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Author: Gerard Cagney
Publisher: Humana Press
ISBN: 9781617792755
Category : Science
Languages : en
Pages : 0
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Book Description
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.
Author: Gerard Cagney
Publisher: Humana Press
ISBN: 9781617792755
Category : Science
Languages : en
Pages : 0
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Book Description
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.
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ISBN: 9780815332183
Category : Cells
Languages : en
Pages : 0
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Book Description
Author: Jesse M. Lingeman
Publisher: Springer Science & Business Media
ISBN: 1461431131
Category : Computers
Languages : en
Pages : 106
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Book Description
Inferring gene regulatory networks is a difficult problem to solve due to the relative scarcity of data compared to the potential size of the networks. While researchers have developed techniques to find some of the underlying network structure, there is still no one-size-fits-all algorithm for every data set. Network Inference in Molecular Biology examines the current techniques used by researchers, and provides key insights into which algorithms best fit a collection of data. Through a series of in-depth examples, the book also outlines how to mix-and-match algorithms, in order to create one tailored to a specific data situation. Network Inference in Molecular Biology is intended for advanced-level students and researchers as a reference guide. Practitioners and professionals working in a related field will also find this book valuable.
Author: Denis Thieffry
Publisher: Humana Press
ISBN: 9781493961566
Category : Science
Languages : en
Pages : 560
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Book Description
Bacterial Molecular Networks provides authoritative descriptions of various experimental and computational methods that enable the characterization and analysis of molecular interaction networks.
Author: Mark Buchanan
Publisher: Cambridge University Press
ISBN: 0521882737
Category : Science
Languages : en
Pages : 282
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Book Description
Key introductory text for graduate students and researchers in physics, biology and biochemistry.
Author: Narsis A. Kiani
Publisher: Cambridge University Press
ISBN: 1108428878
Category : Computers
Languages : en
Pages : 215
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Book Description
Introduces network inspired approaches for the analysis and integration of large, heterogeneous data sets in the life sciences.
Author: Nataša Pržulj
Publisher: Cambridge University Press
ISBN: 1108432239
Category : Language Arts & Disciplines
Languages : en
Pages : 647
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Book Description
Introduces biological concepts and biotechnologies producing the data, graph and network theory, cluster analysis and machine learning, using real-world biological and medical examples.
Author: Luonan Chen
Publisher: John Wiley & Sons
ISBN: 9780470488058
Category : Computers
Languages : en
Pages : 416
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Book Description
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.
Author: Sangdun Choi
Publisher: Springer Science & Business Media
ISBN: 1441957979
Category : Science
Languages : en
Pages : 900
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Book Description
System Biology encompasses the knowledge from diverse fields such as Molecular Biology, Immunology, Genetics, Computational Biology, Mathematical Biology, etc. not only to address key questions that are not answerable by individual fields alone, but also to help in our understanding of the complexities of biological systems. Whole genome expression studies have provided us the means of studying the expression of thousands of genes under a particular condition and this technique had been widely used to find out the role of key macromolecules that are involved in biological signaling pathways. However, making sense of the underlying complexity is only possible if we interconnect various signaling pathways into human and computer readable network maps. These maps can then be used to classify and study individual components involved in a particular phenomenon. Apart from transcriptomics, several individual gene studies have resulted in adding to our knowledge of key components that are involved in a signaling pathway. It therefore becomes imperative to take into account of these studies also, while constructing our network maps to highlight the interconnectedness of the entire signaling pathways and the role of that particular individual protein in the pathway. This collection of articles will contain a collection of pioneering work done by scientists working in regulatory signaling networks and the use of large scale gene expression and omics data. The distinctive features of this book would be: Act a single source of information to understand the various components of different signaling network (roadmap of biochemical pathways, the nature of a molecule of interest in a particular pathway, etc.), Serve as a platform to highlight the key findings in this highly volatile and evolving field, and Provide answers to various techniques both related to microarray and cell signaling to the readers.
Author: Pietro Hiram Guzzi
Publisher: Elsevier
ISBN: 0128193514
Category : Science
Languages : en
Pages : 212
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Book Description
Biological Network Analysis: Trends, Approaches, Graph Theory, and Algorithms considers three major biological networks, including Gene Regulatory Networks (GRN), Protein-Protein Interaction Networks (PPIN), and Human Brain Connectomes. The book's authors discuss various graph theoretic and data analytics approaches used to analyze these networks with respect to available tools, technologies, standards, algorithms and databases for generating, representing and analyzing graphical data. As a wide variety of algorithms have been developed to analyze and compare networks, this book is a timely resource. - Presents recent advances in biological network analysis, combining Graph Theory, Graph Analysis, and various network models - Discusses three major biological networks, including Gene Regulatory Networks (GRN), Protein-Protein Interaction Networks (PPIN) and Human Brain Connectomes - Includes a discussion of various graph theoretic and data analytics approaches
