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Systems Biology (Advanced Biotechnology)

Editat de Jens Nielsen, Stefan Hohmann Sang Yup Lee, J. Nielsen, Gregory Stephanopoulos
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en Limba Engleză Carte Hardback – 26 Apr 2017
Comprehensive coverage of the many different aspects of systems biology, resulting in an excellent overview of the experimental and computational approaches currently in use to study biological systems.

Each chapter represents a valuable introduction to one specific branch of systems biology, while also including the current state of the art and pointers to future directions. Following different methods for the integrative analysis of omics data, the book goes on to describe techniques that allow for the direct quantification of carbon fluxes in large metabolic networks, including the use of 13C labelled substrates and genome–scale metabolic models. The latter is explained on the basis of the model organism Escherichia coli as well as the human metabolism. Subsequently, the authors deal with the application of such techniques to human health and cell factory engineering, with a focus on recent progress in building genome–scale models and regulatory networks. They highlight the importance of such information for specific biological processes, including the ageing of cells, the immune system and organogenesis. The book concludes with a summary of recent advances in genome editing, which have allowed for precise genetic modifications, even with the dynamic control of gene expression.

This is part of the Advances Biotechnology series, covering all pertinent aspects of the field with each volume prepared by eminent scientists who are experts on the topic in question.
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Specificații

ISBN-13: 9783527335589
ISBN-10: 3527335587
Pagini: 432
Dimensiuni: 172 x 249 x 26 mm
Greutate: 0.95 kg
Editura: Wiley Vch
Seria Advanced Biotechnology

Locul publicării: Weinheim, Germany

Public țintă

Biological Institutes, Biotechnologists, Biotechnological Industry, Biotechnological Institutes, Cell Biologists

Cuprins

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
1.4.2 Gene Set Analysis 14
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
5.2 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
5.3.2 Metabolic–Reconstruction–Facilitated Rational Drug Target Identification 147
5.3.3 Repurposing and Expanding Utility of Antibiotics 149
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
5.4.3 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
6.3.4 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
6.4 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
7.4.4 Signaling Inhibitors 202
7.4.5 Metabolites 203
7.4.6 Metabolite Receptors and Enzymes 204
7.4.7 Microbiome–Aided Drug Metabolism 205
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
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
9.4 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– B Signaling 266
10.2.1 Functional Role and Regulation of NF– B 266
10.2.2 Dynamics of the NF– B 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
11.4.3 Probing Protein Protein Interactions 304
11.4.4 Measuring Protein Synthesis 307
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
12.2.3 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
12.3 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
14.7 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

Notă biografică

Jens Nielsen has a PhD degree (1989) in Biochemical Engineering from the Danish Technical University (DTU), and after that established his independent research group and was appointed full Professor there in 1998. He was Fulbright visiting professor at MIT in 1995–1996. At DTU he founded and directed the Center for Microbial Biotechnology. In 2008 he was recruited as Professor and Director to Chalmers University of Technology, Sweden. Jens Nielsen has received numerous Danish and international awards including the Nature Mentor Award, and is member of several academies, including the National Academy of Engineering in USA and the Royal Swedish Academy of Science. He is a founding president of the International Metabolic Engineering Society.

Stefan Hohmann is Head of the Department of Biology and Biological Engineering at Chalmers University (Sweden). He studied biology and microbiology at the Technische Universität Darmstadt (Germany), where he received his PhD in 1987 and became professor in 1993. He held positions as visiting professor at the Katholieke Universiteit Leuven (Belgium) and the University of the Orange Free State (South Africa), before joining the University of Gothenburg in 1999 as professor, a position he hold until his change to Chalmers University in 2015. Stefan Hohmann serves as chairman of several committees and is the Swedish representative at the European Molecular Biology Laboratory (EMBL) Research Council.

Sang Yup Lee is Distinguished Professor at the Department of Chemical and Biomolecular Engineering at the Korea Advanced Institute of Science and Technology (KAIST). He is currently the Director of the Center for Systems and Synthetic Biotechnology, Director of the BioProcess Engineering Research Center, and Director of the Bioinformatics Research Center. He received numerous awards, including the National Order of Merit, the Merck Metabolic Engineering Award and the Elmer Gaden Award. Lee is the Editor–in–Chief of the Biotechnology Journal and Associate Editor and board member of numerous other journals. Lee is currently serving as a member of Presidential Advisory Committee on Science and Technology (Korea).

Professor Gregory Stephanopoulos is the W. H. Dow Professor of Chemical Engineering at the Massachusetts Institute of Technology (MIT, USA) and Director of the MIT Metabolic Engineering Laboratory. He is also Instructor of Bioengineering at Harvard Medical School (since 1997). He has been recognized by numerous awards from the American Institute of Chemical Engineers (AIChE) (Wilhelm, Walker and Founders awards), American Chemical Society (ACS), Society of industrial Microbiology (SIM), BIO (Washington Carver Award), the John Fritz Medal of the American Association of Engineering Societies, and others. In 2003 he was elected member of the National Academy of Engineering (USA) and in 2014 President of AIChE.