基于DNA/RNA的逻辑门与逻辑运算

Bioinformatics is an interdisciplinary subject of bonded relationship in between computer science, mathematics, and molecular biology. Biological information keeps growing tremendously. Molecular biologists are specialized in solving bioinformatics issues such as to store, analyze, and retrieve biological data by applying algorithm and techniques of computer science. This review is from the computer science perspective. The fundamental terminology of bioinformatics and its definition are essential to understand bioinformatics in depth. There are main three components of bioinformatics and data types. Data types are input format for tools or software. Real-life databases of bioinformatics are also discussed which are important for analyzing the algorithms. We then provide bioinformatics applications in various areas. As bioinformatics is a fusion from many disciplines, there are lots of research issues and challenges, but computational and biological research issues and challenges are quite significant. Nowadays, the tremendous amount of biological data are being generated, Due to them, bioinformatics has emerging future research trends in big data, machine learning, and deep learning which are presented at last.

Book Reviews

Contributors

Surveying the last sixty years of research, this book describes the physical properties of DNA in the context of its biological functioning. It is designed to enable both students and researchers of molecular biology, biochemistry and physics to better understand the biophysics of DNA, addressing key questions and facilitating further research. The chapters integrate theoretical and experimental approaches, emphasising throughout the importance of a quantitative knowledge of physical properties in building and analysing models of DNA functioning. For example, the book shows how the relationship between DNA mechanical properties and the sequence specificity of DNA-protein binding can be analyzed quantitatively by using our current knowledge of the physical and structural properties of DNA. Theoretical models and experimental methods in the field are critically considered to enable the reader to engage effectively with the current scientific literature on the physical properties of DNA.

Bioinformatics is the field of science in which biology, computer science, and information technology merge to form a single discipline. The revolutionary growth in the computation speed and memory storage capability has fueled a new era in the analysis of biological data. The ultimate goal of the field is to enable the discovery of new biological insights as well as to create a global perspective from which unifying principles in biology can be discerned. Bioinformatics, also known as genomics, computational genomics, or computational molecular biology is a rate.We have used the analogy of genome analysis and VIRUS (vital information recourse under siege) and analyzed KCNQ1, KCNH2 and SCN5A genes, which are playing an important role in LQTS disease. We tried to find out whether the presence of microsatellites or simple sequence repeats in the KCNQ1, KCNH2 and SCN5A genes, are having any significance in the generation of these mutations and checked whether these mutations are fallen in the regions of those microsatillites and if so, is there any significance of these microsatillites in the functional domains of the each gene?.Our analysis revealed that 24 of the 26 mutations of the KCNQ1 gene, 19 of the 21 mutations of the KCNH2 gene and 3 of the 7 mutations of the SCN5A gene, which are existing in the microsatellite regions are fallen in the domain regions of the respective genes and thus indicating a positive role of microsatellites in mutagenesis.

The shortest superstring problem (SSP) is a combinatorial optimization problem which has attracted the interest of many researchers due to its applications in computational molecular biology and in computer science. The SSP is an NP-hard problem, and therefore great effort to develop exact algorithms for it has not been made. On the other hand, several approximation and heuristic algorithms have been implemented indicating the strong effectiveness of the greedy strategies to this problem. Variations of these algorithms can be parallelized providing computational strength in solving real-world instances. Polynomially solvable versions of the problem obtained under specific restrictions to its parameters reveal the boundaries between hard and easy cases. The computational bounds on the approximability of the SSP are a realization of its Max-SNP-hardness, but the weak proved values of them reflect the potential strength of the greedy approximation techniques. The strength of the greedy methods for the SSP is enhanced also by the asymptotic behaviour and the smoothed analysis of the problem in random and real-world instances, respectively. All these issues are presented in this chapter in a concise way covering the whole relevant literature, revealing the knowledge that is already conquered, and paving the path for further development in the study of shortest superstrings. The order of the sections highlights the pass from hardness complexity results for the SSP to efficient algorithms for the problem based on greedy strategies, and to theoretical results that establish the strength of the greedy techniques.

This talk will trace the growing influence of fundamental ideas from computer science on the nature of research in a number of scientific fields. There is a growing awareness that information processing lies at the heart of the processes studied in fields as diverse as quantum mechanics, statistical physics, nanotechnology, neuroscience, linguistics, economics and sociology. Increasingly, mathematical models in these fields are expressed in algorithmic languages and describe algorithmic processes. The speaker will briefly describe connections between quantum computing and the foundations of quantum mechanics, and between statistical mechanics and phase transitions in computation. He will indicate how the growth of the Web has created new phenomena to be investigated by sociologists and economists. He will then focus in greater detail on computational molecular biology, where the view of living cells as complex information processing systems has become the dominant paradigm, and will discuss specific algorithmic problems arising in the sequencing of genomes, the comparative analysis of the resulting genomic sequences, the modeling of networks of interacting proteins, and the associations between genetic variation and disease.

Molecular biology has posed a number of fascinating and sometimes daunting computational problems, which came naturally expressed in its native language of character strings. Through the years, some such problems have found elegant and even useful solutions in response to the needs that originally motivated them. What is perhaps even more remarkable, several of the ideas inspired by computational molecular biology have found application in remote and diverse domains, so that it may be argued that molecular biology did more for computing than the latter did for it. As a modest tribute, this paper reviews a small sample of these cases drawing from the personal exposure of the author.

The onset of addiction is marked with drug induced positive experiences that keep being repeated. During that time, adaptation occurs and addiction is stabilized. Interruption of those processes induces polysymptomatic withdrawal syndromes. Abstinence is accompanied by risks of relapse. These features of addiction suggest adaptive brain dynamics with common pathways in complex neuronal networks. Addiction research has used animal models, where some of those phenomena could be reproduced, to find correlates of addictive behavior. The major thrust of those approaches has been on the involvement of genes and proteins. Recently, an enormous amount of data has been obtained by high throughput technologies in these fields. Therefore, (Computational) "Systems Biology" had to be implemented as a new approach in molecular biology and biochemistry. Conceptually, Systems Biology can be understood as a field of theoretical biology that tries to identify patterns in complex data sets and that reconstructs the cell and cellular networks as complex dynamic, self-organizing systems. This approach is embedded in systems science as an interdisciplinary effort to understand complex dynamical systems and belongs to the field of theoretical neuroscience (Computational Neuroscience). Systems biology, in a similar way as computational neuroscience is based on applied mathematics, computer-based computation and experimental simulation. In terms of addiction research, building up "computational molecular systems biology of the (addicted) neuron" could provide a better molecular biological understanding of addiction on the cellular and network level. Some key issues are addressed in this article.

We describe a method to extract quantitative information on DNA structural and configurational properties from high-resolution topographic maps recorded by atomic force microscopy (AFM). DNA molecules are deposited on mica surfaces from an aqueous solution, carefully dehydrated, and imaged in air in Tapping Mode. Upon extraction of the spatial coordinates of the DNA backbones from AFM images, several parameters characterizing DNA structure and configuration can be calculated. Here, we explain how to obtain the distribution of contour lengths, end-to-end distances, and gyration radii. This modular protocol can be also used to characterize other statistical parameters from AFM topographies.

Structure and electronic properties of single DNA and large molecules on solid surfaces

It is impossible to pinpoint the exact moment at which computational biology became a discipline of its own, but one could say that it was in 1997 when the society of computational biology was formed. Regardless of its exact birthday, the research community has rapidly adopted computational biology and its applications are being vigorously explored. The study and application of medicine is a dynamic challenge. Changes in medicine usually take place as a result of new knowledge acquired through observation and experimentation. When a tamping rod 1-inch thick went through Phineas Gage’s head in 1848, his survival gave the medical field an unusual opportunity to observe behavior of a person missing their prefrontal cortex. This observation lead to the short-lived psychosurgical procedure known as a lobotomy, which attempted to change a person’s behavior by separating two portions of a person’s brain (Pols, 2001). Countless observations, experiments and mistakes represent how almost all medical knowledge has been acquired. The relatively new field of computational biology offers a nontraditional approach to contribute to the medical body of knowledge. Computational biology is a new field combining biology, computer science, and mathematics to solve problems that are unworkable with traditional biological techniques. It includes traditional areas such as systems biology, molecular biology, biochemistry, biophysics, statistics, and computer science, as well as recently developed disciplines including bioinformatics and computational genomics. Algorithms, which are able to closely model biological behavior, validate the medical understanding of the observed processes and can be used to model scenarios that might not be able to be physically reproduced. The goal of computational biology is to use mathematics and computer science to model biological systems on the molecular level. Instead of taking on large complex systems, computational biology is starting small, literally. Modeling problems in molecular biology and biochemistry is a far less daunting task. At a microscopic level, patient’s characteristics drop out of the equation and all information behavior affecting is known. This creates a deterministic model which, given the same input, will always produce the same output. Some of the major subdisciplines of computational biology are computational genomics, systems biology, protein structure prediction, and evolutionary biology, all of which model microscopic structures.

Chapter 1 - Mathematical preliminaries

Sir Kenneth Murray

Biological organisms are beautiful examples of programming. The program and data are stored in biological molecules such as DNA, RNA, and proteins; the algorithms are carried out by molecular and biochemical processes; and the end result is the creation and function of an organism. If we understood how to program molecular systems, what could we create? Lifelike technologies whose basic operations are chemical reactions? The fields of chemistry, physics, biology, and computer science are converging as we begin to synthesize molecules, molecular machines, and molecular systems of ever increasing complexity, leading to subdisciplines such as DNA nanotechnology, DNA computing, and synthetic biology. Having demonstrated simple devices and systems -- self-assembled structures, molecular motors, chemical logic gates -- researchers are now turning to the question of how to create large-scale integrated systems. To do so, we must learn how to manage complexity: how to efficiently specify the structure and behavior of intricate molecular systems, how to compile such specifications down to the design of molecules to be synthesized in the lab, and how to ensure that such systems function robustly. These issues will be illustrated for chemical logic circuits based on cascades of DNA hybridization reactions. Bio Erik Winfree is an Associate Professor in Computer Science, Computation & Neural Systems, and Bioengineering at Caltech. Winfree is the recipient of the Feynman Prize for Nanotechnology (2006), the NSF PECASE/CAREER Award (2001), the ONR Young Investigators Award (2001), a MacArthur Fellowship (2000), and MIT Technology Review's first TR100 list of "top young innovators" (1999). Prior to joining the faculty at Caltech in 2000, Winfree was a Lewis Thomas Postdoctoral Fellow in Molecular Biology at Princeton, and a Visiting Scientist at the MIT AI Lab. Winfree received a B.S. in Mathematics w/ Computer Science from the University of Chicago in 1991, and a Ph.D. in Computation & Neural Systems from Caltech in 1998. His website is http://dna.caltech.edu/~winfree/.