Search

Praise for the first edition:… superb, beautifully written and organized work that takes an engineering approach to systems biology. Alon provides nicely written appendices to explain the basic mathematical and biological concepts clearly and succinctly without interfering with the main text. He starts with a mathematical description of transcriptional activation and then describes some basic transcription-network motifs (patterns) that can be combined to form larger networks. – Nature[This text deserves] serious attention from any quantitative scientist who hopes to learn about modern biology … It assumes no prior knowledge of or even interest in biology … One final aspect that must be mentioned is the wonderful set of exercises that accompany each chapter. … Alon's book should become a standard part of the training of graduate students. – Physics TodayWritten for students and researchers, 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 systems. It highlights simple, recurring circuit elements that make up the regulation of cells and tissues. Rigorously classroom-tested, this edition includes new chapters on exciting advances made in the last decade.Features: Includes seven new chapters The new edition has 189 exercises, the previous edition had 66 Offers new examples relevant to human physiology and disease The book website including course videos can be found here: https://www.weizmann.ac.il/mcb/UriAlon/introduction-systems-biology-design-principles-biological-circuits.

A First Course in Systems Biology is an introduction for advanced undergraduate and graduate students to the growing field of systems biology. Its main focus is the development of computational models and their applications to diverse biological systems. The book begins with the fundamentals of modeling, then reviews features of the molecular inventories that bring biological systems to life and discusses case studies that represent some of the frontiers in systems biology and synthetic biology. In this way, it provides the reader with a comprehensive background and access to methods for executing standard systems biology tasks, understanding the modern literature, and launching into specialized courses or projects that address biological questions using theoretical and computational means.New topics in this edition include: default modules for model design, limit cycles and chaos, parameter estimation in Excel, model representations of gene regulation through transcription factors, derivation of the Michaelis-Menten rate law from the original conceptual model, different types of inhibition, hysteresis, a model of differentiation, system adaptation to persistent signals, nonlinear nullclines, PBPK models, and elementary modes. The format is a combination of instructional text and references to primary literature, complemented by sets of small-scale exercises that enable hands-on experience, and large-scale, often open-ended questions for further reflection.

Gene-environment (GE) interaction analysis is a statistical method for clarifying GE interactions applicable to a phenotype or a disease that is the result of interactions between genes and the environment. This book is the first to deal with the theme of GE interaction analysis. It compiles and details cutting-edge research in bioinformatics

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

Since the time of Isaac Newton, physicists have used mathematics to describe the behavior of matter of all sizes, from subatomic particles to galaxies. In the past three decades, as advances in molecular biology have produced an avalanche of data, computational and mathematical techniques have also become necessary tools in the arsenal of biologists. But while quantitative approaches are now providing fundamental insights into biological systems, the college curriculum for biologists has not caught up, and most biology majors are never exposed to the computational and probabilistic mathematical approaches that dominate in biological research. With Quantifying Life, Dmitry A. Kondrashov offers an accessible introduction to the breadth of mathematical modeling used in biology today. Assuming only a foundation in high school mathematics, Quantifying Life takes an innovative computational approach to developing mathematical skills and intuition. Through lessons illustrated with copious examples, mathematical and programming exercises, literature discussion questions, and computational projects of various degrees of difficulty, students build and analyze models based on current research papers and learn to implement them in the R programming language. This interplay of mathematical ideas, systematically developed programming skills, and a broad selection of biological research topics makes Quantifying Life an invaluable guide for seasoned life scientists and the next generation of biologists alike.

Computational Systems Biology: Inference and Modelling provides an introduction to, and overview of, network analysis inference approaches which form the backbone of the model of the complex behavior of biological systems. This book addresses the challenge to integrate highly diverse quantitative approaches into a unified framework by highlighting the relationships existing among network analysis, inference, and modeling. The chapters are light in jargon and technical detail so as to make them accessible to the non-specialist reader. The book is addressed at the heterogeneous public of modelers, biologists, and computer scientists. Provides a unified presentation of network inference, analysis, and modeling Explores the connection between math and systems biology, providing a framework to learn to analyze, infer, simulate, and modulate the behavior of complex biological systems Includes chapters in modular format for learning the basics quickly and in the context of questions posed by systems biology Offers a direct style and flexible formalism all through the exposition of mathematical concepts and biological applications

As a component of post-genome science, the field of proteomics has assumed great prominence in recent years. Whereas quantitative analyses focussed initially on relative quantification, a greater emphasis is now placed on absolute quantification and consideration of proteome dynamics. Coverage of the topic of quantitative proteomics requires consideration both of the analytical fundamentals of quantitative mass spectrometry and the specific demands of the problem being addressed. Quantitative Proteomics aims to outline the state of the art in mass spectrometry-based quantitative proteomics, describing recent advances and current limitations in the instrumentation used, together with the various methods employed for generating high quality data. Details on both strategies describing how stable isotope labelling can be applied and methods for performing quantitative analysis of proteins in a label-free manner are given. The utility of these strategies to understanding cellular protein dynamics are then exemplified with chapters looking at spatial proteomics, dynamics of protein function as determined by quantifying changes in protein post-translational modification and protein turnover. Finally, a key application of these techniques to biomarker discovery and validation is presented, together with the rapidly developing area of quantitative analysis of protein-based foodstuffs. This exemplary book is essential reading for analytical and biological mass spectrometrists working in proteomics research, as well as those undertaking either fundamental or clinical-based investigations with an interest in understanding protein dynamics and/or biomarker assessment.

Practical Bioinformatics is specifically designed for biology majors, with a heavy emphasis on the steps required to perform bioinformatics analysis to answer biological questions. It is written for courses that have a practical, hands-on element and contains many exercises (for example, database searches, protein analysis, data interpretation) to

An Introduction to Bioinformatics is intended to be a complete study companion for the advanced undergraduate or beginning graduate student. It is self-contained in the sense that whatever the starting point may be, the reader will gain insight into bioinformatics. Underlying the work is the belief that bioinformatics is a kind of metaphoric lens through which the entire field of biology can be brought into focus, admittedly as yet imperfect, and understood in a unified way. Reflecting the highly incomplete present state of the field, emphasis is placed on the underlying fundamentals and acquisitions of a broad and comprehensive grasp of the field as a whole. Bioinformatics is interpreted as the application of information science to biology, in which it plays a fundamental and all-pervasive role. This interpretation enables a remarkably unified view of the entire field of biology to be taken and hence offers an excellent entry point into the life sciences for those for whom biology is unfamiliar.

'This book offers information on the state-of-the-art development in the fields of computational biology and systems biology, presenting methods, tools, and applications of these fields by many leading experts around the globe'--Provided by publisher

'This book presents cutting-edge research in the field of computational and systems biology, presenting studies ranging from the atomic/molecular level to the genomic level and covering a wide spectrum of important biological problems and applications'--Provided by publisher.

Mapping the genomic landscapes is one of the most exciting frontiers of science. We have the opportunity to reverse engineer the blueprints and the control systems of living organisms. Computational tools are key enablers in the deciphering process. This book provides an in-depth presentation of some of the important computational biology approaches to genomic sequence analysis. The first section of the book discusses methods for discovering patterns in DNA and RNA. This is followed by the second section that reflects on methods in various ways, including performance, usage and paradigms.

Networks provide a very useful way to describe a wide range of different data types in biology, physics and elsewhere. Apart from providing a convenient tool to visualize highly dependent data, networks allow stringent mathematical and statistical analysis.In recent years, much progress has been achieved to interpret various types of biological network data such as transcriptomic, metabolomic and protein interaction data as well as epidemiological data. Of particular interest is to understand the organization, complexity and dynamics of biological networks and how these are influenced by network evolution and functionality.This book reviews and explores statistical, mathematical and evolutionary theory and tools in the understanding of biological networks. The book is divided into comprehensive and self-contained chapters, each of which focuses on an important biological network type, explains concepts and theory and illustrates how these can be used to obtain insight into biologically relevant processes and questions. There are chapters covering metabolic, transcriptomic, protein interaction and epidemiological networks as well as chapters that deal with theoretical and conceptual material. The authors, who contribute to the book, are active, highly regarded and well-known in the network community.

'This book provides a compendium of terms, definitions, and explanations of concepts, processes, and acronyms'--Provided by publisher.

Cet ouvrage en impression à la demande sera envoyé sous 3 semaines environ (France métropolitaine) et dans un colis séparé en cas de commande avec un autre livre papier.Située à l'interface entre la biologie et l'informatique, la bio-informatique et ses outils font aujourd'hui partie du « paysage » des laboratoires s'intéressant de près ou de loin à la structure, au fonctionnement et à l'évolution des génomes. Pour tous les intervenants, s'approprier les outils d'analyse, de stockage et de visualisation des séquences d'acides nucléiques et d'acides aminés est devenu une nécessité. Ce livre contribuera à une meilleure compréhension des outils à disposition et de leurs principes de fonctionnement et permettra ainsi d'en faire un usage raisonné.

Due to the complexity and non-linearity of most ecological problems, artificial neural networks (ANNs) have attracted attention from ecologists and environmental scientists in recent years. As these networks are increasingly being used in ecology for modeling, simulation, function approximation, prediction, classification and data mining, this unique and self-contained book will be the first comprehensive treatment of this subject, by providing readers with overall and in-depth knowledge on algorithms, programs, and applications of ANNs in ecology. Moreover, a new area of ecology, i.e., computational ecology, is proposed and its scopes and objectives are defined and discussed.Computational Ecology consists of two parts: the first describes the methods and algorithms of ANNs, interpretability and mathematical generalization of neural networks, Matlab neural network toolkit, etc., while the second provides case studies of applications of ANNs in ecology, Matlab codes, and comparisons of ANNs with conventional methods. This publication will be a valuable reference for research scientists, university teachers, graduate students and high-level undergraduates in the areas of ecology, environmental sciences, and computational science.

Biological and biomedical studies have entered a new era over the past two decades thanks to the wide use of mathematical models and computational approaches. A booming of computational biology, which sheerly was a theoretician's fantasy twenty years ago, has become a reality. Obsession with computational biology and theoretical approaches is evidenced in articles hailing the arrival of what are va- ously called quantitative biology, bioinformatics, theoretical biology, and systems biology. New technologies and data resources in genetics, such as the International HapMap project, enable large-scale studies, such as genome-wide association st- ies, which could potentially identify most common genetic variants as well as rare variants of the human DNA that may alter individual's susceptibility to disease and the response to medical treatment. Meanwhile the multi-electrode recording from behaving animals makes it feasible to control the animal mental activity, which could potentially leadto the development of useful brain–machine interfaces. - bracing the sheer volume of genetic, genomic, and other type of data, an essential approach is,?rst of all, to avoid drowning the true signal in the data. It has been witnessed that theoretical approach to biology has emerged as a powerful and st- ulating research paradigm in biological studies, which in turn leads to a new - search paradigm in mathematics, physics, and computer science and moves forward with the interplays among experimental studies and outcomes, simulation studies, and theoretical investigations.

This book presents current advances in the emerging interdisciplinary field of microRNA research of human cancers from a unique perspective of quantitative sciences: bioinformatics, computational and systems biology, and mathematical modeling. This volume contains adaptations and critical reviews of recent state-of-the-art studies, ranging from technological advances in microRNA detection and profiling, clinically oriented microRNA profiling in several human cancers, to a systems biology analysis of global patterns of microRNA regulation of signaling and metabolic pathways. Interactions with transcription factor regulatory networks and mathematical modeling of microRNA regulation are also discussed.

This book covers a wide range of subjects in applying machine learning approaches for bioinformatics projects. The book succeeds on two key unique features. First, it introduces the most widely used machine learning approaches in bioinformatics and discusses, with evaluations from real case studies, how they are used in individual bioinformatics projects. Second, it introduces state-of-the-art bioinformatics research methods. The theoretical parts and the practical parts are well integrated for readers to follow the existing procedures in individual research.Unlike most of the bioinformatics books on the market, the content coverage is not limited to just one subject. A broad spectrum of relevant topics in bioinformatics including systematic data mining and computational systems biology researches are brought together in this book, thereby offering an efficient and convenient platform for teaching purposes.An essential reference for both final year undergraduates and graduate students in universities, as well as a comprehensive handbook for new researchers, this book will also serve as a practical guide for software development in relevant bioinformatics projects.

This volume contains 17 peer-reviewed papers based on the presentations at the 9th Annual International Workshop on Bioinformatics and Systems Biology (IBSB 2009) held at the Life Science Engineering Building of Boston University from July 27 to 29, 2009. This workshop started in 2001 as a platform for doctoral students and young researchers to present and discuss their research results and approaches in bioinformatics and systems biology. It is part of a collaborative educational program involving leading institutions and leaders committed to the following institutions and programs:Boston University Graduate Program in BioinformaticsCharité - Universitätsmedizin BerlinFreie Universität BerlinGlobal COE Program — Center of Education and Research for Advanced Genome-Based Medicine, University of TokyoThe International Research Training Group (IRTG) Genomics and Systems Biology of Molecular NetworksInternational Research and Training Program on Bioinformatics and Systems Biology, Kyoto University Bioinformatics CenterMax-Delbrück Center for Molecular Medicine in BerlinMax Planck Institute for Molecular Genetics in BerlinMax Planck Institute of Molecular Plant Physiology in Potsdam

This volume contains papers presented at the 20th International Conference on Genome Informatics (GIW 2009) held at the Pacifico Yokohama, Japan from December 14 to 16, 2009. The GIW Series provides an international forum for the presentation and discussion of original research papers on all aspects of bioinformatics, computational biology and systems biology. Its scope includes biological sequence analysis, protein structure prediction, genetic regulatory networks, bioinformatic algorithms, comparative genomics, and biomolecular data integration and analysis. Boasting a history of 20 years, GIW is the longest-running international bioinformatics conference.A total of 18 contributed papers were selected for presentation at GIW 2009 and for inclusion in this book. In addition, this book contains abstracts from the five invited speakers: Sean Eddy (HHMI's Janelia Farm, USA), Minoru Kanehisa (Kyoto University, Japan), Sang Yup Lee (KAIST, Korea), Hideyuki Okano (Keio University, Japan) and Mark Ragan (University of Queensland, Australia).

'This book provides methodologies and developments of grid technologies applied in different fields of life sciences'--Provided by publisher.

Bioinformatics is interpreted as the application of information science to biology, in which it plays a fundamental and all-pervasive role. The field continues to develop intensively in both academia and commercially, and is highly interdisciplinary. This broad-ranging and thoroughly updated second edition covers new findings while retaining the successful formula of the original text. Bioinformatics: An Introduction (2nd Edition) is structured into three parts devoted to Information, Biology, and Applications. Every section of the book has been completely revised for currency, and expanded where relevant, to take account of significant new discoveries and realizations of the importance of key concepts. Furthermore, two new chapters provide instruction about algorithms and knowledge representation. Emphases are placed on the underlying fundamentals and on acquisition of a broad and comprehensive grasp of the field as a whole. Features: • Provides a solid foundation in, and self-contained introduction to, the field of bioinformatics and its state-of-the-art as it relates to computational biology research • Offers additional chapters on algorithms and knowledge representation, including text mining [NEW] • Imparts a thorough grounding of core concepts, enabling the reader to understand contemporary work within an optimal context • Contains a greatly expanded chapter on interactions and regulatory networks [NEW] • Includes examples, definitions, problems and a comprehensive and useful bibliography • Presents new experimental methods, and serves as a springboard for new research [NEW] • Incorporates discussion of the method of drawing inferences from abstract sequence analysis based on frequency dictionaries • Contains an extensively revised chapter on medical applications[NEW] • Emphasizes the underlying fundamentals and acquisition of a broad and comprehensive grasp of the field as a whole This significantly improved second edition of a successful textbook is intended to be a complete study companion for the advanced undergraduate or graduate student. It is self-contained, bringing together the multiple disciplines necessary for a profound grasp of the field into a coherent whole, thereby allowing the reader to gain much insight into the state-of-the-art of bioinformatics.

Computational biology involves the use of techniques including applied mathematics, informatics, statistics, computer science, artificial intelligence, chemistry, and biochemistry to solve biological problems usually on the molecular level. The core principle of these techniques is using computing resources in order to solve problems on scales of magnitude far too great for human discernment. Research in computational biology often overlaps with systems biology. Major research efforts in the field include sequence alignment, gene finding, genome assembly, protein structure alignment, protein structure prediction, prediction of gene expression and protein-protein interactions, and the modeling of evolution.

Computational structural biology has made tremendous progress over the last two decades, and this book provides a recent and broad overview of such computational methods in structural biology. It covers the impact of computational structural biology on protein structure prediction methods, macromolecular function and protein design, and key methods in drug discovery. It also addresses the computational challenges of experimental approaches in structural biology. In addition to reviewing the current state of computational structural biology, each chapter ends with a brief, visionary discussion on the future outlook, whereby the main challenges for the coming years are elucidated. Written by an international panel of expert contributors, this book can serve as a reference manual for students and practitioners alike.

Collaborative research in bioinformatics and systems biology is a key element of modern biology and health research. This book highlights and provides access to many of the methods, environments, results and resources involved, including integral laboratory data generation and experimentation and clinical activities. Collaborative projects embody a research paradigm that connects many of the top scientists, institutions, their resources and research worldwide, resulting in first-class contributions to bioinformatics and systems biology. Central themes include describing processes and results in collaborative research projects using computational biology and providing a guide for researchers to access them. The book is also a practical guide on how science is managed. It shows how collaborative researchers are putting results together in a way accessible to the entire biomedical community.

While the field of computational structural biology or structural bioinformatics is rapidly developing, there are few books with a relatively complete coverage of such diverse research subjects studied in the field as X-ray crystallography computing, NMR structure determination, potential energy minimization, dynamics simulation, and knowledge-based modeling. This book helps fill the gap by providing such a survey on all the related subjects. Comprising a collection of lecture notes for a computational structural biology course for the Program on Bioinformatics and Computational Biology at Iowa State University, the book is in essence a comprehensive summary of computational structural biology based on the author's own extensive research experience, and a review of the subject from the perspective of a computer scientist or applied mathematician. Readers will gain a deeper appreciation of the biological importance and mathematical novelty of the research in the field.

Systems biology is the study of organisms as interacting networks of genes, proteins and reactions. Practical Systems Biology provides a detailed overview of the different approaches used in this relatively new discipline, integrating bioinformatics, genomics, proteomics and metabolomics. Various areas of research are also discussed, including the use of computational models of biological processes, and post-genomic research. Each chapter is written by an experienced researcher and gives an excellent account of various issues of systems biology that is suitable for postgraduate and postdoctoral researchers who are interested in this expanding area of science.

This volume presents a timely and comprehensive overview of biological networks at all organization levels in the spirit of the complex systems approach. It discusses the transversal issues and fundamental principles as well as the overall structure, dynamics, and modeling of a wide array of biological networks at the molecular, cellular, and population levels. Anchored in both empirical data and a strong theoretical background, the book therefore lends valuable credence to the complex systems approach.

Every researcher in genomics and proteomics now has access to public domain databases containing literally billions of data entries. However, without the right analytical tools, and an understanding of the biological significance of the data, cataloging and interpreting the molecular evolutionary processes buried in those databases is difficult, if

'Molecular Computation Models: Unconventional Approaches is looking into new computational paradigms from both a theoretical perspective which offers a solid foundation of the models developed, as well as from a modeling angle, in order to reveal their effectiveness in modeling and simulating, especially biological systems. Tools and programming concepts and implementation issues are also discussed in the context of some experiments and comparative studies'--Provided by publisher.

This book constitutes the refereed proceedings of the Brazilian Symposium on Bioinformatics, BSB 2005, held in Sao Leopoldo, Brazil in July 2005. The 15 revised full papers and 10 revised extended abstracts presented together with 3 invited papers were carefully reviewed and selected from 55 submissions. The papers address a broad range of current topics in computational biology and bioinformatics.

This book provides simultaneously a design blueprint, user guide, research agenda, and communication platform for current and future developments in artificial intelligence (AI) approaches to systems biology. It places an emphasis on the molecular dimension of life phenomena and in one chapter on anatomical and functional modeling of the brain. As design blueprint, the book is intended for scientists and other professionals tasked with developing and using AI technologies in the context of life sciences research. As a user guide, this volume addresses the requirements of researchers to gain a basic understanding of key AI methodologies for life sciences research. Its emphasis is not on an intricate mathematical treatment of the presented AI methodologies. Instead, it aims at providing the users with a clear understanding and practical know-how of the methods. As a research agenda, the book is intended for computer and life science students, teachers, researchers, and managers who want to understand the state of the art of the presented methodologies and the areas in which gaps in our knowledge demand further research and development. Our aim was to maintain the readability and accessibility of a textbook throughout the chapters, rather than compiling a mere reference manual. The book is also intended as a communication platform seeking to bride the cultural and technological gap among key systems biology disciplines. To support this function, contributors have adopted a terminology and approach that appeal to audiences from different backgrounds.

A shift in culture Only a decade ago, the?rst thing a molecular biologist would have had to learn when he or she started the lab work was how to handle pipettes, extract DNA, useenzymesandcloneagene. Now,the?rstthing thatheorsheshould learn is how to handle databases and to extract all the information that is already known about the gene that he or she wants to study. In all likelihood, he or she will?nd that the gene has already been sequenced from several organisms, that it was recovered in a variety of EST projects, that expression data are available from microarray and SAGE studies, that it was included in linkage studies, that proteomics data are rapidly accumulating, that lists of interacting proteins are being compiled, that domain structure data are available and that it is part of a network of genetic interactions which is intensively modelled. He or she will discover that all this information resides in many di?erent databases with di?erent data formats and with di?erent levels of analyses and linking. Starting to work on this gene will make sense only, if all this information is put together in a project-speci?c manner and set into the context of what is known about related genes and processes. At this point he or she may decide to walk up to the bioinformatics group in house and ask for help with arranging the data in a useful manner.

Designed primarily as an introduction to realistic modeling methods, Computational Neuroscience: Realistic Modeling for Experimentalists focuses on methodological approaches, selecting appropriate methods, and identifying potential pitfalls. The author addresses varying levels of complexity, from molecular interactions within single neurons to the processing of information by neural networks. He avoids theoretical mathematics and provides just enough of the basic math used by experimentalists.What makes this resource unique is the inclusion of downloadable resources that furnish interactive modeling examples. It contains tutorials and demos, movies and images, and the simulation scripts necessary to run the full simulation described in the chapter examples. Each chapter covers: the theoretical foundation; parameters needed; appropriate software descriptions; evaluation of the model; future directions expected; examples in text boxes linked to the downloadable resources; and references. The first book to bring you cutting-edge developments in neuronal modeling. It provides an introduction to realistic modeling methods at levels of complexity varying from molecular interactions to neural networks. The book and downloadable resources combine to make Computational Neuroscience: Realistic Modeling for Experimentalists the complete package for understanding modeling techniques.

The Pacific Symposium on Biocomputing (PSB) is an international, multidisciplinary conference for the presentation and discussion of current research in the theory and application of computational methods in problems of biological significance. Papers and presentations are rigorously peer-reviewed and are published in an archival volume that will prove to be a valuable reference for all biochemists and computer scientists.PSB-97 will focus on rapidly advancing areas of research in the field.