Statistical and Evolutionary Analysis of Biological Networks.
Title:
Statistical and Evolutionary Analysis of Biological Networks.
Author:
Stumpf, Michael P. H.
ISBN:
9781848164345
Personal Author:
Physical Description:
1 online resource (179 pages)
Contents:
Contents -- Preface -- 1. A Network Analysis Primer Michael P.H. Stumpf and Carsten Wiuf -- 1.1. Introduction -- 1.2. Types of Biological Networks -- 1.3. A Primer on Networks -- 1.3.1. Mathematical descriptions of networks -- 1.3.1.1. Characteristics of a node -- 1.3.1.2. Paths, components and trees -- 1.3.1.3. Distance and diameter -- 1.3.2. Network properties -- 1.3.2.1. The degree distribution -- 1.3.2.2. Clustering -- 1.3.2.3. Average path length -- 1.3.3. Mathematical representation of networks -- 1.3.3.1. The adjacency matrix -- 1.3.3.2. The adjacency list -- 1.3.3.3. The edge list -- 1.3.3.4. Some remarks on complexity -- 1.4. Comparing Biological Networks -- 1.4.1. Identity of networks -- 1.4.2. Subnets and patterns -- 1.4.3. The challenges of the data -- References -- 2. Evolutionary Analysis of Protein Interaction Networks Carsten Wiuf and Oliver Ratmann -- 2.1. Introduction -- 2.1.1. Molecular genetic uptake -- 2.1.2. Expansion by gene duplication -- 2.1.3. Redeployment of existing genetic systems -- 2.2. Protein Interaction Network Data -- 2.3. Mathematical Models of Networks and Network Growth -- 2.3.1. Simplistic models of network growth -- 2.3.2. Complex models of network growth by repeated node addition -- 2.3.3. Asymptotics of the node degree DD+RA and DD+PA -- 2.4. Inferring Evolutionary Dynamics in Terms of Mixture Models of Network Growth -- 2.4.1. The likelihood of PIN data under DD+RA or DD+PA -- 2.4.2. Simple methods to account for incomplete datasets -- 2.4.3. Approximating the likelihood with many summaries -- 2.4.4. Approximate Bayesian computation -- 2.4.5. Evolutionary analysis of the PIN topologies of T. pallidum, H. pylori and P. falciparum -- 2.4.6. The size of the interactome -- 2.5. Conclusion -- Acknowledgements -- Appendix A. Proofs of Theorems. -- References.
3. Motifs in Biological Networks Falk Schreiber and Henning Schw obbermeyer -- 3.1. Introduction -- 3.2. Characterisation of Network Motifs -- 3.2.1. Definitions -- 3.2.2. Modelling of biological data as graphs -- 3.2.3. Complexity of motif search -- 3.2.4. Frequency concepts -- 3.2.5. Statistical significance of network motifs -- 3.2.6. Randomisation algorithm for generation of null model networks -- 3.2.7. Calculation of the P-value and Z-score -- 3.3. Methods and Tools for the Analysis of Network Motifs -- 3.3.1. Mfinder -- 3.3.2. Pajek -- 3.3.3. MAVisto -- 3.4. Analyses of Motifs in Networks -- 3.4.1. Analysis of gene regulatory networks -- 3.4.2. Motifs in cortical networks -- 3.4.3. Analysis of other networks -- 3.4.4. Superstructures formed by overlapping motif matches -- 3.4.5. Dynamic properties of network motifs -- 3.4.6. Comparison of networks using motif distributions -- 3.4.7. On the function of network motifs in biological networks -- References -- 4. Bayesian Analysis of Biological Networks: Clusters, Motifs, Cross- Species Correlations Johannes Berg and Michael L assig -- 4.1. Introduction -- 4.2. Measuring Biological Networks -- 4.3. Random Networks in Biology -- 4.4. Network Clusters -- 4.4.1. Clusters in protein interaction networks -- 4.5. Network Motifs -- 4.5.1. Network motifs in regulatory networks -- 4.6. Cross-Species Analysis of Networks -- 4.6.1. Alignment of co-expression networks -- 4.7. Towards an Evolutionary Theory -- 4.7.1. Genetic interactions between different links -- 4.7.2. Gene duplications -- 4.7.3. Neutral and selective dynamics -- Acknowledgements -- Appendix: Bayesian Analysis of Network Data -- References -- 5. Network Concepts and Epidemiological Models Rowland R. Kao and Istvan Z. Kiss -- 5.1. Introduction -- 5.2. Simple Epidemiological Models -- 5.2.1. Introducing R0.
5.2.2. Density vs. frequency dependent contact -- 5.3. Some De nitions and Their Application to Poisson Random Networks -- 5.4. Networks With Localisation of Contacts: Small Worlds, Clustering, Pairwise Approximations and Moment Closure -- 5.4.1. Small worlds -- 5.4.2. Moment closure -- 5.5. Networks With Heterogeneity in Contacts Per Individual -- 5.5.1. Models for sexually transmitted diseases -- 5.5.2. Disease transmission on scale-free networks -- 5.5.3. Preferential attachment or the `Matthew e ect' -- 5.5.4. STI partnership models -- 5.6. Integrating Networks and Epidemiology -- 5.6.1. Component sizes and the nal epidemic size -- 5.6.2. R0 on epidemiological networks and network percolation thresholds -- 5.6.3. Contact frequency distributions on social and epidemiological networks -- 5.7. Conclusion -- References -- 6. Evolutionary Origin and Consequences of Design Properties of Metabolic Networks Thomas Pfeiffer and Sebastian Bonhoeffer -- 6.1. Introduction -- 6.2. Optimal Design of Metabolic Pathways -- 6.3. Game-Theoretical Approaches to Studying Optimal Pathway Design -- 6.4. Genetic Robustness and Epistasis in Metabolic Pathways -- 6.5. Large-Scale Properties of Metabolic Networks and Their Evolution -- 6.5.1. Hubs and robustness in metabolic networks -- 6.5.2. Computer simulations of scenarios for the evolution of metabolism -- 6.5.3. Robustness and epistasis in the emerging networks -- 6.6. Conclusion -- References -- 7. Protein Interactions from an Evolutionary Perspective Florencio Pazos and Alfoso Valencia -- 7.1. Introduction -- 7.2. Computational Prediction of Protein Interactions -- 7.2.1. Experimental vs. computational methods -- 7.2.2. Conservation of gene neighbouring -- 7.2.3. Gene fusion -- 7.2.4. Similarity of phylogenetic pro les -- 7.2.5. Similarity of phylogenetic trees -- 7.2.6. Correlated mutations.
7.2.7. Other methods -- 7.3. Conclusion -- Acknowledgements -- References -- 8. Statistical Null Models for Biological Network Analysis William P. Kelly, Thomas Thorne and Michael P.H. Stumpf -- 8.1. Introduction -- 8.1.1. Protein interaction networks -- 8.1.2. Statistical analysis of network data -- 8.2. Network Ensembles -- 8.2.1. Ensembles in statistical physics -- 8.2.2. Bender-Can eld (BC) networks -- 8.2.3. Beyond BC networks -- 8.3. Generating Con dence Intervals on Networks -- 8.3.1. Random permutation of node properties
Abstract:
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 Schwöbbermeyer); Bayesian Analysis of Biological Networks: Clusters, Motifs, Cross-Species Correlations (J Berg & M Lässig); 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.
Local Note:
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2017. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
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