Methods in Bioengineering: Systems Analysis of Biological Networks (The Artech House Methods in Bioengineering)

329 Pages·2009·6.46 MB·English

by  Arul Jayaraman

Methods in Bioengineering
Systems Analysis of Biological Networks
The Artech House Methods in Bioengineering Series
Series Editors-in-Chief
Martin L.Yarmush, M.D., Ph.D.
Robert S.Langer, Sc.D.
Methods in Bioengineering: Biomicrofabrication andBiomicrofluidics,
Jeffrey D.Zahnand Luke P. Lee, editors
Methods in Bioengineering: Microdevices in Biology and Medicine,
YaakovNahmias andSangeeta N.Bhatia, editors
Methods in Bioengineering: Nanoscale Bioengineering and Nanomedicine,
KaushalRegeand IgorMedintz, editors
Methods in Bioengineering: Stem Cell Bioengineering,
BijuParekkadanand Martin L. Yarmush, editors
Methods in Bioengineering: Systems Analysis of Biological Networks,
ArulJayaraman andJuergenHahn, editors
Methods in Bioengineering
Systems Analysis of Biological Networks
ArulJayaraman
Department of Chemical Engineering, Texas A&M University
JuergenHahn
Department of Chemical Engineering, Texas A&M University
Editors
artechhouse.com
Library of Congress Cataloging-in-Publication Data
A catalog record for this book is available from the U.S. Library of Congress.
British Library Cataloguing in Publication Data
A catalogue record for this book is available from the British Library.
ISBN-13: 978-1-59693-406-1
Cover design byYekaterinaRatner
© 2009ArtechHouse. All rights reserved.
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Contents
CHAPTER 1
Quantitative Immunofluorescence for Measuring SpatialCompartmentationof
Covalently Modified Signaling Proteins 1
1.1 Introduction 2
1.2 Experimental Design 3
1.3 Materials 3
1.3.1 Cell culture 3
1.3.2 Buffers/reagents 3
1.3.3 Immunofluorescence reagents 4
1.4 Methods 4
1.4.1 Cell culture and stimulation forphospho-ERKmeasurements 4
1.4.2 Antibody labeling ofphosphorylatedERK(ppERK) 4
1.4.3 Fluorescence microscopy imaging ofppERKand 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 8
Acknowledgments 9
References 10
CHAPTER 2
Development of Green Fluorescent Protein-Based Reporter Cell Lines for Dynamic
Profiling of Transcription Factor andKinaseActivation 11
2.1 Introduction 12
2.2 Materials 13
2.2.1 Cell and bacterial culture 13
2.2.2 Buffers and reagents 13
2.2.3 Cloning 14
2.2.4 Microscopy 14
2.3 Methods 14
2.3.1 3T3-L1 cell culture 14
2.3.2 Transcription factor reporter development 14
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2.3.3 Kinasereporter development 17
2.4 Application Notes 23
2.4.1 ElectroporationofTFreporterplasmidsinto 3T3-L1
preadipocytes 23
2.4.2 Monitoring activation ofERKin HepG2 cells 26
2.5 Data Acquisition, Anticipated Results, and Interpretation 28
2.6 Discussion and Commentary 29
2.7 Summary Points 30
Acknowledgments 31
References 31
CHAPTER 3
Comparison of Algorithms for Analyzing Fluorescent Microscopy Images and
Computation of Transcription Factor Profiles 33
3.1 Introduction 34
3.2 Preliminaries 35
3.2.1 Principles ofGFPreporter 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 andPCA 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 53
Acknowledgments 54
References 54
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 59
4.2.2 Data processing and normalization 60
4.2.3 Suitability of models used in conjunction with quantitative data 62
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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 thePDGFreceptor
signaling network 64
4.3.2 Example 2: Computational analysis of signal specificity in yeast 69
Acknowledgments 72
References 72
CHAPTER 5
Construction of Phenotype-Specific Gene Network by Synergy Analysis 75
5.1 Introduction 76
5.2 Experimental Design 78
5.3 Materials 79
5.3.1 Cell culture and reagents 79
5.3.2 Fatty acid salt treatment 79
5.4 Methods 79
5.4.1 Cytotoxicitymeasurement 79
5.4.2 Gene expression profiling 79
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 80
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 83
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 90
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 98
6.2.2 Model development 99
6.2.3 Objective function 100
6.2.4 Optimization 104
6.3 Data Acquisition, Anticipated Results, and Interpretation 105
6.3.1 Feasible solution determined 105
6.3.2 No feasible solution determined 106
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6.4 Discussion and Commentary 106
6.5 Summary Points 107
Acknowledgments 107
References 108
CHAPTER 7
Modeling the Dynamics of Cellular Networks 111
7.1 Introduction 112
7.2 Materials 113
7.2.1 Cell culture 113
7.2.2 Database 113
7.3 Methods 113
7.3.1 Network reconstruction 113
7.3.2 Network reduction 113
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 126
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 EmpiricalGramians 136
8.4.1 Gramiansand linear sensitivity analysis 136
8.4.2 EmpiricalGramiansfor nonlinear systems 137
8.4.3 A new sensitivity measure based onGramians 138
8.4.4 Example: covalent modification system 140
8.5 Sensitivity Analysis Via Infeasibility Certificates 141
8.5.1 Feasibility problem andsemidefiniterelaxation 142
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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 ofSaccharomyces Cerevisiae
Using Dynamic Flux Balance Analysis 149
9.1 Introduction 150
9.2 Methods 151
9.2.1 Stoichiometricmodels of cellular metabolism 151
9.2.2 Classical flux balance analysis 152
9.2.3 Dynamic flux balance analysis 154
9.3 Results and Interpretation 155
9.3.1 Stoichiometricmodels ofS. cerevisiaemetabolism 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 175
References 176
Related Resources and Supplementary Electronic Information 178
CHAPTER 10
Experimental Design for ParameterIdentifiabilityin Biological Signal
Transduction Modeling 179
10.1 Introduction 180
10.1.1 Model structure 180
10.1.2 Parameter estimation 181
10.1.3 Identifiabilitymetrics 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 Identifiabilityanalysis 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:Identifiabilityanalysis 193
10.3.3 Step 3: Impact analysis 194
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