Chapter
1.2.2 Measurement Platforms
1.3 Data Processing: Quality Assessment, Quantification, Normalization, and Statistical Analysis
1.3.4 Statistical Analysis
1.4 Data Integration: From a List of Genes to Biological Meaning
1.4.1 Data Resources for Constructing Gene Sets
1.4.1.1 Gene Ontology Terms
1.4.1.2 KEGG and Reactome
1.4.1.3 Genome-Scale Metabolic Reconstructions
1.4.2.1 Gene Set Overenrichment Tests
1.4.2.2 Rank-Based Enrichment Tests
1.4.3 Networks and Network Topology
1.5 Outlook and Perspectives
Chapter 2 13C Flux Analysis in Biotechnology and Medicine
2.1.1 Why Study Metabolic Fluxes?
2.1.2 Why are Isotope Tracers Important for Flux Analysis?
2.1.3 How are Fluxes Determined?
2.2 Theoretical Foundations of 13C MFA
2.2.1 Elementary Metabolite Units (EMUs)
2.2.2 Flux Uncertainty Analysis
2.2.3 Optimal Design of Isotope Labeling Experiments
2.2.4 Isotopically Nonstationary MFA (INST-MFA)
2.3 Metabolic Flux Analysis in Biotechnology
2.3.1 13C MFA for Host Characterization
2.3.2 13C MFA for Pinpointing Yield Losses and Futile Cycles
2.3.3 13C MFA for Bottleneck Identification
2.4 Metabolic Flux Analysis in Medicine
2.4.1 Liver Glucose and Oxidative Metabolism
2.4.2 Cancer Cell Metabolism
2.4.3 Fuel Oxidation and Anaplerosis in the Heart
2.4.4 Metabolism in Other Tissues: Pancreas, Brain, Muscle, Adipose, and Immune Cells
2.5 Emerging Challenges for 13C MFA
2.5.1 Theoretical and Computational Advances: Multiple Tracers, Co-culture MFA, Dynamic MFA
2.5.2 Genome-Scale 13C MFA
2.5.3 New Measurement Strategies
2.5.4 High-Throughput MFA
2.5.5 Application of MFA to Industrial Bioprocesses
2.5.6 Integrating MFA with Omics Measurements
Chapter 3 Metabolic Modeling for Design of Cell Factories
3.2 Building and Refining Genome-Scale Metabolic Models
3.2.1 Generate a Draft Metabolic Network (Step 1)
3.2.2 Manually Curate the Draft Metabolic Network (Step 2)
3.2.3 Develop a Constraint-Based Model (Step 3)
3.2.4 Revise the Metabolic Model through Reconciliation with Experimental Data (Step 4)
3.2.5 Predicting the Effects of Genetic Manipulations
3.3 Strain Design Algorithms
3.3.1 Fundamentals of Bilevel Optimization
3.3.2 Algorithms Involving Only Gene/Reaction Deletions
3.3.3 Algorithms Involving Gene Additions
3.3.4 Algorithms Involving Gene Over/Underexpression
3.3.5 Algorithms Involving Cofactor Changes
3.3.6 Algorithms Involving Multiple Design Criteria
3.4.1 Strains Producing Lactate
3.4.2 Strains Co-utilizing Sugars
3.4.3 Strains Producing 1,4-Butanediol
Chapter 4 Genome-Scale Metabolic Modeling and In silico Strain Design of Escherichia coli
4.3 History of E. coli Metabolic Modeling
4.3.1 Pre-genomic-era Models
4.3.2 Genome-Scale Models
4.4 In silico Model-Based Strain Design of E. coli Cell Factories
4.4.2 Gene Up/Downregulations
4.4.4 Cofactor Engineering
4.5 Future Directions of Model-Guided Strain Design in E. coli
Chapter 5 Accelerating the Drug Development Pipeline with Genome-Scale Metabolic Network Reconstructions
5.1.1 Drug Development Pipeline
5.1.2 Overview of Genome-Scale Metabolic Network Reconstructions
5.1.3 Analytical Tools and Mathematical Evaluation
5.1.3.1 Flux Balance Analysis (FBA)
5.1.3.2 Flux Variability Analysis (FVA)
5.2 Metabolic Reconstructions in the Drug Development Pipeline
5.2.1 Target Identification
5.3 Species-Level Microbial Reconstructions
5.3.1 Microbial Reconstructions in the Antibiotic Development Pipeline
5.3.1.1 Applications in the Drug Development Pipeline
5.3.2 Metabolic-Reconstruction-Facilitated Rational Drug Target Identification
5.3.2.1 Targeting Genes Essential for Biomass Production
5.3.2.2 Targeting Virulence Factors
5.3.2.3 Metabolite-centric Targeting
5.3.3 Repurposing and Expanding Utility of Antibiotics
5.3.3.1 Virtual Drug Screens Informed by Metabolic Reconstructions
5.3.3.2 Limiting Resistance with Drug Combinations
5.3.3.3 Improving Treatment Options by Increasing Sensitivity to Antibiotics
5.3.4 Improving Toxicity Screens with the Human Metabolic Network Reconstruction
5.4 The Human Reconstruction
5.4.1 Approaches for the Human Reconstruction
5.4.2 Target Identification
5.4.2.1 Drug Targeting in Cancer
5.4.2.2 Drug Targeting in Metabolic Diseases
5.4.3 Toxicity and Other Side Effects
5.5.1 Host-Pathogen Community Models
5.5.2 Eukaryotic Community Models
5.6 Personalized Medicine
Chapter 6 Computational Modeling of Microbial Communities
6.1.1 Microbial Communities
6.1.2 Modeling Microbial Communities
6.1.4 Quantitative Approaches
6.2.1 Generalized Predator-Prey Model
6.2.2 Evolutionary Game Theory
6.2.3 Models Including Additional Dimensions
6.2.4 Advantages and Disadvantages
6.3 Genome-Scale Metabolic Models
6.3.1 Introduction and Applications
6.3.2 Genome-Scale Metabolic Modeling of Microbial Communities
6.3.3 Simulation of Microbial Communities Assuming Steady State
6.3.3.1 Predicting Interactions Using FBA
6.3.3.2 Identifying Minimal Media by Mixed Integer Linear Programming
6.3.3.3 Pareto Optimality Analysis by FBA
6.3.3.4 Modeling Chemostat Co-culture
6.3.3.5 Community FBA with Community Mass Balance
6.3.4 Dynamic Simulation of Multispecies Models
6.3.5 Spatial and Temporal Modeling of Communities
6.3.6 Using Bilevel Optimization to Capture Multiple Objective Functions
6.3.6.4 Advantages and Disadvantages
6.3.6.5 Current Challenges and Future Directions
Chapter 7 Drug Targeting of the Human Microbiome
7.3 Association of the Human Microbiome with Human Diseases
7.3.1 Nasal-Sinus Diseases
7.3.3 Cardiovascular Diseases
7.3.4 Metabolic Disorders
7.3.5 Autoimmune Disorders
7.4 Drug Targeting of the Human Microbiome
7.4.3.2 Antimicrobial Peptides
7.4.4 Signaling Inhibitors
7.4.5.1 Short-Chain Fatty Acids
7.4.6 Metabolite Receptors and Enzymes
7.4.6.1 Metabolite Receptors
7.4.6.2 Metabolic Enzymes
7.4.7 Microbiome-Aided Drug Metabolism
7.4.7.1 Drug Delivery and Release
7.4.9 Synthetic Commensal Microbes
Chapter 8 Toward Genome-Scale Models of Signal Transduction Networks
8.2 The Potential of Network Reconstruction
8.3 Information Transfer Networks
8.4 Approaches to Reconstruction of ITNs
8.5 The rxncon Approach to ITNWR
8.6 Toward Quantitative Analysis and Modeling of Large ITNs
8.7 Conclusion and Outlook
Chapter 9 Systems Biology of Aging
9.3 The Mathematics of Aging
9.3.1 Databases Devoted to Aging Research
9.3.2 Mathematical Modeling in Aging Research
9.3.3 Distribution of Damaged Proteins during Cell Division: A Mathematical Perspective
Chapter 10 Modeling the Dynamics of the Immune Response
10.2 Dynamics of NF-KB Signaling
10.2.1 Functional Role and Regulation of NF-kB
10.2.2 Dynamics of the NF-KB Response to Cytokine Stimulation
10.3.1 Functional Roles of the STAT Proteins
10.3.2 Regulation of the JAK/STAT Pathway
10.3.3 Multiplicity and Cross-talk in JAK/STAT Signaling
10.3.4 Early Modeling of STAT Signaling
10.3.5 Minimal Models of STAT Activation Dynamics
10.3.6 Cross-talk with Other Immune Pathways
10.3.7 Population Dynamics of the Immune System
Chapter 11 Dynamics of Signal Transduction in Single Cells Quantified by Microscopy
11.2 Single-Cell Measurement Techniques
11.2.3 Single-Cell Transcriptomics
11.2.4 Single-Cell Mass Spectrometry
11.3.1 Epi-Fluorescence Microscopy
11.3.2 Fluorescent Proteins
11.3.3 Relocation Sensors
11.3.4 Förster Resonance Energy Transfer
11.4 Imaging Signal Transduction
11.4.1 Quantifying Small Molecules
11.4.2 Monitoring Enzymatic Activity
11.4.2.1 Endogenous Relocation Sensors
11.4.2.2 Passive Relocation Sensors
11.4.2.3 Active Relocation Sensors
11.4.3 Probing Protein-Protein Interactions
11.4.3.1 FRET in Protein Complexes
11.4.3.2 Bimolecular Fluorescence Complementation
11.4.3.3 Dimerization-Dependent FP
11.4.4 Measuring Protein Synthesis
11.4.4.1 mRNA Transcription
11.4.4.2 Protein Synthesis
11.4.4.3 Expression Dynamics Visualized by Protein Relocation
Chapter 12 Image-Based In silico Models of Organogenesis
12.2 Typical Workflow of Image-Based In silico Modeling Experiments
12.2.1 In silico Models of Organogenesis
12.2.2 Imaging as a Source of (Semi-)Quantitative Data
12.2.2.1 Imaging a Growing Organ
12.2.3 Image Analysis and Quantification
12.2.4 Computational Simulations of Models Describing Organogenesis
12.2.5 Image-Based Parameter Estimation
12.2.6 In silico Model Validation and Exchange
12.2.6.1 In silico Model Validation
12.2.6.2 Model Exchange via the Systems Biology Markup Language (SBML)
12.3 Application: Image-Based Modeling of Branching Morphogenesis
12.3.1 Image-Based Model Selection
Chapter 13 Progress toward Quantitative Design Principles of Multicellular Systems
13.1 Toward Quantitative Design Principles of Multicellular Systems
13.2 Breaking Multicellular Systems into Distinct Functional and Spatial Modules May Be Possible
13.3 Communication among Cells as a Means of Cell-Cell Interaction
13.4 Making Sense of the Combinatorial Possibilities Due to Many Ways that Cells Can Be Arranged in Space
13.5 From Individual Cells to Collective Behaviors of Cell Populations
13.6 Tuning Multicellular Behaviors
13.7 A New Framework for Quantitatively Understanding Multicellular Systems
Chapter 14 Precision Genome Editing for Systems Biology - A Temporal Perspective
14.1 Early Techniques in DNA Alterations
14.2 Zinc-Finger Nucleases
14.5 Considerations of Gene-Editing Nuclease Technologies
14.5.1 Repairing Nuclease-Induced DNA Damage
14.5.2 Nuclease Specificity
14.6.1 CRISPR Nuclease Genome-Wide Loss-of-Function Screens (CRISPRn)
14.6.2 CRISPR Interference: CRISPRi
14.6.3 CRISPR Activation: CRISPRa
14.6.4 Further Scalable Additions to the CRISPR-Cas Gene Editing Tool Arsenal
14.6.5 In vivo Applications
14.6.5.1 Animal Disease Models
14.7 A Focus on the Application of Genome-Engineering Nucleases on Chromosomal Rearrangements
14.7.1 Introduction to Chromosomal Rearrangements: The First Disease-Related Translocation
14.7.2 A Global Look at the Mechanisms behind Chromosomal Rearrangements
14.7.3 Creating Chromosomal Rearrangements Using CRISPR-Cas
Systems Biology
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