Note: Intro -- Systems Biology in Practice -- Preface -- Foreword -- Contents -- Part I General Introduction -- 1 Basic Principles -- 1.1 Systems Biology is Biology! -- 1.2 Systems Biology is Modeling -- 1.2.1 Properties of Models -- 1.2.1.1 Model Assignment is not Unique -- 1.2.1.2 System State -- 1.2.1.3 Steady States -- 1.2.1.4 Variables, Parameters, and Constants -- 1.2.1.5 Model Behavior -- 1.2.1.6 Process Classification -- 1.2.1.7 Purpose and Adequateness of Models -- 1.2.1.8 Advantages of Computational Modeling -- 1.2.1.9 Model Development -- 1.2.2 Typical Aspects of Biological Systems and Corresponding Models -- 1.2.2.1 Network Versus Elements -- 1.2.2.2 Modularity -- 1.2.2.3 Robustness and Sensitivity are Two Sides of the Same Coin -- 1.3 Systems Biology is Data Integration -- 1.4 Systems Biology is a Living Science -- References -- 2 Biology in a Nutshell -- Introduction -- 2.1 The Origin of Life -- 2.2 Molecular Biology of the Cell -- 2.2.1 Chemical Bonds and Forces Important in Biological Molecules -- 2.2.2 Functional Groups in Biological Molecules -- 2.2.3 Major Classes of Biological Molecules -- 2.2.3.1 Carbohydrates -- 2.2.3.2 Lipids -- 2.2.3.3 Proteins -- 2.2.3.4 Nucleic Acids -- 2.3 Structural Cell Biology -- 2.3.1 Structure and Function of Biological Membranes -- 2.3.2 Nucleus -- 2.3.3 Cytosol -- 2.3.4 Mitochondria -- 2.3.5 Endoplasmatic Reticulum and Golgi Complex -- 2.3.6 Other Organelles -- 2.4 Expression of Genes -- 2.4.1 Transcription -- 2.4.2 Processing of the mRNA -- 2.4.3 Translation -- 2.4.4 Protein Sorting and Posttranslational Modifications -- 2.4.5 Regulation of Gene Expression -- 2.5 Cell Cycle -- 2.5.1 Mitosis -- 2.5.2 Meiosis and Genetic Recombination -- References -- 3 Mathematics in a Nutshell -- Introduction -- 3.1 Linear Algebra -- 3.1.1 Linear Equations -- 3.1.1.1 The Gaussian Elimination Algorithm. , 3.1.1.2 Systematic Solution of Linear Systems -- 3.1.2 Matrices -- 3.1.2.1 Basic Notions -- 3.1.2.2 Linear Dependency -- 3.1.2.3 Basic Matrix Operations -- 3.1.2.4 Dimension and Rank -- 3.1.2.5 Eigenvalues and Eigenvectors of a Square Matrix -- 3.2 Ordinary Differential Equations -- 3.2.1 Notions -- 3.2.2 Linearization of Autonomous Systems -- 3.2.3 Solution of Linear ODE Systems -- 3.2.4 Stability of Steady States -- 3.2.4.1 Global Stability of Steady States -- 3.2.4.2 Limit Cycles -- 3.3 Difference Equations -- 3.4 Statistics -- 3.4.1 Basic Concepts of Probability Theory -- 3.4.1.1 Random Variables, Densities, and Distribution Functions -- 3.4.1.2 Transforming Probability Densities -- 3.4.1.3 Product Experiments and Independence -- 3.4.1.4 Limit Theorems -- 3.4.2 Descriptive Statistics -- 3.4.2.1 Statistics for Sample Location -- 3.4.2.2 Statistics for Sample Variability -- 3.4.2.3 Density Estimation -- 3.4.2.4 Correlation of Samples -- 3.4.3 Testing Statistical Hypotheses -- 3.4.3.1 Statistical Framework -- 3.4.3.2 Two-sample Location Tests -- 3.4.4 Linear Models -- 3.4.4.1 ANOVA -- 3.4.4.2 Multiple Linear Regression -- 3.5 Graph and Network Theory -- 3.5.1 Introduction -- 3.5.2 Regulatory Networks -- 3.5.2.1 Linear Networks -- 3.5.2.2 Boolean Networks -- 3.5.2.3 Bayesian Networks -- 3.6 Stochastic Processes -- 3.6.1 Gillespie's Direct Method -- 3.6.2 Other Algorithms -- 3.6.3 Stochastic and Macroscopic Rate Constants -- 3.6.3.1 First-order Reaction -- 3.6.3.2 Second-order Reaction -- References -- 4 Experimental Techniques in a Nutshell -- Introduction -- 4.1 Elementary Techniques -- 4.1.1 Restriction Enzymes and Gel Electrophoresis -- 4.1.2 Cloning Vectors and DNA Libraries -- 4.1.3 1D and 2D Protein Gels -- 4.1.4 Hybridization and Blotting Techniques -- 4.1.4.1 Southern Blotting -- 4.1.4.2 Northern Blotting -- 4.1.4.3 Western Blotting. , 4.1.4.4 In situ Hybridization -- 4.1.5 Further Protein Separation Techniques -- 4.1.5.1 Centrifugation -- 4.1.5.2 Column Chromatography -- 4.2 Advanced Techniques -- 4.2.1 PCR -- 4.2.2 DNA and Protein Chips -- 4.2.2.1 DNA Chips -- 4.2.2.2 Protein Chips -- 4.2.3 Yeast Two-hybrid System -- 4.2.4 Mass Spectrometry -- 4.2.5 Transgenic Animals -- 4.2.6 RNA Interference -- References -- Part II Standard Models and Approaches in Systems Biology -- 5 Metabolism -- Introduction -- 5.1 Enzyme Kinetics and Thermodynamics -- 5.1.1 The Law of Mass Action -- 5.1.2 Reaction Kinetics and Thermodynamics -- 5.1.3 Michaelis-Menten Kinetics -- 5.1.3.1 How to Derive a Rate Equation -- 5.1.3.2 Parameter Estimation and Linearization of the Michaelis-Menten Equation -- 5.1.3.3 The Michaelis-Menten Equation for Reversible Reactions -- 5.1.4 Regulation of Enzyme Activity by Protein Interaction -- 5.1.5 Inhibition by Irreversible Binding of Inhibitor to Enzyme -- 5.1.6 Substrate Inhibition -- 5.1.7 Inhibition by Binding of Inhibitor to Substrate -- 5.1.8 Binding of Ligands to Proteins -- 5.1.9 Positive Homotropic Cooperativity and the Hill Equation -- 5.1.10 The Monod-Wyman-Changeux Rate Expression for Enzymes with Sigmoid Kinetics -- 5.2 Metabolic Networks -- 5.2.1 Systems Equations -- 5.2.2 Information Contained in the Stoichiometric Matrix N -- 5.2.3 Elementary Flux Modes and Extreme Pathways -- 5.2.4 Flux Balance Analysis -- 5.2.5 Conservation Relations: Null Space of N(T) -- 5.2.6 Compartments and Transport across Membranes -- 5.2.7 Characteristic Times -- 5.2.8 Approximations Based on Timescale Separation -- 5.2.8.1 The Quasi-steady-state Approximation -- 5.2.8.2 Quasi-equilibrium Approximation -- 5.3 Metabolic Control Analysis -- 5.3.1 The Coefficients of Control Analysis -- 5.3.1.1 The Elasticity Coefficients -- 5.3.1.2 Control Coefficients. , 5.3.1.3 Response Coefficients -- 5.3.1.4 Matrix Representation of the Coefficients -- 5.3.2 The Theorems of Metabolic Control Theory -- 5.3.2.1 The Summation Theorems -- 5.3.2.2 The Connectivity Theorems -- 5.3.2.3 Derivation of Matrix Expressions for Control Coefficients -- 5.3.3 Extensions of Metabolic Control Analysis -- 5.3.3.1 Control Analysis for Variables other than Fluxes and Concentrations -- 5.3.3.2 Time-dependent Control Coefficients -- 5.3.3.3 Spatially Heterogeneous and Time-varying Cellular Reaction Networks -- Suggested Further Reading -- References -- 6 Signal Transduction -- Introduction -- 6.1 Function and Structure of Intra- and Intercellular Communication -- 6.2 Receptor-Ligand Interactions -- 6.3 Structural Components of Signaling Pathways -- 6.3.1 G Proteins -- 6.3.2 Ras Proteins -- 6.3.3 Phosphorelay Systems -- 6.3.4 MAP Kinase Cascades -- 6.3.5 Jak-Stat Pathways -- 6.4 Signaling: Dynamic and Regulatory Features -- 6.4.1 Simple Motifs -- 6.4.2 Adaptation Motif -- 6.4.3 Negative Feedback -- 6.4.4 Quantitative Measures for Properties of Signaling Pathways -- References -- 7 Selected Biological Processes -- Introduction -- 7.1 Biological Oscillations -- 7.1.1 Glycolytic Oscillations: The Higgins-Sel'kov Oscillator -- 7.1.2 Other Modes of Behavior -- 7.1.3 Coupling of Oscillators -- 7.1.4 Sustained Oscillations in Signaling Cascades -- 7.2 Cell Cycle -- 7.2.1 Steps in the Cycle -- 7.2.2 Minimal Cascade Model of a Mitotic Oscillator -- 7.2.3 Models of Budding Yeast Cell Cycle -- 7.3 Aging -- 7.3.1 Evolution of the Aging Process -- 7.3.2 Accumulation of Defective Mitochondria -- 7.3.2.1 Synthesis Rates -- 7.3.2.2 Radical Levels -- 7.3.2.3 Dilution of Membrane Damage -- 7.3.2.4 The Equations -- 7.3.2.5 Choice of Parameters and Simulation Results -- References -- 8 Modeling of Gene Expression -- Introduction. , 8.1 Modules of Gene Expression -- 8.2 Promoter Identification -- 8.2.1 General Promoter Structure -- 8.2.2 Sequence-based Prediction of Promoter Elements -- 8.2.3 Approaches that Incorporate Additional Information -- 8.3 Modeling Specific Processes in Eukaryotic Gene Expression -- 8.3.1 One Example, Different Approaches -- 8.3.1.1 Description with Ordinary Differential Equations -- 8.3.1.2 Representation of Gene Network as Directed and Undirected Graphs -- 8.3.1.3 Bayesian Networks -- 8.3.1.4 Boolean Networks -- 8.3.1.5 Gene Expression Modeling with Stochastic Equations -- 8.3.2 Time Delay in Gene Regulation -- 8.3.3 Modeling the Elongation of a Peptide Chain -- 8.4 Modeling the Regulation of Operons in E. coli -- 8.4.1 Mechanism of the Lac Operon in E. coli -- 8.4.2 The Model According to Griffith -- 8.4.3 The Model According to Nicolis and Prigogine -- References -- 9 Analysis of Gene Expression Data -- Introduction -- 9.1 Data Capture -- 9.1.1 DNA Array Platforms -- 9.1.2 Image Analysis and Data Quality Control -- 9.1.2.1 Grid Finding -- 9.1.2.2 Quantification of Signal Intensities -- 9.1.2.3 Signal Validity -- 9.1.3 Pre-processing -- 9.1.3.1 Global Measures -- 9.1.3.2 Linear Model Approaches -- 9.1.3.3 Nonlinear and Spatial Effects -- 9.1.3.4 Other Approaches -- 9.2 Fold-change Analysis -- 9.2.1 Planning and Designing Experiments -- 9.2.2 Tests for Differential Expression -- 9.2.3 Multiple Testing -- 9.2.4 ROC Curve Analysis -- 9.2.5 Validation Methods -- 9.3 Clustering Algorithms -- 9.3.1 Hierarchical Clustering -- 9.3.2 Self-organizing Maps (SOMs) -- 9.3.3 K-means -- 9.4 Validation of Gene Expression Data -- 9.4.1 Cluster Validation -- 9.4.2 Principal Component Analysis -- 9.4.3 Functional Categorization -- 9.5 Classification Methods -- 9.5.1 Basic Concepts -- 9.5.2 Support Vector Machines -- 9.5.3 Other Approaches -- 9.5.4 Cross-validation. , 9.5.4.1 The Holdout Method.

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