<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>