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Fundamentals of Network Biology 1st Edition Wenjun Zhang
Fundamentals of Network Biology 1st Edition Wenjun
Zhang Digital Instant Download
Author(s): WenJun Zhang
ISBN(s): 9781786345080, 1786345080
Edition: 1
File Details: PDF, 6.89 MB
Year: 2010
Language: english
Fundamentals of Network Biology 1st Edition Wenjun Zhang
Fundamentals of
Network Biology
Q0149hc_9781786345080_TP.indd 1 13/10/17 5:11 PM
Other World Scientific Titles by the Author
Computational Ecology: Artificial Neural Networks and
Their Applications
ISBN: 978-981-4282-62-8
Computational Ecology: Graphs, Networks and Agent-based Modeling
ISBN: 978-981-4343-61-9
Selforganizology: The Science of Self-Organization
ISBN: 978-981-4699-48-8
Sajani - Q0149 - Fundamentals of Network Biology.indd 1 20-04-18 10:03:29 AM
NEW JERSEY • LONDON • SINGAPORE • BEIJING • SHANGHAI • HONG KONG • TAIPEI • CHENNAI • TOKYO
World Scientific
Fundamentals of
Network Biology
WenJun Zhang
Sun Yat-sen University, China
Q0149hc_9781786345080_TP.indd 2 13/10/17 5:11 PM
Published by
World Scientific Publishing Europe Ltd.
57 Shelton Street, Covent Garden, London WC2H 9HE
Head office: 5 Toh Tuck Link, Singapore 596224
USA office: 27 Warren Street, Suite 401-402, Hackensack, NJ 07601
Library of Congress Cataloging-in-Publication Data
Names: Zhang, WenJun, 1963– author.
Title: Fundamentals of network biology / [by] WenJun Zhang (Sun Yat-sen University, China).
Description: New Jersey : World Scientific, [2018] | Includes bibliographical references.
Identifiers: LCCN 2017060719 | ISBN 9781786345080 (hc : alk. paper)
Subjects: LCSH: Biometry--Data processing. | Systems biology--Methodology. | Bioinformatics.
Classification: LCC QH323.5 .Z45 2018 | DDC 570.1/5195--dc23
LC record available at https://lccn.loc.gov/2017060719
British Library Cataloguing-in-Publication Data
A catalogue record for this book is available from the British Library.
Copyright © 2018 by World Scientific Publishing Europe Ltd.
All rights reserved. This book, or parts thereof, may not be reproduced in any form or by any means,
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For photocopying of material in this volume, please pay a copying fee through the Copyright Clearance
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For any available supplementary material, please visit
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Desk Editors: Dr. Sree Meenakshi Sajani/Jennifer Brough/Koe Shi Ying
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Sajani - Q0149 - Fundamentals of Network Biology.indd 2 20-04-18 10:03:29 AM
April 20, 2018 10:53 Fundamentals of Network Biology 9in x 6in b3136-fm page v
Preface
Why have human cancers not been conquered so far?
Why does brain research emphasize the structure and function of
neurons and brain networks?
Why did the successful cases of drug development meet a signifi-
cant reduction in the past years?
Can the traditional Chinese medicine form a theoretical basis
coincident with the modern science?
Why has ecological structure and some of the core ecological prob-
lems not yet been solved so far?
A lot of research has demonstrated that many problems in
life sciences, medicine, and ecological science have been unable to
be resolved relying on the local research of single factors, single
molecules, and single reactions, and must be analyzed globally and
systematically at the level of networks. In recent years, these methods
and theories of network biology have been successfully used to solve
problems such as ecological structure, coextinction, drug discovery,
and so on, which have made significant progress and breakthroughs.
Network science has begun to become the core theory and methodol-
ogy for dealing with complex biological systems. Network science
is mainly based on graph theory, topology, statistics, operational
research, computational science, etc. It has been successfully applied
in many areas, including statistical physics, particle physics, com-
puter science, internet, biology, ecology, economics, sociology, etc.
Network science focuses on the network structure, network dynamics
and network functionality, etc.
v
April 20, 2018 10:53 Fundamentals of Network Biology 9in x 6in b3136-fm page vi
vi Preface
The initial concept of network biology was first put forward
by Barabasi and Oltvai in 2004. Since then network biology has
developed rapidly. In recent years, I further expanded the scope and
contents of network biology. So far, the scientific frame of network
biology has been basically shaped. It is becoming a new frontier sci-
ence that needs to be understood by university students and is a
central research field for researchers.
Up till now, the research in network biology is mainly concen-
trated in such biological networks as protein–protein interactions,
gene regulatory networks, metabolic networks, signaling networks,
neural networks, food webs, etc. At the micro- and macro-level, all
kinds of these biological networks offer a variety of sources for the
development of theory and methods of network biology.
At present, studies of biological networks mainly focus on the
following basic aspects: (1) Network structure: connection structure,
degree distribution, network modules, patterns, motifs, robustness
and stability of biological networks, etc. (2) Network dynamics:
rule-based network evolution, biological adaptability of network evo-
lution; dynamics of biological networks over time, among which net-
work model for continuous variables can be used to analyze phase
diagram, to conduct sensitivity analysis, and bifurcation analysis,
etc. (3) Network control: positive and negative feedbacks, contin-
uous/discrete time control, time delay control, etc. (4) Network
design: design and formulation of biological networks. (5) Other
topics.
In general, the scope and topics of network biology focus on, but
are not limited to the following areas:
• Theories, algorithms, and programs of network analysis.
• Evolution, dynamics, optimization and control of biological net-
works.
• Network construction and link prediction.
• Network topology, topological analysis, relationship between topo-
logical structure and network functions, sensitivity analysis, net-
work robustness, and stability.
• Network flow analysis.
• Design and formulation of biological networks.
April 20, 2018 10:53 Fundamentals of Network Biology 9in x 6in b3136-fm page vii
Preface vii
• Ecological networks, food webs and natural equilibrium, coevolu-
tion, coextinction, biodiversity conservation.
• Metabolic networks, protein–protein interaction networks, bio-
chemical reaction networks, gene networks, transcriptional regula-
tory networks, cell cycle networks, phylogenetic networks, network
motifs, and modules.
• Physiological networks, social networks, epidemiological networks.
• Network regulation of metabolic processes, human diseases, and
ecological systems.
• System complexity, self-organized systems, emergence of biological
systems, agent-based modeling, neural network modeling and other
network-based modeling, etc.
• Big data analytics of biological networks.
This book is the first comprehensive book on network biology.
From this integrated and self-contained book, scientists, university
teachers, and students will be provided with an in-depth and com-
plete insight on knowledge, methodology, and recent advances of net-
work biology. Matlab codes are presented in the book for easy use.
It covers such contents as scientific fundamentals of network biol-
ogy, construction and analysis of biological networks, methods for
identifying crucial nodes in biological networks, link prediction, flow
analysis, network dynamics and evolution, network simulation and
control, ecological networks, social networks, molecular and cellular
networks, network pharmacology and network toxicology, big data
analytics, etc. From this unique and self-contained book, undergrad-
uates, postgraduates, teachers, and researchers will thoroughly have
an in-depth and complete insight on network biology. It is a valuable
book for high-level undergraduates, postgraduates and scientists in
the areas of biology, ecology, environmental sciences, medical science,
computational science, applied mathematics, and social science, etc.
b2530   International Strategic Relations and China’s National Security: World at the Crossroads
b2530_FM.indd 6 01-Sep-16 11:03:06 AM
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April 28, 2018 10:40 Fundamentals of Network Biology 9in x 6in b3136-fm page ix
About the Author
Professor WenJun Zhang graduated from
Northwestern Agricultural University in 1984,
and earned his PhD degree in 1993. Professor
Zhang was a postdoctoral fellow at Sun Yat-sen
University during 1994–1996, and postdoctoral
fellow and project scientist of International Rice
Research Institute during 1997–2000. He was a
visiting scientist to University of Arizona (1996)
and Wageningen University (2002). He is the
president of International Society of Network
Biology and International Society of Computational Ecology. He is
also the editor-in-chief of international journals Network Biology,
Computational Ecology and Software, etc. Professor Zhang has pub-
lished many papers and books. He is now working on ecology and
biological science.
ix
b2530   International Strategic Relations and China’s National Security: World at the Crossroads
b2530_FM.indd 6 01-Sep-16 11:03:06 AM
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April 20, 2018 10:53 Fundamentals of Network Biology 9in x 6in b3136-fm page xi
Acknowledgments
I am so grateful to the people who gave me guidance: Professors
ShiZe Wang, HonSheng Shang, ZhenQi Li, DeXiang Gu, and KG
Schoenly. I am also indebted to the people who contributed their
valuable suggestions: Professors Yi Pang, RunJie Zhang, Yan Zhang,
Jie Zhang, ChangLong Shu, and YanHong Qi. Special thanks is given
to the anonymous reviewers for their comments and suggestions on
this book. I thank Qi Zhang for her help in the manuscript prepa-
ration. This book is supported by The National Key Research and
Development Program of China (2017YFD0201204), High-Quality
Textbook Network Biology Project for Engineering of Teaching Qual-
ity and Teaching Reform of Undergraduate Universities of Guang-
dong Province (2015.6–2018.6), China, and Guangzhou Science and
Technology Project (No. 201707020003).
xi
b2530   International Strategic Relations and China’s National Security: World at the Crossroads
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April 20, 2018 10:53 Fundamentals of Network Biology 9in x 6in b3136-fm page xiii
Contents
Preface v
About the Author ix
Acknowledgments xi
Part 1 Mathematical Fundamentals 1
1 Fundamentals of Graph Theory 3
1.1 Definitions and Concepts . . . . . . . . . . . . . . . 3
1.2 Topological Definition of Graph . . . . . . . . . . . 14
2 Graph Algorithms 21
2.1 Matrix Representation of Graphs . . . . . . . . . . . 21
2.2 Computer Storage of Graph . . . . . . . . . . . . . 30
2.3 Graph Algorithms . . . . . . . . . . . . . . . . . . . 32
3 Fundamentals of Network Theory 63
3.1 Network Topology . . . . . . . . . . . . . . . . . . . 64
3.2 Random and Complex Networks . . . . . . . . . . . 66
3.3 Network Analysis . . . . . . . . . . . . . . . . . . . 70
3.4 Basic Algorithms . . . . . . . . . . . . . . . . . . . . 71
4 Other Fundamentals 81
4.1 Bayes’ Rule . . . . . . . . . . . . . . . . . . . . . . . 81
xiii
April 20, 2018 10:53 Fundamentals of Network Biology 9in x 6in b3136-fm page xiv
xiv Contents
4.2 Linear Regression . . . . . . . . . . . . . . . . . . . 83
4.3 Randomization, Bootstrap, and Monte Carlo
Methods . . . . . . . . . . . . . . . . . . . . . . . . 84
4.4 Stochastic Process . . . . . . . . . . . . . . . . . . . 90
4.5 Optimization Methods . . . . . . . . . . . . . . . . 90
4.6 Functional Analysis . . . . . . . . . . . . . . . . . . 94
4.7 Algebraic Topology . . . . . . . . . . . . . . . . . . 98
4.8 Entropy of Systems . . . . . . . . . . . . . . . . . . 98
Part 2 Crucial Nodes/Subnetworks/Modules,
Network Types, and Structural
Comparison 103
5 Identification of Crucial Nodes and
Subnetworks/Modules 105
5.1 Features of Crucial Nodes . . . . . . . . . . . . . . . 105
5.2 Indices and Methods of Crucial Nodes . . . . . . . . 106
5.3 Further Discussion . . . . . . . . . . . . . . . . . . . 117
5.4 Application Examples . . . . . . . . . . . . . . . . . 118
5.5 Identification of Subnetworks/Modules . . . . . . . 121
6 Detection of Network Types 129
6.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . 130
6.2 Application Example . . . . . . . . . . . . . . . . . 135
7 Comparison of Network Structure 139
7.1 Nonparametric Statistic Comparison
of Network Structure . . . . . . . . . . . . . . . . . 139
7.2 Nonparametric Statistic Comparison
of Community Structure . . . . . . . . . . . . . . . 146
7.3 Network Matrix-based Methods . . . . . . . . . . . 152
7.4 Other Methods . . . . . . . . . . . . . . . . . . . . . 154
April 20, 2018 10:53 Fundamentals of Network Biology 9in x 6in b3136-fm page xv
Contents xv
Part 3 Network Dynamics, Evolution,
Simulation, and Control 155
8 Network Dynamics 157
8.1 Differential Equations and Motion Stability . . . . . 157
8.2 Dynamics of Some Networks . . . . . . . . . . . . . 164
9 Network Robustness and Sensitivity Analysis 175
9.1 Network Robustness . . . . . . . . . . . . . . . . . . 175
9.2 Sensitivity Analysis . . . . . . . . . . . . . . . . . . 185
10 Network Control 195
10.1 Conventional Control . . . . . . . . . . . . . . . . . 195
10.2 New Perspectives of Network Control . . . . . . . . 201
11 Network Evolution 203
11.1 A Generalized Network Evolution Model
for Community Assembly Dynamics . . . . . . . . . 203
11.2 A Model for Perturbed Food Web Dynamics . . . . 214
11.3 A Network Evolution Algorithm Based
on Node Attraction . . . . . . . . . . . . . . . . . . 215
11.4 Phase Recognition of Network Evolution . . . . . . 221
12 Cellular Automata 229
12.1 Classification of Cellular Automata . . . . . . . . . 230
12.2 Algorithms of Cellular Automata . . . . . . . . . . . 232
12.3 Some Cases of Cellular Automata . . . . . . . . . . 232
13 Self-Organization 237
13.1 Basic Concept of Self-Organization . . . . . . . . . 237
13.2 Theories and Principles of Self-Organization . . . . 239
13.3 Existing Algorithms of Self-Organization . . . . . . 249
13.4 Self-Organization in Life Sciences . . . . . . . . . . 249
13.5 Selforganizology . . . . . . . . . . . . . . . . . . . . 252
April 20, 2018 10:53 Fundamentals of Network Biology 9in x 6in b3136-fm page xvi
xvi Contents
14 Agent-based Modeling 255
14.1 Complex Systems . . . . . . . . . . . . . . . . . . . 255
14.2 Principle and Methods of Agent-based
Modeling . . . . . . . . . . . . . . . . . . . . . . . . 258
Part 4 Flow Analysis 263
15 Flow/Flux Analysis 265
15.1 Flux Balance Analysis . . . . . . . . . . . . . . . . . 265
15.2 Flow Indices for Ecological Network
Analysis . . . . . . . . . . . . . . . . . . . . . . . . 283
Part 5 Link and Node Prediction 291
16 Link Prediction: Sampling-based Methods 293
16.1 Linear Correlation Analysis for Finding
Interactions . . . . . . . . . . . . . . . . . . . . . . . 293
16.2 Partial Correlation of General Correlation
Measures . . . . . . . . . . . . . . . . . . . . . . . . 301
16.3 Combined Use of Linear Correlation
and Rank Correlation: A Hierarchical Method
for Finding Interactions . . . . . . . . . . . . . . . . 313
17 Link Prediction: Structure- and Perturbation-based
Methods 321
17.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . 322
17.2 Performance Assessment of Link Prediction
Methods . . . . . . . . . . . . . . . . . . . . . . . . 339
18 Link Prediction: Node-Similarity-based Methods 345
18.1 Link Prediction Based on Node Similarity . . . . . . 345
18.2 Screening Node Attributes that Significantly
Influence Node Centrality . . . . . . . . . . . . . . . 351
April 20, 2018 10:53 Fundamentals of Network Biology 9in x 6in b3136-fm page xvii
Contents xvii
19 Node Prediction 361
19.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . 361
19.2 Application Example . . . . . . . . . . . . . . . . . 363
Part 6 Network Construction 365
20 Construction of Biological Networks 367
20.1 Prediction Methods of Protein–Protein
Interactions . . . . . . . . . . . . . . . . . . . . . . . 367
20.2 Gene Coexpression Network Analysis . . . . . . . . 370
20.3 Classification-based Machine Learning . . . . . . . . 372
20.4 Statistic Network . . . . . . . . . . . . . . . . . . . 374
20.5 Homogeneity Test of Samples and Sampling
Completeness . . . . . . . . . . . . . . . . . . . . . . 376
20.6 Network Construction Based on Power-Law
or Exponential-Law Distribution of Node
Degree . . . . . . . . . . . . . . . . . . . . . . . . . 381
Part 7 Pharmacological and Toxicological
Networks 389
21 Network Pharmacology and Toxicology 391
21.1 Network Pharmacology . . . . . . . . . . . . . . . . 391
21.2 Network Toxicology . . . . . . . . . . . . . . . . . . 404
Part 8 Ecological Networks 413
22 Food Webs 415
22.1 Fundamentals of Food Webs . . . . . . . . . . . . . 416
22.2 Food Web Models . . . . . . . . . . . . . . . . . . . 422
22.3 Arthropod Food Webs . . . . . . . . . . . . . . . . . 435
April 20, 2018 10:53 Fundamentals of Network Biology 9in x 6in b3136-fm page xviii
xviii Contents
Part 9 Microscopic Networks 439
23 Molecular and Cellular Networks 441
23.1 Metabolic Pathway of Nonalcoholic Fatty
Liver Disease . . . . . . . . . . . . . . . . . . . . . . 442
23.2 E. coli Transcriptional Regulatory Network . . . . . 444
23.3 Common Disease Regulatory Network
for Metabolic Disorders . . . . . . . . . . . . . . . . 447
23.4 Find Molecular Pathological Events in Different
Cancer Tissues . . . . . . . . . . . . . . . . . . . . . 449
23.5 Crucial Metabolites/Reactions in Tumor Signaling
Networks . . . . . . . . . . . . . . . . . . . . . . . . 452
23.6 Brain Network . . . . . . . . . . . . . . . . . . . . . 453
Part 10 Social Networks 459
24 Social Network Analysis 461
24.1 Definition of Social Networks . . . . . . . . . . . . . 461
24.2 Network Criminology . . . . . . . . . . . . . . . . . 467
Part 11 Software 469
25 Software for Network Analysis 471
25.1 Software for Comprehensive Network
Analysis . . . . . . . . . . . . . . . . . . . . . . . . 471
25.2 Software for Network Layout and Others . . . . . . 485
Part 12 Big Data Analytics 489
26 Big Data Analytics for Network Biology 491
26.1 Fundamentals of Big Data Analytics . . . . . . . . . 492
April 20, 2018 10:53 Fundamentals of Network Biology 9in x 6in b3136-fm page xix
Contents xix
26.2 Text Mining with Python and R . . . . . . . . . . . 496
26.3 Big Data Analytics of Medicinal Attributes
and Functions of Chinese Herbal Medicines . . . . . 499
References 511
Index 543
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Fundamentals of Network Biology 1st Edition Wenjun Zhang

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  • 5. Fundamentals of Network Biology 1st Edition Wenjun Zhang Digital Instant Download Author(s): WenJun Zhang ISBN(s): 9781786345080, 1786345080 Edition: 1 File Details: PDF, 6.89 MB Year: 2010 Language: english
  • 8. Other World Scientific Titles by the Author Computational Ecology: Artificial Neural Networks and Their Applications ISBN: 978-981-4282-62-8 Computational Ecology: Graphs, Networks and Agent-based Modeling ISBN: 978-981-4343-61-9 Selforganizology: The Science of Self-Organization ISBN: 978-981-4699-48-8 Sajani - Q0149 - Fundamentals of Network Biology.indd 1 20-04-18 10:03:29 AM
  • 9. NEW JERSEY • LONDON • SINGAPORE • BEIJING • SHANGHAI • HONG KONG • TAIPEI • CHENNAI • TOKYO World Scientific Fundamentals of Network Biology WenJun Zhang Sun Yat-sen University, China Q0149hc_9781786345080_TP.indd 2 13/10/17 5:11 PM
  • 10. Published by World Scientific Publishing Europe Ltd. 57 Shelton Street, Covent Garden, London WC2H 9HE Head office: 5 Toh Tuck Link, Singapore 596224 USA office: 27 Warren Street, Suite 401-402, Hackensack, NJ 07601 Library of Congress Cataloging-in-Publication Data Names: Zhang, WenJun, 1963– author. Title: Fundamentals of network biology / [by] WenJun Zhang (Sun Yat-sen University, China). Description: New Jersey : World Scientific, [2018] | Includes bibliographical references. Identifiers: LCCN 2017060719 | ISBN 9781786345080 (hc : alk. paper) Subjects: LCSH: Biometry--Data processing. | Systems biology--Methodology. | Bioinformatics. Classification: LCC QH323.5 .Z45 2018 | DDC 570.1/5195--dc23 LC record available at https://lccn.loc.gov/2017060719 British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library. Copyright © 2018 by World Scientific Publishing Europe Ltd. All rights reserved. This book, or parts thereof, may not be reproduced in any form or by any means, electronic or mechanical, including photocopying, recording or any information storage and retrieval system now known or to be invented, without written permission from the Publisher. For photocopying of material in this volume, please pay a copying fee through the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, USA. In this case permission to photocopy is not required from the publisher. For any available supplementary material, please visit http://www.worldscientific.com/worldscibooks/10.1142/Q0149#t=suppl Desk Editors: Dr. Sree Meenakshi Sajani/Jennifer Brough/Koe Shi Ying Typeset by Stallion Press Email: enquiries@stallionpress.com Printed in Singapore Sajani - Q0149 - Fundamentals of Network Biology.indd 2 20-04-18 10:03:29 AM
  • 11. April 20, 2018 10:53 Fundamentals of Network Biology 9in x 6in b3136-fm page v Preface Why have human cancers not been conquered so far? Why does brain research emphasize the structure and function of neurons and brain networks? Why did the successful cases of drug development meet a signifi- cant reduction in the past years? Can the traditional Chinese medicine form a theoretical basis coincident with the modern science? Why has ecological structure and some of the core ecological prob- lems not yet been solved so far? A lot of research has demonstrated that many problems in life sciences, medicine, and ecological science have been unable to be resolved relying on the local research of single factors, single molecules, and single reactions, and must be analyzed globally and systematically at the level of networks. In recent years, these methods and theories of network biology have been successfully used to solve problems such as ecological structure, coextinction, drug discovery, and so on, which have made significant progress and breakthroughs. Network science has begun to become the core theory and methodol- ogy for dealing with complex biological systems. Network science is mainly based on graph theory, topology, statistics, operational research, computational science, etc. It has been successfully applied in many areas, including statistical physics, particle physics, com- puter science, internet, biology, ecology, economics, sociology, etc. Network science focuses on the network structure, network dynamics and network functionality, etc. v
  • 12. April 20, 2018 10:53 Fundamentals of Network Biology 9in x 6in b3136-fm page vi vi Preface The initial concept of network biology was first put forward by Barabasi and Oltvai in 2004. Since then network biology has developed rapidly. In recent years, I further expanded the scope and contents of network biology. So far, the scientific frame of network biology has been basically shaped. It is becoming a new frontier sci- ence that needs to be understood by university students and is a central research field for researchers. Up till now, the research in network biology is mainly concen- trated in such biological networks as protein–protein interactions, gene regulatory networks, metabolic networks, signaling networks, neural networks, food webs, etc. At the micro- and macro-level, all kinds of these biological networks offer a variety of sources for the development of theory and methods of network biology. At present, studies of biological networks mainly focus on the following basic aspects: (1) Network structure: connection structure, degree distribution, network modules, patterns, motifs, robustness and stability of biological networks, etc. (2) Network dynamics: rule-based network evolution, biological adaptability of network evo- lution; dynamics of biological networks over time, among which net- work model for continuous variables can be used to analyze phase diagram, to conduct sensitivity analysis, and bifurcation analysis, etc. (3) Network control: positive and negative feedbacks, contin- uous/discrete time control, time delay control, etc. (4) Network design: design and formulation of biological networks. (5) Other topics. In general, the scope and topics of network biology focus on, but are not limited to the following areas: • Theories, algorithms, and programs of network analysis. • Evolution, dynamics, optimization and control of biological net- works. • Network construction and link prediction. • Network topology, topological analysis, relationship between topo- logical structure and network functions, sensitivity analysis, net- work robustness, and stability. • Network flow analysis. • Design and formulation of biological networks.
  • 13. April 20, 2018 10:53 Fundamentals of Network Biology 9in x 6in b3136-fm page vii Preface vii • Ecological networks, food webs and natural equilibrium, coevolu- tion, coextinction, biodiversity conservation. • Metabolic networks, protein–protein interaction networks, bio- chemical reaction networks, gene networks, transcriptional regula- tory networks, cell cycle networks, phylogenetic networks, network motifs, and modules. • Physiological networks, social networks, epidemiological networks. • Network regulation of metabolic processes, human diseases, and ecological systems. • System complexity, self-organized systems, emergence of biological systems, agent-based modeling, neural network modeling and other network-based modeling, etc. • Big data analytics of biological networks. This book is the first comprehensive book on network biology. From this integrated and self-contained book, scientists, university teachers, and students will be provided with an in-depth and com- plete insight on knowledge, methodology, and recent advances of net- work biology. Matlab codes are presented in the book for easy use. It covers such contents as scientific fundamentals of network biol- ogy, construction and analysis of biological networks, methods for identifying crucial nodes in biological networks, link prediction, flow analysis, network dynamics and evolution, network simulation and control, ecological networks, social networks, molecular and cellular networks, network pharmacology and network toxicology, big data analytics, etc. From this unique and self-contained book, undergrad- uates, postgraduates, teachers, and researchers will thoroughly have an in-depth and complete insight on network biology. It is a valuable book for high-level undergraduates, postgraduates and scientists in the areas of biology, ecology, environmental sciences, medical science, computational science, applied mathematics, and social science, etc.
  • 14. b2530   International Strategic Relations and China’s National Security: World at the Crossroads b2530_FM.indd 6 01-Sep-16 11:03:06 AM This page intentionally left blank This page intentionally left blank This page intentionally left blank This page intentionally left blank
  • 15. April 28, 2018 10:40 Fundamentals of Network Biology 9in x 6in b3136-fm page ix About the Author Professor WenJun Zhang graduated from Northwestern Agricultural University in 1984, and earned his PhD degree in 1993. Professor Zhang was a postdoctoral fellow at Sun Yat-sen University during 1994–1996, and postdoctoral fellow and project scientist of International Rice Research Institute during 1997–2000. He was a visiting scientist to University of Arizona (1996) and Wageningen University (2002). He is the president of International Society of Network Biology and International Society of Computational Ecology. He is also the editor-in-chief of international journals Network Biology, Computational Ecology and Software, etc. Professor Zhang has pub- lished many papers and books. He is now working on ecology and biological science. ix
  • 16. b2530   International Strategic Relations and China’s National Security: World at the Crossroads b2530_FM.indd 6 01-Sep-16 11:03:06 AM This page intentionally left blank This page intentionally left blank This page intentionally left blank This page intentionally left blank
  • 17. April 20, 2018 10:53 Fundamentals of Network Biology 9in x 6in b3136-fm page xi Acknowledgments I am so grateful to the people who gave me guidance: Professors ShiZe Wang, HonSheng Shang, ZhenQi Li, DeXiang Gu, and KG Schoenly. I am also indebted to the people who contributed their valuable suggestions: Professors Yi Pang, RunJie Zhang, Yan Zhang, Jie Zhang, ChangLong Shu, and YanHong Qi. Special thanks is given to the anonymous reviewers for their comments and suggestions on this book. I thank Qi Zhang for her help in the manuscript prepa- ration. This book is supported by The National Key Research and Development Program of China (2017YFD0201204), High-Quality Textbook Network Biology Project for Engineering of Teaching Qual- ity and Teaching Reform of Undergraduate Universities of Guang- dong Province (2015.6–2018.6), China, and Guangzhou Science and Technology Project (No. 201707020003). xi
  • 18. b2530   International Strategic Relations and China’s National Security: World at the Crossroads b2530_FM.indd 6 01-Sep-16 11:03:06 AM This page intentionally left blank This page intentionally left blank This page intentionally left blank This page intentionally left blank
  • 19. April 20, 2018 10:53 Fundamentals of Network Biology 9in x 6in b3136-fm page xiii Contents Preface v About the Author ix Acknowledgments xi Part 1 Mathematical Fundamentals 1 1 Fundamentals of Graph Theory 3 1.1 Definitions and Concepts . . . . . . . . . . . . . . . 3 1.2 Topological Definition of Graph . . . . . . . . . . . 14 2 Graph Algorithms 21 2.1 Matrix Representation of Graphs . . . . . . . . . . . 21 2.2 Computer Storage of Graph . . . . . . . . . . . . . 30 2.3 Graph Algorithms . . . . . . . . . . . . . . . . . . . 32 3 Fundamentals of Network Theory 63 3.1 Network Topology . . . . . . . . . . . . . . . . . . . 64 3.2 Random and Complex Networks . . . . . . . . . . . 66 3.3 Network Analysis . . . . . . . . . . . . . . . . . . . 70 3.4 Basic Algorithms . . . . . . . . . . . . . . . . . . . . 71 4 Other Fundamentals 81 4.1 Bayes’ Rule . . . . . . . . . . . . . . . . . . . . . . . 81 xiii
  • 20. April 20, 2018 10:53 Fundamentals of Network Biology 9in x 6in b3136-fm page xiv xiv Contents 4.2 Linear Regression . . . . . . . . . . . . . . . . . . . 83 4.3 Randomization, Bootstrap, and Monte Carlo Methods . . . . . . . . . . . . . . . . . . . . . . . . 84 4.4 Stochastic Process . . . . . . . . . . . . . . . . . . . 90 4.5 Optimization Methods . . . . . . . . . . . . . . . . 90 4.6 Functional Analysis . . . . . . . . . . . . . . . . . . 94 4.7 Algebraic Topology . . . . . . . . . . . . . . . . . . 98 4.8 Entropy of Systems . . . . . . . . . . . . . . . . . . 98 Part 2 Crucial Nodes/Subnetworks/Modules, Network Types, and Structural Comparison 103 5 Identification of Crucial Nodes and Subnetworks/Modules 105 5.1 Features of Crucial Nodes . . . . . . . . . . . . . . . 105 5.2 Indices and Methods of Crucial Nodes . . . . . . . . 106 5.3 Further Discussion . . . . . . . . . . . . . . . . . . . 117 5.4 Application Examples . . . . . . . . . . . . . . . . . 118 5.5 Identification of Subnetworks/Modules . . . . . . . 121 6 Detection of Network Types 129 6.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . 130 6.2 Application Example . . . . . . . . . . . . . . . . . 135 7 Comparison of Network Structure 139 7.1 Nonparametric Statistic Comparison of Network Structure . . . . . . . . . . . . . . . . . 139 7.2 Nonparametric Statistic Comparison of Community Structure . . . . . . . . . . . . . . . 146 7.3 Network Matrix-based Methods . . . . . . . . . . . 152 7.4 Other Methods . . . . . . . . . . . . . . . . . . . . . 154
  • 21. April 20, 2018 10:53 Fundamentals of Network Biology 9in x 6in b3136-fm page xv Contents xv Part 3 Network Dynamics, Evolution, Simulation, and Control 155 8 Network Dynamics 157 8.1 Differential Equations and Motion Stability . . . . . 157 8.2 Dynamics of Some Networks . . . . . . . . . . . . . 164 9 Network Robustness and Sensitivity Analysis 175 9.1 Network Robustness . . . . . . . . . . . . . . . . . . 175 9.2 Sensitivity Analysis . . . . . . . . . . . . . . . . . . 185 10 Network Control 195 10.1 Conventional Control . . . . . . . . . . . . . . . . . 195 10.2 New Perspectives of Network Control . . . . . . . . 201 11 Network Evolution 203 11.1 A Generalized Network Evolution Model for Community Assembly Dynamics . . . . . . . . . 203 11.2 A Model for Perturbed Food Web Dynamics . . . . 214 11.3 A Network Evolution Algorithm Based on Node Attraction . . . . . . . . . . . . . . . . . . 215 11.4 Phase Recognition of Network Evolution . . . . . . 221 12 Cellular Automata 229 12.1 Classification of Cellular Automata . . . . . . . . . 230 12.2 Algorithms of Cellular Automata . . . . . . . . . . . 232 12.3 Some Cases of Cellular Automata . . . . . . . . . . 232 13 Self-Organization 237 13.1 Basic Concept of Self-Organization . . . . . . . . . 237 13.2 Theories and Principles of Self-Organization . . . . 239 13.3 Existing Algorithms of Self-Organization . . . . . . 249 13.4 Self-Organization in Life Sciences . . . . . . . . . . 249 13.5 Selforganizology . . . . . . . . . . . . . . . . . . . . 252
  • 22. April 20, 2018 10:53 Fundamentals of Network Biology 9in x 6in b3136-fm page xvi xvi Contents 14 Agent-based Modeling 255 14.1 Complex Systems . . . . . . . . . . . . . . . . . . . 255 14.2 Principle and Methods of Agent-based Modeling . . . . . . . . . . . . . . . . . . . . . . . . 258 Part 4 Flow Analysis 263 15 Flow/Flux Analysis 265 15.1 Flux Balance Analysis . . . . . . . . . . . . . . . . . 265 15.2 Flow Indices for Ecological Network Analysis . . . . . . . . . . . . . . . . . . . . . . . . 283 Part 5 Link and Node Prediction 291 16 Link Prediction: Sampling-based Methods 293 16.1 Linear Correlation Analysis for Finding Interactions . . . . . . . . . . . . . . . . . . . . . . . 293 16.2 Partial Correlation of General Correlation Measures . . . . . . . . . . . . . . . . . . . . . . . . 301 16.3 Combined Use of Linear Correlation and Rank Correlation: A Hierarchical Method for Finding Interactions . . . . . . . . . . . . . . . . 313 17 Link Prediction: Structure- and Perturbation-based Methods 321 17.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . 322 17.2 Performance Assessment of Link Prediction Methods . . . . . . . . . . . . . . . . . . . . . . . . 339 18 Link Prediction: Node-Similarity-based Methods 345 18.1 Link Prediction Based on Node Similarity . . . . . . 345 18.2 Screening Node Attributes that Significantly Influence Node Centrality . . . . . . . . . . . . . . . 351
  • 23. April 20, 2018 10:53 Fundamentals of Network Biology 9in x 6in b3136-fm page xvii Contents xvii 19 Node Prediction 361 19.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . 361 19.2 Application Example . . . . . . . . . . . . . . . . . 363 Part 6 Network Construction 365 20 Construction of Biological Networks 367 20.1 Prediction Methods of Protein–Protein Interactions . . . . . . . . . . . . . . . . . . . . . . . 367 20.2 Gene Coexpression Network Analysis . . . . . . . . 370 20.3 Classification-based Machine Learning . . . . . . . . 372 20.4 Statistic Network . . . . . . . . . . . . . . . . . . . 374 20.5 Homogeneity Test of Samples and Sampling Completeness . . . . . . . . . . . . . . . . . . . . . . 376 20.6 Network Construction Based on Power-Law or Exponential-Law Distribution of Node Degree . . . . . . . . . . . . . . . . . . . . . . . . . 381 Part 7 Pharmacological and Toxicological Networks 389 21 Network Pharmacology and Toxicology 391 21.1 Network Pharmacology . . . . . . . . . . . . . . . . 391 21.2 Network Toxicology . . . . . . . . . . . . . . . . . . 404 Part 8 Ecological Networks 413 22 Food Webs 415 22.1 Fundamentals of Food Webs . . . . . . . . . . . . . 416 22.2 Food Web Models . . . . . . . . . . . . . . . . . . . 422 22.3 Arthropod Food Webs . . . . . . . . . . . . . . . . . 435
  • 24. April 20, 2018 10:53 Fundamentals of Network Biology 9in x 6in b3136-fm page xviii xviii Contents Part 9 Microscopic Networks 439 23 Molecular and Cellular Networks 441 23.1 Metabolic Pathway of Nonalcoholic Fatty Liver Disease . . . . . . . . . . . . . . . . . . . . . . 442 23.2 E. coli Transcriptional Regulatory Network . . . . . 444 23.3 Common Disease Regulatory Network for Metabolic Disorders . . . . . . . . . . . . . . . . 447 23.4 Find Molecular Pathological Events in Different Cancer Tissues . . . . . . . . . . . . . . . . . . . . . 449 23.5 Crucial Metabolites/Reactions in Tumor Signaling Networks . . . . . . . . . . . . . . . . . . . . . . . . 452 23.6 Brain Network . . . . . . . . . . . . . . . . . . . . . 453 Part 10 Social Networks 459 24 Social Network Analysis 461 24.1 Definition of Social Networks . . . . . . . . . . . . . 461 24.2 Network Criminology . . . . . . . . . . . . . . . . . 467 Part 11 Software 469 25 Software for Network Analysis 471 25.1 Software for Comprehensive Network Analysis . . . . . . . . . . . . . . . . . . . . . . . . 471 25.2 Software for Network Layout and Others . . . . . . 485 Part 12 Big Data Analytics 489 26 Big Data Analytics for Network Biology 491 26.1 Fundamentals of Big Data Analytics . . . . . . . . . 492
  • 25. April 20, 2018 10:53 Fundamentals of Network Biology 9in x 6in b3136-fm page xix Contents xix 26.2 Text Mining with Python and R . . . . . . . . . . . 496 26.3 Big Data Analytics of Medicinal Attributes and Functions of Chinese Herbal Medicines . . . . . 499 References 511 Index 543
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