Systems Biology.

Systems Biology.

Systems biology is a relatively new biological study field that focuses on the systematic study of complex interactions in biological systems, thus using a new perspective (integration instead of reduction) to study them. Particularly from year 2000 onwards, the term is used widely in the bioscience...

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Bibliographic Details
Author / Creator: Meyers, Robert A.
Format: eBook
Language:English
Edition:1st ed.
Imprint: Somerset : John Wiley & Sons, Incorporated, 2012.
Series:Current Topics from the Encyclopedia of Molecular Cell Biology and Molecular Medicine Ser.
Subjects:
Systems biology.
Biology.
Electronic books.
Local Note:Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2022. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
Online Access:Click to View
Table of Contents:
  • Intro
  • Systems Biology
  • Contents
  • Preface and Commentary
  • List of Contributors
  • Part I Biological Basis of Systems Biology
  • 1 Systems Biology
  • 1 Introduction
  • 2 What Is Systems Understanding?
  • 3 Why Are Biological Systems Different?
  • 3.1 Biological Complexity
  • 3.2 Global Properties of Biological Systems
  • 4 Systems Biology Modeling
  • 4.1 Network Biology
  • 4.2 Dynamic Network Models
  • 4.3 Reaction-Diffusion Models
  • 4.4 Holism versus Reductionism: The Global Dynamics of Networks
  • 4.5 Modeling Resources and Standards
  • 5 Future Prospects of Systems Biology
  • 5.1 Synthetic Biology
  • 5.2 Conclusions: Where Are We?
  • References
  • 2 Developmental Cell Biology
  • 1 Historical Perspective
  • 1.1 Origins of Cell Biology
  • 1.2 Origins of Developmental Biology
  • 1.3 Relationship between Cell and Developmental Biology
  • 2 Cell Activities Underlying Development
  • 2.1 Intracellular Signal Transduction
  • 2.2 Cell Signaling
  • 2.3 Cell-Cell Interactions
  • 2.4 Cell-Matrix Interaction
  • 3 Cell Differentiation
  • 4 The Cell Cycle and Development
  • 5 Organogenesis
  • 6 Stem Cells
  • 7 Chimeras
  • 8 microRNAs (miRNAs)
  • 9 In vitro Fertilization
  • References
  • 3 Principles and Applications of Embryogenomics
  • 1 Introduction
  • 2 Approaches
  • 2.1 Overview
  • 2.2 Large-Scale Analysis of Gene Expression at the Transcriptome Level
  • 2.3 Cell-Cell Interactions
  • 2.4 Cell-Matrix Interaction
  • 3 Cell Differentiation
  • 4 The Cell Cycle and Development
  • 5 Organogenesis
  • 6 Stem Cells
  • 7 Chimeras
  • 8 microRNAs (miRNAs)
  • 9 In vitro Fertilization
  • References
  • 3 Principles and Applications of Embryogenomics
  • 1 Introduction
  • 2 Approaches
  • 2.1 Overview
  • 2.2 Large-Scale Analysis of Gene Expression at the Transcriptome Level
  • 2.3 Large-Scale Analysis of Gene Expression at the Proteome Level.
  • 2.4 Development and Evolution: Comparative Genomics
  • 2.5 Functional Genomics/Large-Scale Manipulation of Expression
  • 2.6 Computational Approaches
  • 3 Model Organisms for Embryogenomics
  • 3.1 Non-Mammalian Animals
  • 3.2 Mammalian
  • 3.3 Plants
  • 3.4 Suitability of Approaches for Particular Model Organisms Applied to the Study of Development
  • 4 Conclusions
  • References
  • 4 Interactome
  • 1 Introduction
  • 2 Experimental Techniques for DetectingProtein Interactions
  • 3 Computational Prediction of Protein Interactions
  • 3.1 Interaction Prediction from the Gene Patterns Across Genomes
  • 3.2 Predicting Interaction from Sequence Coevolution
  • 3.3 Domain Interactions
  • 3.4 Coexpression Networks
  • 4 Exploring the Topology of the Interactome
  • 4.1 Global Properties
  • 4.2 Network Centrality and Protein Essentiality
  • 4.3 Network Modules
  • 4.4 Network Motifs and Related Concepts
  • 5 Comparing Protein-Protein Interaction Networks
  • 6 Databases of Protein and Domain Interactions
  • 7 Applications
  • 7.1 Predicting Protein Function
  • 7.2 Application to Human Diseases
  • 8 Looking Ahead: Towards the Dynamic Interactome
  • Acknowledgments
  • References
  • 5 Protein Abundance Variation
  • 1 Introduction
  • 2 Biochemical Aspects Affecting Protein Abundance in Prokaryotes
  • 2.1 Transcription Rate
  • 2.2 mRNA Decay
  • 2.3 Translation Rate
  • 2.4 Protein Stability
  • 3 Extracellular Causes Influencing Protein Abundance in Prokaryotes
  • 3.1 Nutritional Stress
  • 3.2 Thermal Stress
  • 3.3 Oxidative Stress
  • 4 Biochemical Aspects Affecting Protein Abundance in Eukaryotes
  • 4.1 Transcription Rate
  • 4.2 Alternative Splicing
  • 4.3 mRNA Features Regulating Protein Abundance
  • 4.4 mRNA Stability
  • 4.5 Translation Rate
  • 4.6 Protein Stability
  • 5 Other Factors Influencing Protein Abundance in Eukaryotes
  • 5.1 Environmental Stress.
  • 5.2 Infection
  • 5.3 Development
  • 6 Techniques Used to Measure Protein Abundance
  • 6.1 Correlation between mRNA Abundance and Protein Abundance
  • 6.2 Electrophoresis-Based Methods
  • 6.3 Quantitative Proteomics
  • 6.4 Ribosomal Footprinting
  • 6.5 Single-Molecule Real-Time Imaging
  • 7 Concluding Remarks and Outlook
  • Acknowledgments
  • References
  • Part II Systems Biology of Evolution
  • 6 Genetic Variation and Molecular Darwinism
  • 1 Introduction
  • 2 Principles of Molecular Evolution
  • 2.1 Evolutionary Roles of Genetic Variation, Natural Selection, and Isolation
  • 2.2 Molecular Mechanisms of the Generation of Genetic Variation
  • 3 Genetic Variation in Bacteria
  • 4 Local Changes in the DNA Sequences
  • 5 Intragenomic DNA Rearrangements
  • 5.1 Site-Specific DNA Inversion at Secondary Crossover Sites
  • 5.2 Transposition of Mobile Genetic Elements
  • 6 DNA Acquisition
  • 7 The Three Natural Strategies Generating Genetic Variations Contribute Differently to the Evolutionary Process
  • 8 Evolution Genes and Their Own Second-Order Selection
  • 9 Arguments for a General Relevance of the Theory of Molecular Evolution for All Living Organisms
  • 10 Systemic Aspects of Biological and Terrestrial Evolution
  • 11 Conceptual Aspects of the Theory of Molecular Evolution
  • 11.1 Pertinent Scientific Questions
  • 11.2 Philosophical Values of the Knowledge on Molecular Evolution
  • 11.3 Aspects Relating to Practical Applications of Scientific Knowledge on Molecular Evolution
  • References
  • 7 Systematics and Evolution
  • 1 The Beginning of Molecular Systematics
  • 2 The Molecular Assumption
  • 3 DNA Hybridization
  • 4 Mitochondrial DNA
  • 5 DNA Sequences
  • 6 Repeated (Retro)Transposons
  • 7 ''Evo-Devo''
  • 8 Positional Information and Shape
  • 9 ''Mutation''
  • 10 Toward a Theory of Evolutionary Change.
  • 11 Molecules and Systematics: Looking Toward the Future
  • References
  • 8 Evolution of the Protein Repertoire
  • 1 The First Proteins
  • 2 Organization of the Modern Protein Repertoire
  • 3 Protein Sequence and Its Evolution
  • 3.1 Evolution of the Genetic Code
  • 3.3 The Organization of Protein Sequences
  • 3.4 Genetic Mechanisms of Protein Evolution
  • 3.5 Genomic Mechanisms of Protein Evolution
  • 4 Protein Structure and Its Evolution
  • 4.1 Levels of Protein Structure
  • 4.2 Protein Structure In Vivo
  • 4.3 Evolution of Protein Structure
  • 5 Protein Function and Its Evolution
  • 5.1 Types of Protein Function
  • 5.2 Functional Networks in Physiology
  • 5.3 Evolution of Protein Function
  • 6 Protein Evolution in Human Hands
  • 6.1 In Vitro Protein Evolution
  • 6.2 Computational Protein Evolution
  • 7 Lessons from the Evolution of the Protein Repertoire
  • References
  • Part III Modeling of Biological Systems
  • 9 Chaos in Biochemistry and Physiology
  • 1 Introduction
  • 2 Systems Biology and the Complex Systems Approach: Chaos in Context
  • 3 Reconstructing the Underlying Dynamics of Complex Systems
  • 4 Chaos, Randomness, and (Colored) Noise
  • 5 Nonlinear Time Series Analysis: Conceptual Theoretical and Analytic Tools for Chaos Detection and Characterization
  • 6 Periodic and Non-Periodic Dynamics
  • 7 Biochemical and Physiological Chaos
  • 7.1 Emergent Phenomena in Networks at (Sub) Cellular, Tissue, and Organ Levels
  • 7.2 Chaos, Multi-oscillatory Systems, and Inverse Power Laws
  • 8 Chaos in Dynamics of Heart and Brain?
  • 9 Concluding Remarks: The Status and a Prospective for Chaos
  • Acknowledgments
  • References
  • 10 Computational Biology
  • 1 Introduction
  • 2 Sequencing Genomes
  • 3 Molecular Sequence Analysis
  • 3.1 Sequence Alignment
  • 3.2 Phylogeny Construction
  • 3.3 ''Identifying'' Genes
  • 3.4 Analyzing Regulatory Regions.
  • 3.5 Finding Repetitive Elements
  • 3.6 Analyzing Genome Rearrangements
  • 4 Molecular Structure Prediction
  • 4.1 Protein Structure Prediction
  • 4.2 RNA Secondary Structure
  • 5 Analysis of Molecular Interactions
  • 5.1 Protein Ligand Docking and Drug Screening
  • 5.2 Protein-Protein Docking
  • 5.3 Protein Interactions Involving DNA
  • 5.4 Protein Design
  • 6 Molecular Networks
  • 6.1 Different Types of Network
  • 6.2 Metabolic Networks
  • 6.3 Regulatory and Signaling Networks
  • 6.4 Approaches to Analyzing Interaction Networks
  • 7 Analysis of Expression Data
  • 7.1 Configuration of Experiments and Low-Level Analysis
  • 7.2 Classification of Samples
  • 7.3 Classification of Probes
  • 7.4 Analyzing Transcriptomes with RNA-Seq
  • 7.5 Beyond RNA
  • 8 Protein Function Prediction
  • 8.1 What Is Protein Function?
  • 8.2 Function from Sequence
  • 8.3 Genomic Context Methods
  • 8.4 Function from Structure
  • 8.5 Text Mining
  • 9 Computational Biology of Diseases
  • 9.1 Assessing Disease Risk
  • 9.2 Supporting the Prevention of Diseases
  • 9.3 Supporting the Diagnosis and Prognosis of Diseases
  • 9.4 Supporting the Therapy of Diseases
  • 10 Perspectives
  • Acknowledgments
  • Note on the Second Edition on This Chapter
  • References
  • 11 Dynamics of Biomolecular Networks
  • 1 Introduction
  • 2 Boolean Dynamics Models
  • 2.1 Boolean Formalisms
  • 2.2 Generic Properties of (Random) Boolean Networks and Cell Behaviors: Cell Differentiations and the Cell Cycle
  • 2.3 Topological and Dynamical Properties: Homeostasis, Flexibility, and Evolvability
  • 2.4 Biologically Relevant Boolean Rules
  • 2.5 Dynamical Simulation: An Example
  • 2.6 Boolean Networks Inference from Experimental Data: Probabilistic Boolean Networks
  • 2.7 Addition of Noise
  • 3 Continuous Dynamics Models
  • 3.1 ODE Formalisms: From Biochemistry to Mathematics.
  • 3.2 Summing Nodes and Links: From Math to Systems Biology.

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