DNA计算模型的研究

DNA计算模型在DNA计算的各个研究领域中占有重要的地位,对DNA计算模型进行研究是有意义的。首先回顾了DNA计算模型的发展历史;然后从DNA的基本结构入手研究了DNA计算的机理,并对DNA计算的过程进行了详细分析,从而归纳出DNA计算模型的基本概念;再对DNA计算模型按照DNA计算的物质形态进行了分类并对每一类DNA计算模型的理论及其应用进行了详细的分析。     

DNA计算原理、现状及展望

综述了DNA计算原理和特点,接着介绍了DNA计算的研究现状,指出了目前DNA计算的主要研究方向和DNA计算需要解决问题,最后对DNA计算的发展前景进行了展望.     

浅析DNA计算及其发展状况

近年来,基于生化反应机理的DNA计算模型受到科学领域内许多不同学科领域学者们的关注。DNA计算已经形成国际科学前沿领域内研究的一个新的热点。该文主要讨论了DNA计算的原理,综述了DNA计算的特点、DNA计算模型,并指出了DNA计算研究中存在的问题,最后就DNA计算的发展前景进行了展望。     

DNA逻辑计算模型的研究现状与展望

DNA计算因其优异的计算能力已经成为当前研究热点,DNA逻辑计算模型是DNA计算体系与运算实现的重要依托。按应用技术将现有DNA逻辑计算模型进行分类：基于链置换的DNA逻辑计算模型、基于核酶的DNA逻辑计算模型、基于G-quadruplex的DNA逻辑计算模型、基于DNA自组装的逻辑计算模型、基于其他分子技术和分子材料的DNA逻辑计算模型。首先阐述了DNA逻辑计算的研究背景和研究目的以及现阶段在生物分子检测、疾病诊断、多因素分析和生物成像等领域的应用并简述其相关概念;然后梳理各DNA逻辑计算模型的研究历史和现状,分析各类逻辑计算模型所应用的分子操控技术和分子材料以及优缺点和应用前景;最后归纳总结DNA逻辑计算领域当前研究热点和发展前景,为未来提出全新的计算方式奠定基础,为信息、医疗等领域提供更好的服务。     

DNA计算模型及应用综述

自从Adleman博士最早利用DNA计算成功求解了7个顶点有向图的Hamilton问题以来,DNA计算[1]被引入多个研究领域,成为当今科技发展的热点之一。首先介绍DNA计算的基本原理及编码方法,其次阐述了DNA计算的主要模型,再次总结了国内外研究学者应用DNA计算解决的实际问题,最后列举了DNA计算改进的相关方向。     

微流控制系统在DNA计算中的应用

DNA计算系统以人工合成或自然存在的DNA分子作为信息存储的媒介,通过分子生物工程技术例如PCR、凝胶电泳、酶反应实现计算过程。文章简要介绍了DNA计算的原理、特点及研究概况,从对DNA及蛋白质分子的操控及检测两个方面详细分析了微流控制系统在DNA计算中的应用。研究了生物芯片在集成DNA计算系统中的作用,随着可集成的功能通用化、结构三维化生物芯片系统的出现,基于生物芯片的DNA计算系统将可能成为DNA计算机的一种重要实现途径。     

DNA计算与背包问题

该文通过对背包问题这一典型的NP完全问题的DNA计算研究,针对属于组合优化一类的ZKP问题给出了一种DNA计算方法,该算法解决了组合优化一类DNA计算的加权赋值问题,并根据DNA计算的特点给出了一般加权赋值型组合优化问题的DNA计算模式。     

自组装DNA计算的研究进展及展望

近年来,许多研究者已经证明二维自组装模型有通用计算能力,同时证明了自组装DNA计算具有可扩展性。随着分子生物学技术的发展,自组装DNA计算有着广阔的应用前景,在纳米科学、优化计算、密码学、医学等众多科学领域中有突破性的创新与应用。较全面地介绍了自组装DNA计算的研究现状、原理、分子结构和数学模型,以及自组装DNA计算的复杂度和误差分析,并对自组装DNA计算待研究的问题和发展前景进行了分析和展望。     

计算科学的新领域：DNA计算（Ⅲ）

DNA计算是应用分子生物技术进行计算的新方法.从理论上研究DNA计算方法，有利于推动理论计算科学的发展.本系列文章应用形式语言及自动机理论技术，系统地探讨了DNA分子的可计算性及其计算能力.本文主要介绍DNA剪接计算模型的文法结构和剪接计算方法，探讨了不同DNA剪接计算模型的计算能力，证明了所有图灵机可计算的函数理论上都可以通过DNA剪接计算模型来计算.     

DNA计算中的数据与计算模型

对DNA计算的通用性及单链、双链、粘性末端、发夹、质粒、k-臂DNA分子等各种数据作了简单介绍,并对基于DNA分子结构特性和基于DNA计算机研制过程两个方面的DNA计算模型进行了分析对比。针对各种不同的DNA数据及特性,提出了混合DNA计算模型的研究思路,并从不同角度论述了混合DNA计算模型的可行性。     

Biocomputing: an insight from linguistics

This paper is placed in a formal framework in which the interdisciplinary study of natural language is conducted by integrating linguistics, computer science and biology. It provides an overview of the field of research, conveying the main biological ideas that have influenced research in linguistics. Our work highlights the main methods of molecular computing that have been applied to the processing and study of the structure of natural language: DNA computing, membrane computing and networks of evolutionary processors. Moreover, some new challenges and lines of research for the future are pointed out, that can provide important improvements in the understanding of natural language as a structure and a human capacity.     

活体生物计算模型的研究进展及展望(英文)

活体生物计算模型是基于生物体内各种生化分子以特定的形式互相协作、处理信息的能力而出现的一种新的计算模型．由于其计算组成部件是直接镶嵌在生物活体里面,并且显示具有一定的计算能力,这可以使人们深人研究生物体信息处理能力以及获得对这种能力的有效控制．该文介绍了近几年几类体内生物计算模型,用于求解NP完全问题、基因逻辑电路、分子自动机研究状况,并对未来的发展方向进行了展望．     

Is DNA computing viable for 3-SAT problems?

Adleman reported how to solve a 7-vertex instance of the Hamiltonian path problem by means of DNA manipulations. After that a major goal of subsequent research is how to use DNA manipulations to solve NP-hard problems, especially 3-SAT problems. Lipton proposed DNA experiments on test tubes to solve 3-SAT problems. Liu et al. reported how to solve a simple case of 3-SAT using DNA computing on surfaces. Lipton's model of DNA computing is simple and intuitive for 3-SAT problems. The separate (or extract) operation, which is a key manipulation of DNA computing, only extracts some of the required DNA strands and Lipton thinks that a typical percentage might be 90. But it is unknown what would happen due to imperfect extract operation. Let p be the rate, where 0<p<1. Assume that for each distinct string s in a test tube, there are 10^l (l=13 proposed by Adleman) copies of s and that extracting each of the required DNA strands is equally likely. Here, the present paper will report, no matter how large l is and no matter how close to 1 p is, there always exists a class of 3-SAT problems such that DNA computing error must occur. Therefore, DNA computing is not viable for 3-SAT.     

Analysis and experiments with an elephant's trunk robot

The area of trunk/tentacle-type biological manipulation is not new, but there has been little progress in the development and application of a physical device to simulate these types of manipulation. Our research in this area is based on using an 'elephant trunk' robot. In this paper, we review the construction of the robot and how it compares to biological manipulators. We apply our previously designed kinematic model to describe the kinematics of the robot. We finish by providing some examples of motion planning and intelligent manipulation using the robot.     

Analysis and experiments with an elephant's trunk robot.

The area of tentacle and trunk type biological manipulation is not new, but there has been little progress in the development and application of a physical device to simulate these types of manipulation. Our research in this area is based on using an 'elephant trunk' robot. In this paper, we review the construction of the robot and how it compares to biological manipulators. We then apply our previously designed kinematic model to describe the kinematics of the robot. We finish by providing some examples of motion planning and intelligent manipulation using the robot.     

The Haplotyping problem: An overview of computational models and solutions

The investigation of genetic differences among humans has given evidence that mutations in DNA sequences are responsible for some genetic diseases.The most common mutation is the one that involves only a single nucleotide of the DNA sequence,which is called a single nucleotide polymorphism (SNP).As a consequence,computing a complete map of all SNPs occurring in the human populations is one of the primary goals of recent studies in human genomics.The construction of such a map requires to determine the DNA sequences that from allchromosomes.In diploid organisms like humans,each chromosome consists of two sequences called haplotypes.Distinguishing the information contained in both haplotypes when analyzing chromosome sequences poses several new computational issues which collectively form a new emergingtopic of Computational Biology known as Haplotyping.This paper is a comprehensive study of some new combinatorial approaches proposed in thisresearch area and it mainly focuses on the formulations and algorithmic solutions of some basic biological problems.Three statistical approaches are briefly discussed at the end of the paper.     

Cloud computing in e-Science: research challenges and opportunities

Service-oriented architecture (SOA), workflow, the Semantic Web, and Grid computing are key enabling information technologies in the development of increasingly sophisticated e-Science infrastructures and application platforms. While the emergence of Cloud computing as a new computing paradigm has provided new directions and opportunities for e-Science infrastructure development, it also presents some challenges. Scientific research is increasingly finding that it is difficult to handle “big data” using traditional data processing techniques. Such challenges demonstrate the need for a comprehensive analysis on using the above-mentioned informatics techniques to develop appropriate e-Science infrastructure and platforms in the context of Cloud computing. This survey paper describes recent research advances in applying informatics techniques to facilitate scientific research particularly from the Cloud computing perspective. Our particular contributions include identifying associated research challenges and opportunities, presenting lessons learned, and describing our future vision for applying Cloud computing to e-Science. We believe our research findings can help indicate the future trend of e-Science, and can inform funding and research directions in how to more appropriately employ computing technologies in scientific research. We point out the open research issues hoping to spark new development and innovation in the e-Science field.     

DNA computation: theory, practice, and prospects

L. M. Adleman launched the field of DNA computing with a demonstration in 1994 that strands of DNA could be used to solve the Hamiltonian path problem for a simple graph. He also identified three broad categories of open questions for the field. First, is DNA capable of universal computation? Second, what kinds of algorithms can DNA implement? Third, can the error rates in the manipulations of the DNA be controlled enough to allow for useful computation? In the two years that have followed, theoretical work has shown that DNA is in fact capable of universal computation. Furthermore, algorithms for solving interesting questions, like breaking the Data Encryption Standard, have been described using currently available technology and methods. Finally, a few algorithms have been proposed to handle some of the apparently crippling error rates in a few of the common processes used to manipulate DNA. It is thus unlikely that DNA computation is doomed to be only a passing curiosity. However, much work remains to be done on the containment and correction of errors. It is far from clear if the problems in the error rates can be solved sufficiently to ever allow for general-purpose computation that will challenge the more popular substrates for computation. Unfortunately, biological demonstrations of the theoretical results have been sadly lacking. To date, only the simplest of computations have been carried out in DNA. To make significant progress, the field will require both the assessment of the practicality of the different manipulations of DNA and the implementation of algorithms for realistic problems. Theoreticians, in collaboration with experimentalists, can contribute to this research program by settling on a small set of practical and efficient models for DNA computation.     

Algorithmic applications of XPCR

An emerging trend in DNA computing consists of the algorithmic analysis of new molecular biology technologies, and in general of more effective tools to tackle computational biology problems. An algorithmic understanding of the interaction between DNA molecules becomes the focus of some research which was initially addressed to solve mathematical problems by processing data within biomolecules. In this paper a novel mechanism of DNA recombination is discussed, that turned out to be a good implementation key to develop new procedures for DNA manipulation (Franco et al., DNA extraction by cross pairing PCR, 2005; Franco et al., DNA recombination by XPCR, 2006; Manca and Franco, Math Biosci 211:282–298, 2008). It is called XPCR as it is a variant of the polymerase chain reaction (PCR), which was a revolution in molecular biology as a technique for cyclic amplification of DNA segments. A few DNA algorithms are proposed, that were experimentally proven in different contexts, such as, mutagenesis (Franco, Biomolecular computing—combinatorial algorithms and laboratory experiments, 2006), multiple concatenation, gene driven DNA extraction (Franco et al., DNA extraction by cross pairing PCR, 2005), and generation of DNA libraries (Franco et al., DNA recombination by XPCR, 2006), and some related ongoing work is outlined.