Systems Biology for Signaling Networks (eBook)

Preface 5
Contents 6
Contributors 9
Part I Concepts 15
1 Systems Biology Approaches: Solving New Puzzles in a Symphonic Manner 16
References 24
2 Current Progress in Static and Dynamic Modeling of Biological Networks 25
2.1 Introduction 25
2.2 Static Models of Biological Networks 26
2.2.1 Advantages of Static Models 27
2.2.2 Limitations of Static Models 27
2.2.3 Specific Tasks Associated with Static Modeling 27
2.2.4 Data for Static Modeling 30
2.2.4.1 Data Types and Sources 30
2.2.4.2 Data Limits Static Model Complexity 31
2.2.5 Network Reconstruction 32
2.2.5.1 Labels vs. Predictors 32
2.2.5.2 Early Methods for Clustering and Integration 32
2.2.5.3 Data Integration by Supervised Learning 33
2.2.6 Network Representation 34
2.2.6.1 From Reference Assemblies to Reference Networks 34
2.2.6.2 Strongly Typed Static Network Models 36
2.2.7 Applications of Network Models 39
2.2.7.1 Experimental Prioritization 39
2.2.7.2 Network Alignment 40
2.2.7.3 Network Visualization 42
2.2.8 Outstanding Challenges in Static Modeling 43
2.3 Dynamical Models of Biological Networks 43
2.3.1 Discrete Models 44
2.3.2 Continuous Models 44
2.3.3 Advantages of Continuous Dynamical Models 45
2.3.4 Limitations of Dynamical Models 46
2.3.5 Specific Tasks Associated with Dynamical Modeling 46
2.3.6 ODE Systems 47
2.3.6.1 Assumptions of ODE Biological Network Models 47
2.3.6.2 Early Examples of ODE Models Describing Biological Systems 48
2.3.6.3 Modern Applications of ODE Models to Biological Networks 48
2.3.6.4 Outstanding Challenges in ODE Modeling 56
2.3.7 PDE Systems 56
2.3.7.1 Early Examples of PDE Models Describing Biological Systems 57
2.3.7.2 Modern Applications of PDE Models to Biological Networks 58
2.3.7.3 Outstanding Challenges in PDE Modeling 64
2.3.8 SDE Systems 64
2.3.8.1 Assumptions of SDE Biological Network Models 67
2.3.8.2 Modern Application of SDE Models to Biological Networks 67
2.3.8.3 Outstanding Challenges in SDE Modeling 73
2.3.9 Relevant Software 73
2.3.10 Hybrid Dynamical Models of Biological Systems 73
2.4 Conclusions 74
References 75
3 Getting Started in Biological Pathway Construction 86
3.1 Introduction 86
3.2 Approaches and Examples of Pathway Construction 87
3.3 Pathway Databases 88
3.4 Standard Notations for Representing Biological Pathways 89
3.5 Pathway Building Tools 90
3.6 The Pathway Building Process 91
3.7 Summary and Conclusions 91
References 92
4 From Microarray to Biology 95
4.1 Introduction 95
4.2 Biology vs. Information 97
4.3 Microarray History 99
4.4 Microarray Technology 100
4.5 Box 4.1 Gene/Pathway Annotations 100
4.5 Microarray Data Analysis 101
4.6 Experimental Design 102
4.6.1 Replicates 102
4.6.2 Data Preprocessing and Normalization 103
4.6.3 Identification of Genes of Interest 106
4.6.4 Finding Modular and Dynamic Behavior of Gene Networks 109
Box 4.2 Microarray Databases and Meta-Analysis 111
4.6.5 Functional Meaning Behind the Genes of Interest 111
Box 4.3 Enrichment Analysis 112
4.6.6 Gene Networks 113
Box 4.4 Transcription Factors Databases and Tools 114
4.6.7 Transcription Factors 114
4.6.8 Unannotated Genes 115
Box 4.5 All-in-One Analysis 115
4.7 Future Directions 115
Box 4.6 Favorites 116
References 116
Part II Modeling and Reconstruction 118
5 Computational Procedures for Model Identification 119
5.1 Introduction: The Model Building Loop 119
5.2 Parametric Identification: Problem Formulation 121
5.2.1 Mathematical Model Formulation 122
5.2.2 Experimental Scheme and Experimental Data 122
5.2.3 Cost Function 123
5.3 Parametric Identification: Numerical Solution 124
5.3.1 Single Shooting and Multiple Shooting 124
5.3.2 Nonlinear Programming Solvers 125
5.3.2.1 Local Methods 126
5.3.2.2 Global Methods 127
5.3.2.3 An Illustrative Example: The Goodwin Model 129
5.4 Identifiability 131
5.4.1 Structural Versus Practical Identifiability 132
5.4.2 Methods for Practical Identifiability 132
5.4.2.1 Fisher Information Matrix 133
5.4.2.2 The Monte Carlo-Based Approach 133
5.4.3 Illustrative Example: The Brusselator Model 134
5.5 Optimal Experimental Design 136
5.5.1 Numerical Methods: The Control Vector Parameterization Approach 136
5.5.2 An Illustrative Example: The NFB Regulatory Module 138
5.6 Overview 143
References 143
6 Assembly of Logic-Based Diagrams of Biological Pathways 146
6.1 Introduction 147
6.2 Definition of the Modified Edinburgh Pathway Notation (mEPN) Scheme 150
6.2.1 Depiction of Pathway Components 150
6.2.2 Component Annotation 152
6.2.3 Depiction of Biological Processes 153
6.2.4 Boolean Logic Operators 155
6.2.5 Depiction of Other Concepts 155
6.2.6 Depiction of Interactions Between Components and the Use of Edges 156
6.2.7 Cellular Compartments 156
6.3 Collation of Information and Pathway Assembly 158
6.4 Summary 161
References 162
7 Automating Mathematical Modeling of Biochemical Reaction Networks 165
7.1 A Straightforward Modeling Pipeline 166
7.2 Standards in Systems Biology 169
7.2.1 The Systems Biology Markup Language 171
7.2.2 The Systems Biology Ontology 175
7.2.3 The Systems Biology Graphical Notation 178
7.3 Toward Generalized Rate Laws 180
7.3.1 Generalized Mass Action Kinetics 181
7.3.2 Generalizing Enzyme Kinetics 182
7.3.3 The Hill Equation 184
7.4 Computer-Aided Mathematical Modeling of Biological Systems 185
7.4.1 The Graphical Modeling Tool CellDesigner 185
7.4.2 Context-Sensitive Assignment of Rate Equations 187
7.4.3 SBMLsqueezer 189
7.4.4 Model-Merging Using MIRIAM Annotations 190
7.5 Obtaining Model Parameters 195
7.7 Conclusions 202
References 205
8 Strategies to Investigate Signal Transduction Pathways with Mathematical Modelling 212
8.1 Introduction 212
8.1.1 A Definition for Systems Biology 212
8.1.2 Expected Results from Systems Biology 213
8.1.3 Features of Systems Biology as a New Paradigm for Cell Biology 214
8.1.4 Systems Biology for Signalling Pathways 215
8.1.5 A Sketch of the General Methodology Used in Signalling Systems Biology 218
8.1.6 Decision Making on the Modelling Strategy to Investigate a Cell Signalling Problem 219
8.2 Investigation of Non-Linear Dynamics: Signal Amplification in the JAK2STAT5 Pathway 221
8.3 Investigation of Design Principles: Dynamical Implications of Homodimerization in ReceptorTransducer Interactions 225
8.4 Experiment Design and Formulation of Hypothesis: Elucidation of Homodimer ReceptorHomodimer Transducer Mechanism of Interaction 230
8.5 Integration of Different Experimental Data and Biological Scales: A Multi-level Model for Epo-Mediated Modulation of Erythropoiesis 232
8.6 Conclusions 236
References 237
9 Inferring Transcriptional Regulatory Network 240
9.1 Introduction 241
9.2 Methodology 242
9.2.1 Discovery of Transcriptional Modules 242
9.2.2 Inference of Gene Regulatory Networks 245
9.2.3 Identification of Conserved and Divergent Transcriptional Modules 247
9.3 Comparison with Other Methods 250
9.4 Summary 255
References 256
10 Finding Functional Modules 258
10.1 Background 259
10.2 Methods 260
10.2.1 The Notion of Structure-Connected Clusters 260
10.2.2 Structure-Connected Clusters 260
10.2.3 Algorithm Scan 263
10.3 Application and Discussion 265
10.3.1 Protein--Protein Interaction Network 265
10.3.2 Validation Metric Based on Gene Ontology 266
10.3.3 Expert Validation 272
10.3.4 Complexity Analysis 275
10.4 Conclusions and Research Directions 276
References 277
11 Modeling the Dynamics of Biological Networks from Time Course Data 279
11.1 Introduction 279
11.2 Representing Knowledge for Modeling Dynamics 281
11.2.1 Polynomial Models and Constraints 281
11.2.2 Grammar-Based Representation of Domain Knowledge 283
11.2.3 Process-Based Models 287
11.3 Learning Dynamics 289
11.3.1 Formal Task Specification 289
11.3.2 General Algorithm for Learning Dynamics 290
11.3.3 Constrained Induction of Polynomial Equations: CIPER 292
11.3.4 Grammar-Based Equation Discovery: LAGRAMGE 293
11.3.5 Inductive Process Modeling: LAGRAMGE2, IPM, and HIPM 293
11.4 Related Work 295
11.5 Conclusion and Further Work 295
References 296
12 Decision Making in Cells 299
12.1 Introduction 299
12.2 Quantification of Information: Network-Based Information Processing and Decision Making 301
12.2.1 A Mathematical Basis for Understanding Information 301
12.2.2 Information Processing and Dynamics -- Cellular Automata 303
12.2.3 Connecting Dynamics to Computational Capacity 306
12.2.3.1 Cellular Automata 307
12.2.3.2 NK Boolean Networks 308
12.2.3.3 Order, Chaos, and Complexity in NK Boolean Networks 310
12.2.4 Structural and Functional Properties of Emergent Networks 313
12.2.4.1 Nonlinear Functions 313
12.2.4.2 Nonlinear Connectivity 314
12.2.5 A Mathematical Basis for Decision Making 315
12.3 Computational Capacity in Cells: The Feedback Loop 319
12.3.1 Negative Feedback Loops 320
12.3.2 Positive Feedback Loops 321
12.3.3 Combining Positive and Negative Feedback 324
12.3.4 Feedback Loops with Multi-step Signaling Cascades 326
12.4 Decision Making in Cells 328
12.4.1 Nontrivial Biochemical Network Activity in the Execution of Cellular Decisions 329
12.4.1.1 Movement 329
12.4.1.2 Apoptosis 330
12.4.1.3 Growth and Proliferation 330
12.4.1.4 Differentiation 330
12.4.2 Emergent Decision Making in Cells 331
12.5 Conclusion 335
References 336
13 Robustness of Neural Network Models 341
13.1 Introduction 342
13.2 Methods 343
13.2.1 Network Configuration 343
13.2.2 Network Training 344
13.3 Results 345
13.3.1 Varying the Number of Hidden Nodes 345
13.3.2 Varying the Starting Weight Composition 347
13.3.3 Changing the Activation Function to tanh 348
13.3.4 Multi-dimensional Stimuli 349
13.3.5 Multiple Stimuli 350
13.3.6 Evolving Network Weights 352
13.4 Discussion 353
References 355
14 Functional Modules in ProteinProtein Interaction Networks 356
14.1 Data Integration 358
14.1.1 Microarray and Survival Data 358
14.1.2 Network 359
14.2 Scoring and Searching 360
14.2.1 Aggregation of p-Values 361
14.2.2 Signal--noise Decomposition 362
14.2.3 Network Score 364
14.2.4 Searching 365
14.3 Resulting Functional Modules 367
14.4 Comparison and Validation 369
14.5 Summary and Conclusion 371
References 371
15 Mixture Model on Graphs: A Probabilistic Model for Network-Based Analysis of Proteomic Data 373
15.1 Introduction 373
15.1.1 Systems Biology and Biological Networks 374
15.1.2 Functional Correlation in Metabolic Networks 376
15.1.2.1 Metabolite-Centred Network 376
15.1.2.2 Enzyme-Centred Network 376
15.1.2.3 Structure of Metabolic Networks 377
15.1.2.4 Regulatory Correlation 378
15.1.3 Idiosyncrasies of iTRAQ-Based Proteomics 380
15.2 Model 383
15.2.1 Bayesian Statistics 383
15.2.1.1 Definitions and Interpretations 383
15.2.1.2 Computational Issues and Solutions 385
15.2.2 Prior and Posterior Distributions 386
15.3 Results 389
15.3.1 Illustration on a Simple Network 389
15.3.2 Nostoc and Nitrogen-Fixing Processes 391
15.3.3 Pathway Discovery 393
15.3.4 Bootstrap Validation 394
15.4 Conclusions and Future Work 396
References 396
16 Integration of Network Information for Protein FunctionPrediction 400
16.1 Background 400
16.1.1 Protein--Protein Interaction (PPI) Network 401
16.1.2 Gene Ontology 402
16.1.3 Objectives 404
16.2 Predicting Protein Functions by ProteinProtein Interaction Network 404
16.2.1 Introduction and Notations 404
16.2.2 Binomial-Neighborhood (BN) Model 405
16.2.2.1 Binomial-Neighborhood (BN) assumptions on PPI 405
16.2.2.2 Probabilistic Inference from BN Model 406
16.2.3 Limitations of PPI for PFP 407
16.3 Predicting Protein Functions by Integrating Relational and Hierarchical Information 408
16.3.1 Processing the Gene Ontology Hierarchy (i.e., Transforming GO DAG's into Trees) 408
16.3.2 Hierarchical Binomial-Neighborhood (HBN) Assumptions 409
16.3.3 Inference from HBN Model 410
16.3.3.1 Local Hierarchical Conditional Probability 410
16.3.3.2 Global Hierarchical Conditional Probability 410
16.3.4 Case Study: Predicting Intracellular Signal Cascade on Yeast Genes 411
16.3.4.1 Data 411
16.3.4.2 Cross-Validation Design 412
16.3.4.3 Evaluations 413
16.3.4.4 Results 413
16.4 Integrating Network Information with Heterogeneous Genome-Wide Protein Data 416
16.4.1 Introduction 416
16.4.2 String 416
16.4.3 Inference from PHIPA 417
16.4.3.1 Assumptions and Notations 417
16.4.3.2 Calculation for the Feature Component 418
16.4.4 Results 419
16.4.4.1 Data Preparation 419
16.4.4.2 Network Comparison 419
16.4.4.3 Contribution of Protein Feature and GO Hierarchy 420
16.5 Discussion 423
References 425
Part III Applications for Signaling Networks 428
17 Cellular-Level Gene Regulatory Networks: Their Derivation and Properties 429
17.1 Introduction 430
17.2 Form of Large-Scale Cellular-Level Models 432
17.3 Model derivation 434
17.4 Model interpretation 435
17.5 Models Inferred from Alliance for Cell Signaling Data 436
17.5.1 Model Properties 436
17.5.2 Regulatory Influence Statistical Support 439
17.5.3 Temporal Regulation 439
17.6 Multiscale Cellular Networks 440
17.7 Conclusions 443
References 444
18 Tyrosine-Phosphoproteome Dynamics 447
18.1 Introduction 447
18.2 Quantitative Phosphoproteomics for Defining Temporal Dynamics of Tyrosine-Phosphorylated Signaling Molecules in Cellular Networks 448
18.3 Computational Modeling of Signal Transduction Networks on the Basis of Tyrosine-Phosphoproteome Dynamics Data 451
18.4 Future Prospects 452
References 452
19 Systems Biology of the MAPK1,2 Network 455
19.1 The MAPK Signaling Network 455
19.2 The Role of MAPK1,2 in Disease 461
19.3 Systems Biology and the MAPK Signaling Network 463
19.3.1 Feedback Loops in MAPK1,2 Signaling 464
19.3.2 Computational Modeling 467
19.3.3 MAPK1,2 Signaling Models 470
19.3.4 Alternative MAPK1,2 Modeling Methods 475
19.4 The MAPK1,2 Pathway as a Drug Target 477
19.4.1 Ras Inhibitors 477
19.4.2 Raf Inhibitors 478
19.4.3 MEK Inhibitors 479
19.5 Discussion and Conclusion 481
References 481
20 Pathway Crosstalk Network 490
20.1 Limitations of the Current Drug Discovery Strategy 490
20.2 A Network Approach Toward Understanding of Biology 491
20.3 Construction of Pathway Crosstalk Network 493
20.4 Properties of the Pathway Crosstalk Network 495
20.5 Interpreting Transcriptomic Profiling Results Using PCN 497
20.6 Conclusions 499
References 500
21 Crosstalk Between Mitogen-Activated Protein Kinase and Phosphoinositide-3 Kinase Signaling Pathways in Development and Disease 504
21.1 Introduction 505
21.1.1 Overview of the MAP Kinase Signaling 505
21.1.2 Overview of PI3K/Akt Signaling Pathway 510
21.2 Biochemical Crosstalk Between Raf/MAPK and PI3K/Akt Signaling Pathways 511
21.2.1 Crosstalk at the Level of Ras and PI3K: Near the Cell Membrane 512
21.2.2 Crosstalk at the Level of Adaptor Proteins 512
21.2.3 Crosstalk at the Level of Raf and Akt 512
21.2.4 Crosstalk at the Level of ERK and TSC 513
21.3 Crosstalk During Embryogenesis 513
21.3.1 MAPK and PI3K Crosstalk During Artery and Vein Specification 513
21.3.2 Crosstalk in Vertebrate Limb Development 515
21.4 MAPK and PI3K Pathways and Human Cancers: Potential Therapeutic Targets 517
21.4.1 Receptor Tyrosine Kinases in Tumorigenesis: Therapeutic Opportunities 517
21.4.2 MAPK and PI3K Signaling in Tumorigenesis: Additional Therapeutic Opportunities 519
21.4.3 Evidence for Importance of Crosstalk Between MAPK and PI3K for Tumor Formation and Therapeutic Implications 520
21.5 Conclusion 521
References 521
22 Systems-Level Analyses of the Mammalian Innate Immune Response 529
22.1 Introduction to Innate Immunity 530
22.2 Complexity of Innate Immunity Why Systems Approaches are Necessary 531
22.3 Computational Resources for Innate Immunity 535
22.4 A Walk Through the Analysis of a Smallpox Gene Expression Data Set Using InnateDB Pathways, Processes, and Interaction Networks 536
22.4.1 Introduction 536
22.4.2 Preparing Data for Analysis in InnateDB 537
22.4.3 Uploading Data to InnateDB 539
22.4.4 Performing a Gene Ontology Over-Representation Analysis 542
22.4.5 Performing a Pathway ORA 545
22.4.6 Visualizing Pathway Data with Cerebral 548
22.4.7 Generating and Exploring Molecular Interaction Networks Using InnateDB 550
22.5 Conclusions and Future Directions 554
References 555
23 Molecular Basis of Protective Anti-Inflammatory Signalling by Cyclic AMP in the Vascular Endothelium 559
23.1 Introduction 559
23.1.1 Dysfunctional Vascular Endothelium and Disease 559
23.1.2 Cyclic AMP Signalling 561
23.1.2.1 Basic Architecture of cAMP Signalling Modules 561
23.1.2.2 Exchange Proteins Activated by cAMP (Epacs) 561
23.2 The Control of Endothelial Barrier Function by Cyclic AMP 563
23.2.1 Introduction 563
23.2.2 Co-ordination of Barrier Function by PKA and EPAC 564
23.3 Regulation of Pro-inflammatory Signalling by Cyclic AMP 566
23.3.1 Introduction 566
23.3.2 IL-6 Signalling Through gp130 Homodimers 566
23.3.3 SOCS Proteins and Inhibition of Cytokine Receptor Signalling 569
23.3.4 SOCS-3 and the Control of IL-6 Signalling by cAMP 572
23.3.5 C/EBPs as cAMP-Activated EPAC1-Regulated Transcription Factors 574
23.3.6 ERK1,2 Activation and SOCS-3 Induction by cAMP 575
23.4 Systems-Based Future Directions 578
23.4.1 Identification of New SOCS-3 Targets 578
23.4.2 A New PKA-Independent Route for Genome-Wide Control of Transcription by cAMP? 578
23.5 Concluding Remarks 579
References 580
24 Construction of Cancer-Perturbed ProteinProtein Interaction Network of Apoptosis for Drug Target Discovery 586
24.1 Introduction 586
24.2 Methods 588
24.2.1 Construction of Initial PPI Networks 588
24.2.2 Selecting Experimental Data 589
24.2.3 Processing Selected Experimental Data 589
24.2.4 Identification of Interactions in the Initial PPI Network 591
24.2.5 Modification of Initial PPI Networks 593
24.3 Results 595
24.3.1 Construction of a Cancer-Perturbed PPI Network for Apoptosis 595
24.3.2 Prediction of Apoptosis Drug Targets by Means of Cancer-Perturbed PPI Networks for Apoptosis 597
24.3.2.1 Common pathway: CASP2, CASP3, and CASP9 598
24.3.2.2 Extrinsic Pathway and Crosstalk: TNF and TNFRSF6 600
24.3.2.3 Intrinsic Pathway: BAK1, BAX, BCL2, BCL2A1, BCL2L1, BID, and CYCS 600
24.3.2.4 Apoptosis Regulators: CFLAR, EGFR, MYC, and TP53 602
24.3.2.5 Stress-Induced Signaling: MAPK1, MAPK3, and NFKB1 602
24.3.2.6 Others: CCND1, CDKN1A, IGF1, PCNA , and PRKCD 602
24.4 Caspase Activation Through Static and Dynamic Hubs 603
24.5 Conclusions 605
References 605
25 Transcriptional Changes in Alzheimers Disease 608
25.1 Introduction 609
25.2 Results from Postmortem Studies 610
25.2.1 Whole Tissue Studies 611
25.2.2 Pure Cell Population Studies 615
25.2.3 Large-Scale Microarray Atlases 617
25.3 Results from Model Systems 618
25.3.1 In Vitro Models of AD 618
25.3.2 Invertebrate Models of AD 620
25.3.3 Rodent Models of AD 623
25.3.4 Primate Models of AD 625
25.4 Peripheral Transcriptional Changes 625
25.4.1 Changes in Blood 626
25.4.2 Changes in CSF 628
25.5 Systems Biology Approaches to Studying Transcriptional Changes 629
25.5.1 Combining Multiple Transcriptional Studies 629
25.5.2 Combining Transcription with Genomics 632
25.5.3 Combining Transcription with Proteomics 633
25.5.4 Combining Transcription and Imaging 633
25.5.5 A Systems Biology Study of FTD 634
25.6 Conclusions and Future Directions 635
References 637
26 Pathogenesis of Obesity-Related Chronic Liver Diseases as the Study Case for the Systems Biology 641
26.1 Introduction 641
26.2 Liver 643
26.3 The Non-alcoholic Fatty Liver Disease (NAFLD) Spectrum 643
26.4 A Brief Overview of Genomics and Proteomics High-Throughput Approaches to Collect Systems-Level Data 646
26.4.1 High-Throughput Evaluation of mRNA Profiles 646
26.4.2 Methods of Evaluation of Protein Profiles 649
26.4.3 Focused Proteomics Research 655
26.5 Challenges for an Application of High-Throughput Technologies to the Study of the Liver Pathology 655
26.5.1 Heterogenous Composition of Tissues 656
26.5.2 ''Healthy'' Tissue Controls 658
26.5.3 Correlation of mRNA and Protein Levels 662
26.5.4 Issues Related to High-Throughput Nature of the Experiments 662
26.5.5 Sample Size 664
26.5.6 Publicly Available Data Sets and Their Meta-Analysis 665
26.5.7 Putting it All Together 667
26.6 Case Studies 668
26.6.1 A Genomic and Proteomic Study of the Spectrum of Nonalcoholic Fatty Liver Disease (Collantes et al. 2006) 669
26.6.2 A Study of Hepatic Proteome in the Patients with the Diseases of NAFLD Spectrum (Charlton et al. 2009 ) 671
26.6.3 A Comprehensive Study of miRNA Expression in the Liver of NASH patients (Cheung et al. 2008 ) 673
26.6.4 Profilings by Other 0Omics0 (Dezortova et al. 2005 , Xue et al. 2008, Callewaert et al. 2004 , Liu et al. 2007 ) 675
26.7 Conclusion 676
References 677
27 The Evolving Transcriptome of Head and Neck Squamous Cell Carcinoma 683
27.1 Overview 683
27.1.1 Background: Demographics and Clinical Management of HNSCC 684
27.1.2 Initiatives of Systems-Level Analysis 684
27.2 Data Retrieval and Processing 685
27.2.1 Systematic Reviews and Meta-Analysis 685
27.2.2 Search Strategy and Flow Diagram 686
27.2.3 Data Extraction 686
27.2.4 Data Formatting 689
27.2.4.1 A Common Template 689
27.2.4.2 Bounded Fold Changes 690
27.3 Systems-Level Analysis 690
27.3.1 Progressive Trends 690
27.3.1.1 Tissue-Specificity as an Example of Validity Assessment 690
27.3.1.2 Consensus Membership of Gene expression Signatures in Different Stages of Comparisons 691
27.3.2 Topological Analysis of Signaling Pathways 691
27.3.2.1 Inter-modular vs. Intra-modular Hubs 691
27.3.2.2 Integrin Signaling Pathways 694
27.3.2.3 Implication of Invasiveness: siRNA Knockdown of the Integrin Molecules 695
27.3.3 Highly Differentially Expressed Chromosomal Regions 696
27.4 Discussion 697
Appendix: Update and Archiving 698
References 698
28 Peptide Microarrays for a Network Analysis of Changes in Molecular Interactions in Cellular Signalling 699
28.1 Introduction 699
28.1.1 Molecular Complexes in Cellular Signalling 699
28.1.2 Complex Formation in T Cell Signalling 701
28.1.3 Peptide Microarray-Based Detection of Changes in Cellular Signalling 702
28.1.3.1 The Peptide Microarray-Based Approach 702
28.1.3.2 Comparison to Other Methods for Large-Scale Analyses of Protein Interaction Networks 704
28.2 Method: Peptide Microarrays for the Detection of Signalling-Dependent Changes in Molecular Interactions 706
28.2.1 Selection of Peptides 706
28.2.2 Generation and Processing of Microarrays 706
28.3 Applications 708
28.3.1 Signalling-Dependent Changes in Complex Formation 708
28.3.2 Comparison of Different Cell Lines 710
28.3.3 Analysis of the Architecture of Signalling Complexes 710
28.3.4 The Significance of an Interaction Motif for Organizing the Network 711
References 713
Part IV Tools for Systems Biology 715
29 A Primer on Modular Mass-Action Modelling with CellML 716
29.1 Introduction 716
29.2 Mathematical Formalism 717
29.3 Anatomy of a CellML Model 720
29.4 Mass-Action Modelling Examples 721
29.4.1 Bidirectional Reaction Example 722
29.4.2 Multi-Environment Reaction Example 730
29.4.3 Combined Example Model 733
29.4.4 Importing example 737
29.5 Modular Modelling with CellML 739
29.5.1 Motivation for Modularisation 740
29.5.2 Decoupling the Components 740
29.6 The CellML Modelling Community 744
References 744
30 FERN Stochastic Simulation and Evaluation of Reaction Networks 746
30.1 Background 747
30.1.1 Petri Nets 747
30.1.2 Stochastic Chemical Kinetics 749
30.1.3 Stochastic Simulation Methods 751
30.1.3.1 First Reaction Method 751
30.1.3.2 Direct Method 752
30.1.3.3 Next Reaction Method 752
30.1.3.4 Composition/Rejection Method 753
30.1.3.5 Tau-Leaping Methods 753
30.1.3.6 Hybrid Methods 753
30.2 Implementation 754
30.2.1 Other Implementations 754
30.2.2 FERN 755
30.2.3 Implementation Details 755
30.2.3.1 Networks 756
30.2.3.2 Import and Export of Networks 757
30.2.3.3 Simulation Algorithms 758
30.2.3.4 Observer System 759
30.2.3.5 Stochastics 760
30.2.4 Accuracy and Runtime Performance of FERN 760
30.3 Using FERN 761
30.3.1 Command Line Tool 761
30.3.2 Basic Usage of FERN 763
30.3.3 Cytoscape Plugin for Stochastic Simulation 763
30.3.4 Simulation of Cell Growth and Division Using Observers 765
30.4 Discussion 767
30.5 Availability and Requirements 768
References 768
31 Programming Biology in BlenX 771
31.1 Introduction 771
31.2 The BlenX language 772
31.3 The Beta Workbench 783
31.3.1 Usage 787
31.4 Case Studies 794
31.4.1 Actin Polymerization 794
31.4.1.1 The BlenX model 795
31.4.2 Analysis 808
31.4.3 Cell Cycle 809
31.4.3.1 The BlenX model 810
31.4.4 Analysis 813
31.5 Conclusions 814
References 814
32 Discrete Modelling: Petri Net and Logical Approaches 815
32.1 Introduction 815
32.2 Petri Net Foundations 818
32.2.1 Place/Transition Nets 818
32.2.1.1 Places and Transitions in Biochemical Networks 819
32.2.1.2 Firing Rule 819
32.2.2 Petri Net Properties 822
32.2.2.1 Behavioural Properties 822
32.2.2.2 Structural Properties 824
32.2.3 Petri Net Extensions 825
32.3 Specific Modelling Techniques 827
32.3.1 The Role of Place Invariants and Read Arcs 828
32.3.2 Feasible T-Invariants 828
32.3.3 Modularisation Using MCT-Sets and T-Clusters 830
32.3.4 Mauritius Maps and Knockout Analysis 832
32.4 Logical Approach 834
32.4.1 Analysis of Logical Regulatory Graphs 836
32.4.2 From Logical Regulatory Graphs to Petri Nets 837
32.4.3 Illustration: Mating and Filamentous Pathways in Yeast 839
32.5 Summary and Conclusions 841
Appendix 844
References 846
33 ProteoLens: A Database-Driven Visual Data Mining Tool for Network Biology 850
33.1 Introduction 851
33.1.1 Biomolecular Network and Visualization Software 851
33.1.1.1 Multi-Scale Biological Entities and ProteoLens 851
33.2 Concepts and Software Architecture 852
33.2.1 Data Associations: The Concept 852
33.2.2 Functional Layers 853
33.3 Top Features 853
33.3.1 Comprehensive Input and Output Supporting 854
33.3.2 Declarative SQL-Based Visual Analysis 856
33.3.3 Layout Choices of Biological Network 856
33.3.3.1 Hierarchical Layout 856
33.3.3.2 Circular Layout 856
33.3.3.3 Organic Layout 857
33.3.4 Sub-network Retrieving Capability 857
33.4 Using ProteoLens 858
33.4.1 Installing ProteoLens and Launching the Application 858
33.4.2 Connecting to Database and File-Based Input 858
33.4.2.1 Connecting to Database Input 858
33.4.2.2 Connecting to File-Based Input 860
33.4.3 Create Data Association 860
33.4.4 Attach Network Source to the View 861
33.4.5 Add Annotation 862
33.5 Systems Biology Case Studies 863
33.5.1 Case Study 1: Alzheimer' Disease-Related Protein Interaction Network 863
33.5.2 Case Study 2: Gene Ontology Cross-Talk Network 864
33.5.3 Case Study 3: Human Cancer Association Network 866
33.6 ProteoLens Project 868
References 868
34 MADNet: A Web Server for Contextual Analysis and Visualization of High-Throughput Experiments 869
34.1 Introduction 870
34.2 MADNet Web Server 871
34.2.1 Web Server Implementation 871
34.2.2 Data Input 872
34.2.3 Analysis and Visualization 874
34.2.3.1 Metabolic and Signaling Pathways 875
34.2.3.2 Transcription Factors 877
34.2.4 Output 878
34.3 Conclusions and Future Work 879
References 879
Subject Index 881