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| biology and computer science: Bioinformatics Challenges at the Interface of Biology and Computer Science Teresa K. Attwood, Stephen R. Pettifer, David Thorne, 2016-08-26 This innovative book provides a completely fresh exploration of bioinformatics, investigating its complex interrelationship with biology and computer science. It approaches bioinformatics from a unique perspective, highlighting interdisciplinary gaps that often trap the unwary. The book considers how the need for biological databases drove the evolution of bioinformatics; it reviews bioinformatics basics (including database formats, data-types and current analysis methods), and examines key topics in computer science (including data-structures, identifiers and algorithms), reflecting on their use and abuse in bioinformatics. Bringing these disciplines together, this book is an essential read for those who wish to better understand the challenges for bioinformatics at the interface of biology and computer science, and how to bridge the gaps. It will be an invaluable resource for advanced undergraduate and postgraduate students, and for lecturers, researchers and professionals with an interest in this fascinating, fast-moving discipline and the knotty problems that surround it. |
| biology and computer science: A Computer Scientist's Guide to Cell Biology William W. Cohen, 2007-07-23 This book is designed specifically as a guide for Computer Scientists needing an introduction to Cell Biology. The text explores three different facets of biology: biological systems, experimental methods, and language and nomenclature. The author discusses what biologists are trying to determine from their experiments, how various experimental procedures are used and how they relate to accepted concepts in computer science, and the vocabulary necessary to read and understand current literature in biology. The book is an invaluable reference tool and an excellent starting point for a more comprehensive examination of cell biology. |
| biology and computer science: Bioinformatics for Geneticists Michael R. Barnes, Ian C. Gray, 2003-07-01 This timely book illustrates the value of bioinformatics, not simply as a set of tools but rather as a science increasingly essential to navigate and manage the host of information generated by genomics and the availability of completely sequenced genomes. Bioinformatics can be used at all stages of genetics research: to improve study design, to assist in candidate gene identification, to aid data interpretation and management and to shed light on the molecular pathology of disease-causing mutations. Written specifically for geneticists, this book explains the relevance of bioinformatics showing how it may be used to enhance genetic data mining and markedly improve genetic analysis. |
| biology and computer science: Python Programming for Biology Tim J. Stevens, Wayne Boucher, 2015-02-12 This book introduces Python as a powerful tool for the investigation of problems in computational biology, for novices and experienced programmers alike. |
| biology and computer science: Emerging Trends in Computational Biology, Bioinformatics, and Systems Biology Hamid R Arabnia, Quoc Nam Tran, 2015-08-11 Emerging Trends in Computational Biology, Bioinformatics, and Systems Biology discusses the latest developments in all aspects of computational biology, bioinformatics, and systems biology and the application of data-analytics and algorithms, mathematical modeling, and simu- lation techniques. • Discusses the development and application of data-analytical and theoretical methods, mathematical modeling, and computational simulation techniques to the study of biological and behavioral systems, including applications in cancer research, computational intelligence and drug design, high-performance computing, and biology, as well as cloud and grid computing for the storage and access of big data sets. • Presents a systematic approach for storing, retrieving, organizing, and analyzing biological data using software tools with applications to general principles of DNA/RNA structure, bioinformatics and applications, genomes, protein structure, and modeling and classification, as well as microarray analysis. • Provides a systems biology perspective, including general guidelines and techniques for obtaining, integrating, and analyzing complex data sets from multiple experimental sources using computational tools and software. Topics covered include phenomics, genomics, epigenomics/epigenetics, metabolomics, cell cycle and checkpoint control, and systems biology and vaccination research. • Explains how to effectively harness the power of Big Data tools when data sets are so large and complex that it is difficult to process them using conventional database management systems or traditional data processing applications. - Discusses the development and application of data-analytical and theoretical methods, mathematical modeling and computational simulation techniques to the study of biological and behavioral systems. - Presents a systematic approach for storing, retrieving, organizing and analyzing biological data using software tools with applications. - Provides a systems biology perspective including general guidelines and techniques for obtaining, integrating and analyzing complex data sets from multiple experimental sources using computational tools and software. |
| biology and computer science: Where Mathematics, Computer Science, Linguistics and Biology Meet Carlos Martín-Vide, V. Mitrana, 2013-03-14 In the last years, it was observed an increasing interest of computer scientists in the structure of biological molecules and the way how they can be manipulated in vitro in order to define theoretical models of computation based on genetic engineering tools. Along the same lines, a parallel interest is growing regarding the process of evolution of living organisms. Much of the current data for genomes are expressed in the form of maps which are now becoming available and permit the study of the evolution of organisms at the scale of genome for the first time. On the other hand, there is an active trend nowadays throughout the field of computational biology toward abstracted, hierarchical views of biological sequences, which is very much in the spirit of computational linguistics. In the last decades, results and methods in the field of formal language theory that might be applied to the description of biological sequences were pointed out. |
| biology and computer science: Catalyzing Inquiry at the Interface of Computing and Biology National Research Council, Division on Engineering and Physical Sciences, Computer Science and Telecommunications Board, Committee on Frontiers at the Interface of Computing and Biology, 2006-01-01 Advances in computer science and technology and in biology over the last several years have opened up the possibility for computing to help answer fundamental questions in biology and for biology to help with new approaches to computing. Making the most of the research opportunities at the interface of computing and biology requires the active participation of people from both fields. While past attempts have been made in this direction, circumstances today appear to be much more favorable for progress. To help take advantage of these opportunities, this study was requested of the NRC by the National Science Foundation, the Department of Defense, the National Institutes of Health, and the Department of Energy. The report provides the basis for establishing cross-disciplinary collaboration between biology and computing including an analysis of potential impediments and strategies for overcoming them. The report also presents a wealth of examples that should encourage students in the biological sciences to look for ways to enable them to be more effective users of computing in their studies. |
| biology and computer science: Algorithms on Strings, Trees, and Sequences Dan Gusfield, 1997-05-28 String algorithms are a traditional area of study in computer science. In recent years their importance has grown dramatically with the huge increase of electronically stored text and of molecular sequence data (DNA or protein sequences) produced by various genome projects. This book is a general text on computer algorithms for string processing. In addition to pure computer science, the book contains extensive discussions on biological problems that are cast as string problems, and on methods developed to solve them. It emphasises the fundamental ideas and techniques central to today's applications. New approaches to this complex material simplify methods that up to now have been for the specialist alone. With over 400 exercises to reinforce the material and develop additional topics, the book is suitable as a text for graduate or advanced undergraduate students in computer science, computational biology, or bio-informatics. Its discussion of current algorithms and techniques also makes it a reference for professionals. |
| biology and computer science: Molecular Biology - Not Only for Bioinformaticians Wiesława Widłak, 2013-12-05 Bioinformatics, which can be defined as the application of computer science and information technology to the field of biology and medicine, has been rapidly developing over the past few decades. It generates new knowledge as well as the computational tools to create that knowledge. Understanding the basic processes in living organisms is therefore indispensable for bioinformaticians. This book addresses beginners in molecular biology, especially computer scientists who would like to work as bioinformaticians. It presents basic processes in living organisms in a condensed manner. Additionally, principles of several high-throughput technologies in molecular biology, which need the assistance of bioinformaticians, are explained from a biological point of view. It is structured in the following 9 chapters: cells and viruses; protein structure and function; nucleic acids; DNA replication, mutations, and repair; transcription and posttranscriptional processes; synthesis and posttranslational modifications of proteins; cell division; cell signaling pathways; and high-throughput technologies in molecular biology. |
| biology and computer science: Computing for Biologists Ran Libeskind-Hadas, Eliot Bush, 2014-09-22 Computing is revolutionizing the practice of biology. This book, which assumes no prior computing experience, provides students with the tools to write their own Python programs and to understand fundamental concepts in computational biology and bioinformatics. Each major part of the book begins with a compelling biological question, followed by the algorithmic ideas and programming tools necessary to explore it: the origins of pathogenicity are examined using gene finding, the evolutionary history of sex determination systems is studied using sequence alignment, and the origin of modern humans is addressed using phylogenetic methods. In addition to providing general programming skills, this book explores the design of efficient algorithms, simulation, NP-hardness, and the maximum likelihood method, among other key concepts and methods. Easy-to-read and designed to equip students with the skills to write programs for solving a range of biological problems, the book is accompanied by numerous programming exercises, available at www.cs.hmc.edu/CFB. |
| biology and computer science: A Primer for Computational Biology Shawn T. O'Neil, 2017-12-21 A Primer for Computational Biology aims to provide life scientists and students the skills necessary for research in a data-rich world. The text covers accessing and using remote servers via the command-line, writing programs and pipelines for data analysis, and provides useful vocabulary for interdisciplinary work. The book is broken into three parts: Introduction to Unix/Linux: The command-line is the natural environment of scientific computing, and this part covers a wide range of topics, including logging in, working with files and directories, installing programs and writing scripts, and the powerful pipe operator for file and data manipulation. Programming in Python: Python is both a premier language for learning and a common choice in scientific software development. This part covers the basic concepts in programming (data types, if-statements and loops, functions) via examples of DNA-sequence analysis. This part also covers more complex subjects in software development such as objects and classes, modules, and APIs. Programming in R: The R language specializes in statistical data analysis, and is also quite useful for visualizing large datasets. This third part covers the basics of R as a programming language (data types, if-statements, functions, loops and when to use them) as well as techniques for large-scale, multi-test analyses. Other topics include S3 classes and data visualization with ggplot2. |
| biology and computer science: Encyclopedia of Bioinformatics and Computational Biology , 2018-08-21 Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics, Three Volume Set combines elements of computer science, information technology, mathematics, statistics and biotechnology, providing the methodology and in silico solutions to mine biological data and processes. The book covers Theory, Topics and Applications, with a special focus on Integrative –omics and Systems Biology. The theoretical, methodological underpinnings of BCB, including phylogeny are covered, as are more current areas of focus, such as translational bioinformatics, cheminformatics, and environmental informatics. Finally, Applications provide guidance for commonly asked questions. This major reference work spans basic and cutting-edge methodologies authored by leaders in the field, providing an invaluable resource for students, scientists, professionals in research institutes, and a broad swath of researchers in biotechnology and the biomedical and pharmaceutical industries. Brings together information from computer science, information technology, mathematics, statistics and biotechnology Written and reviewed by leading experts in the field, providing a unique and authoritative resource Focuses on the main theoretical and methodological concepts before expanding on specific topics and applications Includes interactive images, multimedia tools and crosslinking to further resources and databases |
| biology and computer science: Artificial Intelligence and Molecular Biology Lawrence Hunter, 1993 These original contributions provide a current sampling of AI approaches to problems of biological significance; they are the first to treat the computational needs of the biology community hand-in-hand with appropriate advances in artificial intelligence. The enormous amount of data generated by the Human Genome Project and other large-scale biological research has created a rich and challenging domain for research in artificial intelligence. These original contributions provide a current sampling of AI approaches to problems of biological significance; they are the first to treat the computational needs of the biology community hand-in-hand with appropriate advances in artificial intelligence. Focusing on novel technologies and approaches, rather than on proven applications, they cover genetic sequence analysis, protein structure representation and prediction, automated data analysis aids, and simulation of biological systems. A brief introductory primer on molecular biology and Al gives computer scientists sufficient background to understand much of the biology discussed in the book. Lawrence Hunter is Director of the Machine Learning Project at the National Library of Medicine, National Institutes of Health. |
| biology and computer science: Fundamentals of Bioinformatics and Computational Biology Gautam B. Singh, 2014-09-24 This book offers comprehensive coverage of all the core topics of bioinformatics, and includes practical examples completed using the MATLAB bioinformatics toolboxTM. It is primarily intended as a textbook for engineering and computer science students attending advanced undergraduate and graduate courses in bioinformatics and computational biology. The book develops bioinformatics concepts from the ground up, starting with an introductory chapter on molecular biology and genetics. This chapter will enable physical science students to fully understand and appreciate the ultimate goals of applying the principles of information technology to challenges in biological data management, sequence analysis, and systems biology. The first part of the book also includes a survey of existing biological databases, tools that have become essential in today’s biotechnology research. The second part of the book covers methodologies for retrieving biological information, including fundamental algorithms for sequence comparison, scoring, and determining evolutionary distance. The main focus of the third part is on modeling biological sequences and patterns as Markov chains. It presents key principles for analyzing and searching for sequences of significant motifs and biomarkers. The last part of the book, dedicated to systems biology, covers phylogenetic analysis and evolutionary tree computations, as well as gene expression analysis with microarrays. In brief, the book offers the ideal hands-on reference guide to the field of bioinformatics and computational biology. |
| biology and computer science: Computer Simulation and Data Analysis in Molecular Biology and Biophysics Victor Bloomfield, 2009-06-05 This book provides an introduction to two important aspects of modern bioch- istry, molecular biology, and biophysics: computer simulation and data analysis. My aim is to introduce the tools that will enable students to learn and use some f- damental methods to construct quantitative models of biological mechanisms, both deterministicandwithsomeelementsofrandomness;tolearnhowconceptsofpr- ability can help to understand important features of DNA sequences; and to apply a useful set of statistical methods to analysis of experimental data. The availability of very capable but inexpensive personal computers and software makes it possible to do such work at a much higher level, but in a much easier way, than ever before. TheExecutiveSummaryofthein?uential2003reportfromtheNationalAcademy of Sciences, “BIO 2010: Transforming Undergraduate Education for Future - search Biologists” [12], begins The interplay of the recombinant DNA, instrumentation, and digital revolutions has p- foundly transformed biological research. The con?uence of these three innovations has led to important discoveries, such as the mapping of the human genome. How biologists design, perform, and analyze experiments is changing swiftly. Biological concepts and models are becoming more quantitative, and biological research has become critically dependent on concepts and methods drawn from other scienti?c disciplines. The connections between the biological sciences and the physical sciences, mathematics, and computer science are rapidly becoming deeper and more extensive. |
| biology and computer science: Genesis Machines Martyn Amos, 2014-04-01 Silicon chips are out. Today's scientists are using real, wet, squishy, living biology to build the next generation of computers. Cells, gels and DNA strands are the 'wetware' of the twenty-first century. Much smaller and more intelligent, these organic computers open up revolutionary possibilities. Tracing the history of computing and revealing a brave new world to come, Genesis Machines describes how this new technology will change the way we think not just about computers - but about life itself |
| biology and computer science: Algorithms in Structural Molecular Biology Bruce R. Donald, 2023-08-15 An overview of algorithms important to computational structural biology that addresses such topics as NMR and design and analysis of proteins.Using the tools of information technology to understand the molecular machinery of the cell offers both challenges and opportunities to computational scientists. Over the past decade, novel algorithms have been developed both for analyzing biological data and for synthetic biology problems such as protein engineering. This book explains the algorithmic foundations and computational approaches underlying areas of structural biology including NMR (nuclear magnetic resonance); X-ray crystallography; and the design and analysis of proteins, peptides, and small molecules. Each chapter offers a concise overview of important concepts, focusing on a key topic in the field. Four chapters offer a short course in algorithmic and computational issues related to NMR structural biology, giving the reader a useful toolkit with which to approach the fascinating yet thorny computational problems in this area. A recurrent theme is understanding the interplay between biophysical experiments and computational algorithms. The text emphasizes the mathematical foundations of structural biology while maintaining a balance between algorithms and a nuanced understanding of experimental data. Three emerging areas, particularly fertile ground for research students, are highlighted: NMR methodology, design of proteins and other molecules, and the modeling of protein flexibility. The next generation of computational structural biologists will need training in geometric algorithms, provably good approximation algorithms, scientific computation, and an array of techniques for handling noise and uncertainty in combinatorial geometry and computational biophysics. This book is an essential guide for young scientists on their way to research success in this exciting field. |
| biology and computer science: Biological Computation Ehud Lamm, Ron Unger, 2011-05-25 The area of biologically inspired computing, or biological computation, involves the development of new, biologically based techniques for solving difficult computational problems. A unified overview of computer science ideas inspired by biology, Biological Computation presents the most fundamental and significant concepts in this area. In the book |
| biology and computer science: Modeling in Computational Biology and Biomedicine Frédéric Cazals, Pierre Kornprobst, 2012-11-06 Computational biology, mathematical biology, biology and biomedicine are currently undergoing spectacular progresses due to a synergy between technological advances and inputs from physics, chemistry, mathematics, statistics and computer science. The goal of this book is to evidence this synergy by describing selected developments in the following fields: bioinformatics, biomedicine and neuroscience. This work is unique in two respects - first, by the variety and scales of systems studied and second, by its presentation: Each chapter provides the biological or medical context, follows up with mathematical or algorithmic developments triggered by a specific problem and concludes with one or two success stories, namely new insights gained thanks to these methodological developments. It also highlights some unsolved and outstanding theoretical questions, with a potentially high impact on these disciplines. Two communities will be particularly interested in this book. The first one is the vast community of applied mathematicians and computer scientists, whose interests should be captured by the added value generated by the application of advanced concepts and algorithms to challenging biological or medical problems. The second is the equally vast community of biologists. Whether scientists or engineers, they will find in this book a clear and self-contained account of concepts and techniques from mathematics and computer science, together with success stories on their favorite systems. The variety of systems described represents a panoply of complementary conceptual tools. On a practical level, the resources listed at the end of each chapter (databases, software) offer invaluable support for getting started on a specific topic in the fields of biomedicine, bioinformatics and neuroscience. |
| biology and computer science: Digital Biology Peter J. Bentley, 2010-05-11 Imagine a future world where computers can create universes -- digital environments made from binary ones and zeros. Imagine that within these universes there exist biological forms that reproduce, grow, and think. Imagine plantlike forms, ant colonies, immune systems, and brains, all adapting, evolving, and getting better at solving problems. Imagine if our computers became greenhouses for a new kind of nature. Just think what digital biology could do for us. Perhaps it could evolve new designs for us, think up ways to detect fraud using digital neurons, or solve scheduling problems with ants. Perhaps it could detect hackers with immune systems or create music from the patterns of growth of digital seashells. Perhaps it would allow our computers to become creative and inventive. Now stop imagining. digital biology is an intriguing glimpse into the future of technology by one of the most creative thinkers working in computer science today. As Peter J. Bentley explains, the next giant step in computing technology is already under way as computer scientists attempt to create digital universes that replicate the natural world. Within these digital universes, we will evolve solutions to problems, construct digital brains that can learn and think, and use immune systems to trap and destroy computer viruses. The biological world is the model for the next generation of computer software. By adapting the principles of biology, computer scientists will make it possible for computers to function as the natural world does. In practical terms, this will mean that we will soon have smart devices, such as houses that will keep the temperature as we like it and automobiles that will start only for drivers they recognize (through voice recognition or other systems) and that will navigate highways safely and with maximum fuel efficiency. Computers will soon be powerful enough and small enough that they can become part of clothing. Digital agents will be able to help us find a bank or restaurant in a city that we have never visited before, even as we walk through the airport. Miniature robots may even be incorporated into our bodies to monitor our health. Digital Biology is also an exploration of biology itself from a new perspective. We must understand how nature works in its most intimate detail before we can use these same biological processes inside our computers. Already scientists engaged in this work have gained new insights into the elegant simplicity of the natural universe. This is a visionary book, written in accessible, nontechnical language, that explains how cutting-edge computer science will shape our world in the coming decades. |
| biology and computer science: Analysis of Biological Data Sanghamitra Bandyopadhyay, 2007 Bioinformatics, a field devoted to the interpretation and analysis of biological data using computational techniques, has evolved tremendously in recent years due to the explosive growth of biological information generated by the scientific community. Soft computing is a consortium of methodologies that work synergistically and provides, in one form or another, flexible information processing capabilities for handling real-life ambiguous situations. Several research articles dealing with the application of soft computing tools to bioinformatics have been published in the recent past; however, they are scattered in different journals, conference proceedings and technical reports, thus causing inconvenience to readers, students and researchers. This book, unique in its nature, is aimed at providing a treatise in a unified framework, with both theoretical and experimental results, describing the basic principles of soft computing and demonstrating the various ways in which they can be used for analyzing biological data in an efficient manner. Interesting research articles from eminent scientists around the world are brought together in a systematic way such that the reader will be able to understand the issues and challenges in this domain, the existing ways of tackling them, recent trends, and future directions. This book is the first of its kind to bring together two important research areas, soft computing and bioinformatics, in order to demonstrate how the tools and techniques in the former can be used for efficiently solving several problems in the latter. Sample Chapter(s). Chapter 1: Bioinformatics: Mining the Massive Data from High Throughput Genomics Experiments (160 KB). Contents: Overview: Bioinformatics: Mining the Massive Data from High Throughput Genomics Experiments (H Tang & S Kim); An Introduction to Soft Computing (A Konar & S Das); Biological Sequence and Structure Analysis: Reconstructing Phylogenies with Memetic Algorithms and Branch-and-Bound (J E Gallardo et al.); Classification of RNA Sequences with Support Vector Machines (J T L Wang & X Wu); Beyond String Algorithms: Protein Sequence Analysis Using Wavelet Transforms (A Krishnan & K-B Li); Filtering Protein Surface Motifs Using Negative Instances of Active Sites Candidates (N L Shrestha & T Ohkawa); Distill: A Machine Learning Approach to Ab Initio Protein Structure Prediction (G Pollastri et al.); In Silico Design of Ligands Using Properties of Target Active Sites (S Bandyopadhyay et al.); Gene Expression and Microarray Data Analysis: Inferring Regulations in a Genomic Network from Gene Expression Profiles (N Noman & H Iba); A Reliable Classification of Gene Clusters for Cancer Samples Using a Hybrid Multi-Objective Evolutionary Procedure (K Deb et al.); Feature Selection for Cancer Classification Using Ant Colony Optimization and Support Vector Machines (A Gupta et al.); Sophisticated Methods for Cancer Classification Using Microarray Data (S-B Cho & H-S Park); Multiobjective Evolutionary Approach to Fuzzy Clustering of Microarray Data (A Mukhopadhyay et al.). Readership: Graduate students and researchers in computer science, bioinformatics, computational and molecular biology, artificial intelligence, data mining, machine learning, electrical engineering, system science; researchers in pharmaceutical industries. |
| biology and computer science: Parallel Computing for Bioinformatics and Computational Biology Albert Y. Zomaya, 2006-04-21 Discover how to streamline complex bioinformatics applications with parallel computing This publication enables readers to handle more complex bioinformatics applications and larger and richer data sets. As the editor clearly shows, using powerful parallel computing tools can lead to significant breakthroughs in deciphering genomes, understanding genetic disease, designing customized drug therapies, and understanding evolution. A broad range of bioinformatics applications is covered with demonstrations on how each one can be parallelized to improve performance and gain faster rates of computation. Current parallel computing techniques and technologies are examined, including distributed computing and grid computing. Readers are provided with a mixture of algorithms, experiments, and simulations that provide not only qualitative but also quantitative insights into the dynamic field of bioinformatics. Parallel Computing for Bioinformatics and Computational Biology is a contributed work that serves as a repository of case studies, collectively demonstrating how parallel computing streamlines difficult problems in bioinformatics and produces better results. Each of the chapters is authored by an established expert in the field and carefully edited to ensure a consistent approach and high standard throughout the publication. The work is organized into five parts: * Algorithms and models * Sequence analysis and microarrays * Phylogenetics * Protein folding * Platforms and enabling technologies Researchers, educators, and students in the field of bioinformatics will discover how high-performance computing can enable them to handle more complex data sets, gain deeper insights, and make new discoveries. |
| biology and computer science: Computer Science Subrata Dasgupta, 2016 While the development of Information Technology has been obvious to all, the underpinning computer science has been less apparent. Subrata Dasgupta provides a thought-provoking introduction to the field and its core principles, considering computer science as a science of symbol processing. |
| biology and computer science: An Introduction to the Mathematics of Biology: with Computer Algebra Models Edward K. Yeargers, James V. Herod, Ronald W. Shonkweiler, 2013-12-01 Biology is a source of fascination for most scientists, whether their training is in the life sciences or not. In particular, there is a special satisfaction in discovering an understanding of biology in the context of another science like mathematics. Fortunately there are plenty of interesting (and fun) problems in biology, and virtually all scientific disciplines have become the richer for it. For example, two major journals, Mathematical Biosciences and Journal of Mathematical Biology, have tripled in size since their inceptions 20-25 years ago. The various sciences have a great deal to give to one another, but there are still too many fences separating them. In writing this book we have adopted the philosophy that mathematical biology is not merely the intrusion of one science into another, but has a unity of its own, in which both the biology and the math ematics should be equal and complete, and should flow smoothly into and out of one another. We have taught mathematical biology with this philosophy in mind and have seen profound changes in the outlooks of our science and engineering students: The attitude of Oh no, another pendulum on a spring problem!, or Yet one more LCD circuit! completely disappeared in the face of applications of mathematics in biology. There is a timeliness in calculating a protocol for ad ministering a drug. |
| biology and computer science: Advanced Scientific Computing in BASIC with Applications in Chemistry, Biology and Pharmacology P Valko, S. Vajda, 1989-01-01 This book gives a practical introduction to numerical methods and presents BASIC subroutines for real-life computations in the areas of chemistry, biology, and pharmacology. The choice of BASIC as the programming language is motivated by its simplicity, its availability on all personal computers and by its power in data acquisition. While most of the scientific packages currently available in BASIC date back to the period of limited memory and speed, the subroutines presented here can handle a broad range of realistic problems with the power and sophistication needed by professionals and with simple, step-by-step instructions for students and beginners.Please note that a diskette containing the 37 program modules and 39 sample programs listed in the book is no longer available.The main task considered in the book is that of extracting useful information from measurements via modelling, simulation, and statistical data evaluations. Efficient and robust numerical methods have been chosen to solve related problems in numerical algebra, nonlinear equations and optimization, parameter estimation, signal processing, and differential equations. For each class of routines an introduction to the relevant theory and techniques is given, so that the reader will recognise and use the appropriate method for solving his or her particular problem. Simple examples illustrate the use and applicability of each method. |
| biology and computer science: Computational Biology and Bioinformatics Ka-Chun Wong, 2016-04-27 The advances in biotechnology such as the next generation sequencing technologies are occurring at breathtaking speed. Advances and breakthroughs give competitive advantages to those who are prepared. However, the driving force behind the positive competition is not only limited to the technological advancement, but also to the companion data analy |
| biology and computer science: Biochemical Adaptation Pater W. Hochachka, George N. Somero, 2014-07-14 This book discusses biochemical adaptation to environments from freezing polar oceans to boiling hot springs, and under hydrostatic pressures up to 1,000 times that at sea level. Originally published in 1984. The Princeton Legacy Library uses the latest print-on-demand technology to again make available previously out-of-print books from the distinguished backlist of Princeton University Press. These editions preserve the original texts of these important books while presenting them in durable paperback and hardcover editions. The goal of the Princeton Legacy Library is to vastly increase access to the rich scholarly heritage found in the thousands of books published by Princeton University Press since its founding in 1905. |
| biology and computer science: Information Processing And Living Systems Vladimir B Bajic, Tin Wee Tan, 2005-06-01 Information processing and information flow occur in the course of an organism's development and throughout its lifespan. Organisms do not exist in isolation, but interact with each other constantly within a complex ecosystem. The relationships between organisms, such as those between prey or predator, host and parasite, and between mating partners, are complex and multidimensional. In all cases, there is constant communication and information flow at many levels.This book focuses on information processing by life forms and the use of information technology in understanding them. Readers are first given a comprehensive overview of biocomputing before navigating the complex terrain of natural processing of biological information using physiological and analogous computing models. The remainder of the book deals with “artificial” processing of biological information as a human endeavor in order to derive new knowledge and gain insight into life forms and their functioning. Specific innovative applications and tools for biological discovery are provided as the link and complement to biocomputing.Since “artificial” processing of biological information is complementary to natural processing, a better understanding of the former helps us improve the latter. Consequently, readers are exposed to both domains and, when dealing with biological problems of their interest, will be better equipped to grasp relevant ideas. |
| biology and computer science: Introduction to Computer-Intensive Methods of Data Analysis in Biology Derek A. Roff, 2006-05-25 Publisher Description |
| biology and computer science: Current Topics in Computational Molecular Biology Tao Jiang, Ying Xu, Michael Q. Zhang, 2002 A survey of current topics in computational molecular biology. Computational molecular biology, or bioinformatics, draws on the disciplines of biology, mathematics, statistics, physics, chemistry, computer science, and engineering. It provides the computational support for functional genomics, which links the behavior of cells, organisms, and populations to the information encoded in the genomes, as well as for structural genomics. At the heart of all large-scale and high-throughput biotechnologies, it has a growing impact on health and medicine. This survey of computational molecular biology covers traditional topics such as protein structure modeling and sequence alignment, and more recent ones such as expression data analysis and comparative genomics. It combines algorithmic, statistical, database, and AI-based methods for studying biological problems. The book also contains an introductory chapter, as well as one on general statistical modeling and computational techniques in molecular biology. Each chapter presents a self-contained review of a specific subject. Not for sale in China, including Hong Kong. |
| biology and computer science: Oxford Textbook of Cancer Biology Francesco Pezzella, Mahvash Tavassoli, David Kerr, 2019-05-02 The study of the biology of tumours has grown to become markedly interdisciplinary, involving chemists, statisticians, epidemiologists, mathematicians, bioinformaticians, and computer scientists alongside biologists, geneticists, and clinicians. The Oxford Textbook of Cancer Biology brings together the most up-to-date developments from different branches of research into one coherent volume, providing a comprehensive and current account of this rapidly evolving field. Structured in eight sections, the book starts with a review of the development and biology of multi-cellular organisms, how they maintain a healthy homeostasis in an individual, and a description of the molecular basis of cancer development. The book then illustrates, as once cells become neoplastic, their signalling network is altered and pathological behaviour follows. It explores the changes that cancer cells can induce in nearby normal tissue, the new relationship established between them and the stroma, and the interaction between the immune system and tumour growth. The authors illustrate the contribution provided by high throughput techniques to map cancer at different levels, from genomic sequencing to cellular metabolic functions, and how information technology, with its vast amounts of data, is integrated with traditional cell biology to provide a global view of the disease. The effect of the different types of treatments on the biology of the neoplastic cells are explored to understand on the one side, why some treatments succeed, and on the other, how they can affect the biology of resistant and recurrent disease. The book concludes by summarizing what we know to date about cancer, and in what direction our understanding of cancer is moving. Edited by leading authorities in the field with an international team of contributors, this book is an essential resource for scholars and professionals working in the wide variety of sub-disciplines that make up today's cancer research and treatment community. It is written not only for consultation, but also for easy cover-to-cover reading. |
| biology and computer science: Developing Bioinformatics Computer Skills Cynthia Gibas, Per Jambeck, 2001 This practical, hands-on guide shows how to develop a structured approach to biological data and the tools needed to analyze it. It's aimed at scientists and students learning computational approaches to biological data, as well as experienced biology researchers starting to use computers to handle data. |
| biology and computer science: Physics & Chemistry Crac, 2007-05-01 Popular among university applicants and their advisers alike, these guides present a wide range of information on a specific degree discipline, laid out in tabular format enabling at-a-glance course comparison. |
| biology and computer science: Systemic Approaches in Bioinformatics and Computational Systems Biology Paola Lecca, Dan Tulpan, Kanagasabai Rajaraman, 2012 This book presents new techniques that have resulted from the application of computer science methods to the organization and interpretation of biological data, covering three subject areas: bioinformatics, computational biology, and computational systems biology-- |
| biology and computer science: Java(tm)2: A Beginner's Guide Herbert Schildt, 2002-12-16 Bestselling author and programming guru Herb Schildt brings you Java 2 essentials in this newly updated introductory guide. Covering the latest I/O classes and features, this book teaches you Java 2 fundamentals through hands-on projects, end-of-module reviews, annotated code samples, and Q&A sections. |
| biology and computer science: Methods in Computational Biology Ross Carlson, Herbert Sauro, 2019-07-03 Modern biology is rapidly becoming a study of large sets of data. Understanding these data sets is a major challenge for most life sciences, including the medical, environmental, and bioprocess fields. Computational biology approaches are essential for leveraging this ongoing revolution in omics data. A primary goal of this Special Issue, entitled “Methods in Computational Biology”, is the communication of computational biology methods, which can extract biological design principles from complex data sets, described in enough detail to permit the reproduction of the results. This issue integrates interdisciplinary researchers such as biologists, computer scientists, engineers, and mathematicians to advance biological systems analysis. The Special Issue contains the following sections: • Reviews of Computational Methods • Computational Analysis of Biological Dynamics: From Molecular to Cellular to Tissue/Consortia Levels • The Interface of Biotic and Abiotic Processes • Processing of Large Data Sets for Enhanced Analysis • Parameter Optimization and Measurement |
| biology and computer science: Simulation Algorithms for Computational Systems Biology Luca Marchetti, Corrado Priami, Vo Hong Thanh, 2017-09-27 This book explains the state-of-the-art algorithms used to simulate biological dynamics. Each technique is theoretically introduced and applied to a set of modeling cases. Starting from basic simulation algorithms, the book also introduces more advanced techniques that support delays, diffusion in space, or that are based on hybrid simulation strategies. This is a valuable self-contained resource for graduate students and practitioners in computer science, biology and bioinformatics. An appendix covers the mathematical background, and the authors include further reading sections in each chapter. |
| biology and computer science: Computational Biology Ralf Blossey, 2006-05-25 Quantitative methods have a particular knack for improving any field they touch. For biology, computational techniques have led to enormous strides in our understanding of biological systems, but there is still vast territory to cover. Statistical physics especially holds great potential for elucidating the structural-functional relationships in bi |
| biology and computer science: Computational Systems Biology of Cancer Emmanuel Barillot, Laurence Calzone, Philippe Hupe, Jean-Philippe Vert, Andrei Zinovyev, 2012-08-25 The future of cancer research and the development of new therapeutic strategies rely on our ability to convert biological and clinical questions into mathematical models—integrating our knowledge of tumour progression mechanisms with the tsunami of information brought by high-throughput technologies such as microarrays and next-generation sequencing. Offering promising insights on how to defeat cancer, the emerging field of systems biology captures the complexity of biological phenomena using mathematical and computational tools. Novel Approaches to Fighting Cancer Drawn from the authors’ decade-long work in the cancer computational systems biology laboratory at Institut Curie (Paris, France), Computational Systems Biology of Cancer explains how to apply computational systems biology approaches to cancer research. The authors provide proven techniques and tools for cancer bioinformatics and systems biology research. Effectively Use Algorithmic Methods and Bioinformatics Tools in Real Biological Applications Suitable for readers in both the computational and life sciences, this self-contained guide assumes very limited background in biology, mathematics, and computer science. It explores how computational systems biology can help fight cancer in three essential aspects: Categorising tumours Finding new targets Designing improved and tailored therapeutic strategies Each chapter introduces a problem, presents applicable concepts and state-of-the-art methods, describes existing tools, illustrates applications using real cases, lists publically available data and software, and includes references to further reading. Some chapters also contain exercises. Figures from the text and scripts/data for reproducing a breast cancer data analysis are available at www.cancer-systems-biology.net. |
| biology and computer science: An Introduction to Systems Biology Uri Alon, 2006-07-07 Thorough and accessible, this book presents the design principles of biological systems, and highlights the recurring circuit elements that make up biological networks. It provides a simple mathematical framework which can be used to understand and even design biological circuits. The textavoids specialist terms, focusing instead on several well-studied biological systems that concisely demonstrate key principles. An Introduction to Systems Biology: Design Principles of Biological Circuits builds a solid foundation for the intuitive understanding of general principles. It encourages the reader to ask why a system is designed in a particular way and then proceeds to answer with simplified models. |
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