LEADER 05413nam 2200661Ia 450 001 9910780891503321 005 20230725041453.0 010 $a1-282-75998-1 010 $a9786612759987 010 $a1-84816-434-3 035 $a(CKB)2490000000001771 035 $a(EBL)1681722 035 $a(OCoLC)729020361 035 $a(SSID)ssj0000428801 035 $a(PQKBManifestationID)11304824 035 $a(PQKBTitleCode)TC0000428801 035 $a(PQKBWorkID)10424431 035 $a(PQKB)10875252 035 $a(MiAaPQ)EBC1681722 035 $a(Au-PeEL)EBL1681722 035 $a(CaPaEBR)ebr10422454 035 $a(CaONFJC)MIL275998 035 $a(EXLCZ)992490000000001771 100 $a20100310d2010 uy 0 101 0 $aeng 135 $aurcuu|||uu||| 181 $ctxt 182 $cc 183 $acr 200 00$aStatistical and evolutionary analysis of biological networks$b[electronic resource] /$feditors, Michael P.H. Stumpf, Carsten Wiuf 210 $aLondon $cImperial College Press$dc2010 215 $a1 online resource (179 p.) 300 $aDescription based upon print version of record. 311 $a1-84816-433-5 320 $aIncludes bibliographical references and index. 327 $aContents; 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 327 $a1.4. 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 327 $a2.3.3. Asymptotics of the node degree DD+RA and DD+PA2. 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 327 $a3. 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 327 $a3.4.1. 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 Lassig; 4.1. Introduction; 4.2. Measuring Biological Networks; 4.3. Random Networks in Biology; 4.4. Network Clusters 327 $a4.4.1. Clusters in protein interaction networks 330 $aNetworks 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 ne 606 $aBiometry 606 $aComputational biology 606 $aGraph theory 615 0$aBiometry. 615 0$aComputational biology. 615 0$aGraph theory. 676 $a570.15195 701 $aStumpf$b M. P. H$g(Michael P. H.)$0276550 701 $aWiuf$b Carsten$01537990 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910780891503321 996 $aStatistical and evolutionary analysis of biological networks$93787677 997 $aUNINA