Choi Systems Biology for Signaling Networks

1. Auflage 2010
ISBN: 978-1-4419-5797-9
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 908 Seiten

Reihe: Systems Biology

System Biology encompasses the knowledge from diverse fields such as Molecular Biology, Immunology, Genetics, Computational Biology, Mathematical Biology, etc. not only to address key questions that are not answerable by individual fields alone, but also to help in our understanding of the complexities of biological systems. Whole genome expression studies have provided us the means of studying the expression of thousands of genes under a particular condition and this technique had been widely used to find out the role of key macromolecules that are involved in biological signaling pathways. However, making sense of the underlying complexity is only possible if we interconnect various signaling pathways into human and computer readable network maps. These maps can then be used to classify and study individual components involved in a particular phenomenon. Apart from transcriptomics, several individual gene studies have resulted in adding to our knowledge of key components that are involved in a signaling pathway. It therefore becomes imperative to take into account of these studies also, while constructing our network maps to highlight the interconnectedness of the entire signaling pathways and the role of that particular individual protein in the pathway. This collection of articles will contain a collection of pioneering work done by scientists working in regulatory signaling networks and the use of large scale gene expression and omics data. The distinctive features of this book would be: Act a single source of information to understand the various components of different signaling network (roadmap of biochemical pathways, the nature of a molecule of interest in a particular pathway, etc.), Serve as a platform to highlight the key findings in this highly volatile and evolving field, and Provide answers to various techniques both related to microarray and cell signaling to the readers.

Autoren/Hrsg.

Choi, Sangdun

Inhaltsverzeichnis

1;Preface;5
2;Contents;6
3;Contributors;9
4;Part I Concepts;15
4.1;1 Systems Biology Approaches: Solving New Puzzles in a Symphonic Manner;16
4.2;References;24
4.3;2 Current Progress in Static and Dynamic Modeling of Biological Networks;25
4.3.1;2.1 Introduction;25
4.3.2;2.2 Static Models of Biological Networks;26
4.3.2.1;2.2.1 Advantages of Static Models;27
4.3.2.2;2.2.2 Limitations of Static Models;27
4.3.2.3;2.2.3 Specific Tasks Associated with Static Modeling;27
4.3.2.4;2.2.4 Data for Static Modeling;30
4.3.2.4.1;2.2.4.1 Data Types and Sources;30
4.3.2.4.2;2.2.4.2 Data Limits Static Model Complexity;31
4.3.2.5;2.2.5 Network Reconstruction;32
4.3.2.5.1;2.2.5.1 Labels vs. Predictors;32
4.3.2.5.2;2.2.5.2 Early Methods for Clustering and Integration;32
4.3.2.5.3;2.2.5.3 Data Integration by Supervised Learning;33
4.3.2.6;2.2.6 Network Representation;34
4.3.2.6.1;2.2.6.1 From Reference Assemblies to Reference Networks;34
4.3.2.6.2;2.2.6.2 Strongly Typed Static Network Models;36
4.3.2.7;2.2.7 Applications of Network Models;39
4.3.2.7.1;2.2.7.1 Experimental Prioritization;39
4.3.2.7.2;2.2.7.2 Network Alignment;40
4.3.2.7.3;2.2.7.3 Network Visualization;42
4.3.2.8;2.2.8 Outstanding Challenges in Static Modeling;43
4.3.3;2.3 Dynamical Models of Biological Networks;43
4.3.3.1;2.3.1 Discrete Models;44
4.3.3.2;2.3.2 Continuous Models;44
4.3.3.3;2.3.3 Advantages of Continuous Dynamical Models;45
4.3.3.4;2.3.4 Limitations of Dynamical Models;46
4.3.3.5;2.3.5 Specific Tasks Associated with Dynamical Modeling;46
4.3.3.6;2.3.6 ODE Systems;47
4.3.3.6.1;2.3.6.1 Assumptions of ODE Biological Network Models;47
4.3.3.6.2;2.3.6.2 Early Examples of ODE Models Describing Biological Systems;48
4.3.3.6.3;2.3.6.3 Modern Applications of ODE Models to Biological Networks;48
4.3.3.6.4;2.3.6.4 Outstanding Challenges in ODE Modeling;56
4.3.3.7;2.3.7 PDE Systems;56
4.3.3.7.1;2.3.7.1 Early Examples of PDE Models Describing Biological Systems;57
4.3.3.7.2;2.3.7.2 Modern Applications of PDE Models to Biological Networks;58
4.3.3.7.3;2.3.7.3 Outstanding Challenges in PDE Modeling;64
4.3.3.8;2.3.8 SDE Systems;64
4.3.3.8.1;2.3.8.1 Assumptions of SDE Biological Network Models;67
4.3.3.8.2;2.3.8.2 Modern Application of SDE Models to Biological Networks;67
4.3.3.8.3;2.3.8.3 Outstanding Challenges in SDE Modeling;73
4.3.3.9;2.3.9 Relevant Software;73
4.3.3.10;2.3.10 Hybrid Dynamical Models of Biological Systems;73
4.3.4;2.4 Conclusions;74
4.4;References;75
4.5;3 Getting Started in Biological Pathway Construction;86
4.5.1;3.1 Introduction;86
4.5.2;3.2 Approaches and Examples of Pathway Construction;87
4.5.3;3.3 Pathway Databases;88
4.5.4;3.4 Standard Notations for Representing Biological Pathways;89
4.5.5;3.5 Pathway Building Tools;90
4.5.6;3.6 The Pathway Building Process;91
4.5.7;3.7 Summary and Conclusions;91
4.6;References;92
4.7;4 From Microarray to Biology;95
4.7.1;4.1 Introduction;95
4.7.2;4.2 Biology vs. Information;97
4.7.3;4.3 Microarray History;99
4.7.4;4.4 Microarray Technology;100
4.7.5;4.5 Box 4.1 Gene/Pathway Annotations;100
4.7.6;4.5 Microarray Data Analysis;101
4.7.7;4.6 Experimental Design;102
4.7.7.1;4.6.1 Replicates;102
4.7.7.2;4.6.2 Data Preprocessing and Normalization;103
4.7.7.3;4.6.3 Identification of Genes of Interest;106
4.7.7.4;4.6.4 Finding Modular and Dynamic Behavior of Gene Networks;109
4.7.8;Box 4.2 Microarray Databases and Meta-Analysis;111
4.7.8.1;4.6.5 Functional Meaning Behind the Genes of Interest;111
4.7.9;Box 4.3 Enrichment Analysis;112
4.7.9.1;4.6.6 Gene Networks;113
4.7.10;Box 4.4 Transcription Factors Databases and Tools;114
4.7.10.1;4.6.7 Transcription Factors;114
4.7.10.2;4.6.8 Unannotated Genes;115
4.7.11;Box 4.5 All-in-One Analysis;115
4.7.12;4.7 Future Directions;115
4.7.13;Box 4.6 Favorites;116
4.8;References;116
5;Part II Modeling and Reconstruction;118
5.1;5 Computational Procedures for Model Identification;119
5.1.1;5.1 Introduction: The Model Building Loop;119
5.1.2;5.2 Parametric Identification: Problem Formulation;121
5.1.2.1;5.2.1 Mathematical Model Formulation;122
5.1.2.2;5.2.2 Experimental Scheme and Experimental Data;122
5.1.2.3;5.2.3 Cost Function;123
5.1.3;5.3 Parametric Identification: Numerical Solution;124
5.1.3.1;5.3.1 Single Shooting and Multiple Shooting;124
5.1.3.2;5.3.2 Nonlinear Programming Solvers;125
5.1.3.2.1;5.3.2.1 Local Methods;126
5.1.3.2.2;5.3.2.2 Global Methods;127
5.1.3.2.3;5.3.2.3 An Illustrative Example: The Goodwin Model;129
5.1.4;5.4 Identifiability;131
5.1.4.1;5.4.1 Structural Versus Practical Identifiability;132
5.1.4.2;5.4.2 Methods for Practical Identifiability;132
5.1.4.2.1;5.4.2.1 Fisher Information Matrix;133
5.1.4.2.2;5.4.2.2 The Monte Carlo-Based Approach;133
5.1.4.3;5.4.3 Illustrative Example: The Brusselator Model;134
5.1.5;5.5 Optimal Experimental Design;136
5.1.5.1;5.5.1 Numerical Methods: The Control Vector Parameterization Approach;136
5.1.5.2;5.5.2 An Illustrative Example: The NFB Regulatory Module;138
5.1.6;5.6 Overview;143
5.2;References;143
5.3;6 Assembly of Logic-Based Diagrams of Biological Pathways;146
5.3.1;6.1 Introduction;147
5.3.2;6.2 Definition of the Modified Edinburgh Pathway Notation (mEPN) Scheme;150
5.3.2.1;6.2.1 Depiction of Pathway Components;150
5.3.2.2;6.2.2 Component Annotation;152
5.3.2.3;6.2.3 Depiction of Biological Processes;153
5.3.2.4;6.2.4 Boolean Logic Operators;155
5.3.2.5;6.2.5 Depiction of Other Concepts;155
5.3.2.6;6.2.6 Depiction of Interactions Between Components and the Use of Edges;156
5.3.2.7;6.2.7 Cellular Compartments;156
5.3.3;6.3 Collation of Information and Pathway Assembly;158
5.3.4;6.4 Summary;161
5.4;References;162
5.5;7 Automating Mathematical Modeling of Biochemical Reaction Networks;165
5.5.1;7.1 A Straightforward Modeling Pipeline;166
5.5.2;7.2 Standards in Systems Biology;169
5.5.2.1;7.2.1 The Systems Biology Markup Language;171
5.5.2.2;7.2.2 The Systems Biology Ontology;175
5.5.2.3;7.2.3 The Systems Biology Graphical Notation;178
5.5.3;7.3 Toward Generalized Rate Laws;180
5.5.3.1;7.3.1 Generalized Mass Action Kinetics;181
5.5.3.2;7.3.2 Generalizing Enzyme Kinetics;182
5.5.3.3;7.3.3 The Hill Equation;184
5.5.4;7.4 Computer-Aided Mathematical Modeling of Biological Systems;185
5.5.4.1;7.4.1 The Graphical Modeling Tool CellDesigner;185
5.5.4.2;7.4.2 Context-Sensitive Assignment of Rate Equations;187
5.5.4.3;7.4.3 SBMLsqueezer;189
5.5.4.4;7.4.4 Model-Merging Using MIRIAM Annotations;190
5.5.5;7.5 Obtaining Model Parameters;195
5.5.6;7.7 Conclusions;202
5.6;References;205
5.7;8 Strategies to Investigate Signal Transduction Pathways with Mathematical Modelling;212
5.7.1;8.1 Introduction;212
5.7.1.1;8.1.1 A Definition for Systems Biology;212
5.7.1.2;8.1.2 Expected Results from Systems Biology;213
5.7.1.3;8.1.3 Features of Systems Biology as a New Paradigm for Cell Biology;214
5.7.1.4;8.1.4 Systems Biology for Signalling Pathways;215
5.7.1.5;8.1.5 A Sketch of the General Methodology Used in Signalling Systems Biology;218
5.7.1.6;8.1.6 Decision Making on the Modelling Strategy to Investigate a Cell Signalling Problem;219
5.7.2;8.2 Investigation of Non-Linear Dynamics: Signal Amplification in the JAK2STAT5 Pathway;221
5.7.3;8.3 Investigation of Design Principles: Dynamical Implications of Homodimerization in ReceptorTransducer Interactions;225
5.7.4;8.4 Experiment Design and Formulation of Hypothesis: Elucidation of Homodimer ReceptorHomodimer Transducer Mechanism of Interaction;230
5.7.5;8.5 Integration of Different Experimental Data and Biological Scales: A Multi-level Model for Epo-Mediated Modulation of Erythropoiesis;232
5.7.6;8.6 Conclusions;236
5.8;References;237
5.9;9 Inferring Transcriptional Regulatory Network;240
5.9.1;9.1 Introduction;241
5.9.2;9.2 Methodology;242
5.9.2.1;9.2.1 Discovery of Transcriptional Modules;242
5.9.2.2;9.2.2 Inference of Gene Regulatory Networks;245
5.9.2.3;9.2.3 Identification of Conserved and Divergent Transcriptional Modules;247
5.9.3;9.3 Comparison with Other Methods;250
5.9.4;9.4 Summary;255
5.10;References;256
5.11;10 Finding Functional Modules;258
5.11.1;10.1 Background;259
5.11.2;10.2 Methods;260
5.11.2.1;10.2.1 The Notion of Structure-Connected Clusters;260
5.11.2.2;10.2.2 Structure-Connected Clusters;260
5.11.2.3;10.2.3 Algorithm Scan;263
5.11.3;10.3 Application and Discussion;265
5.11.3.1;10.3.1 Protein--Protein Interaction Network;265
5.11.3.2;10.3.2 Validation Metric Based on Gene Ontology;266
5.11.3.3;10.3.3 Expert Validation;272
5.11.3.4;10.3.4 Complexity Analysis;275
5.11.4;10.4 Conclusions and Research Directions;276
5.12;References;277
5.13;11 Modeling the Dynamics of Biological Networks from Time Course Data;279
5.13.1;11.1 Introduction;279
5.13.2;11.2 Representing Knowledge for Modeling Dynamics;281
5.13.2.1;11.2.1 Polynomial Models and Constraints;281
5.13.2.2;11.2.2 Grammar-Based Representation of Domain Knowledge;283
5.13.2.3;11.2.3 Process-Based Models;287
5.13.3;11.3 Learning Dynamics;289
5.13.3.1;11.3.1 Formal Task Specification;289
5.13.3.2;11.3.2 General Algorithm for Learning Dynamics;290
5.13.3.3;11.3.3 Constrained Induction of Polynomial Equations: CIPER;292
5.13.3.4;11.3.4 Grammar-Based Equation Discovery: LAGRAMGE;293
5.13.3.5;11.3.5 Inductive Process Modeling: LAGRAMGE2, IPM, and HIPM;293
5.13.4;11.4 Related Work;295
5.13.5;11.5 Conclusion and Further Work;295
5.14;References;296
5.15;12 Decision Making in Cells;299
5.15.1;12.1 Introduction;299
5.15.2;12.2 Quantification of Information: Network-Based Information Processing and Decision Making;301
5.15.2.1;12.2.1 A Mathematical Basis for Understanding Information;301
5.15.2.2;12.2.2 Information Processing and Dynamics -- Cellular Automata;303
5.15.2.3;12.2.3 Connecting Dynamics to Computational Capacity;306
5.15.2.3.1;12.2.3.1 Cellular Automata;307
5.15.2.3.2;12.2.3.2 NK Boolean Networks;308
5.15.2.3.3;12.2.3.3 Order, Chaos, and Complexity in NK Boolean Networks;310
5.15.2.4;12.2.4 Structural and Functional Properties of Emergent Networks;313
5.15.2.4.1;12.2.4.1 Nonlinear Functions;313
5.15.2.4.2;12.2.4.2 Nonlinear Connectivity;314
5.15.2.5;12.2.5 A Mathematical Basis for Decision Making;315
5.15.3;12.3 Computational Capacity in Cells: The Feedback Loop;319
5.15.3.1;12.3.1 Negative Feedback Loops;320
5.15.3.2;12.3.2 Positive Feedback Loops;321
5.15.3.3;12.3.3 Combining Positive and Negative Feedback;324
5.15.3.4;12.3.4 Feedback Loops with Multi-step Signaling Cascades;326
5.15.4;12.4 Decision Making in Cells;328
5.15.4.1;12.4.1 Nontrivial Biochemical Network Activity in the Execution of Cellular Decisions;329
5.15.4.1.1;12.4.1.1 Movement;329
5.15.4.1.2;12.4.1.2 Apoptosis;330
5.15.4.1.3;12.4.1.3 Growth and Proliferation;330
5.15.4.1.4;12.4.1.4 Differentiation;330
5.15.4.2;12.4.2 Emergent Decision Making in Cells;331
5.15.5;12.5 Conclusion;335
5.16;References;336
5.17;13 Robustness of Neural Network Models;341
5.17.1;13.1 Introduction;342
5.17.2;13.2 Methods;343
5.17.2.1;13.2.1 Network Configuration;343
5.17.2.2;13.2.2 Network Training;344
5.17.3;13.3 Results;345
5.17.3.1;13.3.1 Varying the Number of Hidden Nodes;345
5.17.3.2;13.3.2 Varying the Starting Weight Composition;347
5.17.3.3;13.3.3 Changing the Activation Function to tanh;348
5.17.3.4;13.3.4 Multi-dimensional Stimuli;349
5.17.3.5;13.3.5 Multiple Stimuli;350
5.17.3.6;13.3.6 Evolving Network Weights;352
5.17.4;13.4 Discussion;353
5.18;References;355
5.19;14 Functional Modules in ProteinProtein Interaction Networks;356
5.19.1;14.1 Data Integration;358
5.19.1.1;14.1.1 Microarray and Survival Data;358
5.19.1.2;14.1.2 Network;359
5.19.2;14.2 Scoring and Searching;360
5.19.2.1;14.2.1 Aggregation of p-Values;361
5.19.2.2;14.2.2 Signal--noise Decomposition;362
5.19.2.3;14.2.3 Network Score;364
5.19.2.4;14.2.4 Searching;365
5.19.3;14.3 Resulting Functional Modules;367
5.19.4;14.4 Comparison and Validation;369
5.19.5;14.5 Summary and Conclusion;371
5.20;References;371
5.21;15 Mixture Model on Graphs: A Probabilistic Model for Network-Based Analysis of Proteomic Data;373
5.21.1;15.1 Introduction;373
5.21.1.1;15.1.1 Systems Biology and Biological Networks;374
5.21.1.2;15.1.2 Functional Correlation in Metabolic Networks;376
5.21.1.2.1;15.1.2.1 Metabolite-Centred Network;376
5.21.1.2.2;15.1.2.2 Enzyme-Centred Network;376
5.21.1.2.3;15.1.2.3 Structure of Metabolic Networks;377
5.21.1.2.4;15.1.2.4 Regulatory Correlation;378
5.21.1.3;15.1.3 Idiosyncrasies of iTRAQ-Based Proteomics;380
5.21.2;15.2 Model;383
5.21.2.1;15.2.1 Bayesian Statistics;383
5.21.2.1.1;15.2.1.1 Definitions and Interpretations;383
5.21.2.1.2;15.2.1.2 Computational Issues and Solutions;385
5.21.2.2;15.2.2 Prior and Posterior Distributions;386
5.21.3;15.3 Results;389
5.21.3.1;15.3.1 Illustration on a Simple Network;389
5.21.3.2;15.3.2 Nostoc and Nitrogen-Fixing Processes;391
5.21.3.3;15.3.3 Pathway Discovery;393
5.21.3.4;15.3.4 Bootstrap Validation;394
5.21.4;15.4 Conclusions and Future Work;396
5.22;References;396
5.23;16 Integration of Network Information for Protein FunctionPrediction;400
5.23.1;16.1 Background;400
5.23.1.1;16.1.1 Protein--Protein Interaction (PPI) Network;401
5.23.1.2;16.1.2 Gene Ontology;402
5.23.1.3;16.1.3 Objectives;404
5.23.2;16.2 Predicting Protein Functions by ProteinProtein Interaction Network;404
5.23.2.1;16.2.1 Introduction and Notations;404
5.23.2.2;16.2.2 Binomial-Neighborhood (BN) Model;405
5.23.2.2.1;16.2.2.1 Binomial-Neighborhood (BN) assumptions on PPI;405
5.23.2.2.2;16.2.2.2 Probabilistic Inference from BN Model;406
5.23.2.3;16.2.3 Limitations of PPI for PFP;407
5.23.3;16.3 Predicting Protein Functions by Integrating Relational and Hierarchical Information;408
5.23.3.1;16.3.1 Processing the Gene Ontology Hierarchy (i.e., Transforming GO DAG's into Trees);408
5.23.3.2;16.3.2 Hierarchical Binomial-Neighborhood (HBN) Assumptions;409
5.23.3.3;16.3.3 Inference from HBN Model;410
5.23.3.3.1;16.3.3.1 Local Hierarchical Conditional Probability;410
5.23.3.3.2;16.3.3.2 Global Hierarchical Conditional Probability;410
5.23.3.4;16.3.4 Case Study: Predicting Intracellular Signal Cascade on Yeast Genes;411
5.23.3.4.1;16.3.4.1 Data;411
5.23.3.4.2;16.3.4.2 Cross-Validation Design;412
5.23.3.4.3;16.3.4.3 Evaluations;413
5.23.3.4.4;16.3.4.4 Results;413
5.23.4;16.4 Integrating Network Information with Heterogeneous Genome-Wide Protein Data;416
5.23.4.1;16.4.1 Introduction;416
5.23.4.2;16.4.2 String;416
5.23.4.3;16.4.3 Inference from PHIPA;417
5.23.4.3.1;16.4.3.1 Assumptions and Notations;417
5.23.4.3.2;16.4.3.2 Calculation for the Feature Component;418
5.23.4.4;16.4.4 Results;419
5.23.4.4.1;16.4.4.1 Data Preparation;419
5.23.4.4.2;16.4.4.2 Network Comparison;419
5.23.4.4.3;16.4.4.3 Contribution of Protein Feature and GO Hierarchy;420
5.23.5;16.5 Discussion;423
5.24;References;425
6;Part III Applications for Signaling Networks;428
6.1;17 Cellular-Level Gene Regulatory Networks: Their Derivation and Properties;429
6.1.1;17.1 Introduction;430
6.1.2;17.2 Form of Large-Scale Cellular-Level Models;432
6.1.3;17.3 Model derivation;434
6.1.4;17.4 Model interpretation;435
6.1.5;17.5 Models Inferred from Alliance for Cell Signaling Data;436
6.1.5.1;17.5.1 Model Properties;436
6.1.5.2;17.5.2 Regulatory Influence Statistical Support;439
6.1.5.3;17.5.3 Temporal Regulation;439
6.1.6;17.6 Multiscale Cellular Networks;440
6.1.7;17.7 Conclusions;443
6.2;References;444
6.3;18 Tyrosine-Phosphoproteome Dynamics;447
6.3.1;18.1 Introduction;447
6.3.2;18.2 Quantitative Phosphoproteomics for Defining Temporal Dynamics of Tyrosine-Phosphorylated Signaling Molecules in Cellular Networks;448
6.3.3;18.3 Computational Modeling of Signal Transduction Networks on the Basis of Tyrosine-Phosphoproteome Dynamics Data;451
6.3.4;18.4 Future Prospects;452
6.4;References;452
6.5;19 Systems Biology of the MAPK1,2 Network;455
6.5.1;19.1 The MAPK Signaling Network;455
6.5.2;19.2 The Role of MAPK1,2 in Disease;461
6.5.3;19.3 Systems Biology and the MAPK Signaling Network;463
6.5.3.1;19.3.1 Feedback Loops in MAPK1,2 Signaling;464
6.5.3.2;19.3.2 Computational Modeling;467
6.5.3.3;19.3.3 MAPK1,2 Signaling Models;470
6.5.3.4;19.3.4 Alternative MAPK1,2 Modeling Methods;475
6.5.4;19.4 The MAPK1,2 Pathway as a Drug Target;477
6.5.4.1;19.4.1 Ras Inhibitors;477
6.5.4.2;19.4.2 Raf Inhibitors;478
6.5.4.3;19.4.3 MEK Inhibitors;479
6.5.5;19.5 Discussion and Conclusion;481
6.6;References;481
6.7;20 Pathway Crosstalk Network;490
6.7.1;20.1 Limitations of the Current Drug Discovery Strategy;490
6.7.2;20.2 A Network Approach Toward Understanding of Biology;491
6.7.3;20.3 Construction of Pathway Crosstalk Network;493
6.7.4;20.4 Properties of the Pathway Crosstalk Network;495
6.7.5;20.5 Interpreting Transcriptomic Profiling Results Using PCN;497
6.7.6;20.6 Conclusions;499
6.8;References;500
6.9;21 Crosstalk Between Mitogen-Activated Protein Kinase and Phosphoinositide-3 Kinase Signaling Pathways in Development and Disease;504
6.9.1;21.1 Introduction;505
6.9.1.1;21.1.1 Overview of the MAP Kinase Signaling;505
6.9.1.2;21.1.2 Overview of PI3K/Akt Signaling Pathway;510
6.9.2;21.2 Biochemical Crosstalk Between Raf/MAPK and PI3K/Akt Signaling Pathways;511
6.9.2.1;21.2.1 Crosstalk at the Level of Ras and PI3K: Near the Cell Membrane;512
6.9.2.2;21.2.2 Crosstalk at the Level of Adaptor Proteins;512
6.9.2.3;21.2.3 Crosstalk at the Level of Raf and Akt;512
6.9.2.4;21.2.4 Crosstalk at the Level of ERK and TSC;513
6.9.3;21.3 Crosstalk During Embryogenesis;513
6.9.3.1;21.3.1 MAPK and PI3K Crosstalk During Artery and Vein Specification;513
6.9.3.2;21.3.2 Crosstalk in Vertebrate Limb Development;515
6.9.4;21.4 MAPK and PI3K Pathways and Human Cancers: Potential Therapeutic Targets;517
6.9.4.1;21.4.1 Receptor Tyrosine Kinases in Tumorigenesis: Therapeutic Opportunities;517
6.9.4.2;21.4.2 MAPK and PI3K Signaling in Tumorigenesis: Additional Therapeutic Opportunities;519
6.9.4.3;21.4.3 Evidence for Importance of Crosstalk Between MAPK and PI3K for Tumor Formation and Therapeutic Implications;520
6.9.5;21.5 Conclusion;521
6.10;References;521
6.11;22 Systems-Level Analyses of the Mammalian Innate Immune Response;529
6.11.1;22.1 Introduction to Innate Immunity;530
6.11.2;22.2 Complexity of Innate Immunity Why Systems Approaches are Necessary;531
6.11.3;22.3 Computational Resources for Innate Immunity;535
6.11.4;22.4 A Walk Through the Analysis of a Smallpox Gene Expression Data Set Using InnateDB Pathways, Processes, and Interaction Networks;536
6.11.4.1;22.4.1 Introduction;536
6.11.4.2;22.4.2 Preparing Data for Analysis in InnateDB;537
6.11.4.3;22.4.3 Uploading Data to InnateDB;539
6.11.4.4;22.4.4 Performing a Gene Ontology Over-Representation Analysis;542
6.11.4.5;22.4.5 Performing a Pathway ORA;545
6.11.4.6;22.4.6 Visualizing Pathway Data with Cerebral;548
6.11.4.7;22.4.7 Generating and Exploring Molecular Interaction Networks Using InnateDB;550
6.11.5;22.5 Conclusions and Future Directions;554
6.12;References;555
6.13;23 Molecular Basis of Protective Anti-Inflammatory Signalling by Cyclic AMP in the Vascular Endothelium;559
6.13.1;23.1 Introduction;559
6.13.1.1;23.1.1 Dysfunctional Vascular Endothelium and Disease;559
6.13.1.2;23.1.2 Cyclic AMP Signalling;561
6.13.1.2.1;23.1.2.1 Basic Architecture of cAMP Signalling Modules;561
6.13.1.2.2;23.1.2.2 Exchange Proteins Activated by cAMP (Epacs);561
6.13.2;23.2 The Control of Endothelial Barrier Function by Cyclic 563
6.13.2.1;23.2.1 Introduction;563
6.13.2.2;23.2.2 Co-ordination of Barrier Function by PKA and EPAC;564
6.13.3;23.3 Regulation of Pro-inflammatory Signalling by Cyclic 566
6.13.3.1;23.3.1 Introduction;566
6.13.3.2;23.3.2 IL-6 Signalling Through gp130 Homodimers;566
6.13.3.3;23.3.3 SOCS Proteins and Inhibition of Cytokine Receptor Signalling;569
6.13.3.4;23.3.4 SOCS-3 and the Control of IL-6 Signalling by c572
6.13.3.5;23.3.5 C/EBPs as cAMP-Activated EPAC1-Regulated Transcription Factors;574
6.13.3.6;23.3.6 ERK1,2 Activation and SOCS-3 Induction by c575
6.13.4;23.4 Systems-Based Future Directions;578
6.13.4.1;23.4.1 Identification of New SOCS-3 Targets;578
6.13.4.2;23.4.2 A New PKA-Independent Route for Genome-Wide Control of Transcription by cAMP?;578
6.13.5;23.5 Concluding Remarks;579
6.14;References;580
6.15;24 Construction of Cancer-Perturbed ProteinProtein Interaction Network of Apoptosis for Drug Target Discovery;586
6.15.1;24.1 Introduction;586
6.15.2;24.2 Methods;588
6.15.2.1;24.2.1 Construction of Initial PPI Networks;588
6.15.2.2;24.2.2 Selecting Experimental Data;589
6.15.2.3;24.2.3 Processing Selected Experimental Data;589
6.15.2.4;24.2.4 Identification of Interactions in the Initial PPI Network;591
6.15.2.5;24.2.5 Modification of Initial PPI Networks;593
6.15.3;24.3 Results;595
6.15.3.1;24.3.1 Construction of a Cancer-Perturbed PPI Network for Apoptosis;595
6.15.3.2;24.3.2 Prediction of Apoptosis Drug Targets by Means of Cancer-Perturbed PPI Networks for Apoptosis;597
6.15.3.2.1;24.3.2.1 Common pathway: CASP2, CASP3, and CASP9;598
6.15.3.2.2;24.3.2.2 Extrinsic Pathway and Crosstalk: TNF and TNFRSF6;600
6.15.3.2.3;24.3.2.3 Intrinsic Pathway: BAK1, BAX, BCL2, BCL2A1, BCL2L1, BID, and CYCS;600
6.15.3.2.4;24.3.2.4 Apoptosis Regulators: CFLAR, EGFR, MYC, and TP53;602
6.15.3.2.5;24.3.2.5 Stress-Induced Signaling: MAPK1, MAPK3, and NFKB1;602
6.15.3.2.6;24.3.2.6 Others: CCND1, CDKN1A, IGF1, PCNA , and PRKCD;602
6.15.4;24.4 Caspase Activation Through Static and Dynamic Hubs;603
6.15.5;24.5 Conclusions;605
6.16;References;605
6.17;25 Transcriptional Changes in Alzheimers Disease;608
6.17.1;25.1 Introduction;609
6.17.2;25.2 Results from Postmortem Studies;610
6.17.2.1;25.2.1 Whole Tissue Studies;611
6.17.2.2;25.2.2 Pure Cell Population Studies;615
6.17.2.3;25.2.3 Large-Scale Microarray Atlases;617
6.17.3;25.3 Results from Model Systems;618
6.17.3.1;25.3.1 In Vitro Models of AD;618
6.17.3.2;25.3.2 Invertebrate Models of AD;620
6.17.3.3;25.3.3 Rodent Models of AD;623
6.17.3.4;25.3.4 Primate Models of AD;625
6.17.4;25.4 Peripheral Transcriptional Changes;625
6.17.4.1;25.4.1 Changes in Blood;626
6.17.4.2;25.4.2 Changes in CSF;628
6.17.5;25.5 Systems Biology Approaches to Studying Transcriptional Changes;629
6.17.5.1;25.5.1 Combining Multiple Transcriptional Studies;629
6.17.5.2;25.5.2 Combining Transcription with Genomics;632
6.17.5.3;25.5.3 Combining Transcription with Proteomics;633
6.17.5.4;25.5.4 Combining Transcription and Imaging;633
6.17.5.5;25.5.5 A Systems Biology Study of FTD;634
6.17.6;25.6 Conclusions and Future Directions;635
6.18;References;637
6.19;26 Pathogenesis of Obesity-Related Chronic Liver Diseases as the Study Case for the Systems Biology;641
6.19.1;26.1 Introduction;641
6.19.2;26.2 Liver;643
6.19.3;26.3 The Non-alcoholic Fatty Liver Disease (NAFLD) Spectrum;643
6.19.4;26.4 A Brief Overview of Genomics and Proteomics High-Throughput Approaches to Collect Systems-Level Data;646
6.19.4.1;26.4.1 High-Throughput Evaluation of mRNA Profiles;646
6.19.4.2;26.4.2 Methods of Evaluation of Protein Profiles;649
6.19.4.3;26.4.3 Focused Proteomics Research;655
6.19.5;26.5 Challenges for an Application of High-Throughput Technologies to the Study of the Liver Pathology;655
6.19.5.1;26.5.1 Heterogenous Composition of Tissues;656
6.19.5.2;26.5.2 ''Healthy'' Tissue Controls;658
6.19.5.3;26.5.3 Correlation of mRNA and Protein Levels;662
6.19.5.4;26.5.4 Issues Related to High-Throughput Nature of the Experiments;662
6.19.5.5;26.5.5 Sample Size;664
6.19.5.6;26.5.6 Publicly Available Data Sets and Their Meta-Analysis;665
6.19.5.7;26.5.7 Putting it All Together;667
6.19.6;26.6 Case Studies;668
6.19.6.1;26.6.1 A Genomic and Proteomic Study of the Spectrum of Nonalcoholic Fatty Liver Disease (Collantes et al. 2006);669
6.19.6.2;26.6.2 A Study of Hepatic Proteome in the Patients with the Diseases of NAFLD Spectrum (Charlton et al. 2009 );671
6.19.6.3;26.6.3 A Comprehensive Study of miRNA Expression in the Liver of NASH patients (Cheung et al. 2008 );673
6.19.6.4;26.6.4 Profilings by Other 0Omics0 (Dezortova et al. 2005 , Xue et al. 2008, Callewaert et al. 2004 , Liu et al. 2007 );675
6.19.7;26.7 Conclusion;676
6.20;References;677
6.21;27 The Evolving Transcriptome of Head and Neck Squamous Cell Carcinoma;683
6.21.1;27.1 Overview;683
6.21.1.1;27.1.1 Background: Demographics and Clinical Management of HNSCC;684
6.21.1.2;27.1.2 Initiatives of Systems-Level Analysis;684
6.21.2;27.2 Data Retrieval and Processing;685
6.21.2.1;27.2.1 Systematic Reviews and Meta-Analysis;685
6.21.2.2;27.2.2 Search Strategy and Flow Diagram;686
6.21.2.3;27.2.3 Data Extraction;686
6.21.2.4;27.2.4 Data Formatting;689
6.21.2.4.1;27.2.4.1 A Common Template;689
6.21.2.4.2;27.2.4.2 Bounded Fold Changes;690
6.21.3;27.3 Systems-Level Analysis;690
6.21.3.1;27.3.1 Progressive Trends;690
6.21.3.1.1;27.3.1.1 Tissue-Specificity as an Example of Validity Assessment;690
6.21.3.1.2;27.3.1.2 Consensus Membership of Gene expression Signatures in Different Stages of Comparisons;691
6.21.3.2;27.3.2 Topological Analysis of Signaling Pathways;691
6.21.3.2.1;27.3.2.1 Inter-modular vs. Intra-modular Hubs;691
6.21.3.2.2;27.3.2.2 Integrin Signaling Pathways;694
6.21.3.2.3;27.3.2.3 Implication of Invasiveness: siRNA Knockdown of the Integrin Molecules;695
6.21.3.3;27.3.3 Highly Differentially Expressed Chromosomal Regions;696
6.21.4;27.4 Discussion;697
6.21.5;Appendix: Update and Archiving;698
6.22;References;698
6.23;28 Peptide Microarrays for a Network Analysis of Changes in Molecular Interactions in Cellular Signalling;699
6.23.1;28.1 Introduction;699
6.23.1.1;28.1.1 Molecular Complexes in Cellular Signalling;699
6.23.1.2;28.1.2 Complex Formation in T Cell Signalling;701
6.23.1.3;28.1.3 Peptide Microarray-Based Detection of Changes in Cellular Signalling;702
6.23.1.3.1;28.1.3.1 The Peptide Microarray-Based Approach;702
6.23.1.3.2;28.1.3.2 Comparison to Other Methods for Large-Scale Analyses of Protein Interaction Networks;704
6.23.2;28.2 Method: Peptide Microarrays for the Detection of Signalling-Dependent Changes in Molecular Interactions;706
6.23.2.1;28.2.1 Selection of Peptides;706
6.23.2.2;28.2.2 Generation and Processing of Microarrays;706
6.23.3;28.3 Applications;708
6.23.3.1;28.3.1 Signalling-Dependent Changes in Complex Formation;708
6.23.3.2;28.3.2 Comparison of Different Cell Lines;710
6.23.3.3;28.3.3 Analysis of the Architecture of Signalling Complexes;710
6.23.3.4;28.3.4 The Significance of an Interaction Motif for Organizing the Network;711
6.24;References;713
7;Part IV Tools for Systems Biology;715
7.1;29 A Primer on Modular Mass-Action Modelling with CellML;716
7.1.1;29.1 Introduction;716
7.1.2;29.2 Mathematical Formalism;717
7.1.3;29.3 Anatomy of a CellML Model;720
7.1.4;29.4 Mass-Action Modelling Examples;721
7.1.4.1;29.4.1 Bidirectional Reaction Example;722
7.1.4.2;29.4.2 Multi-Environment Reaction Example;730
7.1.4.3;29.4.3 Combined Example Model;733
7.1.4.4;29.4.4 Importing example;737
7.1.5;29.5 Modular Modelling with CellML;739
7.1.5.1;29.5.1 Motivation for Modularisation;740
7.1.5.2;29.5.2 Decoupling the Components;740
7.1.6;29.6 The CellML Modelling Community;744
7.2;References;744
7.3;30 FERN Stochastic Simulation and Evaluation of Reaction Networks;746
7.3.1;30.1 Background;747
7.3.1.1;30.1.1 Petri Nets;747
7.3.1.2;30.1.2 Stochastic Chemical Kinetics;749
7.3.1.3;30.1.3 Stochastic Simulation Methods;751
7.3.1.3.1;30.1.3.1 First Reaction Method;751
7.3.1.3.2;30.1.3.2 Direct Method;752
7.3.1.3.3;30.1.3.3 Next Reaction Method;752
7.3.1.3.4;30.1.3.4 Composition/Rejection Method;753
7.3.1.3.5;30.1.3.5 Tau-Leaping Methods;753
7.3.1.3.6;30.1.3.6 Hybrid Methods;753
7.3.2;30.2 Implementation;754
7.3.2.1;30.2.1 Other Implementations;754
7.3.2.2;30.2.2 FERN;755
7.3.2.3;30.2.3 Implementation Details;755
7.3.2.3.1;30.2.3.1 Networks;756
7.3.2.3.2;30.2.3.2 Import and Export of Networks;757
7.3.2.3.3;30.2.3.3 Simulation Algorithms;758
7.3.2.3.4;30.2.3.4 Observer System;759
7.3.2.3.5;30.2.3.5 Stochastics;760
7.3.2.4;30.2.4 Accuracy and Runtime Performance of FERN;760
7.3.3;30.3 Using FERN;761
7.3.3.1;30.3.1 Command Line Tool;761
7.3.3.2;30.3.2 Basic Usage of FERN;763
7.3.3.3;30.3.3 Cytoscape Plugin for Stochastic Simulation;763
7.3.3.4;30.3.4 Simulation of Cell Growth and Division Using Observers;765
7.3.4;30.4 Discussion;767
7.3.5;30.5 Availability and Requirements;768
7.4;References;768
7.5;31 Programming Biology in BlenX;771
7.5.1;31.1 Introduction;771
7.5.2;31.2 The BlenX language;772
7.5.3;31.3 The Beta Workbench;783
7.5.3.1;31.3.1 Usage;787
7.5.4;31.4 Case Studies;794
7.5.4.1;31.4.1 Actin Polymerization;794
7.5.4.1.1;31.4.1.1 The BlenX model;795
7.5.4.2;31.4.2 Analysis;808
7.5.4.3;31.4.3 Cell Cycle;809
7.5.4.3.1;31.4.3.1 The BlenX model;810
7.5.4.4;31.4.4 Analysis;813
7.5.5;31.5 Conclusions;814
7.6;References;814
7.7;32 Discrete Modelling: Petri Net and Logical Approaches;815
7.7.1;32.1 Introduction;815
7.7.2;32.2 Petri Net Foundations;818
7.7.2.1;32.2.1 Place/Transition Nets;818
7.7.2.1.1;32.2.1.1 Places and Transitions in Biochemical Networks;819
7.7.2.1.2;32.2.1.2 Firing Rule;819
7.7.2.2;32.2.2 Petri Net Properties;822
7.7.2.2.1;32.2.2.1 Behavioural Properties;822
7.7.2.2.2;32.2.2.2 Structural Properties;824
7.7.2.3;32.2.3 Petri Net Extensions;825
7.7.3;32.3 Specific Modelling Techniques;827
7.7.3.1;32.3.1 The Role of Place Invariants and Read Arcs;828
7.7.3.2;32.3.2 Feasible T-Invariants;828
7.7.3.3;32.3.3 Modularisation Using MCT-Sets and T-Clusters;830
7.7.3.4;32.3.4 Mauritius Maps and Knockout Analysis;832
7.7.4;32.4 Logical Approach;834
7.7.4.1;32.4.1 Analysis of Logical Regulatory Graphs;836
7.7.4.2;32.4.2 From Logical Regulatory Graphs to Petri Nets;837
7.7.4.3;32.4.3 Illustration: Mating and Filamentous Pathways in Yeast;839
7.7.5;32.5 Summary and Conclusions;841
7.7.6;Appendix;844
7.8;References;846
7.9;33 ProteoLens: A Database-Driven Visual Data Mining Tool for Network Biology;850
7.9.1;33.1 Introduction;851
7.9.1.1;33.1.1 Biomolecular Network and Visualization Software;851
7.9.1.1.1;33.1.1.1 Multi-Scale Biological Entities and ProteoLens;851
7.9.2;33.2 Concepts and Software Architecture;852
7.9.2.1;33.2.1 Data Associations: The Concept;852
7.9.2.2;33.2.2 Functional Layers;853
7.9.3;33.3 Top Features;853
7.9.3.1;33.3.1 Comprehensive Input and Output Supporting;854
7.9.3.2;33.3.2 Declarative SQL-Based Visual Analysis;856
7.9.3.3;33.3.3 Layout Choices of Biological Network;856
7.9.3.3.1;33.3.3.1 Hierarchical Layout;856
7.9.3.3.2;33.3.3.2 Circular Layout;856
7.9.3.3.3;33.3.3.3 Organic Layout;857
7.9.3.4;33.3.4 Sub-network Retrieving Capability;857
7.9.4;33.4 Using ProteoLens;858
7.9.4.1;33.4.1 Installing ProteoLens and Launching the Application;858
7.9.4.2;33.4.2 Connecting to Database and File-Based Input;858
7.9.4.2.1;33.4.2.1 Connecting to Database Input;858
7.9.4.2.2;33.4.2.2 Connecting to File-Based Input;860
7.9.4.3;33.4.3 Create Data Association;860
7.9.4.4;33.4.4 Attach Network Source to the View;861
7.9.4.5;33.4.5 Add Annotation;862
7.9.5;33.5 Systems Biology Case Studies;863
7.9.5.1;33.5.1 Case Study 1: Alzheimer' Disease-Related Protein Interaction Network;863
7.9.5.2;33.5.2 Case Study 2: Gene Ontology Cross-Talk Network;864
7.9.5.3;33.5.3 Case Study 3: Human Cancer Association Network;866
7.9.6;33.6 ProteoLens Project;868
7.10;References;868
7.11;34 MADNet: A Web Server for Contextual Analysis and Visualization of High-Throughput Experiments;869
7.11.1;34.1 Introduction;870
7.11.2;34.2 MADNet Web Server;871
7.11.2.1;34.2.1 Web Server Implementation;871
7.11.2.2;34.2.2 Data Input;872
7.11.2.3;34.2.3 Analysis and Visualization;874
7.11.2.3.1;34.2.3.1 Metabolic and Signaling Pathways;875
7.11.2.3.2;34.2.3.2 Transcription Factors;877
7.11.2.4;34.2.4 Output;878
7.11.3;34.3 Conclusions and Future Work;879
7.12;References;879
8;Subject Index;881