Systems Biomedicine: Concepts and Perspectives

Copyright page......Page 2
Overview......Page 3
Foundations of systems biology......Page 7
Introduction......Page 8
Experimental strategies in systems biology......Page 11
Systems biomedicine......Page 12
Circadian cycles as a relevant model for systems biomedicine......Page 14
Conclusion......Page 16
References......Page 17
Genomic Technologies for Systems Biology......Page 19
Introduction......Page 20
Roche 454 Life Sciences: GS FLX Titanium......Page 21
Illumina: Genome Analyzer......Page 24
Applied Biosystems: SOLiD Sequencing......Page 26
Applications of sequencing in systems biology: the transcriptome and transcriptional regulation......Page 28
Expression Arrays......Page 30
ChIP-on-chip Arrays......Page 32
Interaction analysis: Two-hybrid screens......Page 37
Forward Genetics......Page 38
RNA Interference Approaches in Systems Biology......Page 39
References......Page 47
Introduction......Page 49
Cell-based proteomics......Page 50
Proteomics of biological fluids......Page 52
Microarray-based approaches......Page 54
Computational aspects of proteomics......Page 56
References......Page 57
Cellular Regulatory Networks......Page 60
Part I. transcriptional networks......Page 61
Transcriptional Machinery......Page 62
Transcriptional Regulatory Elements......Page 64
Epigenetic Control of Transcription......Page 66
Post-transcriptional Control: MicroRNAs......Page 67
Transcriptional Regulation on a Genomic Scale in Medicine......Page 68
Principles of Transcriptional Network Analysis......Page 75
Estrogen Receptor as a Model for Transcriptional Complexity......Page 85
Part II. Protein phosphorylation networks......Page 87
Phosphopeptide Databases......Page 88
Kinase–Substrate Connections......Page 89
In-vivo Functions of Protein Phosphorylation......Page 92
Protein Networks......Page 95
Analysis of Protein Interaction and Signaling Networks......Page 96
Epidermal Growth Factor Receptor as a Model for Protein Signaling Networks......Page 100
Acknowledgements......Page 102
References......Page 103
Summary......Page 112
Transcription Network in Cellular Systems......Page 113
MicroRNA biogenesis and function......Page 114
A stem cell model for the transcriptional control of miRNAs......Page 116
One mRNA is Targeted by Several miRNAs......Page 118
Predicting miRNA Targets......Page 120
Engineering miRNA networks......Page 122
Coordinated Regulation of Target Genes......Page 123
Distributing the Load Across Regulatory Fabrics......Page 125
Autoregulation......Page 127
Molecular Switches......Page 129
Deciphering the Rules for a miRNA Switch......Page 131
The Roadmap to systems biology: Deconvoluting complexities......Page 132
Concluding Remarks......Page 133
References......Page 135
Summary......Page 141
Introduction......Page 142
Constructing the Adhesome Network......Page 143
Functional Families of the Adhesome......Page 144
Interrelationships Between the Functional Protein Families......Page 146
Resolving Functional Subnets......Page 147
Protein Domains and Switchable Links Enriched in the Adhesome Net......Page 149
Conclusion......Page 152
References......Page 153
Systems Biology and Stem Cell Biology......Page 154
Stem Cells......Page 155
Stem Cells for Systems Biology (Defining Molecular Networks, Interactions and Functionality)......Page 156
Transcription Networks......Page 157
Mapping Transcription Factor and DNA Interactions......Page 158
Computational Tools for Network Construction and Modeling......Page 162
Bayesian Network......Page 163
Genetic Perturbation of the ES Cell Systems to Dissect Regulatory Pathways......Page 164
Epigenetic and Chromatin Features of Pluripotent Stem Cells......Page 167
MicroRNAs in Stem Cells......Page 169
Forwarding Engineering of ES Cell Regulatory Network......Page 170
Concluding Remarks......Page 171
References......Page 172
Computational modeling in systems biology......Page 175
Summary......Page 176
INTRODUCTION......Page 177
DIFFERENT LEVELS OF MODELS OFBIOCHEMICAL SYSTEMS......Page 180
Complexity of Proteomic States andInteractions......Page 183
Integration of Diverse Data to InferBiochemical Interactions......Page 185
Temporal State of Biochemical Models......Page 187
CHALLENGES IN BUILDINGMATHEMATICAL MODELS......Page 189
Utilizing Qualitative Constraints inMathematical Models......Page 190
Incomplete Knowledge andCoarse-graining......Page 192
Unknown Parameters......Page 198
Variation in Local Concentrations andMultiple Scales of Distance and Time......Page 200
Mechanisms......Page 203
Mathematical Representation of theModel......Page 206
Results for Simulation and ParameterEstimation Using Data from RAW 264.7Cells......Page 207
FUTURE DIRECTIONS AND CONCLUDING REMARKS......Page 210
References......Page 212
Summary......Page 223
Morphogens and Shh Signal Transduction......Page 224
Theory and Computational Algorithms......Page 226
Application to the Shh Signaling Network......Page 229
Theory and Computational Algorithms......Page 233
Data......Page 235
Results......Page 236
Introduction......Page 237
Agent-based Theory and Computational Algorithms......Page 238
Application to Multicell Morphogen-regulated Patterning......Page 242
References......Page 243
Summary......Page 246
Introduction......Page 247
Tool for Model Refinement......Page 248
Sensitivity Extensions: Stochastic Systems......Page 250
Other Metrics for Systems Analysis......Page 252
The Unfolded Protein Response......Page 255
Apoptotic Signaling Pathway......Page 257
Circadian Rhythm Gene Regulation in Fly, Mouse and Plants......Page 259
Arabidopsis thaliana......Page 260
Acknowledgements......Page 267
References......Page 268
Summary......Page 270
Introduction......Page 271
The Virtual Cell Database......Page 272
Modeling process in the virtual cell–biomodel interface......Page 275
Numerical algorithms in virtual cell......Page 279
Conclusion and future prospects......Page 282
References......Page 284
Summary......Page 286
Introduction......Page 287
The Problem......Page 288
Functionality......Page 289
A brief survey of commonly used tools......Page 290
COPASI......Page 291
MathSBML......Page 292
Systems Biology Workbench......Page 293
Visual Editors......Page 294
CellML-based Applications......Page 295
Other Tools of Interest......Page 296
Dedicated Libraries......Page 298
SOSLib......Page 299
Dedicated tools for stochastic simulation......Page 300
SBML......Page 302
MIRIAM......Page 303
Human-readable Formats......Page 304
Databases......Page 305
Future prospects......Page 306
References......Page 307
Applications of systems biology......Page 312
Definitions......Page 313
Introduction......Page 314
Conceptual modularization......Page 315
Component-based implementation......Page 317
Implications of modularity......Page 320
Incorporating CellML models into tissue and organ models......Page 321
References......Page 323
Introduction......Page 325
Short-range Specification......Page 326
Long-range Specification and Morphogens......Page 328
Processing, refining and integrating signals downstream of specification cues......Page 337
Conclusions......Page 342
References......Page 343
Applications of Immunologic Modeling to Drug Discovery and Development......Page 347
Challenges to Drug Discovery and Development in Immune-related Diseases......Page 348
Systems Biology for Drug Discovery and Development......Page 349
Basic Immunology......Page 350
Challenges in Treating Infectious Diseases, Specifically HIV......Page 351
Central Mathematical Model of HIV Infection......Page 352
Optimizing Treatment Administration......Page 354
Cancer......Page 356
Immune Cell–Cancer Interactions: Homogenous Single-compartment Models......Page 357
Lymphocyte Trafficking and Tumor Infiltration: Spatial and Multicompartment Models......Page 360
Allergic and autoimmune diseases......Page 363
T Cell Vaccination......Page 364
Disease-specific Efficacy of Cytokine Blockade......Page 365
Evaluation and Prioritization of Drug Targets for Rheumatoid Arthritis......Page 366
Conclusion......Page 368
References......Page 369
From molecule to system......Page 372
Complexity of human diseases and drug action: the need for a systems approach to pharmacology highlighted......Page 373
Target-based therapy and its limitations......Page 374
Combination therapy and synthetic lethality......Page 377
Deconvoluting mechanisms......Page 380
Perturbation analysis of constrained systems to discover new anti-cancer drugs......Page 381
Networks, robustness and cancer therapeutic strategies......Page 383
Mining the regulatory program in the cancer transcriptome for novel targets......Page 385
Integrating experimental and primary tumor expression data for novel biomarkers for therapeutic efficacy......Page 388
Gene signatures to reveal the drug action–gene function connectivity map......Page 389
Concluding remarks......Page 390
References......Page 391
Summary......Page 393
Introduction......Page 394
Systems biology in drug discovery......Page 395
From Clinical Genomics to Model Systems......Page 397
Integrating and Extending Cancer Cell Model Data......Page 398
Systems-oriented Target Identification......Page 399
RNA Interference Synthetic Lethality to Identify Targets, Biomarkers and Combinations......Page 400
Translating from Discovery to the Clinic......Page 401
Predicting Patient Benefit from Preclinical Data for Novel Agents......Page 403
References......Page 406
The role of clinical trials in modern medicine......Page 409
Clinical trials of therapeutics......Page 411
Understanding the problem......Page 413
Another view of “systems” biomedicine......Page 415
Conclusion and summary......Page 416
References......Page 417
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