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.     

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.     

Multiobjective evolutionary optimization of DNA sequences for reliable DNA computing

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.     

Similarity/dissimilarity calculation methods of DNA sequences: A survey

DNA sequence similarity/dissimilarity analysis is a fundamental task in computational biology, which is used to analyze the similarity of different DNA sequences for learning their evolutionary relationships. In past decades, a large number of similarity analysis methods for DNA sequence have been proposed due to the ever-growing demands. In order to learn the advances of DNA sequence similarity analysis, we make a survey and try to promote the development of this field. In this paper, we first introduce the related knowledge of DNA similarities analysis, including the data sets, similarities distance and output data. Then, we review recent algorithmic developments for DNA similarity analysis to represent a survey of the art in this field. At last, we summarize the corresponding tendencies and challenges in this research field. This survey concludes that although various DNA similarity analysis methods have been proposed, there still exist several further improvements or potential research directions in this field.     

一种基于模糊聚类的构造进化树方法

各种生物之间的进化史可以通过构建进化树来讨论,因此进化树的研究成了一个研究热点。提出将利用DNA序列的4D表示所得相似矩阵视为模糊矩阵,再利用最大树法来构建进化树的方法。该方法不需要多序列比对,计算简单,实验验证了该方法的有效性。     

Multiple Sequence Alignment with Evolutionary Computation

In this paper we provide a brief review of current work in the area of multiple sequence alignment (MSA) for DNA and protein sequences using evolutionary computation (EC). We detail the strengths and weaknesses of EC techniques for MSA. In addition, we present two novel approaches for inferring MSA using genetic algorithms. Our first novel approach utilizes a GA to evolve an optimal guide tree in a progressive alignment algorithm and serves as an alternative to the more traditional heuristic techniques such as neighbor-joining. The second novel approach facilitates the optimization of a consensus sequence with a GA using a vertically scalable encoding scheme in which the number of iterations needed to find the optimal solution is approximately the same regardless the number of sequences being aligned. We compare both of our novel approaches to the popular progressive alignment program Clustal W. Experiments have confirmed that EC constitutes an attractive and promising alternative to traditional heuristic algorithms for MSA.     

最小自由能约束的DNA编码设计研究

首先介绍了DNA编码设计中自由能约束的重要性,以及自由能约束的计算公式,进而采用一种改进的蚁群优化算法来求解。仿真实验表明此算法产生一组能满足特定自由能约束和统一的解链温度约束的DNA序列,算法利用蚁群算法的并行性提高了编码设计算法的效率,利用最小自由能约束产生更稳定的DNA序列。     

A DNA-based memory with <Emphasis Type="Italic">in vitro</Emphasis> learning and associative recall

A DNA-based memory was implemented with in vitro learning and associative recall.The learning protocol stored the sequences to which it was exposed, and memories were recalled by sequence content through DNA-to-DNA template annealing reactions. Experiments demonstrated that biological DNA could be learned, that sequences similar to the training DNA were recalled correctly, and that unlike sequences were differentiated. Theoretically, the memory has a pattern separation capability that is very large, and can learn long DNA sequences. The learning and recall protocols are massively parallel, as well as simple, inexpensive, and quick. The memory has several potential applications in detection and classification of biological sequences, as well as a massive storage capacity for non-biological data.     

Automated purification of dye terminator sequencing reactions: an approach to high-throughput capillary electrophoresis sequencing of large templates

Demands for higher quantity and quality of sequence data during genome sequencing projects have led to a need for completely automated reagent systems designed to isolate, process, and analyze DNA samples. While much attention has been given to methodologies aimed at increasing the throughput of sample preparation and reaction setup, purification of the products of sequencing reactions has received less scrutiny despite the profound influence that purification has on sequence quality. Commonly used and commercially available sequencing reaction cleanup methods are not optimal for purifying sequencing reactions generated from larger templates, including bacterial artificial chromosomes (BACs) and those generated by rolling circle amplification. Theoretically, these methods would not remove the original template since they only exclude small molecules and retain large molecules in the sample. If the large template remains in the purified sample, it could understandably interfere with electrokinetic injection and capillary performance. We demonstrate that the use of MagneSil^® paramagnetic particles (PMPs) to purify ABI PRISM^® BigDye^® sequencing reactions increases the quality and read length of sequences from large templates. The high-quality sequence data obtained by our procedure is independent of the size of template DNA used and can be completely automated on a variety of automated platforms.     

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.     

有向图k顶点导出子图的DNA粘贴算法

在经典的电子计算中,有向图k顶点导出子图是一个高度复杂的问题。DNA计算是近年来发展的以DNA为载体求解计算问题的非经典计算技术。文中研究了使用DNA计算解决有向图k顶点导出子图的问题,从而提出了一种在粘贴机上运行的子图生成算法。首先,以粘贴机的标准生化元操作作为算法调用的基本算子;其次,使用顺序与循环等程序结构,把上述基本算子按照一定的逻辑方式组织起来;最后,读取生化反应结果,即可获得给定有向图的所有k顶点导出子图。仿真实验结果表明,与经典算法相比,新算法在理想条件下大幅缩短了子图生成时间。     

基于DEA混合算法的模糊车间作业计划问题的研究*

针对以最小化制造跨度为目标,具有模糊加工时间的车间作业计划问题,采用梯形模糊数来表征时间参数,并应用可能性理论,在此基础上构建车间作业计划问题目标函数。为了对模糊环境下的车间作业计划问题进行有效求解,给出了一种DEA-GA混合求解算法,混合算法采用了DNA进化算法的分裂、变异和水平选择算子,然后利用遗传算法的交叉算子实现个体之间的交互,避免早熟收敛。仿真实验表明,该算法高效可行,与GA等优化算法相比,具有更快的收敛速度。     

Improved genetic algorithm for generalized transportation problem

In this paper, we introduce the genetic algorithm approach to the generalized transportation problem (GTP) and GTP with a fixed charge (fc-GTP). We focus on the use of Prüfer number encoding based on a spanning tree, which is adopted because it is capable of equally and uniquely representing all possible trees. From this point, we also design the criteria by which chromosomes can always be converted to a GTP tree. The genetic crossover and mutation operators are designed to correspond to the genetic representations. With the spanning-tree-based genetic algorithm, less memory space will be used than in the matrix-based genetic algorithm for solving the problem; thereby computing time will also be saved. In order to improve the efficiency of the genetic algorithm, we use the reduced cost for the optimality of a solution and the genetic algorithm to avoid degeneration of the evolutionary process. A comparison of results of numerical experiments between the matrix-based genetic algorithm and the spanning-tree-based genetic algorithm for solving GTP and fc-GTP problems is given. This work was presented, in part, at the Fourth International Symposium on Artificial Life and Robotics, Oita, Japan, January 19–22, 1999     

Multi-commodity flow dynamic resource assignment and matrix-based job dispatching for multi-relay transfer in complex material handling systems (MHS)

Mass management and production of customized products requires material handling systems (MHS) which are flexible and responsive enough to accommodate dynamic and real-time changes in material handling tasks. Towards this goal, we develop a novel control framework to improve the flexibility and responsiveness of material handling systems. Flexibility is achieved by using multi-commodity flow network optimization to find the most optimized job sequence in terms of minimum transfer steps. Responsiveness is achieved by the use of a matrix-based discrete event (DE) supervisory controller to dispatch equipment control commands in real-time based on real-time sensor information, according to the optimized sequence. By modeling the MHS network as multi-commodity flow network to define job routes, and using the matrix-based DE controller to implement the job routes in real-time, the users achieve a seamlessly integrated solution to control the execution of transfer jobs that covers the supervisory planning stage through the real-time actual dispatching decisions. The proposed control framework is evaluated on an industrial case study of airfreight terminal material handling and simulation results show its effectiveness.     

Software Tools for DNA Sequence Design

The design of DNA sequences is a key problem for implementing molecular self-assembly with nucleic acid molecules. These molecules must meet several physical, chemical and logical requirements, mainly to avoid mishybridization. Since manual selection of proper sequences is too time-consuming for more than a handful of molecules, the aid of computer programs is advisable. In this paper two software tools for designing DNA sequences are presented, the DNASequenceGenerator and the DNASequenceCompiler. Both employ an approach of sequence dissimilarity based on the uniqueness of overlapping subsequences and a graph based algorithm for sequence generation. Other sequence properties like melting temperature or forbidden subsequences are also regarded, but not secondary structure errors or equilibrium chemistry. Fields of application are DNA computing and DNA-based nanotechnology. In the second part of this paper, sequences generated with the DNASequenceGenerator are compared to those from several publications of other groups, an example application for the DNASequenceCompiler is presented, and the advantages and disadvantages of the presented approach are discussed.     

连通度问题的三维DNA结构进化算法

针对求连通度这一难解问题,提出了三维DNA图结构进化算法。并提出了一种由k-臂DNA分子构建的图结构。在所述方法中3、4臂DNA分子的顶点构造块被选择性地用来构建一些不同的图结构。然后通过凝胶电泳分离,图的连通度便可确定。并且通过引入进化算法的思想,避免了解空间的穷举。     

DNA计算与DNA计算机研究进展与展望

DNA computing is a new method based on biochemical reactions and molecular biology technology.The paper first introduces the basic principle and advantages of DNA computing, and then surveys DNA computing and DNA computer, finally, points out current existing problems and future search directions of DNA computing and DNA computer.     

基于纠错码编码理论的DNA编码研究*

作为一种新的计算模式,DNA计算有着强大的计算能力,编码问题在DNA计算中占据重要的位置,有效的编码设计能够提高DNA计算的可靠性。基于纠错码编码理论,提出了一种新的DNA编码方法,该方法可以找出具有一定长度且满足汉明距离约束的DNA编码序列。最后,给出了该算法的仿真,结果表明了该算法的有效性。     

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.     

Pattern recognition techniques for the emerging field of bioinformatics: A review

The emerging field of bioinformatics has recently created much interest in the computer science and engineering communities. With the wealth of sequence data in many public online databases and the huge amount of data generated from the Human Genome Project, computer analysis has become indispensable. This calls for novel algorithms and opens up new areas of applications for many pattern recognition techniques. In this article, we review two major avenues of research in bioinformatics, namely DNA sequence analysis and DNA microarray data analysis. In DNA sequence analysis, we focus on the topics of sequence comparison and gene recognition. For DNA microarray data analysis, we discuss key issues such as image analysis for gene expression data extraction, data pre-processing, clustering analysis for pattern discovery and gene expression time series data analysis. We describe current methods and show how computational techniques could be useful in these areas. It is our hope that this review article could demonstrate how the pattern recognition community could have an impact on the fascinating and challenging area of genomic research.