## Methods in Bioengineering: Systems Analysis of Biological Networks"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

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17 Discussion and Commentary | 8 |

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 |

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 |

301 | |

About the Editors | 303 |

List of Contributors | 304 |

307 | |

### Common terms and phrases

activation adaptive sparse algorithm approach Bayesian network binding Bioinformatics Biol biological networks biomass Biotechnol calculated cell cellular computational concentration cost function cross Gramian culture database determined DFBA differential dynamic flux balance engineering enzyme equations ethanol ethanol production evaluations example experimental data experimental design fed-batch Figure fluorescence intensity flux balance analysis gene expression gene regulatory networks genome genome-scale global glucose growth identifiability image analysis inference initial input interactions interpolation intracellular kinase kinetic linear matrix measurements metabolic modeling metabolic network metabolite methods microarray ng/ml nodes nonlinear objective function optimization p-value parameter estimation parameter set parameter space parameter values pathways perturbations phenotype phosphorylation pixel plasmid points preadipocytes predicted problem profiles protein regulatory networks Saccharomyces cerevisiae sample Section sensitivity analysis signal transduction simulation solution sparse grid steady-state step stoichiometric model substrate synergy network Systems Biology tion transcription factor uptake variables vector