Methods in bioengineering: systems analysis of biological networks
Gespeichert in:
| Format: | Buch |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
Boston, Mass. [u.a.]
Artech House
2009
|
| Schriftenreihe: | The Artech House methods in bioengineering series
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Literaturangaben |
| Beschreibung: | XII, 316 S. Ill., graph. Darst. |
| ISBN: | 1596934069 9781596934061 |
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Datensatz im Suchindex
| _version_ | 1804147990162571264 |
|---|---|
| adam_text | Methods in Bioengineering
Systems Analysis of Biological Networks
Ami Jayaraman
Department of Chemical Engineering, Texas A amp;M University
Juergen Hahn
Department of Chemical Engineering, Texas A amp;M University
Editors
ARTECH
HOUSE
BOSTON|LONDON
artechhouse.com
Contents
CHAPTER 1
Quantitative Immunofluorescence for Measuring Spatial Compartmentation of
Covalently Modified Signaling Proteins 1
1.1 Introduction 2
1.2 Experimental Design 3
1.3 Materials 3
1.3.1 Cell culture
1.3.2 Buffers/reagents 3
1.3.3 Immunofluorescence reagents 4
1.4 Methods 4
1.4.1 Cell culture and stimulation for phospho-ERK measurements 4
1.4.2 Antibody labeling of phosphorylated ERK (ppERK) 4
1.4.3 Fluorescence microscopy imaging of ppERK and automated
image analysis 5
1.5 Data Acquisition, Anticipated Results, and Interpretation 6
1.6 Statistical Guidelines 7
1.7 Discussion and Commentary 8
1.8 Application Notes 8
1.9 Summary Points
Acknowledgments 9
References 10
CHAPTER 2
Development of Green Fluorescent Protein-Based Reporter Cell Lines for Dynamic
Profiling of Transcription Factor and Kinase Activation 11
2.1 Introduction 12
2.2 Materials 3
2.2.1 Cell and bacterial culture 13
2.2.2 Buffers and reagents 3
2.2.3 Cloning 14
2.2.4 Microscopy 4
2.3 Methods 4
2.3.1 3T3-L1 cell culture 14
2.3.2 Transcription factor reporter development 14
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intents ,-,
2.3.3 Kinase reporter development 17
2.4 Application Notes 23
2.4.1 Electroporation of TF reporter plasmids into 3T3-L1
preadipocytes 3
2.4.2 Monitoring activation of ERK in HepG2 cells 26
2.5 Data Acquisition, Anticipated Results, and Interpretation 28
2.6 Discussion and Commentary 29
2.7 Summary Points 30
Acknowledgments 1
References 1
CHAPTER 3
Comparison of Algorithms for Analyzing Fluorescent Microscopy Images and
Computation of Transcription Factor Profiles 33
3.1 Introduction 34
3.2 Preliminaries 5
3.2.1 Principles of GFP reporter systems 35
3.2.2 Wavelets 36
3.2.3 K-means clustering 36
3.2.4 Principal component analysis 37
3.2.5 Mathematical description of digital images and image analysis 37
3.3 Methods 38
3.3.1 Image analysis based on wavelets and a bidirectional search 38
3.3.2 Image analysis based on K-means clustering and PCA 41
3.3.3 Determining fluorescence intensity of an image 43
3.3.4 Comparison of the two image analysis procedures 45
3.4 Data Acquisition, Anticipated Results, and Interpretation 46
3.4.1 Developing a model describing the relationship between the
transcription factor concentration and the observed fluorescence
intensity 46
3.4.2 Solution of an inverse problem for determining transcription
factor concentrations 47
3.5 Application Notes 50
3.6 Summary and Conclusions 3
Acknowledgments 4
References 4
CHAPTER 4
Data-Driven, Mechanistic Modeling of Biochemical Reaction Networks 57
4.1 Introduction 58
4.2 Principles of Data-Driven Modeling 59
4.2.1 Types of experimental data 9
4.2.2 Data processing and normalization 60
4.2.3 Suitability of models used in conjunction with quantitative data 62
sfl)
Contents
4.2.4 Issues related to parameter specification and estimation 63
4.3 Examples of Data-Driven Modeling 64
4.3.1 Example 1: Systematic analysis of crosstalk in the PDGF receptor .
signaling network 64
4.3.2 Example 2: Computational analysis of signal specificity in yeast 69
Acknowledgments 72
References 2
CHAPTER 5
Construction of Phenotype-Specific Gene Network by Synergy Analysis 75
5.1 Introduction 76
5.2 Experimental Design 8
5.3 Materials 9
5.3.1 Cell culture and reagents 79
5.3.2 Fatty acid salt treatment 9
5.4 Methods 79
5.4.1 Cytotoxicity measurement 79
5.4.2 Gene expression profiling 9
5.4.3 Metabolites measurements 80
5.4.4 Gene selection based on trends of metabolites 80
5.4.5 Calculation of the synergy scores of gene pairs 0
5.4.6 Permutation test to evaluate the significance of the synergy 82
5.4.7 Characterization of the network topology 82
5.5 Data Acquisition, Anticipated Results, and Interpretation 82
5.6 Discussion and Commentary 83
5.7 Applications Notes 3
5.7.1 Topological characteristics of the synergy network 84
5.7.2 Hub genes in the network 85
5.8 Summary Points 89
Acknowledgments 90
References 0
CHAPTER 6
Genome-Scale Analysis of Metabolic Networks 95
6.1 Introduction 96
6.2 Materials and Methods 98
6.2.1 Flux analysis theory 8
6.2.2 Model development 9
6.2.3 Objective function 100
6.2.4 Optimization 4
6.3 Data Acquisition, Anticipated Results, and Interpretation 105
6.3.1 Feasible solution determined 105
6.3.2 No feasible solution determined 6
sntents
6.4 Discussion and Commentary 106
6.5 Summary Points 107
Acknowledgments 7
References 8
CHAPTER 7
Modeling the Dynamics of Cellular Networks 111
7.1 Introduction 112
7.2 Materials 3
7.2.1 Cell culture 113
7.2.2 Database 3
7.3 Methods 113
7.3.1 Network reconstruction 113
7.3.2 Network reduction 3
7.3.3 Kinetic modeling 117
7.3.4 Parameter estimation 120
7.4 Data Acquisition, Anticipated Results, and Interpretation 121
7.4.1 Model network 121
7.4.2 Dynamic simulation parameters 122
7.5 Discussion and Commentary 122
7.5.1 Modularity 122
7.5.2 Generalized kinetic expressions 123
7.5.3 Population heterogeneity 124
7.6 Application Notes 125
7.7 Summary Points 6
Acknowledgments 126
References 126
CHAPTER 8
Steady-State Sensitivity Analysis of Biochemical Reaction Networks: A Brief
Review and New Methods 129
8.1 Introduction 130
8.2 Considered System Class and Parametric Sensitivity 131
8.2.1 Example system: reversible covalent modification 131
8.2.2 Parametric steady-state sensitivity 132
8.3 Linear Sensitivity Analysis 134
8.4 Sensitivity Analysis Via Empirical Gramians 136
8.4.1 Gramians and linear sensitivity analysis 136
8.4.2 Empirical Gramians for nonlinear systems 137
8.4.3 A new sensitivity measure based on Gramians 138
8.4.4 Example: covalent modification system 140
8.5 Sensitivity Analysis Via Infeasibility Certificates 141
8.5.1 Feasibility problem and semidefinite relaxation 142
Contents
8.5.2 Infeasibility certificates from the dual problem 143
8.5.3 Algorithm to bound feasible steady states 144
8.5.4 Example: covalent modification system 145
8.6 Discussion and Outlook 146
References 147
CHAPTER 9
Determining Metabolite Production Capabilities of Saccharomyces Cerevisiae
Using Dynamic Flux Balance Analysis 149
9.1 Introduction 150
9.2 Methods 1
9.2.1 Stoichiometric models of cellular metabolism 151
9.2.2 Classical flux balance analysis 152
9.2.3 Dynamic flux balance analysis 4
9.3 Results and Interpretation 155
9.3.1 Stoichiometric models of S. cerevisiae metabolism 155
9.3.2 Dynamic simulation of fed-batch cultures 157
9.3.3 Dynamic optimization of fed-batch cultures 159
9.3.4 Identification of ethanol overproduction mutants 164
9.3.5 Exploration of novel metabolic capabilities 167
9.4 Discussion and Commentary 172
9.5 Summary Points 175
Acknowledgments 5
References 6
Related Resources and Supplementary Electronic Information 178
CHAPTER 10
Experimental Design for Parameter Identiflability in Biological Signal
Transduction Modeling 179
10.1 Introduction 180
10.1.1 Model structure 180
10.1.2 Parameter estimation 181
10.1.3 Identifiability metrics and conditions 182
10.1.4 Overview of the experimental design procedure 184
10.2 Methods 185
10.2.1 Initial perturbation and measurement design 185
10.2.2 Identifiability analysis 186
10.2.3 Impact analysis 188
10.2.4 Design modification and reduction 190
10.2.5 Design implementation 191
10.3 Data Acquisition, Anticipated Results, and Interpretation 192
10.3.1 Step 1: Initial perturbation and measurement design 193
10.3.2 Step 2: Identifiability analysis 193
10.3.3 Step 3: Impact analysis 194
Contents
10.3.4 Step 4: Design reduction 196
10.3.5 Step 5: Identifiability analysis 197
10.4 Application Notes 197
10.4.1 Step 1: Initial perturbation and measurement design 198
10.4.2 Step 2: Identifiability analysis 200
10.4.3 Steps 3 to 5: Impact analysis, design reduction, and
identifiability analysis 200
10.5 Discussion and Commentary 205
10.6 Summary Points 207
Acknowledgments 8
References 8
CHAPTER 11
Parameter Identification with Adaptive Sparse Grid-Based Optimization for
Models of Cellular Processes 211
11.1 Introduction 2
11.1.1 Adaptive sparse grid interpolation 213
11.2 Experimental Design 215
11.3 Materials 217
11.4 Methods 8
11.5 Data Acquisition, Anticipated Results, and Interpretation 221
11.5.1 Sorted grid points 222
11.5.2 Unique points 2
11.5.3 Unstable points 223
11.5.4 Interpretation and conclusions 223
11.6 Troubleshooting 224
11.6.1 Troubleshooting special cases: small and large problems 224
11.7 Discussion and Commentary 227
11.8 Application Notes 228
11.8.1 Comparison of adaptive sparse grid and GA-based
optimization 8
11.8.2 Adaptive sparse grid-based optimization 228
11.8.3 Genetic algorithm 229
11.9 Summary Points 230
Acknowledgments 1
References 1
Related sources and supplementary information 232
CHAPTER 12
Reverse Engineering of Biological Networks 233
12.1 Introduction: Biological Networks and Reverse Engineering 234
12.1.1 Biological networks 234
12.1.2 Network representation 6
12.1.3 Motivation and design principles 237
Contents
12.1.4 Reverse engineering 238
12.2 Material: Time Series and Omics Data 239
12.2.1 Metabolomics 240-
12.2.2 Proteomics and protein interaction networks 240
12.2.3 Transcriptomics 241
12.3 Approaches for Inference of Biological Networks 242
12.3.1 Genome-scale metabolic modeling 243
12.3.2 Boolean networks 245
12.3.3 Network topology from correlation or hierarchical clustering 247
12.3.4 Bayesian networks 248
12.3.5 Ordinary differential equations 250
12.4 Network Biology—Exploring the Inferred Networks 256
12.4.1 Graph theory 257
12.4.2 Motifs and modules 258
12.4.3 Stoichiometric analysis 260
12.4.4 Simulation of dynamics, sensitivity analysis, control analysis 261
12.5 Discussion and Comparison of Approaches 264
12.6 Summary Points 266
Acknowledgments 6
References 7
CHAPTER 13
Transcriptome Analysis of Regulatory Networks 271
13.1 Introduction 272
13.2 Methods 3
13.2.1 Materials 273
13.2.2 Cell harvesting 4
13.2.3 RNA purification 274
13.2.4 Transcriptional profiling using DNA microarrays 276
13.3 Data Acquisition, Anticipated Results, and Interpretation 281
13.3.1 Acquisition of DNA microarray data 281
13.3.2 Normalization 281
13.3.3 Network Component Analysis (NCA) 282
13.4 Discussion and Commentary 284
13.5 Application Notes 284
13.6 Summary Points 5
References 285
CHAPTER 14
A Workflow from Time Series Gene Expression to Transcriptional Regulatory
Networks 287
14.1 Introduction 288
14.2 Materials 9
Contents
14.3 Methods 291
14.3.1 Identification of differentially expressed genes 291
14.3.2 Robust clustering of differential gene expression time series
data using computational negative control approach 292
14.3.3 Transcriptional regulatory network analysis using PAINT 293
14.4 Data Acquisition, Anticipated Results, and Interpretation 296
14.4.1 Selection of number of clusters 296
14.4.2 PAINT result interpretation for gene coexpression clusters 296
14.5 Discussion and Commentary 296
14.5.1 Estimation of nondifferentially expressed genes
(pi.not value) 297
14.5.2 Threshold for local false discovery rate analysis 297
14.5.3 Format of gene identifiers 298
14.5.4 Cluster size issues 298
14.5.5 TRANSFAC version issues 298
14.5.6 Annotation redundancy in the gene list and multiple
promoters 299
14.5.7 Reference Feasnet selection/generation 299
14.5.8 Multiple testing correction in PAINT 299
14.6 Application Notes 300
14.7 Summary Points 0
Acknowledgments 301
References 301
About the Editors 303
List of Contributors 304
Index 307
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| spelling | Methods in bioengineering systems analysis of biological networks Arul Jayaraman ... eds. Systems analysis of biological networks Boston, Mass. [u.a.] Artech House 2009 XII, 316 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier The Artech House methods in bioengineering series Literaturangaben Biochemische Analyse (DE-588)4255721-5 gnd rswk-swf Biochemische Analyse (DE-588)4255721-5 s DE-604 Jayaraman, Arul Sonstige oth HEBIS Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024166033&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
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| title | Methods in bioengineering systems analysis of biological networks |
| title_alt | Systems analysis of biological networks |
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| title_full | Methods in bioengineering systems analysis of biological networks Arul Jayaraman ... eds. |
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| title_short | Methods in bioengineering |
| title_sort | methods in bioengineering systems analysis of biological networks |
| title_sub | systems analysis of biological networks |
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