基于混合蚁群算法的DNA编码序列设计方法

通过将遗传算法中的交叉、变异操作与蚁群算法中的协同模型进行结合,提出了一种基于混合蚁群算法的DNA编码序列设计方案.实验表明,该算法具有较高的收敛速度,能为DNA计算提供可靠的编码序列.     

基于改进非支配遗传算法的DNA编码序列优化方法

针对DNA计算中的编码序列设计问题,分析了DNA编码序列设计的目标和需要满足的约束条件,并建立了相应的数学模型。通过将约束条件引入非支配排序过程,提出了一种改进的NSGA-Ⅱ算法。实验结果表明,该算法具有良好的收敛特性和种群多样性,能为可控的DNA计算提供可靠的编码序列。     

DNA计算中核酸序列设计方法比较研究(英文)

DNA计算是将现实问题进行编码,映射到DNA分子上,然后通过分子生物实验产生出代表问题解的DNA分子,最后通过检测技术提取出该DNA分子．高质量的DNA编码可以尽可能避免或减少计算过程中出现的错误,并使检测阶段易于提取出代表问题解的DNA分子．文中对基于汉明距离和基于自由能的DNA核酸编码方法进行研究,分析了两类方法的约束条件对DNA编码质量的影响,比较了两类方法排除非特异性杂交的完备性和计算量,进一步分析了两类方法编码DNA序列的效率．通过分析和比较得到,两类DNA计算编码方法都能有效地限制DNA分子间的非特异性杂交,其中基于汉明距离的DNA编码方法的计算量比较小,但是它仅能近似地估计DNA分子间杂交的热力学稳定性,不能完全替代最小自由能的编码方法．在满足DNA计算试验精度要求的条件下,采用基于汉明距离的DNA编码设计方法不仅能有效地的挑选出特异性杂交和非特异性杂交的DNA序列,还能有效地减少计算量,从而提高DNA序列设计的效率．     

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。     

DNA计算中编码序列的过滤函数研究

构造了用于DNA编码序列过滤的函数,并给出了DNA序列编码的算法,采用该文设计的过滤函数和算法所得到的DNA编码序列,能够满足一定的组合约束条件,并满足一定热力学条件,大大提高了DNA编码字的质量,有利于提高DNA计算的可靠性。     

一种新型DNA自组装磁珠光电检测系统及其在DNA计算机研制中的应用

文中提出了一种基于硅芯片集成自组装磁珠颗粒的新型DNA光电检测系统,该系统利用普通照射光源及光电二极管进行光电信号转换,通过比较DNA杂交反应前后的光电流值,来识别DNA杂交信号.该系统是一种首次将磁珠和光电二极管相结合的新型DNA杂交检测系统,具有成本低廉、快速检测及高精度的特点.这种检测方法不需要信号增强步骤,就能够有效区分DNA单碱基错配及完全杂交的情况;由于采用了磁珠颗粒,易于在DNA计算中删除问题的非解.文中给出了求解图的最小顶点覆盖问题DNA计算模型实例,该实例证实了文中所提出的检测系统较传统检测系统具有明显的优势,有利于实现DNA计算机检测系统中解的自动化检测.     

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。     

一种实现三值逻辑电路的DNA计算模型

近年来,随着生物计算和量子计算研究的深入,多值逻辑电路的各种实现成为一个热门的研究方向.发夹结构是DNA分子一种特殊杂交方式的产物,具有结果稳定、特异性强的优点.本文首次提出了一种利用DNA分子来实现多值逻辑电路的方法,用DNA分子的多发夹结构来表示三值逻辑的值,并给出"与"运算和"或"运算的计算模型,该模型适合应用于大规模的多值逻辑电路.     

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。     

基于离散粒子群的DNA编码序列组合优化方法

本文分析了DNA编码序列设计的目标及需要满足的约束条件H-measure、连续性、相似度、发夹结构、GC含量等约束,建立一种组合优化评价模型,通过引入基于权重的适应度函数来评价DNA序列集合的优劣,最后提出基于该模型的离散粒子群优化算法(DPSO)生成有效的DNA编码序列.根据优化问题的约束条件及离散量的特点,对粒子的...     

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。     

Application of a novel IWO to the design of encoding sequences for DNA computing

Encoding and processing information in DNA-, RNA- and other biomolecule-based devices is an important requirement for DNA based computing with potentially important applications. To make DNA computing more reliable, much work has focused on designing the good DNA sequences. However, this is a bothersome task as encoding problem is an NP problem. In this paper, a new methodology based on the IWO algorithm is developed to optimize encoding sequences. Firstly, the mathematics models of constrained objective optimization design for encoding problems based on the thermodynamic criteria are set up. Then, a modified IWO method is developed by defining the colonizing behavior of weeds to overcome the obstacles of the original IWO algorithm, which cannot be applied to discrete problems directly. The experimental results show that the proposed method is effective and convenient for the user to design and select effective DNA sequences in silicon for controllable DNA computing.     

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.     

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.     

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.     

DNA编码问题及其复杂性研究*

高质量的DNA编码可以避免DNA分子间的非特异性杂交,提高DNA计算的有效性和可靠性。首先对DNA编码的约束条件进行归类,分析了各编码约束对编码质量的影响;然后研究了编码质量、编码数量、序列长度与DNA计算可靠性、有效性、可扩充性之间的关系;最后通过类比DNA编码问题和图的独立集问题,说明了求解最大DNA序列集合问题是NP完全的。     

基于BP神经网络的DNA解链温度预测模型

在DNA计算中,为了确保计算过程的可靠性,要求编码信息的DNA序列必须具有相似的热力学稳定性。解链温度是目前评价DNA序列热力学稳定性的一个主要的参数,目前,生物工程中常用的各种预测方法都存在某些序列的误差偏大的缺点,因此难以满足像DNA计算这种大量DNA序列进行各种生化反应的计算过程的要求。论文以DNA序列的邻近法参数为基础,建立了一个基于BP神经网络的解链温度的预测模型。计算结果表明,DNA序列的解链温度的误差可以达到±5.5℃的范围。     

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.