Systems Biology E-Book

Edda Klipp

• Herausgeber: Wiley-VCH
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 1464

Veröffentlichungsjahr: 2016

Beschreibung

Completely revised and expanded, this new edition now includes a section on medical systems biology and comes in a large format with a vastly improved layout, professional artwork throughout, and student-friendly reading lists. The experienced author team has worked closely to ensure a homogenous style and comprehensive coverage without overlaps. The text retains its easily accessible style and includes numerous work examples and study questions in each chapter. The greatly improved companion website features a complete set of figures, a summary for exam preparations and modeling software for study purposes. Now that systems biology is becoming an integral part of the life science curriculum, this textbook is geared towards biologists, engineers and computer scientists.

Leseprobe

Contents

Cover

Title Page

Copyright

Preface

Guide to Different Topics of the Book

About the Authors

Part One: Introduction to Systems Biology

1: Introduction

1.1 Biology in Time and Space

1.2 Models and Modeling

1.2.1 What Is a Model?

1.2.2 Purpose and Adequateness of Models

1.2.3 Advantages of Computational Modeling

1.3 Basic Notions for Computational Models

1.3.1 Model Scope

1.3.2 Model Statements

1.3.3 System State

1.3.4 Variables, Parameters, and Constants

1.3.5 Model Behavior

1.3.6 Model Classification

1.3.7 Steady States

1.3.8 Model Assignment is Not Unique

1.4 Networks

1.5 Data Integration

1.6 Standards

1.7 Model Organisms

1.7.1 Escherichia Coli

1.7.2 Saccharomyces Cerevisiae

1.7.3 Caenorhabditis Elegans

1.7.4 Drosophila Melanogaster

1.7.5 Mus Musculus

References

Further Reading

2: Modeling of Biochemical Systems

2.1 Overview of Common Modeling Approaches for Biochemical Systems

2.2 ODE Systems for Biochemical Networks

2.2.1 Basic Components of ODE Models

2.2.2 Illustrative Examples of ODE Models

References

Further Reading

3: Structural Modeling and Analysis of Biochemical Networks

3.1 Structural Analysis of Biochemical Systems

3.1.1 System Equations

3.1.2 Information Encoded in the Stoichiometric Matrix N

3.1.3 The Flux Cone

3.1.4 Elementary Flux Modes and Extreme Pathways

3.1.5 Conservation Relations – Null Space of NT

3.2 Constraint-Based Flux Optimization

3.2.1 Flux Balance Analysis

3.2.2 Geometric Interpretation of Flux Balance Analysis

3.2.3 Thermodynamic Constraints

3.2.4 Applications and Tests of the Flux Optimization Paradigm

3.2.5 Extensions of Flux Balance Analysis

Exercises

References

Further Reading

4: Kinetic Models of Biochemical Networks: Introduction

4.1 Reaction Kinetics and Thermodynamics

4.1.1 Kinetic Modeling of Enzymatic Reactions

4.1.2 The Law of Mass Action

4.1.3 Reaction Thermodynamics

4.1.4 Michaelis–Menten Kinetics

4.1.5 Regulation of Enzyme Activity by Effectors

4.1.6 Generalized Mass Action Kinetics

4.1.7 Approximate Kinetic Formats

4.1.8 Convenience Kinetics and Modular Rate Laws

4.2 Metabolic Control Analysis

4.2.1 The Coefficients of Control Analysis

4.2.2 The Theorems of Metabolic Control Theory

4.2.3 Matrix Expressions for Control Coefficients

4.2.4 Upper Glycolysis as Realistic Model Example

4.2.5 Time-Dependent Response Coefficients

Exercises

References

Further Reading

5: Data Formats, Simulation Techniques, and Modeling Tools

5.1 Simulation Techniques and Tools

5.1.1 Differential Equations

5.1.2 Stochastic Simulations

5.1.3 Simulation Tools

5.2 Standards and Formats for Systems Biology

5.2.1 Systems Biology Markup Language

5.2.2 BioPAX

5.2.3 Systems Biology Graphical Notation

5.3 Data Resources for Modeling of Cellular Reaction Systems

5.3.1 General-Purpose Databases

5.3.2 Pathway Databases

5.3.3 Model Databases

5.4 Sustainable Modeling and Model Semantics

5.4.1 Standards for Systems Biology Models

5.4.2 Model Semantics and Model Comparison

5.4.3 Model Combination

5.4.4 Model Validity

References

Further Reading

6: Model Fitting, Reduction, and Coupling

Introduction

6.1 Parameter Estimation

6.1.1 Regression, Estimators, and Maximal Likelihood

6.1.2 Parameter Identifiability

6.1.3 Bootstrapping

6.1.4 Bayesian Parameter Estimation

6.1.5 Probability Distributions for Rate Constants

6.1.6 Optimization Methods

6.2 Model Selection

6.2.1 What Is a Good Model?

6.2.2 The Problem of Model Selection

6.2.3 Likelihood Ratio Test

6.2.4 Selection Criteria

6.2.5 Bayesian Model Selection

6.3 Model Reduction

6.3.1 Model Simplification

6.3.2 Reduction of Fast Processes

6.3.3 Quasi-Equilibrium and Quasi-Steady State

6.3.4 Global Model Reduction

6.4 Coupled Systems and Emergent Behavior

6.4.1 Modeling of Coupled Systems

6.4.2 Combining Rate Laws into Models

6.4.3 Modular Response Analysis

6.4.4 Emergent Behavior in Coupled Systems

6.4.5 Causal Interactions and Global Behavior

Exercises

References

Further Reading

7: Discrete, Stochastic, and Spatial Models

7.1 Discrete Models

7.1.1 Boolean Networks

7.1.2 Petri Nets

7.2 Stochastic Modeling of Biochemical Reactions

7.2.1 Chance in Biochemical Reaction Systems

7.2.2 The Chemical Master Equation

7.2.3 Stochastic Simulation

7.2.4 Chemical Langevin Equation and Chemical Noise

7.2.5 Dynamic Fluctuations

7.2.6 From Stochastic to Deterministic Modeling

7.3 Spatial Models

7.3.1 Types of Spatial Models

7.3.2 Compartment Models

7.3.3 Reaction–Diffusion Systems

7.3.4 Robust Pattern Formation in Embryonic Development

7.3.5 Spontaneous Pattern Formation

7.3.6 Linear Stability Analysis of the Activator–Inhibitor Model

Exercises

References

Further Reading

8: Network Structure, Dynamics, and Function

8.1 Structure of Biochemical Networks

8.1.1 Random Graphs

8.1.2 Scale-Free Networks

8.1.3 Connectivity and Node Distances

8.1.4 Network Motifs and Significance Tests

8.1.5 Explanations for Network Structures

8.2 Regulation Networks and Network Motifs

8.2.1 Structure of Transcription Networks

8.2.2 Regulation Edges and Their Steady-State Response

8.2.3 Negative Feedback

8.2.4 Adaptation Motif

8.2.5 Feed-Forward Loops

8.3 Modularity and Gene Functions

8.3.1 Cell Functions Are Reflected in Structure, Dynamics, Regulation, and Genetics

8.3.2 Metabolic Pathways and Elementary Modes

8.3.3 Epistasis Can Indicate Functional Modules

8.3.4 Evolution of Function and Modules

8.3.5 Independent Systems as a Tacit Model Assumption

8.3.6 Modularity and Biological Function Are Conceptual Abstractions

Exercises

References

Further Reading

9: Gene Expression Models

9.1 Mechanisms of Gene Expression Regulation

9.1.1 Transcription Factor-Initiated Gene Regulation

9.1.2 General Promoter Structure

9.1.3 Prediction and Analysis of Promoter Elements

9.1.4 Posttranscriptional Regulation through microRNAs

9.2 Dynamic Models of Gene Regulation

9.2.1 A Basic Model of Gene Expression and Regulation

9.2.2 Natural and Synthetic Gene Regulatory Networks

9.2.3 Gene Expression Modeling with Stochastic Equations

9.3 Gene Regulation Functions

9.3.1 The Lac Operon in E. coli

9.3.2 Gene Regulation Functions Derived from Equilibrium Binding

9.3.3 Thermodynamic Models of Promoter Occupancy

9.3.4 Gene Regulation Function of the Lac Promoter

9.3.5 Inferring Transcription Factor Activities from Transcription Data

9.3.6 Network Component Analysis

9.3.7 Correspondences between mRNA and Protein Levels

9.4 Fluctuations in Gene Expression

9.4.1 Stochastic Model of Transcription and Translation

9.4.2 Intrinsic and Extrinsic Variability

9.4.3 Temporal Fluctuations in Gene Cascades

Exercises

References

Further Reading

10: Variability, Robustness, and Information

10.1 Variability and Biochemical Models

10.1.1 Variability and Uncertainty Analysis

10.1.2 Flux Sampling

10.1.3 Elasticity Sampling

10.1.4 Propagation of Parameter Variability in Kinetic Models

10.1.5 Models with Parameter Fluctuations

10.2 Robustness Mechanisms and Scaling Laws

10.2.1 Robustness in Biochemical Systems

10.2.2 Robustness by Backup Elements

10.2.3 Feedback Control

10.2.4 Perfect Robustness by Structure

10.2.5 Scaling Laws

10.2.6 Time Scaling, Summation Laws, and Robustness

10.2.7 The Role of Robustness in Evolution and Modeling

10.3 Adaptation and Exploration Strategies

10.3.1 Information Transmission in Signaling Pathways

10.3.2 Adaptation and Fold-Change Detection

10.3.3 Two Adaptation Mechanisms: Sensing and Random Switching

10.3.4 Shannon Information and the Value of Information

10.3.5 Metabolic Shifts and Anticipation

10.3.6 Exploration Strategies

Exercises

References

Further Reading

11: Optimality and Evolution

11.1 Optimality in Systems Biology Models

11.1.1 Mathematical Concepts for Optimality and Compromise

11.1.2 Metabolism Is Shaped by Optimality

11.1.3 Optimality Approaches in Metabolic Modeling

11.1.4 Metabolic Strategies

11.1.5 Optimal Metabolic Adaptation

11.2 Optimal Enzyme Concentrations

11.2.1 Optimization of Catalytic Properties of Single Enzymes

11.2.2 Optimal Distribution of Enzyme Concentrations in a Metabolic Pathway

11.2.3 Temporal Transcription Programs

11.3 Evolution and Self-Organization

11.3.1 Introduction

11.3.2 Selection Equations for Biological Macromolecules

11.3.3 The Quasispecies Model: Self-Replication with Mutations

11.3.4 The Hypercycle

11.3.5 Other Mathematical Models of Evolution: Spin Glass Model

11.3.6 The Neutral Theory of Molecular Evolution

11.4 Evolutionary Game Theory