Methods in Bioengineering: Systems Analysis of Biological Networks

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Artech House, 2009 - Technology & Engineering - 316 pages
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"This cutting-edge volume provides a detailed look at the two main aspects of systems biology: the design of sophisticated experimental methods and the development of complex models to analyze the data. Focusing on methods that are being used to solve current problems in biomedical science and engineering, this comprehensive, richly illustrated resource shows you how to: design of state-of-the art methods for analyzing biological systems Implement experimental approaches for investigating cellular behavior in health and disease; use algorithms and modeling techniques for quantitatively describing biomedical problems; and integrate experimental and computational approaches for a more complete view of biological systems." --Book Jacket.
 

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Contents

922 Classical flux balance analysis
152
923 Dynamic flux balance analysis
154
93 Results and Interpretation
155
932 Dynamic simulation of fedbatch cultures
157
933 Dynamic optimization of fedbatch cultures
159
934 Identification of ethanol overproduction mutants
164
935 Exploration of novel metabolic capabilities
167
94 Discussion and Commentary
172

Acknowledgments
9
References
10
Development of Green Fluorescent ProteinBased Reporter Cell Lines for Dynamic Profiling of Transcription Factor and Kinase Activation
11
21 Introduction
12
22 Materials
13
223 Cloning
14
233 Kinase reporter development
17
24 Application Notes
23
242 Monitoring activation of ERK in HepG2 cells
26
25 Data Acquisition Anticipated Results and Interpretation
28
26 Discussion and Commentary
29
27 Summary Points
30
Acknowledgments
31
Comparison of Algorithms for Analyzing Fluorescent Microscopy Images and Computation of Transcription Factor Profiles
33
31 Introduction
34
32 Preliminaries
35
322 Wavelets
36
324 Principal component analysis
37
33 Methods
38
332 Image analysis based on Kmeans clustering and PCA
41
333 Determining fluorescence intensity of an image
43
334 Comparison of the two image analysis procedures
45
34 Data Acquisition Anticipated Results and Interpretation
46
342 Solution of an inverse problem for determining transcription factor concentrations
47
35 Application Notes
50
36 Summary and Conclusions
53
Acknowledgments
54
DataDriven Mechanistic Modeling of Biochemical Reaction Networks
57
41 Introduction
58
42 Principles of DataDriven Modeling
59
422 Data processing and normalization
60
423 Suitability of models used in conjunction with quantitative data
62
424 Issues related to parameter specification and estimation
63
43 Examples of DataDriven Modeling
64
Computational analysis of signal specificity in yeast
69
Acknowledgments
72
Construction of PhenotypeSpecific Gene Network by Synergy Analysis
75
51 Introduction
76
52 Experimental Design
78
53 Materials
79
543 Metabolites measurements
80
546 Permutation test to evaluate the significance of the synergy
82
56 Discussion and Commentary
83
571 Topological characteristics of the synergy network
84
572 Hub genes in the network
85
58 Summary Points
89
Acknowledgments
90
GenomeScale Analysis of Metabolic Networks
95
61 Introduction
96
62 Materials and Methods
98
622 Model development
99
623 Objective function
100
624 Optimization
104
63 Data Acquisition Anticipated Results and Interpretation
105
632 No feasible solution determined
106
65 Summary Points
107
References
108
Modeling the Dynamics of Cellular Networks
111
71 Introduction
112
72 Materials
113
733 Kinetic modeling
117
734 Parameter estimation
120
74 Data Acquisition Anticipated Results and Interpretation
121
742 Dynamic simulation parameters
122
752 Generalized kinetic expressions
123
753 Population heterogeneity
124
76 Application Notes
125
77 Summary Points
126
SteadyState Sensitivity Analysis of Biochemical Reaction Networks A Brief Review and New Methods
129
81 Introduction
130
82 Considered System Class and Parametric Sensitivity
131
822 Parametric steadystate sensitivity
132
83 Linear Sensitivity Analysis
134
84 Sensitivity Analysis Via Empirical Gramians
136
842 Empirical Gramians for nonlinear systems
137
843 A new sensitivity measure based on Gramians
138
covalent modification system
140
85 Sensitivity Analysis Via Infeasibility Certificates
141
851 Feasibility problem and semidefinite relaxation
142
852 Infeasibility certificates from the dual problem
143
853 Algorithm to bound feasible steady states
144
covalent modification system
145
86 Discussion and Outlook
146
References
147
Determining Metabolite Production Capabilities of Saccharomyces Cerevisiae Using Dynamic Flux Balance Analysis
149
91 Introduction
150
92 Methods
151
95 Summary Points
175
References
176
Related Resources and Supplementary Electronic Information
178
Experimental Design for Parameter Identifiability in Biological Signal Transduction Modeling
179
101 Introduction
180
1012 Parameter estimation
181
1013 Identifiability metrics and conditions
182
1014 Overview of the experimental design procedure
184
102 Methods
185
1022 Identifiability analysis
186
1023 Impact analysis
188
1024 Design modification and reduction
190
1025 Design implementation
191
103 Data Acquisition Anticipated Results and Interpretation
192
Initial perturbation and measurement design
193
Impact analysis
194
Design reduction
196
Identifiability analysis
197
Initial perturbation and measurement design
198
Identifiability analysis
200
105 Discussion and Commentary
205
106 Summary Points
207
Acknowledgments
208
Parameter Identification with Adaptive Sparse GridBased Optimization for Models of Cellular Processes
211
111 Introduction
212
1111 Adaptive sparse grid interpolation
213
112 Experimental Design
215
113 Materials
217
114 Methods
218
115 Data Acquisition Anticipated Results and Interpretation
221
1151 Sorted grid points
222
1153 Unstable points
223
116 Troubleshooting
224
117 Discussion and Commentary
227
118 Application Notes
228
1183 Genetic algorithm
229
119 Summary Points
230
Acknowledgments
231
Related sources and supplementary information
232
Reverse Engineering of Biological Networks
233
Biological Networks and Reverse Engineering
234
1212 Network representation
236
1213 Motivation and design principles
237
1214 Reverse engineering
238
Time Series and Omics Data
239
1221 Metabolomics
240
1223 Transcriptomics
241
123 Approaches for Inference of Biological Networks
242
1231 Genomescale metabolic modeling
243
1232 Boolean networks
245
1233 Network topology from correlation or hierarchical clustering
247
1234 Bayesian networks
248
1235 Ordinary differential equations
250
124 Network BiologyExploring the Inferred Networks
256
1241 Graph theory
257
1242 Motifs and modules
258
1243 Stoichiometric analysis
260
1244 Simulation of dynamics sensitivity analysis control analysis
261
125 Discussion and Comparison of Approaches
264
126 Summary Points
266
References
267
Transcriptome Analysis of Regulatory Networks
271
131 Introduction
272
132 Methods
273
1322 Cell harvesting
274
1324 Transcriptional profiling using DNA microarrays
276
133 Data Acquisition Anticipated Results and Interpretation
281
1333 Network Component Analysis NCA
282
134 Discussion and Commentary
284
136 Summary Points
285
A Workflow from Time Series Gene Expression to Transcriptional Regulatory Networks
287
141 Introduction
288
142 Materials
289
143 Methods
291
1432 Robust clustering of differential gene expression time series data using computational negative control approach
292
1433 Transcriptional regulatory network analysis using PAINT
293
144 Data Acquisition Anticipated Results and Interpretation
296
1451 Estimation of nondifferentially expressed genes pinot value
297
1453 Format of gene identifiers
298
1456 Annotation redundancy in the gene list and multiple promoters
299
146 Application Notes
300
Acknowledgments
301
About the Editors
303
List of Contributors
304
Index
307
Copyright

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About the author (2009)

Arul Jayaraman is an assistant professor in the Department of Chemical Engineering at Texas A&M University. He holds an M.S. and Ph.D. in biochemical engineering from Tufts University and the University of California, respectively. Juergen Hahn is an assistant professor in the Department of Chemical Engineering at Texas A&M University, where he earned his M.S. and Ph.D.

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