Author: Michael P. H. Stumpf Publisher: World Scientific ISBN: 1848164335 Category : Science Languages : en Pages : 179
Book Description
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.
Author: Michael P. H. Stumpf Publisher: World Scientific ISBN: 1848164343 Category : Mathematics Languages : en Pages : 179
Book Description
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. Sample Chapter(s). Chapter 1: A Network Analysis Primer (350 KB). Contents: A Network Analysis Primer (M P H Stumpf & C Wiuf); Evolutionary Analysis of Protein Interaction Networks (C Wiuf & O Ratmann); Motifs in Biological Networks (F Schreiber & H SchwAbbermeyer); Bayesian Analysis of Biological Networks: Clusters, Motifs, Cross-Species Correlations (J Berg & M Lnssig); Network Concepts and Epidemiological Models (R R Kao & I Z Kiss); Evolutionary Origin and Consequences of Design Properties of Metabolic Networks (T Pfeiffer & S Bonhoeffer); Protein Interactions from an Evolutionary Perspective (F Pazos & A Valencia); Statistical Null Models for Biological Network Analysis (W P Kelly et al.). Readership: Academics, researchers, postgraduates and advanced undergraduates in bioinformatics. Biologists, mathematicians/statisticians, physicists and computer scientists.
Author: Björn H. Junker Publisher: John Wiley & Sons ISBN: 1118209915 Category : Computers Languages : en Pages : 278
Book Description
An introduction to biological networks and methods for their analysis Analysis of Biological Networks is the first book of its kind to provide readers with a comprehensive introduction to the structural analysis of biological networks at the interface of biology and computer science. The book begins with a brief overview of biological networks and graph theory/graph algorithms and goes on to explore: global network properties, network centralities, network motifs, network clustering, Petri nets, signal transduction and gene regulation networks, protein interaction networks, metabolic networks, phylogenetic networks, ecological networks, and correlation networks. Analysis of Biological Networks is a self-contained introduction to this important research topic, assumes no expert knowledge in computer science or biology, and is accessible to professionals and students alike. Each chapter concludes with a summary of main points and with exercises for readers to test their understanding of the material presented. Additionally, an FTP site with links to author-provided data for the book is available for deeper study. This book is suitable as a resource for researchers in computer science, biology, bioinformatics, advanced biochemistry, and the life sciences, and also serves as an ideal reference text for graduate-level courses in bioinformatics and biological research.
Author: Olga Valba Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
Complex organization is found in many biological systems. For example, biopolymers could possess very hierarchic structure, which provides their functional peculiarity. Understating such, complex organization allows describing biological phenomena and predicting molecule functions. Besides, we can try to characterize the specific phenomenon by some probabilistic quantities (variances, means, etc), assuming the primary biopolymer structure to be randomly formed according to some statistical distribution. Such a formulation is oriented toward evolutionary problems.Artificially constructed biological network is another common object of statistical physics with rich functional properties. A behavior of cells is a consequence of complex interactions between its numerous components, such as DNA, RNA, proteins and small molecules. Cells use signaling pathways and regulatory mechanisms to coordinate multiple processes, allowing them to respond and to adapt to changing environment. Recent theoretical advances allow us to describe cellular network structure using graph concepts to reveal the principal organizational features shared with numerous non-biological networks.The aim of this thesis is to develop bunch of methods for studying statistical and dynamic objects of complex architecture and, in particular, scale-free structures, which have no characteristic spatial and/or time scale. For such systems, the use of standard mathematical methods, relying on the average behavior of the whole system, is often incorrect or useless, while a detailed many-body description is almost hopeless because of the combinatorial complexity of the problem. Here we focus on two problems.The first part addresses to statistical analysis of random biopolymers. Apart from the evolutionary context, our studies cover more general problems of planar topology appeared in description of various systems, ranging from gauge theory to biophysics. We investigate analytically and numerically a phase transition of a generic planar matching problem, from the regime, where almost all the vertices are paired, to the situation, where a finite fraction of them remains unmatched.The second part of this work focus on statistical properties of networks. We demonstrate the possibility to define co-expression gene clusters within a network context from their specific motif distribution signatures. We also show how a method based on the shortest path function (SPF) can be applied to gene interactions sub-networks of co-expression gene clusters, to efficiently predict novel regulatory transcription factors (TFs). The biological significance of this method by applying it on groups of genes with a shared regulatory locus, found by genetic genomics, is presented. Finally, we discuss formation of stable patters of motifs in networks under selective evolution in context of creation of islands of "superfamilies".
Author: Matthias Dehmer Publisher: John Wiley & Sons ISBN: 111834698X Category : Mathematics Languages : en Pages : 269
Book Description
Explore the multidisciplinary nature of complex networks through machine learning techniques Statistical and Machine Learning Approaches for Network Analysis provides an accessible framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for classification. By providing different approaches based on experimental data, the book uniquely sets itself apart from the current literature by exploring the application of machine learning techniques to various types of complex networks. Comprised of chapters written by internationally renowned researchers in the field of interdisciplinary network theory, the book presents current and classical methods to analyze networks statistically. Methods from machine learning, data mining, and information theory are strongly emphasized throughout. Real data sets are used to showcase the discussed methods and topics, which include: A survey of computational approaches to reconstruct and partition biological networks An introduction to complex networks—measures, statistical properties, and models Modeling for evolving biological networks The structure of an evolving random bipartite graph Density-based enumeration in structured data Hyponym extraction employing a weighted graph kernel Statistical and Machine Learning Approaches for Network Analysis is an excellent supplemental text for graduate-level, cross-disciplinary courses in applied discrete mathematics, bioinformatics, pattern recognition, and computer science. The book is also a valuable reference for researchers and practitioners in the fields of applied discrete mathematics, machine learning, data mining, and biostatistics.
Author: Rasmus Nielsen Publisher: Springer Science & Business Media ISBN: 0387277331 Category : Science Languages : en Pages : 503
Book Description
In the field of molecular evolution, inferences about past evolutionary events are made using molecular data from currently living species. With the availability of genomic data from multiple related species, molecular evolution has become one of the most active and fastest growing fields of study in genomics and bioinformatics. Most studies in molecular evolution rely heavily on statistical procedures based on stochastic process modelling and advanced computational methods including high-dimensional numerical optimization and Markov Chain Monte Carlo. This book provides an overview of the statistical theory and methods used in studies of molecular evolution. It includes an introductory section suitable for readers that are new to the field, a section discussing practical methods for data analysis, and more specialized sections discussing specific models and addressing statistical issues relating to estimation and model choice. The chapters are written by the leaders of field and they will take the reader from basic introductory material to the state-of-the-art statistical methods. This book is suitable for statisticians seeking to learn more about applications in molecular evolution and molecular evolutionary biologists with an interest in learning more about the theory behind the statistical methods applied in the field. The chapters of the book assume no advanced mathematical skills beyond basic calculus, although familiarity with basic probability theory will help the reader. Most relevant statistical concepts are introduced in the book in the context of their application in molecular evolution, and the book should be accessible for most biology graduate students with an interest in quantitative methods and theory. Rasmus Nielsen received his Ph.D. form the University of California at Berkeley in 1998 and after a postdoc at Harvard University, he assumed a faculty position in Statistical Genomics at Cornell University. He is currently an Ole Rømer Fellow at the University of Copenhagen and holds a Sloan Research Fellowship. His is an associate editor of the Journal of Molecular Evolution and has published more than fifty original papers in peer-reviewed journals on the topic of this book. From the reviews: "...Overall this is a very useful book in an area of increasing importance." Journal of the Royal Statistical Society "I find Statistical Methods in Molecular Evolution very interesting and useful. It delves into problems that were considered very difficult just several years ago...the book is likely to stimulate the interest of statisticians that are unaware of this exciting field of applications. It is my hope that it will also help the 'wet lab' molecular evolutionist to better understand mathematical and statistical methods." Marek Kimmel for the Journal of the American Statistical Association, September 2006 "Who should read this book? We suggest that anyone who deals with molecular data (who does not?) and anyone who asks evolutionary questions (who should not?) ought to consult the relevant chapters in this book." Dan Graur and Dror Berel for Biometrics, September 2006 "Coalescence theory facilitates the merger of population genetics theory with phylogenetic approaches, but still, there are mostly two camps: phylogeneticists and population geneticists. Only a few people are moving freely between them. Rasmus Nielsen is certainly one of these researchers, and his work so far has merged many population genetic and phylogenetic aspects of biological research under the umbrella of molecular evolution. Although Nielsen did not contribute a chapter to his book, his work permeates all its chapters. This book gives an overview of his interests and current achievements in molecular evolution. In short, this book should be on your bookshelf." Peter Beerli for Evolution, 60(2), 2006
Author: Jinhu Lü Publisher: Springer Nature ISBN: 981159144X Category : Science Languages : en Pages : 464
Book Description
This book addresses a number of questions from the perspective of complex systems: How can we quantitatively understand the life phenomena? How can we model life systems as complex bio-molecular networks? Are there any methods to clarify the relationships among the structures, dynamics and functions of bio-molecular networks? How can we statistically analyse large-scale bio-molecular networks? Focusing on the modeling and analysis of bio-molecular networks, the book presents various sophisticated mathematical and statistical approaches. The life system can be described using various levels of bio-molecular networks, including gene regulatory networks, and protein-protein interaction networks. It first provides an overview of approaches to reconstruct various bio-molecular networks, and then discusses the modeling and dynamical analysis of simple genetic circuits, coupled genetic circuits, middle-sized and large-scale biological networks, clarifying the relationships between the structures, dynamics and functions of the networks covered. In the context of large-scale bio-molecular networks, it introduces a number of statistical methods for exploring important bioinformatics applications, including the identification of significant bio-molecules for network medicine and genetic engineering. Lastly, the book describes various state-of-art statistical methods for analysing omics data generated by high-throughput sequencing. This book is a valuable resource for readers interested in applying systems biology, dynamical systems or complex networks to explore the truth of nature.
Author: Francois Kepes Publisher: World Scientific ISBN: 9812772367 Category : Medical Languages : en Pages : 531
Book Description
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. Sample Chapter(s). Chapter 1: Scale-Free Networks in Biology (821 KB). Contents: Scale-Free Networks in Biology (E Almaas et al.); Modularity in Biological Networks (R V Sol(r) et al.); Inference of Biological Regulatory Networks: Machine Learning Approaches (F d''Alch(r)-Buc); Transcriptional Networks (F K(r)p s); Protein Interaction Networks (K Tan & T Ideker); Metabolic Networks (D A Fell); Heterogeneous Molecular Networks (V Schnchter); Evolution of Regulatory Networks (A Veron et al.); Complexity in Neuronal Networks (Y Fr(r)gnac et al.); Networks of the Immune System (R E Callard & J Stark); A History of the Study of Ecological Networks (L-F Bersier); Dynamic Network Models of Ecological Diversity, Complexity, and Nonlinear Persistence (R J Williams & N D Martinez); Infection Transmission through Networks (J S Koopman). Readership: Graduate students and industry experts in systems biology and complex systems; biologists; chemists; physicists; mathematicians; computer scientists
Author: Mark R. T. Dale Publisher: Cambridge University Press ISBN: 1108632971 Category : Nature Languages : en Pages : 250
Book Description
Network thinking and network analysis are rapidly expanding features of ecological research. Network analysis of ecological systems include representations and modelling of the interactions in an ecosystem, in which species or factors are joined by pairwise connections. This book provides an overview of ecological network analysis including generating processes, the relationship between structure and dynamic function, and statistics and models for these networks. Starting with a general introduction to the composition of networks and their characteristics, it includes details on such topics as measures of network complexity, applications of spectral graph theory, how best to include indirect species interactions, and multilayer, multiplex and multilevel networks. Graduate students and researchers who want to develop and understand ecological networks in their research will find this volume inspiring and helpful. Detailed guidance to those already working in network ecology but looking for advice is also included.
Author: Michael P. H. Stumpf
Author: Michael P. H. Stumpf
Author: Björn H. Junker
Author: Olga Valba
Author: Matthias Dehmer
Author: Rasmus Nielsen
Author: Jinhu Lü
Author: Francois Kepes
Author: Mark R. T. Dale
Author: