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Statistical And Evolutionary Analysis Of Biological Networks.
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 ac...
| Clasificación: | Libro Electrónico |
|---|---|
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
World Scientific
2009.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 505 | 0 | |a Cover13; -- 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 -- 1.3.2. Network properties -- 1.3.3. Mathematical representation of networks -- 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. | |
| 520 | |a 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 netw. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
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