Systems Biology

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.

• List of Contributors XVAbout the Series Editors XXIII1 Integrative Analysis of Omics Data 1Tobias Österlund, Marija Cvijovic, and Erik KristianssonSummary 11.1 Introduction 11.2 Omics Data and Their Measurement Platforms 41.2.1 Omics Data Types 41.2.2 Measurement Platforms 51.3 Data Processing: Quality Assessment, Quantification, Normalization, and Statistical Analysis 61.3.1 Quality Assessment 71.3.2 Quantification 91.3.3 Normalization 101.3.4 Statistical Analysis 111.4 Data Integration: From a List of Genes to Biological Meaning 121.4.1 Data Resources for Constructing Gene Sets 131.4.2 Gene Set Analysis 141.4.3 Networks and Network Topology 171.5 Outlook and Perspectives 18References 192 13C Flux Analysis in Biotechnology and Medicine 25Yi Ern Cheah, Clinton M. Hasenour, and Jamey D. Young2.1 Introduction 252.1.1 Why Study Metabolic Fluxes? 252.1.2 Why are Isotope Tracers Important for Flux Analysis? 262.1.3 How are Fluxes Determined? 282.2 Theoretical Foundations of 13C MFA 292.2.1 Elementary Metabolite Units (EMUs) 302.2.2 Flux Uncertainty Analysis 312.2.3 Optimal Design of Isotope Labeling Experiments 322.2.4 Isotopically Nonstationary MFA (INST-MFA) 342.3 Metabolic Flux Analysis in Biotechnology 362.3.1 13C MFA for Host Characterization 362.3.2 13C MFA for Pinpointing Yield Losses and Futile Cycles 392.3.3 13C MFA for Bottleneck Identification 412.4 Metabolic Flux Analysis in Medicine 422.4.1 Liver Glucose and Oxidative Metabolism 432.4.2 Cancer Cell Metabolism 472.4.3 Fuel Oxidation and Anaplerosis in the Heart 482.4.4 Metabolism in Other Tissues: Pancreas, Brain, Muscle, Adipose, and Immune Cells 492.5 Emerging Challenges for 13C MFA 502.5.1 Theoretical and Computational Advances: Multiple Tracers, Co-culture MFA, Dynamic MFA 502.5.2 Genome-Scale 13C MFA 512.5.3 New Measurement Strategies 522.5.4 High-Throughput MFA 532.5.5 Application of MFA to Industrial Bioprocesses 532.5.6 Integrating MFA with Omics Measurements 542.6 Conclusion 55Acknowledgments 55Disclosure 55References 553 Metabolic Modeling for Design of Cell Factories 71Mingyuan Tian, Prashant Kumar, Sanjan T. P. Gupta, and Jennifer L. ReedSummary 713.1 Introduction 713.2 Building and Refining Genome-Scale Metabolic Models 723.2.1 Generate a Draft Metabolic Network (Step 1) 743.2.2 Manually Curate the Draft Metabolic Network (Step 2) 753.2.3 Develop a Constraint-Based Model (Step 3) 773.2.4 Revise the Metabolic Model through Reconciliation with Experimental Data (Step 4) 793.2.5 Predicting the Effects of Genetic Manipulations 813.3 Strain Design Algorithms 833.3.1 Fundamentals of Bilevel Optimization 843.3.2 Algorithms Involving Only Gene/Reaction Deletions 943.3.3 Algorithms Involving Gene Additions 943.3.4 Algorithms Involving Gene Over/Underexpression 953.3.5 Algorithms Involving Cofactor Changes 983.3.6 Algorithms Involving Multiple Design Criteria 993.4 Case Studies 1003.4.1 Strains Producing Lactate 1003.4.2 Strains Co-utilizing Sugars 1003.4.3 Strains Producing 1,4-Butanediol 1023.5 Conclusions 103Acknowledgments 103References 1044 Genome-Scale Metabolic Modeling and In silico Strain Design of Escherichia coli 109Meiyappan Lakshmanan, Na-Rae Lee, and Dong-Yup Lee4.1 Introduction 1094.2 The COBRA Approach 1104.3 History of E. coli Metabolic Modeling 1114.3.1 Pre-genomic-era Models 1114.3.2 Genome-Scale Models 1124.4 In silico Model-Based Strain Design of E. coli Cell Factories 1154.4.1 Gene Deletions 1274.4.2 Gene Up/Downregulations 1274.4.3 Gene Insertions 1284.4.4 Cofactor Engineering 1284.4.5 Other Approaches 1284.5 Future Directions of Model-Guided Strain Design in E. coli 129References 1305 Accelerating the Drug Development Pipeline with Genome-Scale Metabolic Network Reconstructions 139Bonnie V. Dougherty, Thomas J. Moutinho Jr., and Jason PapinSummary 1395.1 Introduction 1395.1.1 Drug Development Pipeline 1405.1.2 Overview of Genome-Scale Metabolic Network Reconstructions 1405.1.3 Analytical Tools and Mathematical Evaluation 1415.2 Metabolic Reconstructions in the Drug Development Pipeline 1425.2.1 Target Identification 1435.2.2 Drug Side Effects 1455.3 Species-Level Microbial Reconstructions 1465.3.1 Microbial Reconstructions in the Antibiotic Development Pipeline 1465.3.2 Metabolic-Reconstruction-Facilitated Rational Drug Target Identification 1475.3.3 Repurposing and Expanding Utility of Antibiotics 1495.3.4 Improving Toxicity Screens with the Human Metabolic Network Reconstruction 1505.4 The Human Reconstruction 1515.4.1 Approaches for the Human Reconstruction 1525.4.2 Target Identification 1525.4.3 Toxicity and Other Side Effects 1545.5 Community Models 1555.5.1 Host–Pathogen Community Models 1555.5.2 Eukaryotic Community Models 1565.6 Personalized Medicine 1565.7 Conclusion 157References 1586 Computational Modeling of Microbial Communities 163Siu H. J. Chan, Margaret Simons, and Costas D. MaranasSummary 1636.1 Introduction 1636.1.1 Microbial Communities 1636.1.2 Modeling Microbial Communities 1656.1.3 Model Structures 1656.1.4 Quantitative Approaches 1666.2 Ecological Models 1686.2.1 Generalized Predator–Prey Model 1696.2.2 Evolutionary Game Theory 1706.2.3 Models Including Additional Dimensions 1716.2.4 Advantages and Disadvantages 1716.3 Genome-Scale Metabolic Models 1726.3.1 Introduction and Applications 1726.3.2 Genome-Scale Metabolic Modeling of Microbial Communities 1746.3.3 Simulation of Microbial Communities Assuming Steady State 1756.3.4 Dynamic Simulation of Multispecies Models 1776.3.5 Spatial and Temporal Modeling of Communities 1786.3.6 Using Bilevel Optimization to Capture Multiple Objective Functions 1796.4 Concluding Remarks 183References 1837 Drug Targeting of the Human Microbiome 191Hua Ling, Jee L. Foo, Gourvendu Saxena, Sanjay Swarup, and Matthew W. ChangSummary 1917.1 Introduction 1917.2 The Human Microbiome 1927.3 Association of the Human Microbiome with Human Diseases 1947.3.1 Nasal–Sinus Diseases 1947.3.2 Gut Diseases 1947.3.3 Cardiovascular Diseases 1967.3.4 Metabolic Disorders 1967.3.5 Autoimmune Disorders 1977.3.6 Lung Diseases 1977.3.7 Skin Diseases 1977.4 Drug Targeting of the Human Microbiome 1987.4.1 Prebiotics 1987.4.2 Probiotics 2007.4.3 Antimicrobials 2017.4.4 Signaling Inhibitors 2027.4.5 Metabolites 2037.4.6 Metabolite Receptors and Enzymes 2047.4.7 Microbiome-Aided Drug Metabolism 2057.4.8 Immune Modulators 2067.4.9 Synthetic Commensal Microbes 2077.5 Future Perspectives 2077.6 Concluding Remarks 208Acknowledgments 208References 2098 Toward Genome-Scale Models of Signal Transduction Networks 215Ulrike Münzner, Timo Lubitz, Edda Klipp, and Marcus Krantz8.1 Introduction 2158.2 The Potential of Network Reconstruction 2198.3 Information Transfer Networks 2228.4 Approaches to Reconstruction of ITNs 2258.5 The rxncon Approach to ITNWR 2308.6 Toward Quantitative Analysis and Modeling of Large ITNs 2348.7 Conclusion and Outlook 236Acknowledgments 236Glossary 237References 2389 Systems Biology of Aging 243Johannes Borgqvist, Riccardo Dainese, and Marija CvijovicSummary 2439.1 Introduction 2439.2 The Biology of Aging 2459.3 The Mathematics of Aging 2499.3.1 Databases Devoted to Aging Research 2499.3.2 Mathematical Modeling in Aging Research 2499.3.3 Distribution of Damaged Proteins during Cell Division: A Mathematical Perspective 2569.4 Future Challenges 260Conflict of Interest 262References 26210 Modeling the Dynamics of the Immune Response 265Elena Abad, Pablo Villoslada, and Jordi García-Ojalvo10.1 Background 26510.2 Dynamics of NF-κB Signaling 26610.2.1 Functional Role and Regulation of NF-κB 26610.2.2 Dynamics of the NF-κB Response to Cytokine Stimulation 26710.3 JAK/STAT Signaling 27310.3.1 Functional Roles of the STAT Proteins 27310.3.2 Regulation of the JAK/STAT Pathway 27410.3.3 Multiplicity and Cross-talk in JAK/STAT Signaling 27510.3.4 Early Modeling of STAT Signaling 27610.3.5 Minimal Models of STAT Activation Dynamics 27710.3.6 Cross-talk with Other Immune Pathways 27910.3.7 Population Dynamics of the Immune System 28110.4 Conclusions 282Acknowledgments 283References 28311 Dynamics of Signal Transduction in Single Cells Quantified by Microscopy 289Min Ma, Nadim Mira, and Serge Pelet11.1 Introduction 28911.2 Single-Cell Measurement Techniques 29111.2.1 Flow Cytometry 29111.2.2 Mass Cytometry 29111.2.3 Single-Cell Transcriptomics 29211.2.4 Single-Cell Mass Spectrometry 29211.2.5 Live-Cell Imaging 29211.3 Microscopy 29311.3.1 Epi-Fluorescence Microscopy 29411.3.2 Fluorescent Proteins 29511.3.3 Relocation Sensors 29511.3.4 Förster Resonance Energy Transfer 29811.4 Imaging Signal Transduction 30011.4.1 Quantifying Small Molecules 30011.4.2 Monitoring Enzymatic Activity 30111.4.3 Probing Protein–Protein Interactions 30411.4.4 Measuring Protein Synthesis 30711.5 Conclusions 311References 31212 Image-Based In silico Models of Organogenesis 319Harold F. Gómez, Lada Georgieva, Odysse Michos, and Dagmar IberSummary 31912.1 Introduction 31912.2 Typical Workflow of Image-Based In silico Modeling Experiments 32012.2.1 In silico Models of Organogenesis 32212.2.2 Imaging as a Source of (Semi-)Quantitative Data 32312.2.3 Image Analysis and Quantification 32612.2.4 Computational Simulations of Models Describing Organogenesis 32812.2.5 Image-Based Parameter Estimation 32912.2.6 In silico Model Validation and Exchange 32912.3 Application: Image-Based Modeling of Branching Morphogenesis 33112.3.1 Image-Based Model Selection 33112.4 Future Avenues 334References 33413 Progress toward Quantitative Design Principles of Multicellular Systems 341Eduardo P. Olimpio, Diego R. Gomez-Alvarez, and Hyun YoukSummary 34113.1 Toward Quantitative Design Principles of Multicellular Systems 34113.2 Breaking Multicellular Systems into Distinct Functional and Spatial Modules May Be Possible 34213.3 Communication among Cells as a Means of Cell–Cell Interaction 34613.4 Making Sense of the Combinatorial Possibilities Due to Many Ways that Cells Can Be Arranged in Space 35013.5 From Individual Cells to Collective Behaviors of Cell Populations 35213.6 Tuning Multicellular Behaviors 35513.7 A New Framework for Quantitatively Understanding Multicellular Systems 359Acknowledgments 361References 36214 Precision Genome Editing for Systems Biology – A Temporal Perspective 367Franziska Voellmy and Rune LindingSummary 36714.1 Early Techniques in DNA Alterations 36714.2 Zinc-Finger Nucleases 36914.3 TALENs 36914.4 CRISPR-Cas9 37014.5 Considerations of Gene-Editing Nuclease Technologies 37214.5.1 Repairing Nuclease-Induced DNA Damage 37214.5.2 Nuclease Specificity 37314.6 Applications 37614.6.1 CRISPR Nuclease Genome-Wide Loss-of-Function Screens (CRISPRn) 37714.6.2 CRISPR Interference: CRISPRi 37814.6.3 CRISPR Activation: CRISPRa 37814.6.4 Further Scalable Additions to the CRISPR-Cas Gene Editing Tool Arsenal 37914.6.5 In vivo Applications 37914.7 A Focus on the Application of Genome-Engineering Nucleases on Chromosomal Rearrangements 38014.7.1 Introduction to Chromosomal Rearrangements: The First Disease-Related Translocation 38014.7.2 A Global Look at the Mechanisms behind Chromosomal Rearrangements 38214.7.3 Creating Chromosomal Rearrangements Using CRISPR-Cas 38314.8 Future Perspectives 384References 384Index 393