Feedback control in systems biology:
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| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Boca Raton [u.a.]
CRC Press
2012
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | 282 S. Ill., graph. Darst. |
| ISBN: | 9781439816905 1439816905 |
Staff View
MARC
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| 100 | 1 | |a Cosentino, Carlo |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Feedback control in systems biology |c Carlo Cosentino ; Declan Bates |
| 264 | 1 | |a Boca Raton [u.a.] |b CRC Press |c 2012 | |
| 300 | |a 282 S. |b Ill., graph. Darst. | ||
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Record in the Search Index
| _version_ | 1804148423450951680 |
|---|---|
| adam_text | Contents
Preface
ix
Epigraph xiii
1
Introduction
1
1.1
What is feedback control?
.................... 1
1.2
Feedback control in biological systems
............. 4
1.2.1
The tryptophan operon feedback control system
.... 5
1.2.2
The polyamine feedback control system
........ 6
1.2.3
The heat shock feedback control system
........ 7
1.3
Application of control theory to biological systems: A histor¬
ical perspective
.......................... 10
References
................................ 11
2
Linear systems
17
2.1
Introduction
............................ 17
2.2
State-space models
........................ 18
2.3
Linear time-invariant systems and the frequency response
. . 20
2.4
Fourier analysis
.......................... 26
2.5
Transfer functions and the Laplace transform
......... 30
2.6
Stability
.............................. 33
2.7
Change of state variables and canonical representations
... 35
2.8
Characterising system dynamics in the time domain
..... 38
2.9
Characterising system dynamics in the frequency domain
. . 40
2.10
Block diagram representations of interconnected systems
... 42
2.11
Case Study I: Characterising the frequency dependence of
osmo-adaptation in Saccharomyces cemvisiae
......... 47
2.11.1
Introduction.
....................... 48
2.11.2
Frequency domain analysis
............... 48
2.11.3
Time domain analysis
.................. 50
2.12
Case Study
П:
Characterising the dynamics of the Dictymtelmm
external signal receptor network
................· 54
2.12.1
Introduction
........................ 55
2.12.2
A generic structure for ligand-receptor interaction net¬
works
........................... 55
vi
Feedback
Control in Systems Biology
2.12.3
Structure of the ligand-receptor interaction network
in aggregating Dictyostelium cells
............ 57
2.12.4
Dynamic response of the ligand-receptor interaction
network in Dictyostelium
................. 60
References
................................ 63
3
Nonlinear systems
67
3.1
Introduction
............................ 67
3.2
Equilibrium points
........................ 69
3.3
Linearisation around equilibrium points
............ 72
3.4
Stability and regions of attractions
............... 78
3.4.1
Lyapunov stability
.................... 78
3.4.2
Region of attraction
................... 81
3.5
Optimisation methods for nonlinear systems
.......... 85
3.5.1
Local optimisation methods
............... 87
3.5.2
Global optimisation methods
.............. 89
3.5.3
Linear matrix inequalities
................ 91
3.6
Case Study III: Stability analysis of tumour dormancy equi¬
librium
............................... 93
3.6.1
Introduction
........................ 94
3.6.2
Model of cancer development
.............. 95
3.6.3
Stability of the equilibrium points
........... 96
3.6.4
Checking inclusion in the region of attraction
.....97
3.6.5
Analysis of the tumour dormancy equilibrium
.....100
3.7
Case Study IV: Global optimisation of a model of the trypto-
phan control system against multiple experiment data
.... 105
3.7.1
Introduction
........................106
3.7.2
Model of the tryptophan control system
........106
3.7.3
Model analysis using global optimisation
........109
References
................................110
4
Negative feedback systems
115
4.1
Introduction
............................ 115
4.2
Stability of negative feedback systems
............. 119
4.3
Performance of negative feedback systems
........... 122
4.4
Fundamental tradeoffs with negative feedback
......... 127
4.5
Case Study V: Analysis of stability and oscillations in the p53-
Mdm2 feedback system
...................... 132
4.6
Case Study VI: Perfect adaptation via integral feedback con¬
trol in bacterial chemotaxis
................... 137
4.6.1
A mathematical model of bacterial chemotaxis
.... 138
4.6.2
Analysis of the perfect adaptation mechanism
..... 142
4.6.3
Perfect adaptation requires demethylation of active
only receptors
....................... 145
References
................................148
Table
of Contents
vii
5
Positive feedback systems
151
5.1
Introduction
............................151
5.2
Bifurcations, bistability and limit cycles
............151
5.2.1
Bifurcations and bistability
...............151
5.2.2
Limit cycles
........................154
5.3
Monotone systems
........................158
5.4
Chemical reaction network theory
................161
5.4.1
Preliminaries on reaction network structure
......162
5.4.2
Networks of deficiency zero
...............164
5.4.3
Networks of deficiency one
................166
5.5
Case Study
VII:
Positive feedback leads to multistability, bi¬
furcations and hysteresis in
а МАРК
cascade
.........168
5.6
Case Study
VIII:
Coupled positive and negative feedback loops
in the yeast
galactose
pathway
.................175
References
................................182
6
Model validation using robustness analysis
185
6.1
Introduction
........,...................185
6.2
Robustness analysis tools for model validation
.........187
6.2.1
Bifurcation diagrams
...................187
6.2.2
Sensitivity analysis
....................188
6.2.3
^-analysis
.........................192
6.2.4
Optimisation-based robustness analysis
.........195
6.2.5
Sum-of-squares polynomials
...............196
6.2.6
Monte Carlo simulation
.................198
6.3
New robustness analysis tools for biological systems
.....199
6.4
Case Study IX: Validating models of cAMP oscillations in ag¬
gregating Dictyostelinm cells
...................202
6.5
Case Study X: Validating models of the p53-Mdm2 System
.204
References
................................206
7
Reverse engineering biomolecular networks
211
7.1
Introduction
............................211
7.2
Inferring network interactions using linear models
.......211
7.2.1
Discrete-time vs continuous-time model
........213
7.3
Least squares
...........................216
7.3.1
Least squares for dynamical systems
..........220
7.3.2
Methods based on least squares regression
.......223
7.4
Exploiting prior knowledge
... -...............226
7.4.1
Network inference via LMI-based optimisation
.... 227
7.4.2
MAX-PARSE: An algorithm for pruning a fully con¬
nected network according to maximum parsimony
. . 229
7.4.3
GORE-Net: A network growth algorithm using pref¬
erential attachment
....................231
7.5
Dealing with measurement noise
................231
viii
Feedback
Control in Systems Biology
7.5.1
Total least squares
....................232
7.5.2
Constrained total least squares
.............233
7.6
Exploiting time-varying models
.................236
7.7
Case Study XI: Inferring regulatory interactions in the innate
immune system from noisy measurements
...........239
7.8
Case Study
XII:
Reverse engineering a cell cycle regulatory
subnetwork of Saccharomyces cerevisiae from experimental mi-
croarray data
...........................243
7.8.1
PACTLS: An algorithm for reverse engineering par¬
tially known networks from noisy data
.........244
7.8.2
Results
..........................247
References
................................250
8
Stochastic effects in biological control systems
255
8.1
Introduction
............................255
8.2
Stochastic modelling and simulation
..............256
8.3
A framework for analysing the effect of stochastic noise on
stability
..............................259
8.3.1
The effective stability approximation
..........260
8.3.2
A computationally efficient approximation of the dom¬
inant stochastic perturbation
..............261
8.3.3
Analysis using the Nyquist stability criterion
.....263
8.4
Case Study
XIII:
Stochastic effects on the stability of cAMP
oscillations in aggregating Dictyostelium cells
.........266
8.5
Case Study
XIV:
Stochastic effects on the robustness of cAMP
oscillations in aggregating Dictyostelium cells
......... 271
References
................................276
Index
279
|
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| dewey-ones | 611 - Human anatomy, cytology, histology |
| dewey-raw | 611.0181 |
| dewey-search | 611.0181 |
| dewey-sort | 3611.0181 |
| dewey-tens | 610 - Medicine and health |
| discipline | Biologie Mathematik Medizin |
| format | Book |
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| spelling | Cosentino, Carlo Verfasser aut Feedback control in systems biology Carlo Cosentino ; Declan Bates Boca Raton [u.a.] CRC Press 2012 282 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Systembiologie (DE-588)4809615-5 gnd rswk-swf Rückkopplung (DE-588)4050851-1 gnd rswk-swf Rückkopplung (DE-588)4050851-1 s Systembiologie (DE-588)4809615-5 s b DE-604 Bates, Declan Verfasser aut Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024440063&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Cosentino, Carlo Bates, Declan Feedback control in systems biology Systembiologie (DE-588)4809615-5 gnd Rückkopplung (DE-588)4050851-1 gnd |
| subject_GND | (DE-588)4809615-5 (DE-588)4050851-1 |
| title | Feedback control in systems biology |
| title_auth | Feedback control in systems biology |
| title_exact_search | Feedback control in systems biology |
| title_full | Feedback control in systems biology Carlo Cosentino ; Declan Bates |
| title_fullStr | Feedback control in systems biology Carlo Cosentino ; Declan Bates |
| title_full_unstemmed | Feedback control in systems biology Carlo Cosentino ; Declan Bates |
| title_short | Feedback control in systems biology |
| title_sort | feedback control in systems biology |
| topic | Systembiologie (DE-588)4809615-5 gnd Rückkopplung (DE-588)4050851-1 gnd |
| topic_facet | Systembiologie Rückkopplung |
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