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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.
- Written in a didactic manner in order to explain how to investigate Big Mechanisms by big data mining and system identification
- Provides more than 140 diagrams to illustrate Big Mechanism in systems biology
- Presents worked examples in each chapter
Bioinformaticians; graduate students in systems biology; members of biomedical field interested in data mining and systems identification technologies
- Chapter 1. Introduction to Big Mechanisms in Systems Biology
- 1.1 Introduction to Big Mechanisms
- 1.2 Big Mechanisms in Systems Biology
- 1.3 The Scope of Big Mechanisms of Systems Biology in This Book
- Chapter 2. System Modeling and System Identification Methods for Big Mechanisms in Biological Systems
- 2.1 Dynamic System Models and Their Parameter Estimation by Time-Profile Experimental Data
- 2.2 Static Models and Their Parameter Estimation by Sample Microarray Data
- 2.3 Modeling and Identification of Integrated Genetic and Epigenetic Cellular Networks
- 2.4 The Core Network by PNP of the Integrated Genetic and Epigenetic Cellular Network Using PCA
- Chapter 3. Procedure for Exploring Big Mechanisms of Systems Biology Through System Identification and Big Database Mining
- 3.1 Big Mechanisms Based on GRNs by System Identification and Big Database Mining
- 3.2 Big Mechanisms Based on PPINs by System Identification and Big Database Mining
- 3.3 Big Mechanisms Based on the Integrated GRN and PPIN by System Identification and Big Database Mining
- 3.4 Big Mechanisms Based on the Integrated Genetic and Epigenetic Cellular Network by System Identification and Big Database Mining
- Chapter 4. Big Cellular Mechanisms in the Cell Cycle by System Identification and Big Data Mining
- 4.1 Constructing Transcriptional Regulatory Network to Investigate the Big Mechanisms in the Yeast Cell Cycle by System Identification and Big Data Mining
- Appendix A Matched Filter for Selecting More Correlated Regulators in Yeast Cell Cycle
- 4.2 Constructing TRMs for Big Regulatory Mechanisms of the Yeast Cell Cycle
- Appendix B Methods and Figures
- Chapter 5. Big Regulatory Mechanisms in the Transcriptional Regulation Control of Gene Expression Using a Stochastic System Model and Genome-Wide Experimental Data
- 5.1 Identification of TF Cooperativity in Gene Regulation of the Cell Cycle via the Stochastic System Model
- Appendix A Methods in Identifying the TF Cooperativity
- 5.2 Cis-Regulatory Mechanisms for Gene Expression via Cross-Gene Identification and Data Mining
- 5.3 Nonlinear Dynamic Trans/Cis-Regulatory Mechanisms for Gene Transcription via Microarray Data
- Appendix B Figures
- Chapter 6. Big Mechanisms of Information Flow in Cellular Systems in Response to Environmental Stress Signals via System Identification and Data Mining
- 6.1 Constructing Stress-Response Mechanisms via Dynamic Gene Regulatory Modeling and Data Mining
- 6.2 Identifying Protective Mechanisms of Gene and Protein Networks in Response to a Broad Range of Environmental Stress Signals
- 6.3 Constructing GRNs for Control Mechanisms of Photosynthetic Light Acclimation in Response to Different Light Signals
- 6.4 Constructing IGECN for Investigating Whole Cellular Signal Flow Mechanisms in Response to Environmental Stress Signals Using High-Throughput NGS
- Chapter 7. Big Offensive and Defensive Mechanisms in Systems Immunity From System Modeling and Big Data Mining
- 7.1 A Systems Biology Approach to Construct the GRN of Systemic Inflammation Mechanisms via Microarray and Databases Mining
- Appendix A Tables and Figures
- 7.2 Identification of Infection and Defense-Related Mechanisms via a Dynamic Host–Pathogen Interaction Network Using C. albicans-Zebrafish Infection Model
- Appendix B Methods, Tables, and Figures
- 7.3 Investigating Host–Pathogen Interaction Networks to Reveal the Pathogenic Mechanism in HIV Infection: A Systems Biology Approach
- Appendix C Figures
- Chapter 8. Big Regeneration Mechanisms via Systems Biology and Big Database Mining Methods
- 8.1 Dynamic System Mechanisms in the Three Differentiation Stages of Stem Cells to Reveal Essential Proteins and Functional Modules in the Directed Differentiation Process
- Appendix A Figures
- 8.2 Cerebella Regeneration-Related Pathways and Their Crosstalks in Molecular Restoration Mechanisms After TBI in Zebrafish
- Appendix B Methods, Tables, and Figures
- Chapter 9. Big Tumorigenesis Mechanisms in Systems Cancer Biology via Big Database Mining and Network Modeling
- 9.1 Construction and Clarification of Dynamic Networks of the Cancer Cell Cycle via Microarray Data
- Appendix A Methods
- 9.2 Investigating Tumorigenesis Mechanisms by Cancer-Perturbed PPINs
- Appendix B Methods of Constructing Cancer-Perturbed PPINs
- 9.3 A Network-Based Biomarker Approach for Molecular Investigation and Diagnosis of Lung Cancer
- Appendix C Tables and Figures
- 9.4 Network Biomarkers of Bladder Cancer Based on a Genome-Wide Genetic and Epigenetic Network Derived From NGS Data
- Chapter 10. Big Evolutionary Mechanisms of Network Robustness and Signaling Transductivity in Aging and Carcinogenic Process by System Modeling and Database Mining
- 10.1 New Measurement Methods of Network Robustness and Response Ability in Aging and Carcinogenic Process via Microarray Data and Dynamic System Model
- Appendix A Methods and Figures
- 10.2 Evolution of Signal Transductivities of Coupled Signal Pathways in the Carcinogenic Process
- Appendix B Figures
- 10.3 Nonlinear Stochastic Game Strategy for Evolution Mechanisms of Organ Carcinogenesis Under a Natural Selection Scheme
- Appendix C
- Chapter 11. Big Mechanisms of Aging via System Identification and Big Database Mining
- 11.1 On the Systematic Mechanism of GRN in the Aging Process: A Systems Biology Approach via Microarray Data
- 11.2 Investigating Specific Core GEN for Cellular Mechanisms of Human Aging via NGS Data
- Chapter 12. Big Drug Design Mechanisms via Systems Biology and Big Database Mining
- 12.1 Overview of Drug Discovery Using Systems Biology
- 12.2 Investigating Core and Specific Network Markers of Cancers for Multiple Drug Targets
- Appendix A Methods, Tables, and Figures
- 12.3 Systems Drug Design Mechanisms for Multiple Drug Targets
- Appendix B Method and Table
- No. of pages:
- © Academic Press 2017
- 11th November 2016
- Academic Press
- Paperback ISBN:
- eBook ISBN:
Bor-Sen Chen received B.S. degree of electrical Engineering from Tatung Institute of Technology in 1970, M.S. degree of Geophysics from National Central University in 1973, and PhD in Electrical Engineering from University of Southern California in 1982. He is an expert on the topic of nonlinear robust control and filter designs based on stochastic Nash game theory to override the influence of intrinsic random fluctuations and attenuate the effect of environmental disturbances, which can be applied to evolutionary game strategies of biological networks under natural selection to respond to random genetic variations and environmental disturbances in the evolutionary process. Prof. Chen had audited more than 10 courses of biology before his research in systems biology. He has published about 100 papers in bioinformatics and systems biology. Further, he have published more than 100 papers in system theory and control, and more than 80 papers of signal processing and communication. In the last three years, he has also published 7 monographs. He was elected to an IEEE Fellow in 2001 and became an IEEE Life Fellow in 2014.
Tsing Hua Distinguished Chair Professor, Department of Electrical Engineering, National Tsing Hua University, Taiwan
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.
Postdoctoral Researcher, National Tsing Hua University, Taiwan
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