Computational Biology – serie

This study focuses on the evolving linear structure of the genome, from organelles and prokaryotes through to the multichromosomal nuclear genomes of plants and animals, based on modern genome map and sequence data. The collection brings together the diverse range of combinatorial optimization and probabilistic modeling techniques for analyzing genome rearrangements in the context of clinical cytogenetics, molecular genetics, population biology, microbial genomics, and the domesticated plant and animal genome projects. It critically compares functional and neutral conceptions of genome rearrangement and represents a concerted attack on the modeling and analytical problems associated with gene and genome duplication and with multigene families.

A comprehensive account of genomic rearrangement, focusing on the mechanisms of inversion, translocation, gene and genome duplication and gene transfer and on the patterns that result from them in comparative maps. Includes analyses of genomic sequences in organelles, prokaryotes and eukaryotes as well as comparative maps of the nuclear genomes in higher plants and animals. The book showcases a variety of algorithmic and statistical approaches to rearrangement and map data.

This text is based on a set of not es produced for courses given for gradu- ate students in mathematics, computer science and biochemistry during the academic year 1998-1999 at the University of Turku in Turku and at the Royal Institute of Technology (KTH) in Stockholm. The course in Turku was organized by Professor Mats Gyllenberg's groupl and was also included 2 within the postgraduate program ComBi , a Graduate School in Compu- tational Biology, Bioinformatics, and Biometry, directed by Professor Esko Ukkonen at the University of Helsinki. The purpose of the courses was to give a thorough and systematic intro duc ti on to probabilistic modelling in bioinformatics for advanced undergraduate and graduate students who had a fairly limited background in prob ability theory, but were otherwise well trained in mathematics and were already familiar with at least some of the techniques of algorithmic sequence analysis. Portions of the material have also been lectured at shorter graduate courses and seminars both in Finland and in Sweden. The initial set of notes circulated also for a time outside those two countries via the World Wide Web.The intermediate course in probability theory and techniques of discrete mathematics held by the author at the University College of Sodertorn (Hud- dinge, Sweden) during the academic year 1997-1998 has also influenced the presentation. The opportunity to give this course is hereby gratefully ac- knowledged.

This text is based on a set of not es produced for courses given for gradu- ate students in mathematics, computer science and biochemistry during the academic year 1998-1999 at the University of Turku in Turku and at the Royal Institute of Technology (KTH) in Stockholm. The course in Turku was organized by Professor Mats Gyllenberg's groupl and was also included 2 within the postgraduate program ComBi , a Graduate School in Compu- tational Biology, Bioinformatics, and Biometry, directed by Professor Esko Ukkonen at the University of Helsinki. The purpose of the courses was to give a thorough and systematic intro duc ti on to probabilistic modelling in bioinformatics for advanced undergraduate and graduate students who had a fairly limited background in prob ability theory, but were otherwise well trained in mathematics and were already familiar with at least some of the techniques of algorithmic sequence analysis. Portions of the material have also been lectured at shorter graduate courses and seminars both in Finland and in Sweden. The initial set of notes circulated also for a time outside those two countries via the World Wide Web.The intermediate course in probability theory and techniques of discrete mathematics held by the author at the University College of Sodertorn (Hud- dinge, Sweden) during the academic year 1997-1998 has also influenced the presentation. The opportunity to give this course is hereby gratefully ac- knowledged.

This is the first book on "phylogenetic supertrees", a recent, but controversial development for inferring evolutionary trees. Rather than analyze the combined primary character data directly, supertree construction proceeds by combining the tree topologies derived from those data. This difference in strategy has allowed for the exciting possibility of larger, more complete phylogenies than are otherwise currently possible, with the potential to revolutionize evolutionarily-based research. This book provides a comprehensive look at supertrees, ranging from the methods used to build supertrees to the significance of supertrees to bioinformatic and biological research. Reviews of many the major supertree methods are provided and four new techniques, including a Bayesian implementation of supertrees, are described for the first time. The far-reaching impact of supertrees on biological research is highlighted both in general terms and through specific examples from diverse clades such as flowering plants, even-toed ungulates, and primates. The book also critically examines the many outstanding challenges and problem areas for this relatively new field, showing the way for supertree construction in the age of genomics. Interdisciplinary contributions from the majority of the leading authorities on supertree construction in all areas of the bioinformatic community (biology, computer sciences, and mathematics) will ensure that this book is a valuable reference with wide appeal to anyone interested in phylogenetic inference.

This is the first book on "phylogenetic supertrees", a recent, but controversial development for inferring evolutionary trees. Rather than analyze the combined primary character data directly, supertree construction proceeds by combining the tree topologies derived from those data. This difference in strategy has allowed for the exciting possibility of larger, more complete phylogenies than are otherwise currently possible, with the potential to revolutionize evolutionarily-based research. This book provides a comprehensive look at supertrees, ranging from the methods used to build supertrees to the significance of supertrees to bioinformatic and biological research. Reviews of many the major supertree methods are provided and four new techniques, including a Bayesian implementation of supertrees, are described for the first time. The far-reaching impact of supertrees on biological research is highlighted both in general terms and through specific examples from diverse clades such as flowering plants, even-toed ungulates, and primates. The book also critically examines the many outstanding challenges and problem areas for this relatively new field, showing the way for supertree construction in the age of genomics. Interdisciplinary contributions from the majority of the leading authorities on supertree construction in all areas of the bioinformatic community (biology, computer sciences, and mathematics) will ensure that this book is a valuable reference with wide appeal to anyone interested in phylogenetic inference.

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.

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.

Comparative genomics is a new and emerging ?eld, and with the explosion of ava- able biological sequences the requests for faster, more ef?cient and more robust algorithms to analyze all this data are immense. This book is meant to serve as a self-contained instruction of the state-of-the-art of computational gene ?nding in general and of comparative approaches in particular. It is meant as an overview of the various methods that have been applied in the ?eld, and a quick introduction into how computational gene ?nders are built in general. A beginner to the ?eld could use this book as a guide through to the main points to think about when constructing a gene ?nder, and the main algorithms that are in use. On the other hand, the more experienced gene ?nder should be able to use this book as a reference to different methods and to the main components incorporated in these methods. I have focused on the main uses of the covered methods and avoided much of the technical details and general extensions of the models. In exchange I have tried to supply references to more detailed accounts of the different research areas touched upon. The book, however, makes no claim on being comprehensive.

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.

The emerging, multi-disciplinary field of systems biology is devoted to the study of the relationships between various parts of a biological system, and computer modeling plays a vital role in the drive to understand the processes of life from an holistic viewpoint. Advancements in experimental technologies in biology and medicine have generated an enormous amount of biological data on the dependencies and interactions of many different molecular cell processes, fueling the development of numerous computational methods for exploring this data. The mathematical formalism of Petri net theory is able to encompass many of these techniques.This essential text/reference presents a comprehensive overview of cutting-edge research in applications of Petri nets in systems biology, with contributions from an international selection of experts. Those unfamiliar with the field are also provided with a general introduction to systems biology, the foundations of biochemistry, and the basics of Petri net theory. Further chapters address Petri net modeling techniques for building and analyzing biological models, as well as network prediction approaches, before reviewing the applications to networks of different biological classification.Topics and features: investigates the modular, qualitative modeling of regulatory networks using Petri nets, and examines an Hybrid Functional Petri net simulation case study; contains a glossary of the concepts and notation used in the book, in addition to exercises at the end of each chapter; covers the topological analysis of metabolic and regulatory networks, the analysis of models of signaling networks, and the prediction of network structure; provides a biological case study on the conversion of logical networks into Petri nets; discusses discrete modeling, stochastic modeling, fuzzy modeling, dynamic pathway modeling, genetic regulatory network modeling, and quantitative analysis techniques; includes a Foreword by Professor Jens Reich, Professor of Bioinformatics at Humboldt University and Max Delbrück Center for Molecular Medicine in Berlin.This unique guide to the modeling of biochemical systems using Petri net concepts will be of real utility to researchers and students of computational biology, systems biology, bioinformatics, computer science, and biochemistry.

This authoritative text/reference presents a review of the history, current status, and potential future directions of computational biology in molecular evolution. Gathering together the unique insights of an international selection of prestigious researchers, this must-read volume examines the latest developments in the field, the challenges that remain, and the new avenues emerging from the growing influx of sequence data. These viewpoints build upon the pioneering work of David Sankoff, one of the founding fathers of computational biology, and mark the 50th anniversary of his first scientific article. The broad spectrum of rich contributions in this essential collection will appeal to all computer scientists, mathematicians and biologists involved in comparative genomics, phylogenetics and related areas.

Thistimely text presents a comprehensive guide to genetic association, a new andrapidly expanding field that aims to elucidate how our genetic code (genotypes)influences the traits we possess (phenotypes). The book provides a detailedreview of methods of gene mapping used in association with experimentalcrosses, as well as genome-wide association studies. Emphasis is placed onmodel selection procedures for analyzing data from large-scale genome scansbased on specifically designed modifications of the Bayesian informationcriterion. Features: presents a thorough introduction to the theoreticalbackground to studies of genetic association (both genetic and statistical); reviewsthe latest advances in the field; illustrates the properties of methods formapping quantitative trait loci using computer simulations and the analysis ofreal data; discusses open challenges; includes an extensive statisticalappendix as a reference for those who are not totally familiar with thefundamentals of statistics.

This book presents a guide to building computational gene finders, and describes the state of the art in computational gene finding methods, with a focus on comparative approaches. Fully updated and expanded, this new edition examines next-generation sequencing (NGS) technology. The book also discusses conditional random fields, enhancing the broad coverage of topics spanning probability theory, statistics, information theory, optimization theory and numerical analysis. Features: introduces the fundamental terms and concepts in the field; discusses algorithms for single-species gene finding, and approaches to pairwise and multiple sequence alignments, then describes how the strengths in both areas can be combined to improve the accuracy of gene finding; explores the gene features most commonly captured by a computational gene model, and explains the basics of parameter training; illustrates how to implement a comparative gene finder; examines NGS techniques and how to build a genome annotation pipeline.

This comprehensive textbook presents a self-contained guide to bioinformatics, defined in its broadest sense as the application of information science to biology. Thoroughly updated and greatly expanded, this third edition now includes material on the growing array of “-omics”; covering metagenomics, toxicogenomics, glycomics, lipidomics, microbiomics and phenomics. New chapters have also been added on ecosystems management and the nervous system. Emphasis is placed on providing both a firm grounding in the core concepts and a clear overview of the complete field of bioinformatics. Features: explains the fundamentals of information science relevant to biology; covers both organismal (ontogeny and phylogeny, as well as genome structure) and molecular aspects; examines the most important practical applications of bioinformatics, providing detailed descriptions of both the experimental process and the data analysis; provides a varied selection of problems throughout the book, to stimulate further thinking.

Today, as hundreds of genomes have been sequenced and thousands of proteins and more than ten thousand metabolites have been identi?ed, navigating safely through this wealth of information without getting completely lost has become crucial for research in, and teaching of, molecular biology. Consequently, a considerable number of tools have been developed and put on the market in the last two decades that describe the multitude of potential/putative interactions between genes, proteins, metabolites, and other biologically relevant compounds in terms of metabolic, genetic, signaling, and other networks, their aim being to support all sorts of explorations through bio-data bases currently called Systems Biology. As a result, navigating safely through this wealth of information-processing tools has become equally crucial for successful work in molecular biology. To help perform such navigation tasks successfully, this book starts by providing an extremely useful overview of existing tools for ?nding (or designing) and inv- tigating metabolic, genetic, signaling, and other network databases, addressing also user-relevant practical questions like • Is the database viewable through a web browser? • Is there a licensing fee? • What is the data type (metabolic, gene regulatory, signaling, etc. )? • Is the database developed/maintained by a curator or a computer? • Is there any software for editing pathways? • Is it possible to simulate the pathway? It then goes on to introduce a speci?c such tool, that is, the fabulous “Cell - lustrator 3. 0” tool developed by the authors.

This book presents a guide to building computational gene finders, and describes the state of the art in computational gene finding methods, with a focus on comparative approaches. Fully updated and expanded, this new edition examines next-generation sequencing (NGS) technology. The book also discusses conditional random fields, enhancing the broad coverage of topics spanning probability theory, statistics, information theory, optimization theory and numerical analysis. Features: introduces the fundamental terms and concepts in the field; discusses algorithms for single-species gene finding, and approaches to pairwise and multiple sequence alignments, then describes how the strengths in both areas can be combined to improve the accuracy of gene finding; explores the gene features most commonly captured by a computational gene model, and explains the basics of parameter training; illustrates how to implement a comparative gene finder; examines NGS techniques and how to build a genome annotation pipeline.

This authoritative text/reference presents a review of the history, current status, and potential future directions of computational biology in molecular evolution. Gathering together the unique insights of an international selection of prestigious researchers, this must-read volume examines the latest developments in the field, the challenges that remain, and the new avenues emerging from the growing influx of sequence data. These viewpoints build upon the pioneering work of David Sankoff, one of the founding fathers of computational biology, and mark the 50th anniversary of his first scientific article. The broad spectrum of rich contributions in this essential collection will appeal to all computer scientists, mathematicians and biologists involved in comparative genomics, phylogenetics and related areas.

This book is the first of its kind to explain the fundamentals of evolutionary genomics. The comprehensive coverage includes concise descriptions of a variety of genome organizations, a thorough discussion of the methods used, and a detailed review of genome sequence processing procedures. The opening chapters also provide the necessary basics for readers unfamiliar with evolutionary studies. Features: introduces the basics of molecular biology, DNA replication, mutation, phylogeny, neutral evolution, and natural selection; presents a brief evolutionary history of life from the primordial seas to the emergence of humans; describes the genomes of prokaryotes, eukaryotes, vertebrates, and humans; reviews methods for genome sequencing, phenotype data collection, homology searches and analysis, and phylogenetic tree and network building; discusses databases of genome sequences and related information, evolutionary distances, and population genomics; provides supplementary material at an associated website.

Thistimely text presents a comprehensive guide to genetic association, a new andrapidly expanding field that aims to elucidate how our genetic code (genotypes)influences the traits we possess (phenotypes). The book provides a detailedreview of methods of gene mapping used in association with experimentalcrosses, as well as genome-wide association studies. Emphasis is placed onmodel selection procedures for analyzing data from large-scale genome scansbased on specifically designed modifications of the Bayesian informationcriterion. Features: presents a thorough introduction to the theoreticalbackground to studies of genetic association (both genetic and statistical); reviewsthe latest advances in the field; illustrates the properties of methods formapping quantitative trait loci using computer simulations and the analysis ofreal data; discusses open challenges; includes an extensive statisticalappendix as a reference for those who are not totally familiar with thefundamentals of statistics.

This book is about the ontology of anatomy. With respect to the individual ?elds of ontology and anatomy, the ontology of anatomy has aspects of both an old and a new topic area. A new aspect for anatomy is that the ontology of anatomy brings medicine together with molecular biology and its related subjects. Similarly, for the ?eld of ontology, biomedical informatics has seen an explosion in the use of onto- gies and ontology-like resources. There has been a particular interest in ontologies for human anatomy and also the anatomy of other types of organism. This explosion has pushed the ?eld of ontology into the limelight, with new practical applications of ontology being developed and new formalisms to accommodate the things that biologists need to say. The ontology of anatomy covers a broad spectrum of life sciences, but why should medics and geneticists, molecular biologists, etc. really be so interested in anatomy? For medics, the reason for this interest is seemingly self evident—medical things happen to bodies and bits of the body. Surgical procedures are carried out on body parts; illnesses and injuries happen to the body and parts of the body. So, if we are to describe medicine, we need to start with anatomy. For molecular biologists, it is often not immediately obvious that biology and medicine join at the level of anatomy, especially in the study of disease processes and the treatment of disease, particularly through drug action.

Integrates different approaches from bioinformatics, biochemistry, computational analysis and systems biology to offer the reader a global view of the diverse data on protein-protein interactions and protein interaction networksProtein-protein interactions and networks: identification, analysis and prediction brings together the descriptions of experimental techniques and expounds on different computational algorithms for protein network analysis and prediction of protein and domain interactions Each chapter contains a description of the problem, a review of methods and algorithms, a list of online resources, conclusions and future directionsOffering broad coverage on the topic, this book will be of interest to researchers from academia and the bioinformatics industry; will also be of use to graduate students studying the topic

Overview and Goals This book describes how to visualize and compare bacterial genomes. Sequencing technologies are becoming so inexpensive that soon going for a cup of coffee will be more expensive than sequencing a bacterial genome. Thus, there is a very real and pressing need for high-throughput computational methods to compare hundreds and thousands of bacterial genomes. It is a long road from molecular biology to systems biology, and in a sense this text can be thought of as a path bridging these ? elds. The goal of this book is to p- vide a coherent set of tools and a methodological framework for starting with raw DNA sequences and producing fully annotated genome sequences, and then using these to build up and test models about groups of interacting organisms within an environment or ecological niche. Organization and Features The text is divided into four main parts: Introduction, Comparative Genomics, Transcriptomics and Proteomics, and ? nally Microbial Communities. The ? rst ? ve chapters are introductions of various sorts. Each of these chapters represents an introduction to a speci? c scienti? c ? eld, to bring all readers up to the same basic level before proceeding on to the methods of comparing genomes. First, a brief overview of molecular biology and of the concept of sequences as biological inf- mation are given.

Biomolecular sequence comparison is the origin of bioinformatics. This book gives a complete in-depth treatment of the study of sequence comparison.A comprehensive introduction is followed by a focus on alignment algorithms and techniques, proceeded by a discussion of the theory. The book examines alignment methods and techniques, features a new issue of sequence comparison - the spaced seed technique, addresses several new flexible strategies for coping with various scoring schemes, and covers the theory on the significance of high-scoring segment pairs between two unalignment sequences. Useful appendices on basic concepts in molecular biology, primer in statistics and software for sequence alignment are included in this reader-friendly text, as well as chapter-ending exercise and research questionsA state-of-the-art study of sequence alignment and homology search, this is an ideal reference for advanced students studying bioinformatics and will appeal to biologists who wish to know how to use homology search tools.

Today, as hundreds of genomes have been sequenced and thousands of proteins and more than ten thousand metabolites have been identi?ed, navigating safely through this wealth of information without getting completely lost has become crucial for research in, and teaching of, molecular biology. Consequently, a considerable number of tools have been developed and put on the market in the last two decades that describe the multitude of potential/putative interactions between genes, proteins, metabolites, and other biologically relevant compounds in terms of metabolic, genetic, signaling, and other networks, their aim being to support all sorts of explorations through bio-data bases currently called Systems Biology. As a result, navigating safely through this wealth of information-processing tools has become equally crucial for successful work in molecular biology. To help perform such navigation tasks successfully, this book starts by providing an extremely useful overview of existing tools for ?nding (or designing) and inv- tigating metabolic, genetic, signaling, and other network databases, addressing also user-relevant practical questions like • Is the database viewable through a web browser? • Is there a licensing fee? • What is the data type (metabolic, gene regulatory, signaling, etc. )? • Is the database developed/maintained by a curator or a computer? • Is there any software for editing pathways? • Is it possible to simulate the pathway? It then goes on to introduce a speci?c such tool, that is, the fabulous “Cell - lustrator 3. 0” tool developed by the authors.

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.

The emerging, multi-disciplinary field of systems biology is devoted to the study of the relationships between various parts of a biological system, and computer modeling plays a vital role in the drive to understand the processes of life from an holistic viewpoint. Advancements in experimental technologies in biology and medicine have generated an enormous amount of biological data on the dependencies and interactions of many different molecular cell processes, fueling the development of numerous computational methods for exploring this data. The mathematical formalism of Petri net theory is able to encompass many of these techniques.This essential text/reference presents a comprehensive overview of cutting-edge research in applications of Petri nets in systems biology, with contributions from an international selection of experts. Those unfamiliar with the field are also provided with a general introduction to systems biology, the foundations of biochemistry, and the basics of Petri net theory. Further chapters address Petri net modeling techniques for building and analyzing biological models, as well as network prediction approaches, before reviewing the applications to networks of different biological classification.Topics and features: investigates the modular, qualitative modeling of regulatory networks using Petri nets, and examines an Hybrid Functional Petri net simulation case study; contains a glossary of the concepts and notation used in the book, in addition to exercises at the end of each chapter; covers the topological analysis of metabolic and regulatory networks, the analysis of models of signaling networks, and the prediction of network structure; provides a biological case study on the conversion of logical networks into Petri nets; discusses discrete modeling, stochastic modeling, fuzzy modeling, dynamic pathway modeling, genetic regulatory network modeling, and quantitative analysis techniques; includes a Foreword by Professor Jens Reich, Professor of Bioinformatics at Humboldt University and Max Delbrück Center for Molecular Medicine in Berlin.This unique guide to the modeling of biochemical systems using Petri net concepts will be of real utility to researchers and students of computational biology, systems biology, bioinformatics, computer science, and biochemistry.

This book is about the ontology of anatomy. With respect to the individual ?elds of ontology and anatomy, the ontology of anatomy has aspects of both an old and a new topic area. A new aspect for anatomy is that the ontology of anatomy brings medicine together with molecular biology and its related subjects. Similarly, for the ?eld of ontology, biomedical informatics has seen an explosion in the use of onto- gies and ontology-like resources. There has been a particular interest in ontologies for human anatomy and also the anatomy of other types of organism. This explosion has pushed the ?eld of ontology into the limelight, with new practical applications of ontology being developed and new formalisms to accommodate the things that biologists need to say. The ontology of anatomy covers a broad spectrum of life sciences, but why should medics and geneticists, molecular biologists, etc. really be so interested in anatomy? For medics, the reason for this interest is seemingly self evident—medical things happen to bodies and bits of the body. Surgical procedures are carried out on body parts; illnesses and injuries happen to the body and parts of the body. So, if we are to describe medicine, we need to start with anatomy. For molecular biologists, it is often not immediately obvious that biology and medicine join at the level of anatomy, especially in the study of disease processes and the treatment of disease, particularly through drug action.

Integrates different approaches from bioinformatics, biochemistry, computational analysis and systems biology to offer the reader a global view of the diverse data on protein-protein interactions and protein interaction networksProtein-protein interactions and networks: identification, analysis and prediction brings together the descriptions of experimental techniques and expounds on different computational algorithms for protein network analysis and prediction of protein and domain interactions Each chapter contains a description of the problem, a review of methods and algorithms, a list of online resources, conclusions and future directionsOffering broad coverage on the topic, this book will be of interest to researchers from academia and the bioinformatics industry; will also be of use to graduate students studying the topic