Bøker i Chapman & Hall/CRC Computational Biology Series-serien

Molecular biologists are performing increasingly large and complicated experiments, but often have little background in data analysis. The book is devoted to teaching the statistical and computational techniques molecular biologists need to analyze their data. It explains the big-picture concepts in data analysis using a wide variety of real-world molecular biological examples such as eQTLs, ortholog identification, motif finding, inference of population structure, protein fold prediction and many more. The book takes a pragmatic approach, focusing on techniques that are based on elegant mathematics yet are the simplest to explain to scientists with little background in computers and statistics.

From the elucidation and analysis of a genomic sequence to the prediction of a protein structure and the identification of the molecular function, this book describes the rationale and limitations of the bioinformatics methods and tools that can help solve biological problems. It addresses the ways to store and retrieve biological data.

Introduction to Proteins shows how proteins can be analyzed in multiple ways. It refers to the roles of proteins and enzymes in diverse contexts and everyday applications, including medical disorders, drugs, toxins, chemical warfare, and animal behavior.

Focusing on protein classification and meta-organization, this book describes methods for detecting self-organization in complex biological systems. It presents the analysis of biological entities and their cellular counterparts and discusses methods for detecting the building blocks of proteins and for prediction of protein-protein interactions.

This book tackles the steps and problems involved with protein analysis, classification, and meta-organization. It analyzes individual entities as well as more complex entities, from protein families to interactions, cellular pathways, and gene networks. The first part of the book presents methods for identifying the building blocks of the prote

This will be the first book to systematically describe the process and provide corresponding methods for analysing data generated from genetic- and epigenetic-studies. Specifically, the book aims to provide a "pipe line" for genetic and epigenetic data analysis starting from raw genome- and epigenome-scale data.

Applying complex systems science to biology, this book develops mathematical models for understanding biological systems. Based on his one-semester course, the author suggests appropriate control strategies to mediate the effects of past and future pandemics, assuming no prior knowledge of mathematics. Each chapter presents exercises with worked solutions as well as computational and research projects. Topics covered include pattern formation and flocking behavior, the interaction of autonomous agents, hierarchical and structured network topologies, epidemiology, biomedical signal processing, computational neurophysiology, and population dynamics. A solutions manual is available for qualifying instructors.

Written for students and researchers in systems biology, the second edition of this best-selling textbook continues to offer a clear presentation of design principles that govern the structure and behavior of biological networks, highlighting simple, recurring circuit elements that make up the regulation of cells and tissues.

This will be the first book to systematically describe the process and provide corresponding methods for analysing data generated from genetic- and epigenetic-studies. Specifically, the book aims to provide a "pipe line" for genetic and epigenetic data analysis starting from raw genome- and epigenome-scale data.

This self-contained guide enables researchers to examine differential expression at gene, exon, and transcript levels and to discover novel genes, transcripts, and whole transcriptomes. It takes readers through the whole data analysis workflow. Each chapter starts with theoretical background, followed by descriptions of relevant analysis tools and practical examples. Accessible to both bioinformaticians and nonprogramming wet lab scientists, the examples illustrate the use of command-line tools, R, and other open source tools.

This book has fundamental theoretical and practical aspects of data analysis, useful for beginners and experienced researchers that are looking for a recipe or an analysis approach. Since R has many packages, even experienced researchers look for how particular functions are used in an analysis workflow.

Emphasizing the search for patterns within and between biological sequences, trees, and graphs, this book shows how combinatorial pattern matching algorithms can solve computational biology problems that arise in the analysis of genomic, transcriptomic, proteomic, metabolomic, and interactomic data.

Replaces the commercial software with the open source R computing environment. This title contains chapters on cutting-edge microarray topics and provides the R code on an accompanying CD-ROM. It bridges the gap between an introduction to data analysis and advanced material for performing data analysis.

The SeqAn project was initiated to offer access to the algorithms needed by researchers in computational biology and bioinformatics. This book helps you in rapid prototyping of algorithms in the field. It is suitable for bioinformaticians.

Presents the modeling, analysis and design methods for systems biology. This work discusses how to examine experimental data to learn about mathematical models, develop efficient abstraction and simulation methods to analyze these models, and use analytical methods to design new circuits.

This book is intended to be an introductory bioinformatics textbook for mathematicians and computer scientists. It focuses on using algorithms and discrete mathematics to solve biological problems. The book systematically describes biological applications, the corresponding mathematical/computational problems, and various algorithmic solutions. It also discusses the practical use of various algorithmic methods and describes what algorithms should be used in different situations.

Microarrays allow researchers to simultaneously monitor the expression of thousands of genes. Independent of the platform and analysis methods used, the result of a microarray experiment is a list of differentially expressed genes. Presenting a unified analysis of the field, this book explores the tools available to better understand the underlying biological phenomena of differentially expressed genes. It focuses on two major analytic approaches: 1) ontological profiling and 2) gene interaction networks and known pathways. The author presents the fundamentals and tools for each approach.

This second edition incorporates important advancements made in bioinformatics over the last decade. Focusing on practical problem solving, it works through sequence analysis from simple gene family analysis to expression analysis with microarrays and next-gen sequencing.

The past three decades have witnessed an explosion of what is now referred to as high-dimensional `omics' data. This book describes the statistical methods and analytic frameworks that are best equipped to interpret these complex data and how they apply to health-related research.

Exploring high speed computational methods to extrapolate to the rest of the protein universe, this book considers the most significant problems occupying those looking to identify the biological properties and functional roles of proteins.

Covers the various informatics methods pertaining to the study of glycans, which provide crucial functional roles in many biological processes. This book supplies the necessary background information, including glycan classes, motifs, and nomenclature. It offers a list of relevant databases and resources on glycobiology.

Presents techniques for analyzing data from modern biological studies that involve multiple data sets, either of the same type or multiple data sources. This book addresses the combination of similar data types: genotype data from genome-wide linkage scans and data derived from microarray gene expression experiments.

From the elucidation and analysis of a genomic sequence to the prediction of a protein structure and the identification of the molecular function, this book describes the rationale and limitations of the bioinformatics methods and tools that can help solve biological problems. It addresses the ways to store and retrieve biological data.

Gene expression studies merge three disciplines with different historical backgrounds: molecular biology, bioinformatics, and biostatistics. This book explains the entire process of a gene expression study from conception to interpretation. It describes technical and statistical methods conceptually with illustrative examples.

Suitable for advanced undergraduates in computer science programs, this title covers major themes of bio-inspired computing, including cellular automata, molecular computation, genetic algorithms, and neural networks. It provides theoretical and coding exercises.