efficiency of the computation. Independent sequences might be manufactured in the test tube using evolutionary methods. To this end, an in vitro selection has been developed that selects maximally mismatched DNA sequences. In order to understand the behavior of the protocol, a computer simulation of the protocol was done, results of which showed that Watson-Crick pairs of independent oligonucleotides were preferentially selected. In addition, to explore the computational capability of the selection protocol, a design is presented that generates the Fibonacci sequence of numbers.     

An optimality criteria based method for discrete design optimization taking into account buildability constraints

This paper presents a heuristic design optimization method specifically developed for practicing structural engineers. Practical design optimization problems are often governed by buildability constraints. The majority of optimization methods that have recently been proposed for design optimization under buildability constraints are based on evolutionary computing. While these methods are generally easy to implement, they require a large number of function evaluations (finite element analyses), and they involve algorithmic parameters that require careful tuning. As a consequence, both the computation time and the engineering time are high. The discrete design optimization algorithm presented in this paper is based on the optimality criteria method for continuous optimization. It is faster than an evolutionary algorithm and it is free of tuning parameters. The algorithm is successfully applied to two classical benchmark problems (the design of a ten-bar truss and an eight-story frame) and to a practical truss design optimization problem.     

Optimization of retaining wall design using evolutionary algorithms

This paper explores the performance of three evolutionary optimization methods, differential evolution (DE), evolutionary strategy (ES) and biogeography based optimization algorithm (BBO), for nonlinear constrained optimum design of a cantilever retaining wall. These algorithms are based on biological contests for survival and reproduction. The retaining wall optimization problem consists of two criteria, geotechnical stability and structural strength, while the final design minimizes an objective function. The objective function is defined in terms of both cost and weight. Constraints are applied using the penalty function method. The efficiency of the proposed method is examined by means of two numerical retaining wall design examples, one with a base shear key and one without a base shear key. The final designs are compared to the ones determined by genetic algorithms as classical metaheuristic optimization methods. The design results and convergence rate of the BBO algorithm show a significantly better performance than the other algorithms in both design cases.     

DNA序列数据挖掘技术

DNA序列数据是一类重要的生物数据.研究DNA序列数据解读其含义是后基因组时代的主要研究任务.数据挖掘是目前最有效的数据分析手段之一,用于发现大量数据所隐含的各种规律,也是生物信息学采用的主要数据分析技术.将数据挖掘技术用于DNA序列数据分析,已得到了广泛关注和快速发展,并取得了许多研究成果.综述了DNA序列数据挖掘领域的研究状况和进展,提出了3个研究阶段:基于统计的挖掘方法应用阶段、一般化挖掘方法应用阶段和专门的DNA序列数据挖掘方法设计阶段.阐述了DNA序列数据挖掘的基础是序列相似性,评述了DNA序列数据挖掘领域所采用的关键技术,包括DNA序列模式、关联、聚类、分类和异常挖掘等,分析讨论了其相应的生物应用背景和意义.最后给出DNA序列数据挖掘进一步研究的热点问题,包括DNA序列数据新的存储和索引机制的设计、根据生物领域知识的数据挖掘新模型和算法的设计等.     

Combinatorial algorithms for DNA sequence assembly

The trend toward very large DNA sequencing projects, such as those being undertaken as part of the Human Genome Program, necessitates the development of efficient and precise algorithms for assembling a long DNA sequence from the fragments obtained by shotgun sequencing or other methods. The sequence reconstruction problem that we take as our formulation of DNA sequence assembly is a variation of the shortest common superstring problem, complicated by the presence of sequencing errors and reverse complements of fragments. Since the simpler superstring problem is NP-hard, any efficient reconstruction procedure must resort to heuristics. In this paper, however, a four-phase approach based on rigorous design criteria is presented, and has been found to be very accurate in practice. Our method is robust in the sense that it can accommodate high sequencing error rates, and list a series of alternate solutions in the event that several appear equally good. Moreover, it uses a limited form of multiple sequence alignment to detect, and often correct, errors in the data. Our combined algorithm has successfully reconstructed nonrepetitive sequences of length 50,000 sampled at error rates of as high as 10%.This research was supported by the National Library of Medicine under Grant R01-LM4960, by a postdoctoral fellowship from the Program in Mathematics and Molecular Biology of the University of California at Berkeley under National Science Foundation Grant DMS-8720208, and by a fellowship from the Centre de recherches mathématiques of the Université de Montréal.     

ADEMO/D: An adaptive differential evolution for protein structure prediction problem

Protein Structure Prediction (PSP) is the process of determining three-dimensional structures of proteins based on their sequence of amino acids. PSP is of great importance to medicine and biotechnology, e.g., to novel enzymes and drugs design, and one of the most challenging problems in bioinformatics and theoretical chemistry. This paper models PSP as a multi-objective optimization problem and adopts ADEMO/D (Adaptive Differential Evolution for Multi-objective Problems based on Decomposition) on its optimizer platform. ADEMO/D has been previously applied to multi-objective optimization with a lot of success. It incorporates concepts of problem decomposition and mechanisms of mutation strategies adaptation. Decomposition-based multi-objective optimization tends to be more efficient than other techniques in complex problems. Adaptation is particularly important in bioinformatics because it can release practitioners, with a great expertise focused on the application, from tuning optimization algorithm’s parameters. ADEMO/D for PSP needs a decision maker and this work tests four different methods. Experiments consider off-lattice models and ab initio approaches for six real proteins. Results point ADEMO/D as a competitive approach for total energy and conformation similarity metrics. This work contributes to different areas ranging from evolutionary multi-objective optimization to bioinformatics as it extends the application universe of adaptive problem decomposition-based algorithms, which despite the success in various areas are practically unexplored in the PSP context.