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The new research area of genomics-inspired network biology lacks an introductory book that enables both physical/computational scientists and biologists to obtain a general yet sufficiently rigorous perspective of current thinking. Filling this gap, Introduction to Biological Networks provides a thorough introduction to genomics-inspired network biology for physical scientists and biologists involved in interdisciplinary research. The book focuses on the concept of molecular and genetic interaction networks as a paradigm for interpreting the complexity of molecular biology at a genomic scale. The authors describe the experimental methods used to discover and test networks of interaction among biological molecules. They also present computational methods for predicting the interaction networks, discuss general mechanisms of network formation and evolution, and explore the application of network approaches to important problems in biology and medicine. With many examples throughout and clear explanations of key concepts, this book is the first to offer a broad treatment of genomics-inspired network biology with sufficient mathematical and biological rigor. It gives readers a conceptual understanding of this burgeoning scientific field.
This resource aims to help the practitioners of the "new biology" revolution, the molecular biologists who are more at home at a laboratory bench than in front of a computer keyboard, to use the Internet more effectively. It provides a broad introductionto using Internet based computing resources to support research in molecular biology.
While extremely large datasets describing gene sequences, mRNA transcripts, protein abundance, and metabolite concentrations are increasingly commonplace, these represent only starting ‘parts lists’ that are usually insufficient to unlock mechanistic insights on their own right. Fortunately, as Network Biology: Methods and Applications examines, concepts emerging from the study of biological entities such as networks (e.g. functional interactions linking genes, proteins, metabolites, etc.) suggest that order rather than chaos prevails, with such principles as modular and hierarchical organization, reactive information-driven causal-response behaviours, systems robustness, co-evolution, and self-organization guiding the way. This volume presents detailed, practical descriptions of the experimental and computational approaches currently prevalent in network biology as written by practiced experts in the field. Written in the highly successful Methods in Molecular BiologyTM series format, relevant chapters contain introductions to their respective topics, lists of the necessary materials, step-by-step, readily reproducible protocols, and tips on troubleshooting and avoiding known pitfalls. Comprehensive and accessible, Network Biology: Methods and Applications provides an ensemble of procedures that will be of great value to a broad assortment of readers, ranging from graduate students to seasoned professionals looking to polish their skill sets.
Alternative techniques and tools for analyzing biomolecular networks With the recent rapid advances in molecular biology, high-throughput experimental methods have resulted in enormous amounts of data that can be used to study biomolecular networks in living organisms. With this development has come recognition of the fact that a complicated living organism cannot be fully understood by merely analyzing individual components. Rather, it is the interactions of components or biomolecular networks that are ultimately responsible for an organism's form and function. This book addresses the important need for a new set of computational tools to reveal essential biological mechanisms from a systems biology approach. Readers will get comprehensive coverage of analyzing biomolecular networks in cellular systems based on available experimental data with an emphasis on the aspects of network, system, integration, and engineering. Each topic is treated in depth with specific biological problems and novel computational methods: GENE NETWORKS—Transcriptional regulation; reconstruction of gene regulatory networks; and inference of transcriptional regulatory networks PROTEIN INTERACTION NETWORKS—Prediction of protein-protein interactions; topological structure of biomolecular networks; alignment of biomolecular networks; and network-based prediction of protein function METABOLIC NETWORKS AND SIGNALING NETWORKS—Analysis, reconstruction, and applications of metabolic networks; modeling and inference of signaling networks; and other topics and new trends In addition to theoretical results and methods, many computational software tools are referenced and available from the authors' Web sites. Biomolecular Networks is an indispensable reference for researchers and graduate students in bioinformatics, computational biology, systems biology, computer science, and applied mathematics.
Modeling Biomolecular Networks in Cells shows how the interaction between the molecular components of basic living organisms can be modelled mathematically and the models used to create artificial biological entities within cells. Such forward engineering is a difficult task but the nonlinear dynamical methods espoused in this book simplify the biology so that it can be successfully understood and the synthesis of simple biological oscillators and rhythm-generators made feasible. Such simple units can then be co-ordinated using intercellular signal biomolecules. The formation of such man-made multicellular networks with a view to the production of biosensors, logic gates, new forms of integrated circuitry based on "gene-chips" and even biological computers is an important step in the design of faster and more flexible "electronics". The book also provides theoretical frameworks and tools with which to analyze the nonlinear dynamical phenomena which arise from the connection of building units in a biomolecular network.
This book serves as an introduction to the myriad computational approaches to gene regulatory modeling and analysis, and is written specifically with experimental biologists in mind. Mathematical jargon is avoided and explanations are given in intuitive terms. In cases where equations are unavoidable, they are derived from first principles or, at the very least, an intuitive description is provided. Extensive examples and a large number of model descriptions are provided for use in both classroom exercises as well as self-guided exploration and learning. As such, the book is ideal for self-learning and also as the basis of a semester-long course for undergraduate and graduate students in molecular biology, bioengineering, genome sciences, or systems biology.
This volume contains the papers presented at the 9th Annual International Conference on Research in Computational Molecular Biology (RECOMB 2005), which was held in Cambridge, Massachusetts, on May 14–18, 2005. The RECOMB conference series was started in 1997 by Sorin Istrail, Pavel Pevzner and Michael Waterman. The list of previous meetings is shown below in the s- tion “Previous RECOMB Meetings. ” RECOMB 2005 was hosted by the Broad Institute of MIT and Harvard, and Boston University’s Center for Advanced - nomic Technology, and was excellently organized by the Organizing Committee Co-chairs Jill Mesirov and Simon Kasif. This year, 217 papers were submitted, of which the Program Committee - lected 39 for presentation at the meeting and inclusion in this proceedings. Each submission was refereed by at least three members of the Program Committee. After the completion of the referees’ reports, an extensive Web-based discussion took place for making decisions. From RECOMB 2005, the Steering Committee decided to publish the proceedings as a volume of Lecture Notes in Bioinf- matics (LNBI) for which the founders of RECOMB are also the editors. The prominent volume number LNBI 3500 was assigned to this proceedings. The RECOMB conference series is closely associated with the Journal of Compu- tional Biology which traditionally publishes special issues devoted to presenting full versions of selected conference papers. The RECOMB Program Committee consistedof42members,aslistedonaseparatepage. Iwouldliketothank the RECOMB 2005 Program Committee members for their dedication and hard work.
Big data, genomics, and quantitative approaches to network-based analysis are combining to advance the frontiers of medicine as never before. With contributions from leading experts, Network Medicine introduces this rapidly evolving field of research, which promises to revolutionize the diagnosis and treatment of human diseases.
Introduces biological concepts and biotechnologies producing the data, graph and network theory, cluster analysis and machine learning, using real-world biological and medical examples.