Systems Biology: Definitions and Perspectives

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Lilia Alberghina, Hans V. Westerhoff
Springer Science & Business Media, Oct 4, 2007 - Computers - 408 pages
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For life to be understood and disease to become manageable, the wealth of postgenomic data now needs to be made dynamic. This development requires systems biology, integrating computational models for cells and organisms in health and disease; quantitative experiments (high-throughput, genome-wide, living cell, in silico); and new concepts and principles concerning interactions. This book defines the new field of systems biology and discusses the most efficient experimental and computational strategies. The benefits for industry, such as the new network-based drug-target design validation, and testing, are also presented.

 

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Contents

Did we know it all along?
3
2 Is it important?
5
3 What is it?
6
4 Did we know it all along?
8
References
9
METHODS
11
From isolation to integration a systems biology approach for building the Silicon Cell
13
2 What makes systems biology different from other systems approaches?
14
32 Modules and hierarchies
229
33 Functions and design principles
230
4 Conclusions
231
References
232
Integration of metabolic and signaling networks
235
2 Insilico reconstruction of cellular networks
236
22 Reconstruction of largescale cellular networks
237
23 Topological properties of metabolic and signaling networks
239

3 Isolation and characterization
15
4 A modular approach
16
5 Integration
18
7 Yeast glycolysis as an example
19
8 The Silicon Cell
22
9 JWS Online Cellular Systems Modelling
23
10 How far are we and what needs to be done?
26
References
28
Kinetic modelling of the E coli metabolism
31
2 Basic principles of kinetic model construction
32
22 Basic principles of kinetic description of enzymatic reactions using in vitro experimental data
36
23 Derivation of rate equation of histidinol dehydrogenase of Escherichia coli and estimation of its kinetic parameters using in vitro experimental data
37
3 Application of the Escherichia coli branchedchain amino acid biosynthesis model Prediction of possible genetic changes that should maximize isole...
45
31 Model development
47
32 Application of kinetic model to optimize production of isoleucine and valine
61
4 Discussion
65
Metabolic Control Analysis
69
2 Relating system variables to enzyme kinetics
71
3 Generic properties of metabolic systems
72
4 Perspectives for the future
76
References
78
How to understand biochemical systems by understanding their dynamics
81
2 Nonlinear dynamics displayed and used by single enzyme reactions
83
3 Nonlinear dynamics displayed and used by metabolic pathways
85
4 Nonlinear dynamics displayed and used by signal transduction systems
86
5 Recent developments summary and outlook
90
Acknowledgments
91
Mesoscopic kinetics and its applications in protein synthesis
95
2 Chemical reactions in the living cell
96
3 Mesoscopic kinetics for homogenous systems
97
32 Monte Carlo simulations of system trajectories
99
34 The Linear Noise Approximation
100
4 A master equation with an analytical solution
102
5 Stoichiometrically coupled flows
103
6 Stoichiometrically coupled flows in protein synthesis
108
7 Nearcritical fluctuations in the levels of charged tRNA isoacceptors
109
8 Conclusions
114
References
116
What is systems biology? From genes to function and back
119
12 Molecular biology
120
13 Systems molecular biology?
121
2 Limits to systems molecular biology
122
23 Nonlinearities and dependencies prevail in real life
124
Neither the biology of systems nor the biology of all molecules individually
126
4 Systems biology avant la lettre
127
42 Perpetuation
129
44 Nonequilibrium thermodynamics
130
46 Circular causality and emergence
132
47 Networks and hierarchies in life
135
dealing with the circular causation in biology
138
5 Concluding remarks
140
Mechanistic and modular approaches to modeling and inference of cellular regulatory networks
143
2 Network inference
144
22 Modularization of cellular networks
146
23 Inference of connections between network modules
148
3 Bottomup approach
149
32 Differential temporal patterns of signaling responses can be explained using kinetic modeling
150
33 Membrane translocation of SOS and RasGAP shapes Ras activation patterns
152
34 Heterogeneous spatial distribution is an additional factor controlling signaling cascades
153
The future of systems biology
155
METABOLISM
161
The need for computing cooperation and consortia
163
2 Quantitative formal models are essential instruments in systems biology
165
21 Computational modeling is an extension of the scientific method
166
22 Mechanistic models can serve as frameworks for organizing data and hypotheses
167
3 A variety of software resources are available today for computational modeling
168
The Systems Biology Markup Language
172
41 The general form of SBML
173
42 The continued evolution of SBML
174
5 Development of an E coli systems biology project
176
6 An integrated E coli database for community research and systems biology
179
The International E coli Alliance
183
References
184
A key methodology for systems biology of metabolism
191
2 Accessing metabolic network operation through steady state flux analysis
193
Flux analysis in systems biology
198
32 Identification of metabolic systems properties
202
4 Recent developments and future needs in metabolic flux analysis
203
5 Quo vadis metabolic systems biology?
205
Acknowledgements
207
Abbreviations
214
biology meets engineering sciences
215
2 Model setup
217
22 Lactose pathway
218
24 More detailed description of regulatory phenomena
222
25 Regulation by MIc
224
26 Model analysis implications for diauxic growth
225
3 Recent developments and future challenges
227
3 Marrying diverse partners integrated models of signaling and metabolism
242
32 Coupling cell cycle progression and energy metabolism in Saccharomyces cerevisiae
243
33 Establishing a modular model
245
4 Future directions Or How to catch a black cat in a dark room?
249
41 Wet lab
250
5 Concluding remarks
251
Acknowledgment
252
Abbreviations
255
SIGNAL TRANSDUCTION
257
Mathematical modelling of the Wntpathway
259
2 Detailed reaction scheme
261
4 Model reference state
263
5 The stimulated state
265
6 Comparison of theory and experiment
266
7 Effect of ARC on the Bcatenin concentration
268
9 Control and robustness of the Wntpathway
270
10 Discussion
274
References
275
Modelling signalling pathways a yeast approach
277
2 Yeast MAPK pathways
278
3 The yeast pheromone response pathway
279
31 Simulating feedback control mechanisms of the pheromone response pathway
281
4 The high osmolarity glycerol response pathway
286
41 Feedback control of the HOG pathway
288
5 Feedback control with and without pathway desensitisation
292
6 Data for modelling
293
7 Mathematical models
297
8 Conclusions
298
References
299
CELL LIFE AND DEATH
303
Systems biology of the yeast cell cycle engine
305
2 Components of the cell cycle engine
306
3 Feedback loops and regulatory modules
309
4 Mathematical formulation
311
6 Bifurcation diagrams and their biological significance
312
7 Cell cycle progression on the bifurcation diagram
315
8 Effects of cell cycle checkpoints on the bifurcation diagrams
316
9 Endoreplication cycles
318
10 Conclusion
319
Acknowledgements
320
Abbreviations
322
balance equations
323
A modular systems biology analysis of cell cycle entrance into Sphase
325
2 The modular systems biology approach
326
an open question
328
31 Cyclins Cdks and Chiare the evolutionary conserved molecular machines driving the cell cycle
329
4 Global functional analysis of the G1S transition in budding yeast
331
size distribution is a distinctive property of a yeast population
332
42 Are metabolism and DNA division cycle coordinated?
333
5 A new threshold control for the G1 to S transition in budding yeast
335
6 Postgenomic analysis of the G1S transition
338
7 What next?
341
Acknowledgements
342
Abbreviations
347
Systems biology of apoptosis
349
2 Modelling signal transduction networks
350
3 CD95induced apoptosis
351
31 The CD95receptor and the DISC
352
4 Mathematical models of apoptosis
353
5 Structured information models The information problem
354
51 Network decomposition based on information quality
355
53 The model of CD95induced apoptosis
356
54 Black boxes
357
61 The sensitivity matrix
358
62 Local versus global sensitivity analysis
360
64 Sensitivity of sensitivities
362
7 Sensitivitycontrolled parameter estimation
363
72 Parameter estimation algorithm
365
8 Model simulation of apoptosis and experimental results
366
82 Threshold mechanism for CD95induced apoptosis
367
9 Outlook
368
References
370
Scientific and technical challenges for systems biology
373
2 Robustness as a fundamental organizational principle
375
3 Evolvability and tradeoffs of robust systems
377
4 Computational tools in systems biology
378
Acknowledgements
382
AND NOW
387
necessary developments and trends
389
2 Long and mediumterm goals of Systems Biology
390
21 Quantitative measurements on single cells?
391
23 Standard notation and visualization
392
31 The modular approach
394
32 Models at different levels
396
towards new ways of organizing research?
398
References
401
Index
403
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