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ОбложкаSystems biology / ed. by J.Nielsen, S.Hohmann. - Weinheim: Wiley-VCH, 2017. - xxiv, 401 p.: ill., tab. - (Advanced biotechnology; vol.6). - Bibliogr. at the end of the chapters. - Ind.: p.393-401. - ISBN 978-3-527-33558-9; ISSN 2365-3035
Шифр: (И/E-S98) 02

 

Место хранения: 01 | ГПНТБ СО РАН | Новосибирск

Оглавление / Contents
 
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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