Big Mechanisms in Systems Biology : Big Data Mining, Network Modeling, and Genome-Wide Data Identification

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Title:

Personal Author:

Chen, Bor-Sen, author

Physical Description:

viii, 870 pages : illustrations ; 24 cm.

ISBN:

9780128094792

Subject Term:

Systems biology

Added Author:

Li, Cheng Wei,

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Library	Item Barcode	Call Number	Material Type	Item Category 1	Status
Searching... PSZ JB	30000010343282	QH324.2 C544 2017	Open Access Book	Book	Searching... Unknown

Big Mechanisms in Systems Biology: Big Data Mining, Network Modeling, and Genome-Wide Data Identification explains big mechanisms of systems biology by system identification and big data mining methods using models of biological systems. Systems biology is currently undergoing revolutionary changes in response to the integration of powerful technologies. Faced with a large volume of available literature, complicated mechanisms, small prior knowledge, few classes on the topics, and causal and mechanistic language, this is an ideal resource.

This book addresses system immunity, regulation, infection, aging, evolution, and carcinogenesis, which are complicated biological systems with inconsistent findings in existing resources. These inconsistencies may reflect the underlying biology time-varying systems and signal transduction events that are often context-dependent, which raises a significant problem for mechanistic modeling since it is not clear which genes/proteins to include in models or experimental measurements.

The book is a valuable resource for bioinformaticians and members of several areas of the biomedical field who are interested in an in-depth understanding on how to process and apply great amounts of biological data to improve research.

Author Notes

Bor-Sen Chen is currently the Tsing Hua University distinguished chair professor. He received his PhD degree in Electrical Engineering from University of Southern California in 1982. He is major at system control, signal processing and communication system. He also published about 200 SCI journal papers in two fields. He has been elected as an IEEE fellow in 2001 and became a life fellow of IEEE in 2014. In the last decade, he has published more than 70 journal papers in systems biology and bioinformatics after he had audited more than 10 courses in biology from 2010 to 2013. He has also published three books, including Synthetic Gene Network: Modelling, Analysis and Robust Design Method (CRC Press, 2014), Systems Biology: An Integrated Platform for Bioinformatics, Synthetic Biology and Systems Metabolic Engineering (NOVA Science Publisher, 2014), and H Robust Design and Its Application to Control, Signal Processing, Communication, Systems and Synthetic Biology (NOVA Science Publisher, 2015). Professor Chen has been a member of Editorial Board of several international journals, including BMC Systems Biology (2010-2015).

Cheng-Wei Li received the B.S. degree in automatic control engineering from Feng Chia University and the Ph.D. degree in electrical engineering from the National Tsing Hua University (NTHU), Hsinchu, Taiwan, in 2003 and 2010, respectively. He currently joins Dr. Bor-Sen Chen's lab at NTHU (2011-now) to conduct postdoctoral research in systems biology, and computational neuroscience. His research interests include systems biology, bioinformatics and stochastic nonlinear control.

1 Introduction to Big Mechanisms in Systems Biology	p. 1
Introduction	p. 1
1.1 Introduction to Big Mechanisms	p. 2
1.2 Big Mechanisms in Systems Biology	p. 3
1.3 The Scope of Big Mechanisms of Systems Biology in This Book	p. 4
Peferences	p. 7
2 System Modeling and System identification Methods for Big Mechanisms in Biological Systems	p. 9
Introduction	p. 9
2.1 Dynamic System Models and Their Parameter Estimation by Time-Profile Experimental Data	p. 10
2.2 Static Models and Their Parameter Estimation by Sample Microarray Data	p. 20
2.3 Modeling and Identification of Integrated Genetic and Epigenetic Cellular Networks	p. 23
2.4 The Core Network by PNP of the Integrated Genetic and Epigenetic Cellular Network Using PCA	p. 25
References	p. 27
3 Procedure for Exploring Big Mechanisms of Systems Biology Through System Identification and Big Database Mining	p. 29
Introduction	p. 29
3.1 Big Mechanisms Based on GRNs by System Identification and Big Database Mining	p. 29
3.2 Big Mechanisms Based on PPINs by System Identification and Big Database Mining	p. 31
3.3 Big Mechanisms Based on the integrated GRN and PPIN by System Identification and Big Database Mining	p. 35
3.4 Big Mechanisms Based on the Integrated Genetic and Epigenetic Cellular Network by System Identification and Big Database Mining	p. 35
References	p. 37
4 Big Cellular Mechanisms in the Cell Cycle by System Identification and Big Data Mining	p. 39
Introduction	p. 39
4.1 Constructing Transcriptional Regulatory Network to Investigate the Big Mechanisms in the Yeast Cell Cycle by System Identification and Big Data Mining	p. 40
Appendix A Matched Filter for Selecting More Correlated Regulators in Yeast Cell Cycle	p. 40
4.2 Constructing TRMs for Big Regulatory Mechanisms of the Yeast Cell Cycle	p. 61
Appendix B Methods and Figures	p. 78
References	p. 82
5 Big Regulatory Mechanisms in the Transcriptional Regulation Control of Gene Expression Using a Stochastic System Model and Genome-Wide Experimental Data	p. 87
Introduction	p. 87
5.1 Identification of TF Cooperativity in Gene Regulation of the Cell Cycle via the Stochastic System Model	p. 88
Appendix A Methods in Identifying the TF Cooperativity	p. 102
5.2 Cis-Regulatory Mechanisms for Gene Expression via Cross-Gene Identification and Data Mining	p. 105
5.3 Nonlinear Dynamic Trans/Cis-Regulatory Mechanisms for Gene Transcription via Microarray Data	p. 128
Appendix B Figures	p. 150
References	p. 152
6 Big Mechanisms of Information Flow in Cellular Systems in Response to Environmental Stress Signals via System Identification and Data Mining	p. 155
Introduction	p. 155
6.1 Constructing Stress-Response Mechanisms via Dynamic Gene Regulatory Modeling and Data Mining	p. 156
6.2 Identifying Protective Mechanisms of Gene and Protein Networks in Response to a Broad Range of Environmental Stress Signals	p. 167
6.3 Constructing GRNs for Control Mechanisms of Photosynthetic Light Acclimation in Response to Different Light Signals	p. 194
6.4 Constructing IGECN for Investigating Whole Cellular Signal Flow Mechanisms in Response to Environmental Stress Signals Using High-Throughput NGS	p. 213
References	p. 237
7 Big Offensive and Defensive Mechanisms in Systems Immunity From System Modeling and Big Data Mining	p. 249
Introduction	p. 249
7.1 A Systems Biology Approach to Construct the GRN of Systemic Inflammation Mechanisms via Microarray and Databases Mining	p. 250
Appendix A Tables and Figures	p. 276
7.2 Identification of Infection and Defense-Related Mechanisms via a Dynamic Host-Pathogen Interaction Network Using C. albicans-Zebrafish Infection Model	p. 295
Appendix B Methods, Tables, and Figures	p. 321
7.3 Investigating Host-Pathogen Interaction Networks to Reveal the Pathogenic Mechanism in HIV Infection: A Systems Biology Approach	p. 329
Appendix C Figures	p. 359
References	p. 362
8 Big Regeneration Mechanisms via Systems Biology and Big Database Mining Methods	p. 373
Introduction	p. 373
8.1 Dynamic System Mechanisms in the Three Differentiation Stages of Stem Cells to Repeal Essential Proteins and Functional Modules in the Directed Differentiation Process	p. 374
Appendix A Figures	p. 392
8.2 Cerebella Regeneration-Related Pathways and Their Crosstalks in Molecular Restoration Mechanisms After TBI in Zebrafish	p. 393
Appendix B Methods, Tables, and Figures	p. 413
References	p. 426
9 Big Tumorigenesis Mechanisms in Systems Cancer Biology via Big Database Mining and Network Modeling	p. 431
Introduction	p. 431
9.1 Construction and Clarification of Dynamic Networks of the Cancer Cell Cycle via Microarray Data	p. 433
Appendix A Methods	p. 450
9.2 Investigating Tumorigenesis Mechanisms by Cancer-Perturbed PPINs	p. 453
Appendix B Methods of Constructing Cancer-Perturbed PPINs	p. 467
9.3 A Network-Based Biomarker Approach for Molecular Investigation and Diagnosis of Lung Cancer	p. 474
Appendix C Tables and Figures	p. 493
9.4 Network Biomarkers of Bladder Cancer Based on a Genome-Wide Genetic and Epigenetic Network Derived From NGS Data	p. 494
References	p. 513
10 Big Evolutionary Mechanisms of Network Robustness and Signaling Transductivity in Aging and Carcinogenic Process by System Modeling and Database Mining	p. 527
Introduction	p. 527
10.1 New Measurement Methods of Network Robustness and Response Ability in Aging and Carcinogenic Process via Microarray Data and Dynamic System Model	p. 529
Appendix A Methods and Figures	p. 552
10.2 Evolution of Signal Transductivities of Coupled Signal Pathways in the Carcinogenic Process	p. 560
Appendix B Figures	p. 617
10.3 Nonlinear Stochastic Game Strategy for Evolution Mechanisms of Organ Carcinogenesis Under a Natural Selection Scheme	p. 617
Appendix C p. 656
References	p. 662
11 Big Mechanisms of Aging via System Identification and Big Database Mining	p. 671
Introduction	p. 671
11.1 On the Systematic Mechanism of GRN in the Aging Process: A Systems Biology Approach via Microarray Data	p. 672
11.2 Investigating Specific Core GEN for Cellular Mechanisms of Human Aging via NGS Data	p. 696
References	p. 729
12 Big Drug Design Mechanisms via Systems Biology and Big Database Mining	p. 737
Introduction	p. 737
12.1 Overview of Drug Discovery Using Systems Biology	p. 738
12.2 Investigating Core and Specific Network Markers of Cancers for Multiple Drug Targets	p. 750
Appendix A Methods, Tables, and Figures	p. 785
12.3 Systems Drug Design Mechanisms for Multiple Drug Targets	p. 796
Appendix B Method and Table	p. 828
References	p. 833
Index	p. 847

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