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Systems Biology


Systems Biology


Advanced Biotechnology 1. Aufl.

von: Jens Nielsen, Stefan Hohmann, Sang Yup Lee, J. Nielsen, Gregory Stephanopoulos

138,99 €

Verlag: Wiley-Blackwell
Format: EPUB
Veröffentl.: 15.03.2017
ISBN/EAN: 9783527696178
Sprache: englisch
Anzahl Seiten: 432

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Beschreibungen

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

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