Choi Introduction to Systems Biology

1. Auflage 2008
ISBN: 978-1-59745-531-2
Verlag: Humana Press
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 542 Seiten

This book provides an introductory text for undergraduate and graduate students who are interested in comprehensive biological systems. The authors offer a broad overview of the field using key examples and typical approaches to experimental design. The volume begins with an introduction to systems biology and then details experimental omics tools. Other sections introduce the reader to challenging computational approaches. The final sections provide ideas for theoretical and modeling optimization in systemic biological researches. The book is an indispensable resource, providing a first glimpse into the state-of-the-art in systems biology.

Autoren/Hrsg.

Choi, Sangdun

Inhaltsverzeichnis

1;Preface;5
2;Contents;7
3;Contributors;11
4;Part I Introduction;18
4.1;Scientific Challenges in Systems Biology;19
4.1.1;1. Introduction;19
4.1.2;2. Robustness as a Fundamental Organizational Principle;21
4.1.3;3. Intrinsic Nature of Robust Systems;23
4.1.4;4. Technology Platforms in Systems Biology;24
4.2;Bringing Genomes to Life: The Use of Genome-Scale In Silico Models;30
4.2.1;1. Introduction;30
4.2.2;2. Properties of Biological Networks;33
4.2.3;3. Reconstruction of Metabolic Networks;34
4.2.4;4. Mathematical Characterization of Network Capabilities;39
4.2.5;5. Two Sample Studies;45
4.2.6;6. Further Levels of Annotation;49
4.2.7;7. Future Directions;50
4.3;From Gene Expression to Metabolic Fluxes;53
4.3.1;1. Introduction;53
4.3.2;2. Engineering a Cell: A Systems Approach;56
4.3.3;3. Omics Quantification;65
4.3.4;4. Models for Metabolic Engineering in the Systems Biology Era;69
4.3.5;5. Conclusions;75
5;Part II Experimental Techniques for Systems Biology;83
5.1;Handling and Interpreting Gene Groups;85
5.1.1;1. Introduction;85
5.1.2;2. Accession Numbers;86
5.1.3;3. Handling and Comparing Gene Groups;88
5.1.4;4. Functional Interpretation of Gene Groups;90
5.1.5;5. Retrieval and Analysis of Sequences;94
5.1.6;6. Conclusions;96
5.2;The Dynamic Transcriptome of Mice;101
5.2.1;1. Introduction;101
5.2.2;2. Mouse Encyclopedia Project;102
5.2.3;3. Technology Used for the Mouse cDNA Encyclopedia;102
5.2.4;4. FANTOM;110
5.2.5;5. Future Prospects;116
5.2.6;6. Conclusions;118
5.3;Dissecting Transcriptional Control Networks;122
5.3.1;1. Introduction;122
5.3.2;2. Information Theoretic Weight Matrix and the Problem of Thresholds;124
5.3.3;3. Combining Heterogeneous Data: Going Beyond Sequence;130
5.3.4;4. Combining Phylogenetic Footprinting and Motif Search;132
5.3.5;5. Conclusion;135
5.4;Reconstruction and Structural Analysis of Metabolic and Regulatory Networks;140
5.4.1;1. Introduction;140
5.4.2;2. Methods for Reconstruction of Metabolic Networks and Regulatory Networks;141
5.4.3;3. Graph Representation of Biological Networks;147
5.4.4;4. Structural Analysis of Biological Networks;150
5.4.5;5. Conclusions;159
5.5;Cross-Species Comparison Using Expression Data;163
5.5.1;1. Introduction;163
5.5.2;2. Chapter Outline;164
5.5.3;3. Information Required to Compare Expression Between Species;164
5.5.4;4. Available Tools and Methodologies;168
5.5.5;5. Using Expression Data Provides New Insight into Cross- Species Comparisons;170
5.5.6;6. Conclusion;173
5.6;Methods for Protein–Protein Interaction Analysis;176
5.6.1;1. MS-Based Approaches for PPIs;176
5.6.2;2. Two-Hybrid Approaches for Protein Interactions;187
5.6.3;3. Conclusion;195
5.7;Genome-Scale Assessment of Phenotypic Changes During Adaptive Evolution;199
5.7.1;1. Introduction to Adaptive Evolution;199
5.7.2;2. Adaptive Evolution and Systems Biology;200
5.7.3;3. Genotype to Phenotype in Adaptive Evolution;202
5.7.4;4. Genome-Scale Phenotype Assessment;204
5.7.5;5. Summary;210
5.8;Location Proteomics;212
5.8.1;1. Introduction;212
5.8.2;2. Approaches to Determining Subcellular Location;214
5.8.3;3. Automated Analysis of Subcellular Patterns;216
5.8.4;4. Systematic Comparison and Clustering;223
5.8.5;5. Models of Subcellular Patterns;225
6;Part III Theoretical and Modeling Techniques;231
6.1;Reconstructing Transcriptional Networks Using Gene Expression Profiling and Bayesian State- Space Models;233
6.1.1;1. Introduction;234
6.1.2;2. Modeling Time Series with SSMs;236
6.1.3;3. Results;243
6.1.4;4. Conclusions;254
6.2;Modeling Spatiotemporal Dynamics of Multicellular Signaling;258
6.2.1;1. Introduction;258
6.2.2;2. Methods;260
6.2.3;3. Applications;265
6.2.4;4. Discussion;272
6.3;Kinetics of Dimension-Restricted Conditions;277
6.3.1;1. In Vivo–Oriented Modeling;277
6.3.2;2. DRRK;279
6.3.3;3. Planning the Experiments for the Model;285
6.3.4;4. Calculation Cost: Another Benefit of the DRRK Model;292
6.3.5;5. Concluding Remarks;292
6.3.6;Appendix A: Steady-State Conditions and Reaction Order Estimation;293
6.3.7;Appendix B: The Relation Between Rate Constant and Diffusion Coefficient;294
6.4;Mechanisms Generating Ultrasensitivity, Bistability, and Oscillations in Signal Transduction;298
6.4.1;1. Introduction;298
6.4.2;2. Quantification of Ultrasensitivity;300
6.4.3;3. Mechanisms;302
6.4.4;4. Effect of Feedbacks on Ultrasensitive Cascades;307
6.4.5;5. Discussion;312
6.5;Employing Systems Biology to Quantify Receptor Tyrosine Kinase Signaling in Time and Space;316
6.5.1;1. Introduction;316
6.5.2;2. Challenges of Mechanistic Modeling;318
6.5.3;3. Complex Temporal Dynamics of Signaling Networks;321
6.5.4;4. Spatial Dimension of Cell Signaling;322
6.5.5;5. Facilitated Mechanisms for Intracellular Signal Propagation;327
6.5.6;6. Outlook;329
6.6;Dynamic Instabilities Within Living Neutrophils;335
6.6.1;1. Introduction;335
6.6.2;2. Computational Biology of Neutrophil Oscillators;337
6.6.3;3. Biomechanisms;341
6.6.4;4. Conclusions;347
6.7;Efficiency, Robustness, and Stochasticity of Gene Regulatory Networks in Systems Biology: l Switch as a Working Example;352
6.7.1;1. Introduction;352
6.7.2;2. Phage l Genetic Switch;355
6.7.3;3. Towards Quantitative Modeling;358
6.7.4;4. Stochastic Dynamical Modeling;364
6.7.5;5. Quantitative Comparison Between Theory and Experiment;368
6.7.6;6. Perspective on Mathematical Modeling;377
6.7.7;7. Third Age of Phage;380
6.8;Applications, Representation, and Management of Signaling Pathway Information: Introduction to the SigPath Project;388
6.8.1;1. Introduction;388
6.8.2;2. Signaling Pathways;389
6.8.3;3. Knowledge Representation and Modeling;390
6.8.4;4. Applications of Signaling Pathway Information;391
6.8.5;5. Representing Signaling Pathways to Support Biomedical Research;395
6.8.6;6. The SigPath Project;399
7;Part IV Methods and Software Platforms for Systems Biology;409
7.1;SBML Models and MathSBML;411
7.1.1;1. Motivation;411
7.1.2;2. The Evolution of SBML;412
7.1.3;3. SBML Level 2 Models;414
7.1.4;4. Proposed Modifications to SBML;422
7.1.5;5. Resources at http://sbml.org;424
7.1.6;6. BioModels Database;427
7.1.7;7. Managing SBML with MathSBML;428
7.2;CellDesigner: A Graphical Biological Network Editor and Workbench Interfacing Simulator;438
7.2.1;1. Introduction;438
7.2.2;2. Features of CellDesigner;439
7.2.3;3. How Does it Work?;446
7.2.4;4. What Distinguishes CellDesigner’s Technology from Others Currently Available?;447
7.2.5;5. Future Work;448
7.2.6;6. Conclusion;449
7.3;DBRF-MEGN Method: An Algorithm for Inferring Gene Regulatory Networks from Large- Scale Gene Expression Profiles;451
7.3.1;1. Introduction;451
7.3.2;2. Algorithm;452
7.3.3;3. Application;456
7.3.4;4. Software;459
7.3.5;5. Perspectives;461
7.4;Systematic Determination of Biological Network Topology: Nonintegral Connectivity Method ( NICM);465
7.4.1;1. Introduction;465
7.4.2;2. Methods;466
7.4.3;3. Analysis of Yeast Glycolytic Network;479
7.4.4;4. Conclusion;480
7.4.5;Appendix 1;483
7.4.6;Appendix 2;484
7.5;Storing, Searching, and Disseminating Experimental Proteomics Data;488
7.5.1;1. Introduction;488
7.5.2;2. Previous Work;490
7.5.3;3. Modeling Proteomics Data;491
7.5.4;4. Capturing and Disseminating;494
7.5.5;5. Conclusions and Future Perspectives;498
7.6;Representing and Analyzing Biochemical Networks Using BioMaze;500
7.6.1;1. General Introduction;500
7.6.2;2. Data Models for Representing Biochemical Networks;502
7.6.3;3. Data Models for Analyzing Biochemical Networks;509
7.6.4;4. Analysis of Biochemical Networks;513
7.6.5;5. Implementation Aspects;516
7.6.6;6. Concluding Remarks;518
8;Appendices;525
8.1;Software, Databases, and Websites for Systems Biology;527
8.2;Glossary;533
8.3;Index;543