Systems Biology: Definitions and Perspectives (Topics in Current Genetics)

front-matter.pdf......Page 1
1 Is Systems Biology something new?......Page 17
2 Is it important?......Page 19
3 What is it?......Page 20
5 Will it work?......Page 22
References......Page 23
1 Systems biology......Page 24
2 What makes systems biology different from other systems approaches?......Page 25
3 Isolation and characterization......Page 26
4 A modular approach......Page 27
6 Validation......Page 29
7 Yeast glycolysis as an example......Page 30
8 The Silicon Cell......Page 33
9 JWS - Online Cellular Systems Modelling......Page 34
10 How far are we, and what needs to be done?......Page 37
References......Page 39
1 Introduction......Page 42
2.1 Development of system of ordinary differential equations (ODEs) describing dynamics of selected biochemical system......Page 43
2.2 Basic principles of kinetic description of enzymatic reactions using in vitro experimental data......Page 47
2.3 Derivation of rate equation of histidinol dehydrogenase of Escherichia coli and estimation of its kinetic parameters using in vitro experimental data......Page 48
3 Application of the Escherichia coli branched-chain amino acid biosynthesis model. Prediction of possible genetic changes that should maximize isoleucine and valine production......Page 56
3.1 Model development......Page 58
3.1.1 Derivation of the rate equations......Page 60
3.1.2 Detailed description of pathway steps......Page 62
3.1.3 Evaluation of maximal reaction rates......Page 71
3.2 Application of kinetic model to optimize production of isoleucine and valine......Page 72
References......Page 76
1 Introduction......Page 79
2 Relating system variables to enzyme kinetics......Page 81
3 Generic properties of metabolic systems......Page 82
4 Perspectives for the future......Page 86
References......Page 88
1 Introduction: Dynamics is a systems property essential for systems biology......Page 91
2 Nonlinear dynamics displayed and used by singleenzyme reactions......Page 93
3 Nonlinear dynamics displayed and used by metabolic pathways......Page 95
4 Nonlinear dynamics displayed and used by signal transduction systems......Page 96
5 Recent developments, summary, and outlook......Page 100
References......Page 101
1 Introduction......Page 104
2 Chemical reactions in the living cell......Page 105
3.1 The master equation......Page 106
3.3 The Fokker-Planck Approximation......Page 108
3.4 The Linear Noise Approximation......Page 109
4 A master equation with an analytical solution......Page 111
5 Stoichiometrically coupled flows......Page 112
6 Stoichiometrically coupled flows in protein synthesis......Page 117
7 Near-critical fluctuations in the levels of charged tRNA isoacceptors......Page 118
9 Appendix: The moment generating function......Page 123
References......Page 125
1.1. Physiology......Page 128
1.2 Molecular biology......Page 129
1.3 Systems molecular biology?......Page 130
2.2 Nonlinearity......Page 131
2.3 Nonlinearities and dependencies prevail in real life......Page 133
3 Systems biology: Neither the biology of systems nor the biology of all molecules individually......Page 135
4.1 Self-organization......Page 136
4.3 Chemiosmotic coupling......Page 138
4.5 Systems biology avant la lettre: Metabolic Control Analysis; laws of systems biology......Page 139
4.6 Circular causality and emergence......Page 141
4.7 Networks and hierarchies in life......Page 144
4.8 Systems biology: dealing with the circular causation in biology......Page 147
References......Page 149
1 From molecular to systems biology......Page 151
2.1 Modular or top-down approach and the quantification of the network architecture by “connection” coefficients......Page 152
2.2 Modularization of cellular networks......Page 154
2.3 Inference of connections between network modules......Page 156
3.1 Spatio-temporal patterns of growth factor signaling and cell fate decisions......Page 157
3.2 Differential temporal patterns of signaling responses can be explained using kinetic modeling......Page 158
3.3 Membrane translocation of SOS and RasGAP shapes Ras activation patterns......Page 160
4 Rationalization of network function......Page 161
References......Page 163
1 Introduction......Page 168
2 Quantitative, formal models are essential instruments in systems biology......Page 170
2.1 Computational modeling is an extension of the scientific method......Page 171
2.2 Mechanistic models can serve as frameworks for organizing data and hypotheses......Page 172
3 A variety of software resources are available today for computational modeling......Page 173
4 Exchanging models between software tools: The Systems Biology Markup Language......Page 177
4.1 The general form of SBML......Page 178
4.2 The continued evolution of SBML......Page 179
5 Development of an E. coli systems biology project......Page 181
6 An integrated E. coli database for community research and systems biology......Page 184
7 Putting models to work: The International E. coli Alliance......Page 188
References......Page 189
1 Complex systems – Systems biology......Page 195
2 Accessing metabolic network operation through steady state flux analysis......Page 197
3.1 Identification of unexpected or novel pathways and reactions......Page 202
3.2 Identification of metabolic systems properties......Page 206
4 Recent developments and future needs in metabolic flux analysis......Page 207
5 Quo vadis metabolic systems biology?......Page 209
References......Page 211
Abbreviations......Page 218
1 Systems biology: an interdisciplinary approach......Page 219
2.1 Environment – the liquid phase......Page 221
2.3 Glucose uptake......Page 222
2.4 More detailed description of regulatory phenomena......Page 226
2.5 Regulation by Mlc......Page 228
2.6 Model analysis – implications for diauxic growth......Page 229
3.1 Experimentation and Theory......Page 231
3.2 Modules and hierarchies......Page 233
3.3 Functions and design principles......Page 234
4 Conclusions......Page 235
References......Page 236
1 Rationale......Page 239
2.1 Top-down versus bottom-up......Page 240
2.2 Reconstruction of large-scale cellular networks......Page 241
2.3 Topological properties of metabolic and signaling networks......Page 243
3.1 Motivation......Page 246
3.2 Coupling cell cycle progression and energy metabolism in Saccharomyces cerevisiae......Page 247
3.3 Establishing a modular model......Page 249
4 Future directions – Or – How to catch a black cat in a dark room?......Page 253
4.2 Dry lab......Page 254
5 Concluding remarks......Page 255
References......Page 256
1 Introduction......Page 261
3 Systems equations......Page 263
4 Model reference state......Page 265
5 The stimulated state......Page 267
6 Comparison of theory and experiment......Page 268
8 Transient stimulation of the pathway......Page 270
9 Control and robustness of the Wnt-pathway......Page 272
10 Discussion......Page 276
References......Page 277
1 Introduction......Page 278
2 Yeast MAPK pathways......Page 279
3 The yeast pheromone response pathway......Page 280
3.1 Simulating feedback control mechanisms of the pheromone response pathway......Page 282
3.1.3 GTP hydrolysis......Page 283
3.1.4 Ste11 degradation......Page 285
4 The high osmolarity glycerol response pathway......Page 287
4.1.1 Sensor activity......Page 289
4.1.3 Transcriptional activation of the phosphatases......Page 292
5 Feedback control with and without pathway desensitisation......Page 293
6 Data for modelling......Page 294
7 Mathematical models......Page 298
8 Conclusions......Page 299
References......Page 300
1 Introduction......Page 304
2 Components of the cell cycle engine......Page 305
3 Feedback loops and regulatory modules......Page 308
5 The role of the nucleocytoplasmic ratio......Page 310
6 Bifurcation diagrams and their biological significance......Page 311
7 Cell cycle progression on the bifurcation diagram......Page 314
8 Effects of cell cycle checkpoints on the bifurcation diagrams......Page 315
9 Endoreplication cycles......Page 317
10 Conclusion......Page 318
References......Page 319
Abbreviations......Page 321
Supplement: balance equations......Page 322
1 Systems biology and complex cellular processes......Page 324
2 The modular systems biology approach......Page 325
3 The control of cell cycle: an open question......Page 327
3.1 Cyclins, Cdks, and Cki are the evolutionary conserved molecular machines driving the cell cycle......Page 328
4 Global functional analysis of the G1/S transition in budding yeast......Page 330
4.1 Coordination between growth and the DNA division cycle: size distribution is a distinctive property of a yeast population......Page 331
4.3 Analysis of a shift-up......Page 332
5 A new threshold control for the G1 to S transition in budding yeast......Page 334
6 Post-genomic analysis of the G1/S transition......Page 337
7 What next?......Page 340
References......Page 341
Abbreviations......Page 346
1 Systems biology: paradigm shift from reductionism to holism in biology? The whole is greater than the sum of its parts......Page 347
2 Modelling signal transduction networks......Page 348
3 CD95-induced apoptosis......Page 349
3.3 Type I versus type II cells and the regulation of apoptosis......Page 350
4 Mathematical models of apoptosis......Page 351
5 Structured information models - The information problem......Page 352
5.2 Combined model definition......Page 353
5.3 The model of CD95-induced apoptosis......Page 354
6 Model reduction by sensitivity analysis......Page 355
6.1 The sensitivity matrix......Page 356
6.3 Stochastic approach to global sensitivity analysis......Page 358
6.4 Sensitivity of sensitivities......Page 360
7.1 Cluster-based parameter estimation......Page 361
7.2 Parameter estimation algorithm......Page 363
8.1 Parameter estimation based on multiple scenarios......Page 364
8.3 Delay of apoptosis and point of no return......Page 365
9 Outlook......Page 366
References......Page 368
1 Introduction......Page 371
2 Robustness as a fundamental organizational principle......Page 373
3 Evolvability and trade-offs of robust systems......Page 375
4 Computational tools in systems biology......Page 376
References......Page 380
1 Various facets of Systems Biology......Page 384
2 Long and medium-term goals of Systems Biology......Page 385
2.2 Systems Biology data and model-bases......Page 386
3 The challenges of Systems Biology......Page 387
3.1 The modular approach......Page 389
4 Potential applications of Systems Biology......Page 391
5 Systems Biology: towards new ways of organizing research?......Page 393
References......Page 396
back-matter.pdf......Page 398