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Systems Biology: A Textbook [Mīkstie vāki]

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  • Formāts: Paperback / softback, 592 pages, height x width x depth: 239x165x27 mm, weight: 1150 g, Illustrations
  • Izdošanas datums: 24-Jun-2009
  • Izdevniecība: Wiley-VCH Verlag GmbH
  • ISBN-10: 3527318747
  • ISBN-13: 9783527318742
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Systems Biology: A TextbookPalielināt
  • Formāts: Paperback / softback, 592 pages, height x width x depth: 239x165x27 mm, weight: 1150 g, Illustrations
  • Izdošanas datums: 24-Jun-2009
  • Izdevniecība: Wiley-VCH Verlag GmbH
  • ISBN-10: 3527318747
  • ISBN-13: 9783527318742
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9783527318742.html
Keywords:
This advanced textbook is tailored to the needs of introductory course in Systems Biology. It has a compagnion website (WWW.WILEY-VCH.DE/HOME/SYSTEMSBIOLOGY) with solutions to questions in the book and several additional extensive working models. The book is related to the very successful previous title 'Systems Biology in Practice' and has incorporated the feedback and suggestions from many lecturers worldwide. The book addresses biologists as well as engineers and computer scientists. The interdisciplinary team of acclaimed authors worked closely together to ensure a comprehensive coverage with no overlaps in a homogenous and compelling style.

Recenzijas

This clear text is a useful starting point for anyone aspiring to solve a biological question using systems biology approaches." (Oxford University Biochemical Society, 2010)

Preface XVII
Part One Introduction to Systems Biology 1
1 Introduction
3
1.1 Biology in Time and Space
3
1.2 Models and Modeling
4
1.2.1 What is a Model?
5
1.2.2 Purpose and Adequateness of Models
5
1.2.3 Advantages of Computational Modeling
6
1.3 Basic Notions for Computational Models
7
1.3.1 Model Scope
7
1.3.2 Model Statements
8
1.3.3 System State
8
1.3.4 Variables, Parameters, and Constants
8
1.3.5 Model Behavior
9
1.3.6 Model Classification
9
1.3.7 Steady States
9
1.3.8 Model Assignment is not Unique
10
1.4 Data Integration
11
1.5 Standards
12
References
12
2 Modeling of Biochemical Systems
13
2.1 Kinetic Modeling of Enzymatic Reactions
13
2.1.1 The Law of Mass Action
14
2.1.2 Reaction Kinetics and Thermodynamics
15
2.1.3 Michaelis—Menten Kinetics
18
2.1.3.1 How to Derive a Rate Equation
19
2.1.3.2 Parameter Estimation and Linearization of the Michaelis—Menten Equation
20
2.1.3.3 The Michaelis—Menten Equation for Reversible Reactions
22
2.1.4 Regulation of Enzyme Activity by Effectors
22
2.1.4.1 Substrate Inhibition
25
2.1.4.2 Binding of Ligands to Proteins
26
2.1.4.3 Positive Homotropic Cooperativity and the Hill Equation
27
2.1.4.4 The Monod—Wyman—Changeux Model for Sigmoid Kinetics
28
2.1.5 Generalized Mass Action Kinetics
29
2.1.6 Approximate Kinetic Formats
30
2.1.7 Convenience Kinetics
30
2.2 Structural Analysis of Biochemical Systems
31
2.2.1 Systems Equations
31
2.2.2 Information Encoded in the Stoichiometric Matrix N
34
2.2.3 Elementary Flux Modes and Extreme Pathways
36
2.2.3.1 Flux Cone
37
2.2.4 Conservation Relations: Null Space of NT
39
2.3 Kinetic Models of Biochemical Systems
42
2.3.1 Describing Dynamics with ODEs
42
2.3.1.1 Notations
43
2.3.1.2 Linearization of Autonomous Systems
44
2.3.1.3 Solution of Linear ODE Systems
45
2.3.1.4 Stability of Steady States
46
2.3.1.5 Global Stability of Steady States
49
2.3.1.6 Limit Cycles
49
2.3.2 Metabolic Control Analysis
51
2.3.2.1 The Coefficients of Control Analysis
52
2.3.2.2 The Elasticity Coefficients
52
2.3.2.3 Control Coefficients
55
2.3.2.4 Response Coefficients
55
2.3.2.5 Matrix Representation of the Coefficients
55
2.3.2.6 The Theorems of Metabolic Control Theory
56
2.3.2.7 The Summation Theorems
56
2.3.2.8 The Connectivity Theorems
58
2.3.2.9 Derivation of Matrix Expressions for Control Coefficients
59
2.4 Tools and Data Formats for Modeling
63
2.4.1 Simulation Techniques
64
2.4.1.1 Petri Nets
64
2.4.1.2 Cellular Automata
65
2.4.2 Simulation Tools
65
2.4.2.1 CellDesigner
66
2.4.2.2 COPASI
67
2.4.2.3 PyBioS
68
2.4.3 Data Formats
70
2.4.3.1 Systems Biology Markup Language
70
2.4.3.2 BioPAX
73
2.4.3.3 Systems Biology Graphical Notation
73
2.4.3.4 Standards for Systems Biology
74
2.4.4 Data Resources
75
2.4.4.1 Pathway Databases
76
2.4.4.2 Databases of Kinetic Data
77
2.4.4.3 Model Databases
77
References
79
3 Specific Biochemical Systems
83
3.1 Metabolic Systems
83
3.1.1 Basic Elements of Metabolic Modeling
84
3.1.2 Toy Model of Upper Glycolysis
85
3.1.3 Threonine Synthesis Pathway Model
88
3.2 Signaling Pathways
91
3.2.1 Introduction
92
3.2.2 Function and Structure of Intra- and Intercellular Communication
92
3.2.3 Receptor–Ligand Interactions
93
3.2.4 Structural Components of Signaling Pathways
96
3.2.4.1 G proteins
96
3.2.4.2 Small G proteins
99
3.2.4.3 Phosphorelay Systems
100
3.2.4.4 MAP Kinase Cascades
102
3.2.4.5 Jak/Stat Pathways
106
3.2.5 Signaling – Dynamic and Regulatory Features
106
3.2.5.1 Quantitative Measures for Properties of Signaling Pathways
107
3.2.5.2 Crosstalk in Signaling Pathways
109
3.3 The Cell Cycle
111
3.3.1 Steps in the Cycle
114
3.3.2 Minimal Cascade Model of a Mitotic Oscillator
115
3.3.3 Models of Budding Yeast Cell Cycle
117
3.3.4 Modeling Nucleo/Cytoplasmatic Compartmentalization
119
3.4 Spatial Models
121
3.4.1 Types of Spatial Models
122
3.4.1.1 Compai talent Models and Partial Differential Equations
122
3.4.1.2 Stochastic Models
123
3.4.1.3 Cellular Automata
123
3.4.2 Compartment Models
123
3.4.3 Reaction–Diffusion Systems
125
3.4.3.1 The Diffusion Equation
125
3.4.3.2 Solutions of the Diffusion Equation
126
3.4.3.3 Reaction–Diffusion Equation
127
3.4.4 Pattern Formation in Tissue Development
128
3.4.5 Spontaneous Pattern Formation
130
3.5 Apoptosis
132
3.5.1 Molecular Biology of Apoptosis
132
3.5.2 Modeling of Apoptosis
135
References
142
4 Model Fitting
147
4.1 Data for Small Metabolic and Signaling Systems
147
4.1.1 Databases for Kinetic Modeling
148
4.1.2 Measuring Promoter Activities Using GFP Reporter Genes
150
4.2 Parameter Estimation
152
4.2.1 Regression
153
4.2.2 Estimators
153
4.2.2.1 Method of Least Squares and Maximum-Likelihood Estimation
155
4.2.3 Identifiability
155
4.2.4 Bootstrapping
157
4.2.5 Crossvalidation
158
4.2.6 Bayesian Parameter Estimation
159
4.2.7 Local and Global Optimization
160
4.2.7.1 Local Optimization
161
4.2.7.2 Global Optimization
161
4.2.7.3 Sampling Methods
162
4.2.7.4 Genetic Algorithms
163
4.3 Reduction and Coupling of Models
164
4.3.1 Model Simplification
164
4.3.2 Tacit Model Assumptions
166
4.3.3 Reduction of Fast Processes
167
4.3.3.1 Response Time
167
4.3.3.2 Time-Scale Separation
167
4.3.4 Global Model Reduction
170
4.3.4.1 Linearized Biochemical Models
171
4.3.4.2 Linear Relaxation Modes
171
4.3.5 Coupled Systems and Emergent Behavior
172
4.3.6 Modeling of Coupled Systems
174
4.3.6.1 Bottom-Up and Top-Down Modeling
174
4.3.6.2 Modeling the System Boundary
175
4.3.6.3 Coupling of Submodels
175
4.3.6.4 Model Merging
175
4.4 Model Selection
176
4.4.1 What is a Good Model?
177
4.4.2 Statistical Tests and Model Selection
178
4.4.3 Maximum-Likelihood Estimation and x2-Test
180
4.4.4 Overfitting
181
4.4.5 Likelihood Ratio Test
182
4.4.6 Selection Criteria
183
4.4.7 Bayesian Model Selection
184
4.4.8 Cycle of Experiments and Modeling
186
4.4.9 Models are Growing in Complexity
186
References
189
5 Analysis of High-Throughput Data
193
5.1 High-Throughput Experiments
193
5.1.1 DNA Array Platforms
193
5.1.2 Platform Comparison
196
5.1.3 Next Generation Sequencing
196
5.1.4 Image Analysis and Data Quality Control
198
5.1.4.1 Grid Finding
198
5.1.4.2 Spot Quantification
200
5.1.4.3 Signal Validity
200
5.1.5 Preprocessing
202
5.1.5.1 Global Measures
203
5.1.5.2 Linear Models
203
5.1.5.3 Nonlinear and Spatial Effects
204
5.1.5.4 Other Approaches
204
5.2 Analysis of Gene Expression Data
205
5.2.1 Planning and Designing Experiments for Case-Control Studies
205
5.2.2 Tests for Differential Expression
206
5.2.2.1 DNA Arrays
206
5.2.2.2 Next Generation Sequencing
209
5.2.3 Multiple Testing
209
5.2.4 ROC Curve Analysis
211
5.2.5 Clustering Algorithms
213
5.2.5.1 Hierarchical Clustering
215
5.2.5.2 Self-Organizing Maps (SOMs)
218
5.2.5.3 K-Means
218
5.2.6 Cluster Validation
220
5.2.7 Overrepresentation and Enrichment Analyses
223
5.2.8 Classification Methods
226
5.2.8.1 Support Vector Machines
227
5.2.8.2 Other Approaches
229
References
232
6 Gene Expression Models
235
6.1 Mechanisms of Gene Expression Regulation
235
6.1.1 Transcription-Factor Initiated Gene Regulation
235
6.1.2 General Promoter Structure
237
6.1.3 Prediction and Analysis of Promoter Elements
239
6.1.3.1 Sequence-Based Analysis
239
6.1.3.2 Approaches that Incorporate Additional Information
241
6.1.4 Posttranscriptional Regulation Through microRNAs
243
6.1.4.1 Identification of microRNAs in the Genome Sequence
245
6.1.4.2 MicroRNA Target Prediction
246
6.1.4.3 Experimental Implications – RNA Interference
246
6.2 Gene Regulation Functions
248
6.2.1 The Lac Operon in Escherichia coli
249
6.2.2 Gene Regulation Functions Derived from Equilibrium Binding
250
6.2.3 Occupation Probability Derived from Statistical Thermodynamics
251
6.2.4 Gene Regulation Function of the Lac Operon
253
6.2.5 Transcriptional Regulation in Larger Networks
254
6.2.6 Network Component Analysis
254
6.3 Dynamic Models of Gene Regulation
256
6.3.1 One Gene Regulatory Network: Different Approaches
256
6.3.2 Representation of a Gene Regulatory Network as Graph
256
6.3.3 Bayesian Networks
258
6.3.4 Boolean Networks
259
6.3.5 Description with Ordinary Differential Equations
262
6.3.6 Gene Expression Modeling with Stochastic Processes
264
References
267
7 Stochastic Systems and Variability
271
7.1 Stochastic Modeling of Biochemical Reactions
271
7.1.1 Chemical Random Process for Molecule Numbers
272
7.1.2 The Chemical Master Equation
273
7.1.3 Stochastic Simulation
275
7.1.3.1 Direct Method
275
7.1.3.2 Explicit r-Leaping Method
276
7.1.3.3 Stochastic Simulation and Spatial Models
276
7.1.4 The Chemical Langevin Equation
276
7.1.5 Deterministic and Stochastic Modeling Frameworks
278
7.1.6 Temporal Fluctuations
279
7.2 Fluctuations in Gene Expression
281
7.2.1 Stochastic Model of Transcription and Translation
283
7.2.1.1 Macroscopic Kinetic Model
283
7.2.1.2 Microscopic Stochastic Model
284
7.2.1.3 Fluctuations and Protein Bursts
285
7.2.2 Measuring the Intrinsic and Extrinsic Variability
286
7.2.3 Temporal Fluctuations in a Gene Cascade
288
7.2.3.1 Linear Model of Two Genes
288
7.2.3.2 Measuring the Time Correlations in Protein Levels
290
7.2.4 Biological Functions of Noise
291
7.2.4.1 Random Switching
291
7.2.4.2 Exploration Strategies
291
7.3 Variability and Uncertainty
292
7.3.1 Models with Uncertain Constant Parameters
292
7.3.2 Computing the Distribution of Output Variables
293
7.3.2.1 Monte Carlo Simulation
293
7.3.2.2 Approximation for Narrow Parameter Distributions
294
7.3.2.3 Temporal Parameter Fluctuations
295
7.3.3 Uncertainty Analysis of Biochemical Models
295
7.3.3.1 Sampling of Reaction Elasticities
297
7.3.4 Distributions for Kinetic Parameters
298
7.3.4.1 Principle of Minimal Information
298
7.3.4.2 Thermodynamic Constraints on Parameters
299
7.3.4.3 Obtaining Parameter Distributions from Experimental Data
299
7.4 Robustness
300
7.4.1 Robustness Properties in Biochemical Systems
301
7.4.1.1 Biological Robustness Properties
301
7.4.1.2 Mathematical Robustness Criteria
301
7.4.1.3 Precise Robustness in a Bacterial Two-Component System
301
7.4.2 Structural Robustness in Large Networks
303
7.4.2.1 Backup Genes
303
7.4.2.2 Backup Pathways
304
7.4.3 Quantitative Robustness by Feedback
304
7.4.3.1 Negative Feedback
304
7.4.3.2 Integral Feedback
306
7.4.4 Scaling Laws, Invariance, and Dimensional Analysis
306
7.4.5 Summation Laws and Homogeneous Functions
308
7.4.5.1 Summation Theorems
308
7.4.5.2 Conservation Laws for Sensitivity
308
7.4.5.3 Compensation of Correlated Fluctuations
309
7.4.6 Robustness and Evolvability
309
7.4.7 Robustness and Modeling
310
References
312
8 Network Structures, Dynamics, and Function
315
8.1 Structure of Biochemical Networks
315
8.1.1 Mathematical Graphs
317
8.1.2 Random Graphs
318
8.1.2.1 Erdeis–Renyi Random Graphs
318
8.1.2.2 Geometric Random Graphs
319
8.1.2.3 Random Graphs with Predefined Degree Sequence
319
8.1.3 Scale-Free Networks
319
8.1.4 Clustering and Local Structure
321
8.1.4.1 Clustering Coefficient
321
8.1.4.2 Small-World Networks
321
8.1.5 Network Motifs
322
8.1.6 Structure of Metabolic Networks
323
8.1.7 The Network Picture
324
8.2 Network Motifs
325
8.2.1 Transcription Networks and Network Motifs
326
8.2.2 Single Regulation Arrows and Their Steady-State Response
328
8.2.3 Adaptation Motif
329
8.2.4 Negative Feedback
330
8.2.5 Feed-Forward Loops
331
8.2.6 Dynamic Model of the Feed-Forward Loop
332
8.2.7 Dynamics and Function of Network Motifs
333
8.3 Modularity
335
8.3.1 Modularity as a Fact or as an Assumption
336
8.3.2 Aspects of Modularity. Structure, Function, Dynamics, Regulation, and Genetics
337
8.3.3 Structural Modules in Cellular Networks
337
8.3.4 Modular Response Analysis
338
8.3.5 Functional Modules Detected by Epistasis
339
8.3.6 Evolution of Modularity and Complexity
341
8.3.6.1 Tinkering and Engineering
341
8.3.6.2 Analogy in Evolution
342
8.3.6.3 Modularity, Robustness, and Evolvability
342
References
343
9 Optimality and Evolution
349
9.1 Optimality and Constraint-Based Models
349
9.1.1 Optimization by Evolution
350
9.1.2 Optimality Studies in Systems Biology
350
9.1.2.1 The Fitness Function
351
9.1.2.2 Optimality and Compromise
351
9.1.2.3 Cost-Benefit Calculations
351
9.1.2.4 Inequality Constraints
352
9.1.2.5 Local Optima
353
9.1.3 Constraint-Based Flux Optimization
353
9.1.3.1 Flux-Balance Analysis
353
9.1.3.2 Geometric Interpretation of Flux-Balance Analysis
354
9.1.4 Thermodynamic Constraints
355
9.1.5 Applications and Tests of Flux-Optimization Paradigm
356
9.2 Optimal Enzyme Concentrations
357
9.2.1 Optimization of Catalytic Properties of Single Enzymes
358
9.2.2 Optimal Distribution of Enzyme Concentrations in a Metabolic Pathway
360
9.2.3 Temporal Transcription Programs
363
9.3 Evolutionary Game Theory
367
9.3.1 Game Theory
369
9.3.1.1 Hawk–Dove Game and Prisoner's Dilemma
369
9.3.1.2 Best Choices and Nash Equilibrium
370
9.3.2 Evolutionary Game Theory
371
9.3.3 Replicator Equation for Population Dynamics
371
9.3.3.1 The Replicator Equation
372
9.3.3.2 Outcomes of Frequency-Dependent Selection
372
9.3.4 Evolutionary Stable Strategies
373
9.3.5 Dynamical Behavior in the Rock-Scissors-Paper Game
374
9.3.6 Evolution of Cooperative Behavior
375
9.3.6.1 Kin Selection
376
9.3.6.2 Other Scenarios for Evolution of Cooperation
376
9.3.7 Yield and Efficiency in Metabolism
377
9.3.7.1 Trade-off Between Fast and Efficient Energy Metabolism
377
9.3.7.2 Multicellularity Enables Cells to Profit from Respiration
377
References
379
10 Cell Biology
383
10.1 Introduction
383
10.2 The Origin of Life
384
10.3 Molecular Biology of the Cell
387
10.3.1 Chemical Bonds and Forces Important in Biological Molecules
387
10.3.2 Functional Groups in Biological Molecules
390
10.3.3 Major Classes of Biological Molecules
391
10.3.3.1 Carbohydrates
392
10.3.3.2 Lipids
392
10.3.3.3 Proteins
396
10.3.3.4 Nucleic Acids
400
10.4 Structural Cell Biology
402
10.4.1 Structure and Function of Biological Membranes
403
10.4.2 Nucleus
406
10.4.3 Cytosol
406
10.4.4 Mitochondria
407
10.4.5 Endoplasmatic Reticulum and Golgi Complex
408
10.4.6 Other Organelles
409
10.5 Expression of Genes
410
10.5.1 Transcription
412
10.5.2 Processing of the mRNA
412
10.5.3 Translation
413
10.5.4 Protein Sorting and Posttranslational Modifications
415
10.5.5 Regulation of Gene Expression
416
References
417
11 Experimental Techniques in Molecular Biology
419
11.1 Introduction
420
11.2 Restriction Enzymes and Gel Electrophoresis
420
11.3 Cloning Vectors and DNA Libraries
422
11.4 1D and 2D Protein Gels
425
11.5 Hybridization and Blotting Techniques
427
11.5.1 Southern Blotting
428
11.5.2 Northern Blotting
429
11.5.3 Western Blotting
429
11.5.4 In Situ Hybridization
430
11.6 Further Protein Separation Techniques
430
11.6.1 Centrifugation
430
11.6.2 Column Chromatography
431
11.6.3 Polymerase Chain Reaction
432
11.7 DNA and Protein Chips
433
11.7.1 DNA Chips
433
11.7.2 Protein Chips
434
11.8 Yeast Two-Hybrid System
434
11.9 Mass Spectrometry
435
11.10 Transgenic Animals
436
11.11 RNA Interference
437
11.12 ChIP on Chip and ChIP-PET
439
11.13 Surface Plasmon Resonance
441
11.14 Population Heterogeneity and Single Entity Experiments
442
References
444
12 Mathematics
449
12.1 Linear Modeling
449
12.1.1 Linear Equations
449
12.1.1.1 The Gaussian Elimination Algorithm
451
12.1.1.2 Systematic Solution of Linear Systems
452
12.1.2 Matrices
454
12.1.2.1 Basic Notions
454
12.1.2.2 Linear Dependency
454
12.1.2.3 Basic Matrix Operations
454
12.1.2.4 Dimension and Rank
456
12.1.2.5 Eigenvalues and Eigenvectors of a Square Matrix
457
12.2 Ordinary Differential Equations
458
12.2.1 Notions Regarding Differential Equations
459
12.2.2 Linearization of Autonomous Systems
461
12.2.3 Solution of Linear ODE Systems
462
12.2.4 Stability of Steady States
463
12.2.4.1 Global Stability of Steady States
465
12.2.5 Limit Cycles
466
12.3 Difference Equations
467
12.4 Graph and Network Theory
469
12.4.1 Linear Networks
471
12.4.2 Boolean Networks
471
12.4.3 Bayesian Networks
473
References
474
13 Statistics
475
13.1 Basic Concepts of Probability Theory
475
13.1.1 Random Variables, Densities, and Distribution Functions
478
13.1.2 Transforming Probability Densities
481
13.1.3 Product Experiments and Independence
482
13.1.4 Limit Theorems
483
13.2 Descriptive Statistics
483
13.2.1 Statistics for Sample Location
484
13.2.2 Statistics for Sample Variability
485
13.2.3 Density Estimation
486
13.2.4 Correlation of Samples
487
13.3 Testing Statistical Hypotheses
488
13.3.1 Statistical Framework
489
13.3.2 Two Sample Location Tests
491
13.4 Linear Models
493
13.4.1 ANOVA
493
13.4.2 Multiple Linear Regression
495
13.5 Principal Component Analysis
496
References
499
14 Stochastic Processes
501
14.1 Basic Notions for Random Processes
501
14.1.1 Reduced and Conditional Distributions
503
14.2 Markov Processes
505
14.2.1 Markov Chains
506
14.3 Jump Processes in Continuous Time: The Master Equation
507
14.4 Continuous Random Processes
508
14.4.1 Langevin Equations
508
14.4.2 The Fokker–Planck Equation
509
References
510
15 Control of Linear Systems
511
15.1 Linear Dynamical Systems
511
15.2 System Response
512
15.2.1 Random Fluctuations and Spectral Density
514
15.3 The Gramian Matrices
515
16 Databases
517
16.1 Databases of the National Center for Biotechnology
517
16.2 Databases of the European Bioinformatics Institute
518
16.2.1 EMBL Nucleotide Sequence Database
519
16.2.2 Ensembl
519
16.2.3 InterPro
519
16.3 Swiss-Prot, TrEMBL, and UniProt
520
16.4 Protein Databank
520
16.5 BioNumbers
521
16.6 Gene Ontology
521
16.7 Pathway Databases
524
16.7.1 ConsensusPathDB
524
References
525
17 Modeling Tools
527
17.1 Introduction
527
17.2 Mathematica and Matlab
528
17.2.1 Mathematica Example
530
17.2.2 Matlab Example
531
17.3 Dizzy
532
17.4 Systems Biology Workbench
534
17.5 Tools Compendium
536
References
551
Index 553
Edda Klipp (born 1965) studied theoretical biophysics at the Humboldt University Berlin. A member of the Yeast Systems Biology Network, her research interests include mathematical modeling of cellular systems, signal transduction, systems biology, and text mining. Wolfram Liebermeister (born 1972) studied physics in Tubingen and Hamburg and obtained a PhD of theoretical biophysics at the Humboldt University of Berlin. In his works on complex biological systems, he points out functional aspects like variability, information, and optimality. Christoph Wierling (born 1973) studied biology at the University of Munster and recently obtained a PhD degree on the modeling and simulation of biological systems. Axel Kowald (born 1963) holds a PhD in mathematical biology from the National Institute for Medical Research, London. His current research interests focus on the mathematical modeling of processes involved in the biology of aging and systems biology. Hans Lehrach (born 1946) is a director at the Max Planck Institute for Molecular Genetics in Berlin and was spokesman for the German Human Genome Initiative. His research interests focus on functional genomics, technology development and systems biology. Ralf Herwig (born 1967) studied mathematics and physics at the Free University Berlin and completed a PhD on statistical clustering methods. He has specialized in integrative bioinformatics projects covering genomics, proteomics and systems biology.