| | Contents | |
| | | |
| |
| | Preface | V |
| | Foreword | IX |
| | | |
| Part I | General Introduction | |
| | | |
| 1 | Basic Principles | 3 |
| 1.1 | Systems Biology is Biology! | 3 |
| 1.2 | Systems Biology is Modeling | 5 |
| 1.2.1 | Properties of Models | 6 |
| 1.2.1.1 | Model Assignment is not Unique | 6 |
| 1.2.1.2 | System State | 6 |
| 1.2.1.3 | Steady States | 7 |
| 1.2.1.4 | Variables, Parameters, and Constants | 7 |
| 1.2.1.5 | Model Behavior | 8 |
| 1.2.1.6 | Process Classification | 8 |
| 1.2.1.7 | Purpose and Adequateness of Models | 8 |
| 1.2.1.8 | Advantages of Computational Modeling | 8 |
| 1.2.1.9 | Model Development | 9 |
| 1.2.2 | Typical Aspects of Biological Systems and Corresponding Models | 10 |
| 1.2.2.1 | Network Versus Elements | 10 |
| 1.2.2.2 | Modularity | 10 |
| 1.2.2.3 | Robustness and Sensitivity are Two Sides of the Same Coin | 11 |
| 1.3 | Systems Biology is Data Integration | 11 |
| 1.4 | Systems Biology is a Living Science | 14 |
| | References | 15 |
| 2 | Biology in a Nutshell | 19 |
| | Introduction | 19 |
| 2.1 | The Origin of Life | 20 |
| 2.2 | Molecular Biology of the Cell | 23 |
| 2.2.1 | Chemical Bonds and Forces Important in Biological Molecules | 23 |
| 2.2.2 | Functional Groups in Biological Molecules | 26 |
| 2.2.3 | Major Classes of Biological Molecules | 27 |
| 2.2.3.1 | Carbohydrates | 27 |
| 2.2.3.2 | Lipids | 27 |
| 2.2.3.3 | Proteins | 31 |
| 2.2.3.4 | Nucleic Acids | 35 |
| 2.3 | Structural Cell Biology | 37 |
| 2.3.1 | Structure and Function of Biological Membranes | 38 |
| 2.3.2 | Nucleus | 40 |
| 2.3.3 | Cytosol | 41 |
| 2.3.4 | Mitochondria | 42 |
| 2.3.5 | Endoplasmatic Reticulum and Golgi Complex | 43 |
| 2.3.6 | Other Organelles | 44 |
| 2.4 | Expression of Genes | 45 |
| 2.4.1 | Transcription | 45 |
| 2.4.2 | Processing of the mRNA | 47 |
| 2.4.3 | Translation | 48 |
| 2.4.4 | Protein Sorting and Posttranslational Modifications | 50 |
| 2.4.5 | Regulation of Gene Expression | 51 |
| 2.5 | Cell Cycle | 52 |
| 2.5.1 | Mitosis | 54 |
| 2.5.2 | Meiosis and Genetic Recombination | 54 |
| | References | 55 |
| 3 | Mathematics in a Nutshell | 57 |
| | Introduction | 57 |
| 3.1 | Linear Algebra | 57 |
| 3.1.1 | Linear Equations | 57 |
| 3.1.1.1 | The Gaussian Elimination Algorithm | 59 |
| 3.1.1.2 | Systematic Solution of Linear Systems | 60 |
| 3.1.2 | Matrices | 62 |
| 3.1.2.1 | Basic Notions | 62 |
| 3.1.2.2 | Linear Dependency | 62 |
| 3.1.2.3 | Basic Matrix Operations | 62 |
| 3.1.2.4 | Dimension and Rank | 64 |
| 3.1.2.5 | Eigenvalues and Eigenvectors of a Square Matrix | 65 |
| 3.2 | Ordinary Differential Equations | 66 |
| 3.2.1 | Notions | 67 |
| 3.2.2 | Linearization of Autonomous Systems | 69 |
| 3.2.3 | Solution of Linear ODE Systems | 70 |
| 3.2.4 | Stability of Steady States | 71 |
| 3.2.4.1 | Global Stability of Steady States | 73 |
| 3.2.4.2 | Limit Cycles | 74 |
| 3.3 | Difference Equations | 75 |
| 3.4 | Statistics | 77 |
| 3.4.1 | Basic Concepts of Probability Theory | 78 |
| 3.4.1.1 | Random Variables, Densities, and Distribution Functions | 81 |
| 3.4.1.2 | Transforming Probability Densities | 84 |
| 3.4.1.3 | Product Experiments and Independence | 84 |
| 3.4.1.4 | Limit Theorems | 85 |
| 3.4.2 | Descriptive Statistics | 86 |
| 3.4.2.1 | Statistics for Sample Location | 86 |
| 3.4.2.2 | Statistics for Sample Variability | 87 |
| 3.4.2.3 | Density Estimation | 88 |
| 3.4.2.4 | Correlation of Samples | 89 |
| 3.4.3 | Testing Statistical Hypotheses | 91 |
| 3.4.3.1 | Statistical Framework | 91 |
| 3.4.3.2 | Two-sample Location Tests | 93 |
| 3.4.4 | Linear Models | 96 |
| 3.4.4.1 | ANOVA | 96 |
| 3.4.4.2 | Multiple Linear Regression | 98 |
| 3.5 | Graph and Network Theory | 99 |
| 3.5.1 | Introduction | 100 |
| 3.5.2 | Regulatory Networks | 101 |
| 3.5.2.1 | Linear Networks | 101 |
| 3.5.2.2 | Boolean Networks | 101 |
| 3.5.2.3 | Bayesian Networks | 102 |
| 3.6 | Stochastic Processes | 103 |
| 3.6.1 | Gillespie’s Direct Method | 105 |
| 3.6.2 | Other Algorithms | 105 |
| 3.6.3 | Stochastic and Macroscopic Rate Constants | 106 |
| 3.6.3.1 | First-order Reaction | 106 |
| 3.6.3.2 | Second-order Reaction | 107 |
| | References | 107 |
| 4 | Experimental Techniques in a Nutshell | 109 |
| | Introduction | 109 |
| 4.1 | Elementary Techniques | 109 |
| 4.1.1 | Restriction Enzymes and Gel Electrophoresis | 109 |
| 4.1.2 | Cloning Vectors and DNA Libraries | 113 |
| 4.1.3 | 1D and 2D Protein Gels | 117 |
| 4.1.4 | Hybridization and Blotting Techniques | 119 |
| 4.1.4.1 | Southern Blotting | 120 |
| 4.1.4.2 | Northern Blotting | 121 |
| 4.1.4.3 | Western Blotting | 121 |
| 4.1.4.4 | In situ Hybridization | 121 |
| 4.1.5 | Further Protein Separation Techniques | 122 |
| 4.1.5.1 | Centrifugation | 122 |
| 4.1.5.2 | Column Chromatography | 123 |
| 4.2 | Advanced Techniques | 124 |
| 4.2.1 | PCR | 124 |
| 4.2.2 | DNA and Protein Chips | 126 |
| 4.2.2.1 | DNA Chips | 126 |
| 4.2.2.2 | Protein Chips | 127 |
| 4.2.3 | Yeast Two-hybrid System | 128 |
| 4.2.4 | Mass Spectrometry | 129 |
| 4.2.5 | Transgenic Animals | 130 |
| 4.2.6 | RNA Interference | 131 |
| | References | 133 |
| | | |
| Part II | Standard Models and Approaches in Systems Biology | |
| | | |
| 5 | Metabolism | 137 |
| | Introduction | 137 |
| 5.1 | Enzyme Kinetics and Thermodynamics | 140 |
| 5.1.1 | The Law of Mass Action | 141 |
| 5.1.2 | Reaction Kinetics and Thermodynamics | 142 |
| 5.1.3 | Michaelis-Menten Kinetics | 144 |
| 5.1.3.1 | How to Derive a Rate Equation | 146 |
| 5.1.3.2 | Parameter Estimation and Linearization of the Michaelis-Menten Equation | 147 |
| 5.1.3.3 | The Michaelis-Menten Equation for Reversible Reactions | 148 |
| 5.1.4 | Regulation of Enzyme Activity by Protein Interaction | 149 |
| 5.1.5 | Inhibition by Irreversible Binding of Inhibitor to Enzyme | 152 |
| 5.1.6 | Substrate Inhibition | 152 |
| 5.1.7 | Inhibition by Binding of Inhibitor to Substrate | 153 |
| 5.1.8 | Binding of Ligands to Proteins | 153 |
| 5.1.9 | Positive Homotropic Cooperativity and the Hill Equation | 154 |
| 5.1.10 | The Monod-Wyman-Changeux Rate Expression for Enzymes with Sigmoid Kinetics | 155 |
| 5.2 | Metabolic Networks | 157 |
| 5.2.1 | Systems Equations | 158 |
| 5.2.2 | Information Contained in the Stoichiometric Matrix N | 159 |
| 5.2.3 | Elementary Flux Modes and Extreme Pathways | 162 |
| 5.2.4 | Flux Balance Analysis | 164 |
| 5.2.5 | Conservation Relations: Null Space of NT | 165 |
| 5.2.6 | Compartments and Transport across Membranes | 168 |
| 5.2.7 | Characteristic Times | 168 |
| 5.2.8 | Approximations Based on Timescale Separation | 171 |
| 5.2.8.1 | The Quasi-steady-state Approximation | 171 |
| 5.2.8.2 | Quasi-equilibrium Approximation | 172 |
| 5.3 | Metabolic Control Analysis | 174 |
| 5.3.1 | The Coefficients of Control Analysis | 175 |
| 5.3.1.1 | The Elasticity Coefficients | 177 |
| 5.3.1.2 | Control Coefficients | 178 |
| 5.3.1.3 | Response Coefficients | 180 |
| 5.3.1.4 | Matrix Representation of the Coefficients | 180 |
| 5.3.2 | The Theorems of Metabolic Control Theory | 181 |
| 5.3.2.1 | The Summation Theorems | 181 |
| 5.3.2.2 | The Connectivity Theorems | 183 |
| 5.3.2.3 | Derivation of Matrix Expressions for Control Coefficients | 185 |
| 5.3.3 | Extensions of Metabolic Control Analysis | 191 |
| 5.3.3.1 | Control Analysis for Variables other than Fluxes and Concentrations | 191 |
| 5.3.3.2 | Time-dependent Control Coefficients | 193 |
| 5.3.3.3 | Spatially Heterogeneous and Time-varying Cellular Reaction Networks | 194 |
| | Suggested Further Reading | 195 |
| | References | 196 |
| 6 | Signal Transduction | 201 |
| | Introduction | 201 |
| 6.1 | Function and Structure of Intra- and Intercellular Communication | 202 |
| 6.2 | Receptor-Ligand Interactions | 203 |
| 6.3 | Structural Components of Signaling Pathways | 206 |
| 6.3.1 | G Proteins | 206 |
| 6.3.2 | Ras Proteins | 208 |
| 6.3.3 | Phosphorelay Systems | 209 |
| 6.3.4 | MAP Kinase Cascades | 211 |
| 6.3.5 | Jak-Stat Pathways | 216 |
| 6.4 | Signaling: Dynamic and Regulatory Features | 217 |
| 6.4.1 | Simple Motifs | 217 |
| 6.4.2 | Adaptation Motif | 219 |
| 6.4.3 | Negative Feedback | 220 |
| 6.4.4 | Quantitative Measures for Properties of Signaling Pathways | 220 |
| | References | 223 |
| 7 | Selected Biological Processes | 225 |
| | Introduction | 225 |
| 7.1 | Biological Oscillations | 225 |
| 7.1.1 | Glycolytic Oscillations: The Higgins-Sel’kov Oscillator | 226 |
| 7.1.2 | Other Modes of Behavior | 229 |
| 7.1.3 | Coupling of Oscillators | 230 |
| 7.1.4 | Sustained Oscillations in Signaling Cascades | 233 |
| 7.2 | Cell Cycle | 234 |
| 7.2.1 | Steps in the Cycle | 235 |
| 7.2.2 | Minimal Cascade Model of a Mitotic Oscillator | 236 |
| 7.2.3 | Models of Budding Yeast Cell Cycle | 238 |
| 7.3 | Aging | 240 |
| 7.3.1 | Evolution of the Aging Process | 241 |
| 7.3.2 | Accumulation of Defective Mitochondria | 245 |
| 7.3.2.1 | Synthesis Rates | 247 |
| 7.3.2.2 | Radical Levels | 248 |
| 7.3.2.3 | Dilution of Membrane Damage | 248 |
| 7.3.2.4 | The Equations | 249 |
| 7.3.2.5 | Choice of Parameters and Simulation Results | 251 |
| | References | 254 |
| 8 | Modeling of Gene Expression | 257 |
| | Introduction | 257 |
| 8.1 | Modules of Gene Expression | 258 |
| 8.2 | Promoter Identification | 259 |
| 8.2.1 | General Promoter Structure | 260 |
| 8.2.2 | Sequence-based Prediction of Promoter Elements | 262 |
| 8.2.3 | Approaches that Incorporate Additional Information | 264 |
| 8.3 | Modeling Specific Processes in Eukaryotic Gene Expression | 265 |
| 8.3.1 | One Example, Different Approaches | 266 |
| 8.3.1.1 | Description with Ordinary Differential Equations | 266 |
| 8.3.1.2 | Representation of Gene Network as Directed and Undirected Graphs | 269 |
| 8.3.1.3 | Bayesian Networks | 270 |
| 8.3.1.4 | Boolean Networks | 270 |
| 8.3.1.5 | Gene Expression Modeling with Stochastic Equations | 273 |
| 8.3.2 | Time Delay in Gene Regulation | 274 |
| 8.3.3 | Modeling the Elongation of a Peptide Chain | 276 |
| 8.4 | Modeling the Regulation of Operons in E. coli | 278 |
| 8.4.1 | Mechanism of the Lac Operon in E. coli | 278 |
| 8.4.2 | The Model According to Griffith | 280 |
| 8.4.3 | The Model According to Nicolis and Prigogine | 282 |
| | References | 286 |
| 9 | Analysis of Gene Expression Data | 289 |
| | Introduction | 289 |
| 9.1 | Data Capture | 289 |
| 9.1.1 | DNA Array Platforms | 289 |
| 9.1.2 | Image Analysis and Data Quality Control | 291 |
| 9.1.2.1 | Grid Finding | 291 |
| 9.1.2.2 | Quantification of Signal Intensities | 292 |
| 9.1.2.3 | Signal Validity | 293 |
| 9.1.3 | Pre-processing | 296 |
| 9.1.3.1 | Global Measures | 296 |
| 9.1.3.2 | Linear Model Approaches | 297 |
| 9.1.3.3 | Nonlinear and Spatial Effects | 297 |
| 9.1.3.4 | Other Approaches | 298 |
| 9.2 | Fold-change Analysis | 299 |
| 9.2.1 | Planning and Designing Experiments | 299 |
| 9.2.2 | Tests for Differential Expression | 301 |
| 9.2.3 | Multiple Testing | 303 |
| 9.2.4 | ROC Curve Analysis | 306 |
| 9.2.5 | Validation Methods | 307 |
| 9.3 | Clustering Algorithms | 307 |
| 9.3.1 | Hierarchical Clustering | 311 |
| 9.3.2 | Self-organizing Maps (SOMs) | 314 |
| 9.3.3 | K-means | 315 |
| 9.4 | Validation of Gene Expression Data | 316 |
| 9.4.1 | Cluster Validation | 316 |
| 9.4.2 | Principal Component Analysis | 318 |
| 9.4.3 | Functional Categorization | 321 |
| 9.5 | Classification Methods | 322 |
| 9.5.1 | Basic Concepts | 322 |
| 9.5.2 | Support Vector Machines | 323 |
| 9.5.3 | Other Approaches | 326 |
| 9.5.4 | Cross-validation | 327 |
| 9.5.4.1 | The Holdout Method | 327 |
| 9.5.4.2 | k-fold Cross-validation | 327 |
| 9.5.4.3 | Leave-one-out Cross-validation | 327 |
| 9.6 | Reverse Engineering Genetic Networks | 328 |
| 9.6.1 | Reconstructing Boolean Networks | 328 |
| 9.6.2 | Other Approaches | 330 |
| 9.6.3 | Network Motifs | 331 |
| | References | 333 |
| 10 | Evolution and Self-organization | 337 |
| | Introduction | 337 |
| 10.1 | Quasispecies and Hypercycles | 338 |
| 10.1.1 | Selection Equations for Biological Macromolecules | 339 |
| 10.1.1.1 | Self-replication Without Interaction | 340 |
| 10.1.1.2 | Selection at Constant Total Concentration of Self-replicating Molecules | 340 |
| 10.1.1.3 | Self-replication with Mutations: The Quasispecies Model | 342 |
| 10.1.1.4 | The Genetic Algorithm | 343 |
| 10.1.1.5 | Assessment of Sequence Length for Stable Passing-on of Sequence Information | 344 |
| 10.1.1.6 | Coexistence of Self-replicating Sequences: Complementary Replication of RNA | 345 |
| 10.1.2 | The Hypercycle | 346 |
| 10.2 | Other Mathematical Models of Evolution | 349 |
| 10.2.1 | Spin-glass Model of Evolution | 349 |
| 10.2.2 | Neutral Theory of Molecular Evolution | 351 |
| 10.2.3 | Boolean Network Models | 352 |
| 10.3 | Prediction of Biological Systems from Optimality Principles | 355 |
| 10.3.1 | Optimization of Catalytic Properties of Single Enzymes | 356 |
| 10.3.2 | Optimal Distribution of Enzyme Concentrations in a Metabolic Pathway | 359 |
| 10.3.3 | Temporal Transcription Programs | 362 |
| | References | 364 |
| 11 | Data Integration | 367 |
| | Introduction | 367 |
| 11.1 | Database Networks | 368 |
| 11.1.1 | Basic Concepts of Database Integration | 369 |
| 11.1.2 | SRS | 370 |
| 11.1.3 | EnsMart | 371 |
| 11.1.4 | DiscoveryLink | 372 |
| 11.1.5 | Data Exchange | 372 |
| 11.2 | Information Measurement in Heterogeneous Data | 374 |
| 11.2.1 | Information and Entropy | 374 |
| 11.2.2 | Mutual Information | 376 |
| 11.2.3 | Information Correlation: Example | 379 |
| 11.3 | Biclustering | 381 |
| 11.3.1 | The Problem | 381 |
| 11.3.2 | Algorithmic Example | 382 |
| 11.3.3 | Biclustering and Data Integration | 384 |
| | References | 385 |
| 12 | What’s Next? | 387 |
| 12.1 | Systems Biology: The Core of Biological Research and Medical Practice of the Future? | 387 |
| 12.2 | Experimental Planning in the Systems Biology Phase of Biological Research | 388 |
| 12.3 | Publication in the Era of Systems Biology | 389 |
| 12.4 | Systems Biology and Text Mining | 389 |
| 12.5 | Systems Biology in Medicine | 390 |
| 12.6 | Systems Biology in Drug Development | 390 |
| 12.7 | Systems Biology in Food Production and Biotechnology | 391 |
| 12.8 | Systems Biology in Ecology | 391 |
| 12.9 | Systems Biology and Nanotechnology | 391 |
| 12.10 | Guiding the Design of New Organisms | 392 |
| 12.11 | Computational Limitations | 393 |
| 12.12 | Potential Dangers | 394 |
| | References | 394 |
| | | |
| Part III | Computer-based Information Retrieval and Examination | |
| | | |
| 13 | Databases and Tools on the Internet | 399 |
| | Introduction | 399 |
| 13.1 | Gene Ontology | 399 |
| 13.2 | KEGG | 403 |
| 13.3 | BRENDA | 404 |
| 13.4 | Databases of the National Center for Biotechnology | 405 |
| 13.5 | Databases of the European Bioinformatics Institute | 406 |
| 13.5.1 | EMBL Nucleotide Sequence Database | 407 |
| 13.5.2 | Ensembl | 407 |
| 13.5.3 | InterPro | 408 |
| 13.6 | Swiss-Prot, TrEMBL, and UniProt | 408 |
| 13.7 | Reactome | 409 |
| 13.8 | PDB | 410 |
| 13.9 | TRANSFAC and EPD | 413 |
| 13.9.1 | TRANSFAC | 413 |
| 13.9.2 | EPD | 414 |
| 13.10 | Genome Matrix | 415 |
| | References | 417 |
| 14 | Modeling Tools | 419 |
| | Introduction | 419 |
| 14.1 | Modeling and Visualization | 419 |
| 14.1.1 | Mathematica and Matlab | 419 |
| 14.1.1.1 | Mathematica Example | 421 |
| 14.1.1.2 | Matlab Example | 422 |
| 14.1.2 | Gepasi | 422 |
| 14.1.3 | E-Cell | 424 |
| 14.1.4 | PyBioS | 426 |
| 14.1.5 | Systems Biology Workbench | 428 |
| 14.1.5.1 | JDesigner | 429 |
| 14.1.5.2 | CellDesigner | 431 |
| 14.1.6 | Petri Nets | 433 |
| 14.1.7 | STOCKS 2 | 435 |
| 14.1.8 | Genetic Programming | 438 |
| 14.2 | Model Exchange Languages, Data Formats | 440 |
| 14.2.1 | Introduction to XML | 440 |
| 14.2.2 | Systems Biology Markup Language | 442 |
| 14.2.3 | MathML | 447 |
| | References | 448 |
| | | |
| | Subject Index | 451 |
| | | |
