List of Contributors ........................................... XV
About the Series Editors .................................... XXIII
1 Integrative Analysis of Omics Data ......................... 1
Tobias Österlund, Marija Cvijovic, and Erik Kristiansson
Summary .................................................... 1
1.1 Introduction ............................................... 1
1.2 Omics Data and Their Measurement Platforms ................. 4
1.2.1 Omics Data Types .................................... 4
1.2.2 Measurement Platforms ............................... 5
1.3 Data Processing: Quality Assessment, Quantification,
Normalization, and Statistical Analysis .................... 6
1.3.1 Quality Assessment .................................. 7
1.3.2 Quantification ...................................... 9
1.3.3 Normalization ...................................... 10
1.3.4 Statistical Analysis ............................... 11
1.4 Data Integration: From a List of Genes to Biological
Meaning ................................................... 12
1.4.1 Data Resources for Constructing Gene Sets .......... 13
1.4.1.1 Gene Ontology Terms ............................ 13
1.4.1.2 KEGG and Reactome .............................. 13
1.4.1.3 Genome-Scale Metabolic Reconstructions ......... 14
1.4.2 Gene Set Analysis .................................. 14
1.4.2.1 Gene Set Overenrichment Tests .................. 16
1.4.2.2 Rank-Based Enrichment Tests .................... 16
1.4.3 Networks and Network Topology ...................... 17
1.5 Outlook and Perspectives .................................. 18
References ................................................ 19
2 13C Flux Analysis in Biotechnology and Medicine ........... 25
Yi Ern Cheah, Clinton M. Hasenour, and Jamey D. Young
2.1 Introduction .............................................. 25
2.1.1 Why Study Metabolic Fluxes? ........................ 25
2.1.2 Why are Isotope Tracers Important for Flux
Analysis? .......................................... 26
2.1.3 How are Fluxes Determined? ......................... 28
2.2 Theoretical Foundations of 13C MFA ........................ 29
2.2.1 Elementary Metabolite Units (EMUs) ................. 30
2.2.2 Flux Uncertainty Analysis .......................... 31
2.2.3 Optimal Design of Isotope Labeling Experiments ..... 32
2.2.4 Isotopically Nonstationary MFA (INST-MFA) .......... 34
2.3 Metabolic Flux Analysis in Biotechnology .................. 36
2.3.1 13C MFA for Host Characterization .................. 36
2.3.2 13C MFA for Pinpointing Yield Losses and Futile
Cycles ............................................. 39
2.3.3 13C MFA for Bottleneck Identification .............. 41
2.4 Metabolic Flux Analysis in Medicine ....................... 42
2.4.1 Liver Glucose and Oxidative Metabolism ............. 43
2.4.2 Cancer Cell Metabolism ............................. 47
2.4.3 Fuel Oxidation and Anaplerosis in the Heart ........ 48
2.4.4 Metabolism in Other Tissues: Pancreas, Brain,
Muscle, Adipose, and Immune Cells .................. 49
2.5 Emerging Challenges for 13C MFA ........................... 50
2.5.1 Theoretical and Computational Advances: Multiple
Tracers, Co-culture MFA, Dynamic MFA ............... 50
2.5.2 Genome-Scale 13C MFA ............................... 51
2.5.3 New Measurement Strategies ......................... 52
2.5.4 High-Throughput MFA ................................ 53
2.5.5 Application of MFA to Industrial Bioprocesses ...... 53
2.5.6 Integrating MFA with Omics Measurements ............ 54
2.6 Conclusion ................................................ 55
Acknowledgments ........................................... 55
Disclosure ................................................ 55
References ................................................ 55
3 Metabolic Modeling for Design of Cell Factories ........... 71
Mingyuan Tian, Prashant Kumar, Sanjan T.P. Gupta, and
Jennifer L. Reed
Summary ................................................... 71
3.1 Introduction .............................................. 71
3.2 Building and Refining Genome-Scale Metabolic Models ....... 72
3.2.1 Generate a Draft Metabolic Network (Step 1) ........ 74
3.2.2 Manually Curate the Draft Metabolic Network
(Step 2) ........................................... 75
3.2.3 Develop a Constraint-Based Model (Step 3) .......... 77
3.2.4 Revise the Metabolic Model through Reconciliation
with Experimental Data (Step 4) .................... 79
3.2.5 Predicting the Effects of Genetic Manipulations .... 81
3.3 Strain Design Algorithms .................................. 83
3.3.1 Fundamentals of Bilevel Optimization ............... 84
3.3.2 Algorithms Involving Only Gene/Reaction Deletions .. 94
3.3.3 Algorithms Involving Gene Additions ................ 94
3.3.4 Algorithms Involving Gene Over/Underexpression ..... 95
3.3.5 Algorithms Involving Cofactor Changes .............. 98
3.3.6 Algorithms Involving Multiple Design Criteria ...... 99
3.4 Case Studies ............................................. 100
3.4.1 Strains Producing Lactate ......................... 100
3.4.2 Strains Co-utilizing Sugars ....................... 100
3.4.3 Strains Producing 1,4-Butanediol .................. 102
3.5 Conclusions .............................................. 103
Acknowledgments .......................................... 103
References ............................................... 104
4 Genome-Scale Metabolic Modeling and In silico Strain
Design of Escherichia coli ............................... 109
Meiyappan Lakshmanan, Na-Rae Lee, and Dong-Yup Lee
4.1 Introduction ............................................. 109
4.2 The COBRA Approach ....................................... 110
4.3 History of E. coli Metabolic Modeling .................... 111
4.3.1 Pre-genomic-era Models ............................ 111
4.3.2 Genome-Scale Models ............................... 112
4.4 In silico Model-Based Strain Design of E. coli Cell
Factories ................................................ 115
4.4.1 Gene Deletions .................................... 127
4.4.2 Gene Up/Downregulations ........................... 127
4.4.3 Gene Insertions ................................... 128
4.4.4 Cofactor Engineering .............................. 128
4.4.5 Other Approaches .................................. 128
4.5 Future Directions of Model-Guided Strain Design in
E. coli .................................................. 129
References ............................................... 130
5 Accelerating the Drug Development Pipeline with Genome-
Scale Metabolic Network Reconstructions .................. 139
Bonnie V. Dougherty, Thomas J. Moutinho Jr., and Jason
Papin
Summary .................................................. 139
5.1 Introduction ............................................. 139
5.1.1 Drug Development Pipeline ......................... 140
5.1.2 Overview of Genome-Scale Metabolic Network
Reconstructions ................................... 140
5.1.3 Analytical Tools and Mathematical Evaluation ...... 141
5.1.3.1 Flux Balance Analysis (FBA) ................... 141
5.1.3.2 Flux Variability Analysis (FVA) ............... 142
5.2 Metabolic Reconstructions in the Drug Development
Pipeline ................................................. 142
5.2.1 Target Identification ............................. 143
5.2.2 Drug Side Effects ................................. 145
5.3 Species-Level Microbial Reconstructions .................. 146
5.3.1 Microbial Reconstructions in the Antibiotic
Development Pipeline .............................. 146
5.3.1.1 Applications in the Drug Development
Pipeline ...................................... 146
5.3.2 Metabolic-Reconstruction-Facilitated Rational
Drug Target Identification ........................ 147
5.3.2.1 Targeting Genes Essential for Biomass
Production .................................... 147
5.3.2.2 Targeting Virulence Factors ................... 147
5.3.2.3 Metabolite-centric Targeting .................. 148
5.3.3 Repurposing and Expanding Utility of Antibiotics .. 149
5.3.3.1 Virtual Drug Screens Informed by Metabolic
Reconstructions ............................... 149
5.3.3.2 Limiting Resistance with Drug Combinations .... 149
5.3.3.3 Improving Treatment Options by Increasing
Sensitivity to Antibiotics .................... 150
5.3.4 Improving Toxicity Screens with the Human
Metabolic Network Reconstruction .................. 150
5.4 The Human Reconstruction ................................. 151
5.4.1 Approaches for the Human Reconstruction ........... 152
5.4.2 Target Identification ............................. 152
5.4.2.1 Drug Targeting in Cancer ...................... 152
5.4.2.2 Drug Targeting in Metabolic Diseases .......... 153
5.4.3 Toxicity and Other Side Effects ................... 154
5.5 Community Models ......................................... 155
5.5.1 Host-Pathogen Community Models .................... 155
5.5.2 Eukaryotic Community Models ....................... 156
5.6 Personalized Medicine .................................... 156
5.7 Conclusion ............................................... 157
References ............................................... 158
6 Computational Modeling of Microbial Communities .......... 163
Siu H.J. Chan, Margaret Simons, and Costas D. Maranas
Summary .................................................. 163
6.1 Introduction ............................................. 163
6.1.1 Microbial Communities ............................. 163
6.1.2 Modeling Microbial Communities .................... 165
6.1.3 Model Structures .................................. 165
6.1.4 Quantitative Approaches ........................... 166
6.2 Ecological Models ........................................ 168
6.2.1 Generalized Predator-Prey Model ................... 169
6.2.2 Evolutionary Game Theory .......................... 170
6.2.3 Models Including Additional Dimensions ............ 171
6.2.4 Advantages and Disadvantages ...................... 171
6.3 Genome-Scale Metabolic Models ............................ 172
6.3.1 Introduction and Applications ..................... 172
6.3.2 Genome-Scale Metabolic Modeling of Microbial
Communities ....................................... 174
6.3.3 Simulation of Microbial Communities Assuming
Steady State ...................................... 175
6.3.3.1 Predicting Interactions Using FBA ............. 175
6.3.3.2 Identifying Minimal Media by Mixed Integer
Linear Programming ............................ 176
6.3.3.3 Pareto Optimality Analysis by FBA ............. 176
6.3.3.4 Modeling Chemostat Co-culture ................. 177
6.3.3.5 Community FBA with Community Mass Balance ..... 177
6.3.4 Dynamic Simulation of Multispecies Models ......... 177
6.3.5 Spatial and Temporal Modeling of Communities ...... 178
6.3.6 Using Bilevel Optimization to Capture Multiple
Objective Functions ............................... 179
6.3.6.1 OptCom ........................................ 179
6.3.6.2 d-OptCom ...................................... 181
6.3.6.3 CASINO Toolbox ................................ 181
6.3.6.4 Advantages and Disadvantages .................. 182
6.3.6.5 Current Challenges and Future Directions ...... 182
6.4 Concluding Remarks ....................................... 183
References ............................................... 183
7 Drug Targeting of the Human Microbiome ................... 191
Hua Ling, Jee L. Foo, Gourvendu Saxena, Sanjay Swarup,
and Matthew W. Chang
Summary .................................................. 191
7.1 Introduction ............................................. 191
7.2 The Human Microbiome ..................................... 192
7.3 Association of the Human Microbiome with Human
Diseases ................................................. 194
7.3.1 Nasal-Sinus Diseases .............................. 194
7.3.2 Gut Diseases ...................................... 194
7.3.3 Cardiovascular Diseases ........................... 196
7.3.4 Metabolic Disorders ............................... 196
7.3.5 Autoimmune Disorders .............................. 197
7.3.6 Lung Diseases ..................................... 197
7.3.7 Skin Diseases ..................................... 197
7.4 Drug Targeting of the Human Microbiome ................... 198
7.4.1 Prebiotics ........................................ 198
7.4.2 Probiotics ........................................ 200
7.4.3 Antimicrobials .................................... 201
7.4.3.1 Antibiotics ................................... 201
7.4.3.2 Antimicrobial Peptides ........................ 202
7.4.4 Signaling Inhibitors .............................. 202
7.4.5 Metabolites ....................................... 203
7.4.5.1 Short-Chain Fatty Acids ....................... 203
7.4.5.2 Bile Acids .................................... 203
7.4.6 Metabolite Receptors and Enzymes .................. 204
7.4.6.1 Metabolite Receptors .......................... 204
7.4.6.2 Metabolic Enzymes ............................. 204
7.4.7 Microbiome-Aided Drug Metabolism .................. 205
7.4.7.1 Drug Delivery and Release ..................... 205
7.4.7.2 Drug Toxicity ................................. 206
7.4.8 Immune Modulators ................................. 206
7.4.9 Synthetic Commensal Microbes ...................... 207
7.5 Future Perspectives ...................................... 207
7.6 Concluding Remarks ....................................... 208
Acknowledgments .......................................... 208
References ............................................... 209
8 Toward Genome-Scale Models of Signal Transduction
Networks ................................................. 215
Ulrike Münzner, Timo Lubitz, Edda Klipp, and Marcus
Krantz
8.1 Introduction ............................................. 215
8.2 The Potential of Network Reconstruction .................. 219
8.3 Information Transfer Networks ............................ 222
8.4 Approaches to Reconstruction of ITNs ..................... 225
8.5 The rxncon Approach to ITNWR ............................. 230
8.6 Toward Quantitative Analysis and Modeling of Large ITNs .. 234
8.7 Conclusion and Outlook ................................... 236
8 Toward Genome-Scale Models of Signal Transduction
Networks ................................................. 215
Ulrike Münzner, Timo Lubitz, Edda Klipp, and Marcus
Krantz
8.1 Introduction ............................................. 215
8.2 The Potential of Network Reconstruction .................. 219
8.3 Information Transfer Networks ............................ 222
8.4 Approaches to Reconstruction of ITNs ..................... 225
8.5 The rxncon Approach to ITNWR ............................. 230
8.6 Toward Quantitative Analysis and Modeling of Large ITNs .. 234
8.7 Conclusion and Outlook ................................... 236
Acknowledgments .......................................... 236
Glossary ................................................. 237
References ............................................... 238
9 Systems Biology of Aging ................................. 243
Johannes Borgqvist, Riccardo Dainese, and Marija
Cvijovic
Summary .................................................. 243
9.1 Introduction ............................................. 243
9.2 The Biology of Aging ..................................... 245
9.3 The Mathematics of Aging ................................. 249
9.3.1 Databases Devoted to Aging Research ............... 249
9.3.2 Mathematical Modeling in Aging Research ........... 249
9.3.3 Distribution of Damaged Proteins during Cell
Division: A Mathematical Perspective ................... 256
9.3.3.1 Cell Growth ................................... 256
9.3.3.2 Cell Death .................................... 257
9.3.3.3 Cell Division ................................. 257
9.4 Future Challenges ........................................ 260
Conflict of Interest ..................................... 262
References ............................................... 262
10 Modeling the Dynamics of the Immune Response ............. 265
Elena Abad, Pablo Villoslada, and Jordi García-Ojalvo
10.1 Background ............................................... 265
10.2 Dynamics of NF-кВ Signaling .............................. 266
10.2.1 Functional Role and Regulation of NF-kB ........... 266
10.2.2 Dynamics of the NF-кВ Response to Cytokine
Stimulation ....................................... 267
10.3 JAK/STAT Signaling ....................................... 273
10.3.1 Functional Roles of the STAT Proteins ............. 273
10.3.2 Regulation of the JAK/STAT Pathway ................ 274
10.3.3 Multiplicity and Cross-talk in JAK/STAT
Signaling ......................................... 275
10.3.4 Early Modeling of STAT Signaling .................. 276
10.3.5 Minimal Models of STAT Activation Dynamics ........ 277
10.3.6 Cross-talk with Other Immune Pathways ............. 279
10.3.7 Population Dynamics of the Immune System .......... 281
10.4 Conclusions .............................................. 282
Acknowledgments .......................................... 283
References ............................................... 283
11 Dynamics of Signal Transduction in Single Cells
Quantified by Microscopy ................................. 289
Min Ma, Nadim Mira, and Serge Pelet
11.1 Introduction ............................................. 289
11.2 Single-Cell Measurement Techniques ....................... 291
11.2.1 Flow Cytometry .................................... 291
11.2.2 Mass Cytometry .................................... 291
11.2.3 Single-Cell Transcriptomics ....................... 292
11.2.4 Single-Cell Mass Spectrometry ..................... 292
11.2.5 Live-Cell Imaging ................................. 292
11.3 Microscopy ............................................... 293
11.3.1 Epi-Fluorescence Microscopy ....................... 294
11.3.2 Fluorescent Proteins .............................. 295
11.3.3 Relocation Sensors ................................ 295
11.3.4 Förster Resonance Energy Transfer ................. 298
11.4 Imaging Signal Transduction .............................. 300
11.4.1 Quantifying Small Molecules ....................... 300
11.4.2 Monitoring Enzymatic Activity ..................... 301
11.4.2.1 Endogenous Relocation Sensors ................. 301
11.4.2.2 Passive Relocation Sensors .................... 302
11.4.2.3 Active Relocation Sensors ..................... 303
11.4.2.4 FRET Biosensors ............................... 304
11.4.3 Probing Protein-Protein Interactions .............. 304
11.4.3.1 FRET in Protein Complexes ..................... 304
11.4.3.2 Bimolecular Fluorescence Complementation ...... 305
11.4.3.3 Dimerization-Dependent FP ..................... 306
11.4.4 Measuring Protein Synthesis ....................... 307
11.4.4.1 mRNA Transcription ............................ 307
11.4.4.2 Protein Synthesis ............................. 308
11.4.4.3 Expression Dynamics Visualized by Protein
Relocation .................................... 311
11.5 Conclusions .............................................. 311
References ............................................... 312
12 Image-Based In silico Models of Organogenesis ............ 319
Harold F. Gómez, Lada Georgieva, Odysse Michos, and
Dagmar Iber
Summary .................................................. 319
12.1 Introduction ............................................. 319
12.2 Typical Workflow of Image-Based In silico Modeling
Experiments .............................................. 320
12.2.1 In silico Models of Organogenesis ................. 322
12.2.2 Imaging as a Source of (Semi-)Quantitative Data ... 323
12.2.2.1 Imaging a Growing Organ ....................... 324
12.2.3 Image Analysis and Quantification ................. 326
12.2.4 Computational Simulations of Models Describing
Organogenesis ..................................... 328
12.2.5 Image-Based Parameter Estimation .................. 329
12.2.6 In silico Model Validation and Exchange ........... 329
12.2.6.1 In silico Model Validation .................... 329
12.2.6.2 Model Exchange via the Systems Biology
Markup Language (SBML) ............................ 330
12.3 Application: Image-Based Modeling of Branching
Morphogenesis ............................................ 331
12.3.1 Image-Based Model Selection ....................... 331
12.4 Future Avenues ........................................... 334
References ............................................... 334
13 Progress toward Quantitative Design Principles of
Multicellular Systems .................................... 341
Eduardo P. Olimpio, Diego R. Gomez-Alvarez, and Hyun
Youk
Summary .................................................. 341
13.1 Toward Quantitative Design Principles of Multicellular
Systems .................................................. 341
13.2 Breaking Multicellular Systems into Distinct Functional
and Spatial Modules May Be Possible ...................... 342
13.3 Communication among Cells as a Means of Cell-Cell
Interaction .............................................. 346
13.4 Making Sense of the Combinatorial Possibilities Due to
Many Ways that Cells Can Be Arranged in Space ............ 350
13.5 From Individual Cells to Collective Behaviors of Cell
Populations .............................................. 352
13.6 Tuning Multicellular Behaviors ........................... 355
13.7 A New Framework for Quantitatively Understanding
Multicellular Systems .................................... 359
Acknowledgments .......................................... 361
References ............................................... 362
14 Precision Genome Editing for Systems Biology -
A Temporal Perspective ................................... 367
Franziska Voellmy and Rune Linding
Summary .................................................. 367
14.1 Early Techniques in DNA Alterations ...................... 367
14.2 Zinc-Finger Nucleases .................................... 369
14.3 TALENs ................................................... 369
14.4 CRISPR-Cas9 .............................................. 370
14.5 Considerations of Gene-Editing Nuclease Technologies ..... 372
14.5.1 Repairing Nuclease-Induced DNA Damage ............. 372
14.5.2 Nuclease Specificity .............................. 373
14.6 Applications ............................................. 376
14.6.1 CRISPR Nuclease Genome-Wide Loss-of-Function
Screens (CRISPRn) ................................. 377
14.6.2 CRISPR Interference: CRISPRi ...................... 378
14.6.3 CRISPR Activation: CRISPRa ........................ 378
14.6.4 Further Scalable Additions to the CRISPR-Cas
Gene Editing Tool Arsenal ......................... 379
14.6.5 In vivo Applications .............................. 379
14.6.5.1 Animal Disease Models ......................... 379
14.6.5.2 Gene Therapy .................................. 379
14.7 A Focus on the Application of Genome-Engineering
Nucleases on Chromosomal Rearrangements .................. 380
14.7.1 Introduction to Chromosomal Rearrangements:
The First Disease-Related Translocation ........... 380
14.7.2 A Global Look at the Mechanisms behind
Chromosomal Rearrangements ........................ 382
14.7.3 Creating Chromosomal Rearrangements Using
CRISPR-Cas ........................................ 383
14.8 Future Perspectives ...................................... 384
References ............................................... 384
Index ......................................................... 393
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