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E-raamat: Metabolic Engineering: Concepts and Applications

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Learn more about foundational and advanced topics in metabolic engineering in this comprehensive resource edited by leaders in the field

Metabolic Engineering: Concepts and Applications delivers a one-stop resource for readers seeking a complete description of the concepts, models, and applications of metabolic engineering. This guide offers practical insights into the metabolic engineering of major cell lines, including E. Coli, Bacillus and Yarrowia Lipolytica, and organisms, including human, animal, and plant). The distinguished editors also offer readers resources on microbiome engineering and the use of metabolic engineering in bioremediation.

Written in two parts, Metabolic Engineering begins with the essential models and strategies of the field, like Flux Balance Analysis, Quantitative Flux Analysis, and Proteome Constrained Models. It also provides an overview of topics like Pathway Design, Metabolomics, and Genome Editing of Bacteria and Eukarya.

The second part contains insightful descriptions of the practical applications of metabolic engineering, including specific examples that shed light on the topics within. In addition to subjects like the metabolic engineering of animals, humans, and plants, you’ll learn more about:

  • Metabolic engineering concepts and a historical perspective on their development
  • The different modes of analysis, including flux balance analysis and quantitative flux analysis
  • An illuminating and complete discussion of the thermodynamics of metabolic pathways
  • The Genome architecture of E. coli, as well as genome editing of both bacteria and eukarya
  • An in-depth treatment of the application of metabolic engineering techniques to organisms including corynebacterial, bacillus, and pseudomonas, and more

Perfect for students of biotechnology, bioengineers, and biotechnologists, Metabolic Engineering: Concepts and Applications also has a place on the bookshelves of research institutes, biotechnological institutes and industry labs, and university libraries. It's comprehensive treatment of all relevant metabolic engineering concepts, models, and applications will be of use to practicing biotechnologists and bioengineers who wish to solidify their understanding of the field.

Volume 13a
Preface xvii
Part I Concepts
1(338)
1 Metabolic Engineering Perspectives
3(20)
Nian Liu
Gregory Stephanopoulos
1.1 History and Overview of Metabolic Engineering
3(2)
1.2 Understanding Cellular Metabolism and Physiology
5(4)
1.2.1 Computational Methods in Understanding Metabolism
6(1)
1.2.2 Experimental Methods in Understanding Metabolism
7(2)
1.3 General Approaches to Metabolic Engineering
9(6)
1.3.1 Rational Metabolic Engineering
10(2)
1.3.2 Combinatorial Metabolic Engineering
12(2)
1.3.3 Systems Metabolic Engineering
14(1)
1.4 Host Organism Selection
15(1)
1.5 Substrate Considerations
15(1)
1.6 Metabolic Engineering and Synthetic Biology
16(1)
1.7 The Future of Metabolic Engineering
17(6)
References
19(4)
2 Genome-Scale Models: Two Decades of Progress and a 2020 Vision
23(50)
Bernhard O. Palsson
2.1 Introduction
23(1)
2.2 Flux Balance Analysis
23(7)
2.2.1 Dynamic Mass Balances
23(2)
2.2.2 Analogy to Deriving Enzymatic Rate Equations
25(1)
2.2.3 Formulating Flux Balances at the Genome-Scale
25(1)
2.2.4 Constrained Optimization
26(1)
2.2.5 Principles
26(1)
2.2.6 Additional Constraints
27(1)
2.2.7 Flux-Concentration Duality
28(1)
2.2.8 Recap
28(2)
2.3 Network Reconstruction
30(6)
2.3.1 Assembling the Reactome
30(1)
2.3.2 Basic Principles of Network Reconstruction
30(2)
2.3.3 Curation
32(1)
2.3.4 GEMs Have a Genomic Basis
32(1)
2.3.5 Computational Queries
32(1)
2.3.6 Scope Expansion
33(2)
2.3.7 Knowledge Bases
35(1)
2.3.8 Availability of GEMs
35(1)
2.3.9 Recap
35(1)
2.4 Brief History of the GEM for E. coli
36(6)
2.4.1 Origin
36(1)
2.4.2 Model Organism
36(2)
2.4.3 Key Predictions
38(1)
2.4.4 Design Algorithms
38(3)
2.4.5 Scope Expansions
41(1)
2.4.6 Recap
42(1)
2.5 From Metabolism to the Proteome
42(8)
2.5.1 ME Models
42(1)
2.5.2 Capabilities of ME Models
43(1)
2.5.2.1 Growth-Coupled Metabolic Designs Can Be Reproduced in GEMs
43(1)
2.5.2.2 ME Models Can Reflect Properties of the Metalloproteome
44(1)
2.5.2.3 ME Models Can Compute the Biomass Objective Function
44(2)
2.5.2.4 Computing Stresses
46(3)
2.5.3 Recapitulation
49(1)
2.6 Current Developments
50(6)
2.6.1 Kinetics
50(1)
2.6.2 Transcriptional Regulation
51(1)
2.6.2.1 iModulons
52(1)
2.6.2.2 Activities
52(3)
2.6.3 Protein Structures
55(1)
2.7 Broader Perspectives
56(3)
2.7.1 Distal Causation
56(1)
2.7.2 Contextualization of GEMs Within Workflows
57(2)
2.8 What Does the Future Look Like for GEMs?
59(14)
Disclaimer
62(1)
Acknowledgments
63(1)
References
63(10)
3 Quantitative Metabolic Flux Analysis Based on Isotope Labeling
73(64)
Wolfgang Wiechert
Katharina Noh
3.1 Introduction
73(4)
3.1.1 What Metabolic Flux Analysis Is About
73(3)
3.1.2 The Variants of 13C-MFA
76(1)
3.2 A Toy Example Illustrates the Basic Principles
77(6)
3.2.1 Fluxomics: More Than Just a Branch of Metabolomics
77(2)
3.2.2 Isotope Labeling: The Key to Metabolic Fluxes
79(3)
3.2.3 From the Data to the Intracellular Fluxes
82(1)
3.2 A INST-13C-MFA: Metabolic Stationary, but Isotopically Nonstationary
83(14)
3.2.5 From Measurements to Flux Estimates: Parameter Fitting
84(2)
3.2.6 Flux Estimates Have Confidence Bounds: Statistical Analysis
86(4)
3.2.7 The Classical Approach at Metabolic and Isotopic Stationary State
90(1)
3.2.8 An Additional Source of Information: Carbon Atom Transitions
91(2)
3.2.9 Input Labeling Design: How Informative Can an Experiment Be Made?
93(1)
3.2.10 The Isotopomers of a Single Metabolite Can Be a Rich Source of Information
94(1)
3.2.11 Bidirectional Reaction Steps: More Than Just Nuisance Factors
95(1)
3.2.12 Isotopomer Fractions Cannot Be Measured Comprehensively
96(1)
3.3 Lessons Learned from the Example
97(3)
3.3.1 Definition of 13C-MFA Revisited
97(2)
3.3.2 Statistical Evaluation and Optimal Experimental Design
99(1)
3.4 How to Configure an Isotope Labeling Experiment
100(8)
3.4.1 Modeling and Simulation of Isotope Labeling Experiments
101(1)
3.4.2 Metabolic Network Specification
101(2)
3.4.3 Atom Transition Network Specification
103(1)
3.4.4 Input Labeling Composition
104(2)
3.4.5 Measurement Specification
106(1)
3.4.6 Flux Constraints
107(1)
3.4.7 In Silico Experimental ILE Design
108(1)
3.5 Putting Theory into Practice
108(16)
3.5.1 A Recipe How to Start
108(2)
3.5.2 Metabolic and Isotopic Stationarity
110(1)
3.5.3 Measuring Extracellular Fluxes
111(1)
3.5.4 Administering Labeled Substrate(s)
112(1)
3.5.5 Metabolomics: Sampling, Sample Preparation, and Analytical Procedures
113(2)
3.5.6 Adjusting Labeling Enrichments for Isotopic Steady State Approximation
115(1)
3.5.7 Correcting Labeling Enrichments for Natural Isotope Abundance
116(1)
3.5.8 Simulation of Labeling Data and Flux Estimation
117(6)
3.5.9 Delicacies of INST-13C-MFA
123(1)
3.6 Future Challenges of 13C-MFA
124(13)
Acknowledgments
125(1)
Abbreviations
125(1)
References
126(11)
4 Proteome Constraints in Genome-Scale Models
137(16)
Yu Chen
Jens Nielsen
Eduard J. Kerkhoven
4.1 Introduction
137(1)
4.2 Cellular Constraints
137(2)
4.3 Formulation of Proteome Constraints
139(11)
4.3.1 Coarse-Grained Integration of Proteome Constraints
139(5)
4.3.2 Fine-Tuned Integration of Proteome Constraints
144(6)
4.4 Perspectives
150(3)
References
151(2)
5 Kinetic Models of Metabolism
153(18)
Hongzhong Lu
Yu Chen
Jens Nielsen
Eduard J. Kerkhoven
5.1 Introduction
153(1)
5.2 Definition of Enzyme Kinetics
153(2)
5.2.1 Michaelis--Menten Formula
153(2)
5.3 Factors Affecting Intracellular Enzyme Kinetics
155(1)
5.4 Kinetic Model: Definition and Scope
156(2)
5.4.1 What Is a Kinetic Model?
156(1)
5.4.2 Scope of Kinetic Models
156(1)
5.4.3 How to Build a Functional Kinetic Model?
157(1)
5.5 Main Mathematical Expressions in Description of Reaction Rates
158(1)
5.5.1 Mechanistic Rate Expressions
158(1)
5.6 Approximative Rate Expressions
159(1)
5.7 Approaches to Assign Parameters in the Rate Expressions
160(3)
5.7.1 Direct Measurements of Kinetic Parameters in Enzyme Assays
161(1)
5.7.2 Querying Databases
161(1)
5.7.3 Inferring from Measured Fluxes
162(1)
5.7 A Parameters Inference Using the Statistical Analysis
163(3)
5.8 Applications
166(1)
5.9 Perspectives
167(4)
References
168(3)
6 Metabolic Control Analysis
171(42)
David A. Fell
6.1 The Metabolic Engineering Context of Metabolic Control Analysis
171(3)
6.2 MCA Theory
174(16)
6.2.1 Metabolic Steady State
174(1)
6.2.2 Flux Control Coefficients
175(1)
6.2.3 Examples of the Flux-Enzyme Relationship
176(2)
6.2.4 Flux Summation Theorem
178(1)
6.2.5 Concentration Control Coefficients
179(2)
6.2.6 Linking Control Coefficients to Enzyme Properties
181(1)
6.2.6.1 Enzyme Rate Equations and Elasticity Coefficients
181(3)
6.2.6.2 Elasticities and Control Coefficients
184(2)
6.2.6.3 Block Coefficients and Top-Down Analysis
186(1)
6.2.7 Feedback Inhibition
186(2)
6.2.8 Large Alterations of Enzyme Activity
188(2)
6.3 Implications of MCA for Metabolic Engineering Strategies
190(6)
6.3.1 Abolishing Feedback Inhibition
191(3)
6.3.2 Increasing Demand for Product
194(1)
6.3.3 Inhibition of Competing Pathways
195(1)
6.3 A Designing Large Changes in Metabolic Flux
196(9)
6.3.4.1 Yeast Tryptophan Synthesis
197(2)
6.3.4.2 The Universal Method
199(1)
6.3.4.3 Bacterial Production of Aromatic Amino Acids
200(2)
6.3.4.4 Penicillin and Other Instances
202(1)
6.3.5 Impacts on Yield from a Growing System
203(2)
6.4 Conclusion
205(8)
Appendix 6.A Feedback Inhibition Simulation
205(2)
References
207(6)
7 Thermodynamics of Metabolic Pathways
213(24)
Daniel Robert Weilandt
Maria Masid
Vassily Hatzimanikatis
7.1 Bioenergetics in Life and in Metabolic Engineering
213(2)
7.2 Thermodynamics-Based Flux Analysis Workflow
215(13)
7.2.1 Thermodynamic Model Curation
215(1)
7.2.1.1 Estimation of the Standard Free Energies of Formation
216(4)
7.2.1.2 Compensating for Compartment-Specific Ionic Strength and pH
220(1)
7.2.1.3 Compensating the Free Energy of Formation for Isomer Distributions
221(2)
7.2.1.4 Computing the Transformed Free Energies of Reaction
223(4)
7.2.2 Mathematical Formulation
227(1)
7.3 Thermodynamics-Based Flux Analysis Applications
228(3)
7.3.1 Constraining the Flux Space with Metabolomics Data
228(1)
7.3.2 Characterizing the Feasible Concentration Space
229(2)
7.4 Conclusion and Future Perspectives
231(6)
References
233(4)
8 Pathway Design
237(22)
Jasmin Hafner
Homa Mohammadi-Peyhani
Vassily Hatzimanikatis
Definition
237(1)
8.1 De Novo Design of Metabolic Pathways
237(1)
8.1.1 Manual Versus Computational Design
238(1)
8.2 Pathway Design Workflow
238(9)
8.2.1 Biochemical Search Space
238(2)
8.2.1.1 Reaction Prediction
240(1)
8.2.1.2 Retrobiosynthesis
241(1)
8.2.1.3 Network Data Representation
242(1)
8.2.2 Pathway Search
242(1)
8.2.2.1 Stoichiometric Matrix-Based Search
243(1)
8.2.2.2 Graph-Based Search
243(1)
8.2.2.3 Pathway Ranking
244(1)
8.2.3 Enzyme Assignment
244(1)
8.2.3.1 Enzyme Prediction for Orphan and Novel Reactions
244(2)
8.2.3.2 Choice of Protein Sequence
246(1)
8.2.4 Pathway Feasibility
246(1)
8.2.4.1 Chassis Metabolic Model
246(1)
8.2.4.2 Stoichiometric Feasibility
246(1)
8.2.4.3 Thermodynamic Feasibility
246(1)
8.2.4.4 Kinetic Feasibility
247(1)
8.2.4.5 Toxicity of Intermediates
247(1)
8.3 Applications
247(6)
8.3.1 Available Tools for Pathway Design
247(2)
8.3.2 Successful Applications of Pathway Design Tools
249(1)
8.3.3 Practical Example of Pathway Design
249(1)
8.3.3.1 Creating a Biochemical Network Around BDO
249(2)
8.3.3.2 Search for Biosynthetic Pathways
251(1)
8.3.3.3 Finding Enzymes for Novel Reactions
251(1)
8.3.3.4 Stoichiometric and Thermodynamic Pathway Evaluation
251(1)
8.3.3.5 Overall Ranking of Pathways
251(2)
8.4 Conclusions and Future Perspectives
253(6)
References
254(5)
9 Metabolomics
259(42)
Tomek Diederen
Alexis Delabriere
Alaa Othman
Michelle E. Reid
Nicola Zambonl
9.1 Introduction
259(1)
9.2 Fundamentals
260(2)
9.2.1 Experimental Design
260(1)
9.2.2 Targeted and Untargeted Metabolomics
260(1)
9.2.3 Sequences and Standards
261(1)
9.3 Analytical Techniques
262(10)
9.3.1 Sample Preparation
262(2)
9.3.2 Separation Techniques
264(1)
9.3.2.1 Liquid Chromatography
264(2)
9.3.2.2 Gas Chromatography
266(1)
9.3.2.3 Alternative Separation Techniques
266(2)
9.3.3 Mass Spectrometry
268(1)
9.3.3.1 Ionization Techniques
268(1)
9.3.3.2 Low-Resolution MS
269(1)
9.3.3.3 High-Resolution MS
270(1)
9.3.3.4 Acquisition Modes for Targeted MS
271(1)
9.3.3.5 Acquisition Modes for Untargeted Metabolomics
272(1)
9.4 Data Analysis
272(7)
9.4.1 Data Processing in Untargeted Metabolomics
273(1)
9.4.1.1 Preprocessing of Individual MS Runs
273(1)
9.4.1.2 Peak Picking
273(1)
9.4.1.3 Peak Alignment and Retention Time Correction
274(1)
9.4.1.4 Peak Grouping
274(1)
9.4.1.5 Missing Values
274(1)
9.4.1.6 Normalization
274(2)
9.4.1.7 Annotation
276(1)
9.4.2 Data Analysis and Interpretation
277(1)
9.4.2.1 Univariate Statistics
277(1)
9.4.2.2 Multivariate Statistics
278(1)
9.4.2.3 Pathway Analysis
278(1)
9.5 Emerging Trends for Cellular Analyses
279(2)
9.5.1 High-Throughput Metabolomics for Large Scale Screening
279(1)
9.5.2 Single Cell Metabolomics
280(1)
9.5.3 Dynamic Analysis
281(1)
9.6 Applications of Metabolomics in Metabolic Engineering
281(3)
9.6.1 Pathway Design by Thermodynamic Analysis
281(2)
9.6.2 Alleviating Pathway Bottlenecks
283(1)
9.6.3 Reduction of Side Products and Metabolite Damage
284(1)
9.6 A Improving Stress Tolerance
284(1)
9.6.5 Engineer Medium Composition
285(1)
9.7 Final Remarks
285(16)
References
286(15)
10 Genome Editing of Eukarya
301(38)
Jonathan A. Arnesen
Jakob Blaesbjerg Hoof
Helene Faustrup Kildegaard
Irina Borodino
10.1 Basic Principles of Genome Editing
301(3)
10.2 Endonucleases
304(6)
10.2.1 Zinc-Finger Nucleases
304(2)
10.2.2 Transcription Activator-Like Effectors Nucleases
306(2)
10.2.3 CRISPR/Cas
308(2)
10.3 Genome Editing of Industrially Relevant Eukaryotes
310(10)
10.3.1 Yeast
310(3)
10.3.2 Filamentous Fungi
313(3)
10.3.3 Chinese Hamster Ovary Cells
316(4)
10.4 Outlook
320(19)
References
320(19)
Volume 13b
Preface xvii
Part II Applications
339(552)
11 Metabolic Engineering of Escherichia coli
341(62)
Zi Wei Luo
Jung Ho Ahn
Tong Un Chae
So Young Choi
Seon Young Park
Yoojin Choi
Jiyong Kim
Cindy Pricilia Surya Prabowo
Jong An Lee
Dongsoo Yang
Taehee Han
Hanwen Xu
Sang Yup Lee
12 Metabolic Engineering of Corynebacterium glutamicum
403(66)
Judith Becker
Christoph Wittmann
13 Metabolic Engineering of Bacillus -- New Tools, Strains, and Concepts
469(50)
Mathis Appelbaum
Thomas Schweder
14 Metabolic Engineering of Pseudomonas
519(38)
Pablo I. Nikel
Victor de Lorenzo
15 Metabolic Engineering of Lactic Acid Bacteria
557(54)
Robin Dorau
Jianming Liu
Christian Solem
Peter Ruhdal Jensen
16 Metabolic Engineering and the Synthetic Biology Toolbox for Clostridium
611(42)
Rochelle C. Joseph
Susan Q. Kelley
Nancy M. Kim
Nicholas R. Sandoval
17 Metabolic Engineering of Filamentous Actinomycetes
653(36)
Charlotte Beck
Kai Blin
Tetiana Gren
Xinglin Jiang
Omkar Satyavan Mohite
Emilia Palazzotto
Yaojun Jong
Pep Charusanti
Tilmann Weber
18 Metabolic Engineering of Yeast
689(46)
Rui Pereira
Olena P. Ishchuk
Xiaowei Li
Quanli Liu
Yi Liu
Maximilian Otto
Yun Chen
Verena Siewers
Jens Nielsen
19 Harness Yarrowia lipolytica to Make Small Molecule Products
735(30)
Kang Zhou
Gregory Stephanopoulos
20 Metabolic Engineering of Filamentous Fungi
765(38)
Vera Meyer
21 Metabolic Engineering of Photosynthetic Cells -- in Collaboration with Nature
803(56)
Mette Sørensen
Birger Lindberg Møtier
22 Metabolic Engineering for Large-Scale Environmental Bioremediation
859(32)
Pablo I. Nikel
Victor de Lorenzo
Index 891
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 has published more than 500 journal papers, 64 books and book chapters, and more than 580 patents (either registered or applied). He received numerous awards, including the National Order of Merit, the Merck Metabolic Engineering Award, the ACS Marvin Johnson Award, Charles Thom Award, Amgen Biochemical Engineering Award, Elmer Gaden Award, POSCO TJ Park Prize, and HoAm Prize. He currently is Fellow of American Association for the Advancement of Science, the American Academy of Microbiology, American Institute of Chemical Engineers, Society for Industrial Microbiology and Biotechnology, American Institute of Medical and Biological Engineering, the World Academy of Science, the Korean Academy of Science and Technology, and the National Academy of Engineering of Korea. He is also Foreign Member of National Academy of Engineering USA. He is currently honorary professor of the University of Queensland (Australia), honorary professor of the Chinese Academy of Sciences, honorary professor of Wuhan University (China), honorary professor of Hubei University of Technology (China), honorary professor of Beijing University of Chemical Technology (China), and advisory professor of the Shanghai Jiaotong University (China). 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).

Jens Nielsen is Professor and Director to Chalmers University of Technology (Sweden) since 2008. He obtained an MSc degree in Chemical Engineering and a PhD degree (1989) in Biochemical Engineering from the Technical University of Denmark (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. Jens Nielsen has published more than 350 research papers, co-authored more than 40 books and he is inventor of more than 50 patents. He has founded several companies that have raised more than 20 million in venture capital. He has received numerous Danish and international awards and is member of the Academy of Technical Sciences (Denmark), the National Academy of Engineering (USA), the Royal Danish Academy of Science and Letters, the American Institute for Medical and Biological Engineering and the Royal Swedish Academy of Engineering Sciences.

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 received his BS degree from the National Technical University of Athens and his PhD from the University of Minnesota (USA). He has co-authored approximately 400 research papers and 50 patents, along with the first textbook on Metabolic Engineering. 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.