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E-raamat: Analysis of Biological Networks

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Series edited by (University of Western Australia), Edited by (Leibniz Institute of Plant Genetics and Crop Plant Research), Series edited by (Department of Computer Science, Georgia State University), Edited by (IPK Gatersleben, Germany)
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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.

Arvustused

"This book is a wonderful text for biological network analysis. It comprehensively presents a numbers of analysis tools and their applications for understanding real biological problems. This book is a must-read for entry-level students and researchers, and a complete reference source for experts." (Computing Reviews, March 6, 2009) "This book is an excellent introduction to the analysis of biological networks.  The exercise provided after each chapter make the book suitable for self-study, and the extensive references provide the interested reader with good sources for further reading." (Computing Reviews, August 21, 2008)

Foreword xiii
Preface xv
Contributors xix
PART I INTRODUCTION
1(28)
Networks in Biology
3(12)
Bjorn H. Junker
Introduction
3(1)
Biology 101
4(4)
Biochemistry and Molecular Biology
4(2)
Cell Biology
6(1)
Ecology and Evolution
7(1)
Systems Biology
8(1)
Properties of Biological Networks
8(4)
Networks on a Microscopic Scale
9(2)
Networks on a Macroscopic Scale
11(1)
Other Biological Networks
11(1)
Summary
12(1)
Exercises
12(3)
References
12(3)
Graph Theory
15(14)
Falk Schreiber
Introduction
15(1)
Basic Notation
16(3)
Sets
16(1)
Graphs
16(3)
Graph Attributes
19(1)
Special Graphs
19(4)
Undirected, Directed, Mixed, and Multigraphs
19(1)
Hypergraphs and Bipartite Graphs
20(1)
Trees
21(2)
Graph Representation
23(1)
Adjacency Matrix
23(1)
Adjacency List
23(1)
Graph Algorithms
24(3)
Running Times of Algorithms
24(1)
Traversal
25(2)
Summary
27(1)
Exercises
27(2)
References
28(1)
PART II NETWORK ANALYSIS
29(152)
Global Network Properties
31(34)
Ralf Steuer
Gorka Zamora Lopez
Introduction
31(2)
Global Properties of Complex Networks
33(10)
Distance, Average Path Length, and Diameter
33(2)
Six Degrees of Separation: Concepts of a Small World
35(1)
The Degree Distribution
35(3)
Assortative Mixing and Degree Correlations
38(1)
The Clustering Coefficient
39(2)
The Matching Index
41(1)
Network Centralities
42(1)
Eigenvalues and Spectral Properties of Networks
43(1)
Models of Complex Networks
43(5)
The Erdos--Renyi Model
44(1)
The Watts--Strogatz Model
45(1)
The Barabasi--Albert Model
46(2)
Extensions of the BA Model
48(1)
Additional Properties of Complex Networks
48(4)
Structural Robustness and Attack Tolerance
49(1)
Modularity, Community Structures and Hierarchies
50(1)
Subgraphs and Motifs in Networks
51(1)
Statistical Testing of Network Properties
52(5)
Generating Networks and Null Models
53(1)
The Conceptualization of Cellular Networks
54(1)
Bipartite Graphs
55(2)
Correlation Networks
57(1)
Summary
57(1)
Exercises
58(7)
References
59(6)
Network Centralities
65(20)
Dirk Koschutzki
Introduction
65(2)
Centrality Definition and Fundamental Properties
67(2)
Comparison of Centrality Values
68(1)
Disconnected Networks
68(1)
Degree and Shortest Path-Based Centralities
69(8)
Degree Centrality
69(2)
Eccentricity Centrality
71(1)
Closeness Centrality
72(1)
Shortest Path Betweenness Centrality
73(1)
Algorithms
74(2)
Example
76(1)
Feedback-Based Centralities
77(3)
Katz's Status Index
77(1)
Bonacich's Eigenvector Centrality
78(1)
Page Rank
79(1)
Tools
80(1)
Summary
80(1)
Exercises
81(4)
References
81(4)
Network Motifs
85(28)
Henning Schwobbermeyer
Introduction
85(1)
Definitions and Basic Concepts
86(3)
Definitions
86(2)
Modeling of Biological Networks
88(1)
Concepts of Motif Frequency
88(1)
Motif Statistics and Motif-Based Network Distance
89(5)
Determination of Statistical Significance of Network Motifs
89(1)
Randomization Algorithm for Generation of Null Model Networks
90(1)
Influence of the Null Model on Motif Significance
91(1)
Limitations of the Null Model on Motif Detection
91(1)
Measures of Motif Significance and for Network Comparison
91(3)
Complexity of Network Motif Detection
94(2)
Aspects Affecting the Complexity of Network Motif Detection
94(2)
Frequency Estimation by Motif Sampling
96(1)
Methods and Tools for Network Motif Analysis
96(1)
Pajek
96(1)
Mfinder
96(1)
MAVisto
97(1)
FANMOD
97(1)
Analyses and Applications of Network Motifs
97(9)
Network Motifs in Complex Networks
97(1)
Dynamic Properties of Network Motifs
98(4)
Higher Order Structures Formed by Network Motifs
102(2)
Network Comparison Based on Network Motifs
104(2)
Evolutionary Origin of Network Motifs
106(1)
Summary
106(2)
Exercises
108(5)
References
108(5)
Network Clustering
113(26)
Balabhaskar Balasundaram
Sergiy Butenko
Introduction
113(2)
Notations and Definitions
115(3)
Network Clustering Problem
118(1)
Clique-Based Clustering
119(6)
Minimum Clique Partitioning
120(2)
Min--Max k-Clustering
122(3)
Center-Based Clustering
125(6)
Clustering with Dominating Sets
126(3)
k-Center Clustering
129(2)
Conclusion
131(2)
Summary
133(1)
Exercises
133(6)
References
134(5)
Petri Nets
139(42)
Ina Koch
Monika Heiner
Introduction
139(2)
Qualitative Modeling
141(11)
The Model
141(7)
The Behavioral Properties
148(4)
Qualitative Analysis
152(17)
Structural Analysis
152(3)
Invariant Analysis
155(7)
MCT-Sets
162(2)
Dynamic Analysis of General Properties
164(2)
Dynamic Analysis of Special Properties
166(2)
Model Validation Criteria
168(1)
Quantitative Modeling and Analysis
169(2)
Tool Support
171(1)
Case Studies
172(2)
Summary
174(1)
Exercises
175(6)
References
177(4)
PART III BIOLOGICAL NETWORKS
181(154)
Signal Transduction and Gene Regulation Networks
183(24)
Anatolij P. Potapov
Introduction
183(1)
Decisive Role of Regulatory Networks in the Evolution and Existence of Organisms
184(2)
Gene Regulatory Network as a System of Many Subnetworks
186(1)
Databases on Gene Regulation and Software Tools for Network Analysis
187(1)
Peculiarities of Signal Transduction Networks
188(2)
Topology of Signal Transduction Networks
190(1)
Topology of Transcription Networks
191(7)
Intercellular Molecular Regulatory Networks
198(2)
Summary
200(1)
Exercises
201(6)
References
202(5)
Protein Interaction Networks
207(26)
Frederik Bornke
Introduction
207(2)
Detecting Protein Interactions
209(11)
The Yeast Two-Hybrid System
211(5)
Affinity Capture of Protein Complexes
216(2)
Computational Methods to Predict Protein Interactions
218(1)
Other Ways to Identify Protein Interactions
219(1)
Establishing Protein Interaction Networks
220(3)
Data Storage and Network Generation
220(2)
Benchmarking High-Throughput Interaction Data
222(1)
Analyzing Protein Interaction Networks
223(2)
Network Topology and Functional Implications
223(1)
Functional Modules in Protein Interaction Networks
223(1)
Evolution of Protein Interaction Networks
224(1)
Comparative Interactomics
225(1)
Summary
225(1)
Exercises
226(7)
References
227(6)
Metabolic Networks
233(22)
Marcio Rosa da Silva
Jibin Sun
Hongwu Ma
Feng He
An-Ping Zeng
Introduction
233(1)
Visualization and Graph Representation
234(1)
Reconstruction of Genome-Scale Metabolic Networks
234(5)
Connectivity and Centrality in Metabolic Networks
239(3)
Modularity and Decomposition of Metabolic Networks
242(4)
Modularity Coefficient
244(1)
Modularity-Based Decomposition
245(1)
Elementary Flux Modes and Extreme Pathways
246(3)
Summary
249(1)
Exercises
249(6)
References
251(4)
Phylogenetic Networks
255(28)
Birgit Gemeinholzer
Introduction
255(2)
Character Selection, Character Coding, and Matrices for Phylogenetic Reconstruction
257(3)
Tree Reconstruction Methodologies
260(4)
Phylogenetic Networks
264(12)
Galled Trees
266(1)
Statistical Parsimony
267(2)
Median Network
269(1)
Median-Joining Networks
270(1)
Pyramids
271(1)
Example of a Pyramidal Clustering Model
271(3)
Split Decomposition
274(2)
Summary
276(1)
Exercises
276(7)
References
277(6)
Ecological Networks
283(22)
Ursula Gaedke
Introduction
283(6)
Binary Food Webs
289(4)
Introduction and Definitions
289(1)
Descriptors of the Network
289(2)
Operational Problems
291(1)
Aims and Results
291(2)
Conclusion
293(1)
Quantitative Trophic Food Webs
293(5)
Introduction, Definitions, and Database
293(2)
Multiple Commodities
295(1)
Descriptors of the Network and Information to be Gained
295(3)
Conclusion
298(1)
Ecological Information Networks
298(2)
Summary
300(1)
Exercises
301(4)
References
301(4)
Correlation Networks
305(30)
Dirk Steinhauser
Leonard Krall
Carsten Mussig
Dirk Bussis
Bjorn Usadel
Introduction
305(1)
General Remarks
306(1)
Basic Notation
307(7)
Data, Unit, Variable, and Observation
307(1)
Sample, Profiles, and Replica Set
308(1)
Measures of Association
309(1)
Simple Correlation Measures
310(1)
Complex Correlation and Association Measures
311(2)
Probability, Confidence, and Power
313(1)
Matrices
314(1)
Construction and Analyses of Correlation Networks
314(7)
Data and Profiles
315(1)
Data Set and Matrix
316(2)
Correlation Matrix
318(1)
Network Matrix
318(1)
Correlation Network Analysis
319(2)
Interpretation and Validation
321(1)
Biological Use of Correlation Networks
321(7)
The Global Analysis Approach
321(1)
The Guide Gene Approach
322(2)
A Simple Coregulation Test: Photosynthesis
324(3)
A Complex Coregulation Test: Brassinosteroids
327(1)
Summary
328(1)
Exercises
329(6)
References
330(5)
Index 335
Björn H. Junker is a biologist with a strong background in bioinformatics. His current research activities include the quantitative analysis and modeling of metabolic networks, as well as pathway databases and visual data mining. Mr. Junker has been at the Leibniz Institute of Plant Genetics and Crop Plant Research in Germany since 2003. He worked at Brookhaven National Laboratory in New York during 2006 and was appointed as project leader at the Leibniz Institute in 2007. Falk Schreiber is a computer scientist who has worked in bioinformatics for more than ten years. His current research areas include modeling, analysis, and visualization of biological networks; graph algorithms; and data exploration and information visualization in the life sciences. Since 2003, he has been head of the Network Analysis Research Group at the Leibniz Institute of Plant Genetics and Crop Plant Research. He was appointed professor of bioinformatics at the Martin Luther University Halle-Wittenberg, Germany, in 2007.
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