Metabolic Engineering E-Book
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- Herausgeber: Wiley-VCH GmbH
- Kategorie: Fachliteratur
- Serie: Advanced Biotechnology
- Sprache: Englisch
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.
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Veröffentlichungsjahr: 2021
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Table of Contents
Cover
Title Page
Title Page
Copyright
Dedication
Preface
Volume 13a
Part 1: Concepts
1 Metabolic Engineering Perspectives
1.1 History and Overview of Metabolic Engineering
1.2 Understanding Cellular Metabolism and Physiology
1.3 General Approaches to Metabolic Engineering
1.4 Host Organism Selection
1.5 Substrate Considerations
1.6 Metabolic Engineering and Synthetic Biology
1.7 The Future of Metabolic Engineering
References
2 Genome‐Scale Models
2.1 Introduction
2.2 Flux Balance Analysis
2.3 Network Reconstruction
2.4 Brief History of the GEM for E. coli
2.5 From Metabolism to the Proteome
2.6 Current Developments
2.7 Broader Perspectives
2.8 What Does the Future Look Like for GEMs?
Disclaimer
Acknowledgments
References
3 Quantitative Metabolic Flux Analysis Based on Isotope Labeling
3.1 Introduction
3.2 A Toy Example Illustrates the Basic Principles
3.3 Lessons Learned from the Example
3.4 How to Configure an Isotope Labeling Experiment
3.5 Putting Theory into Practice
3.6 Future Challenges of 13C‐MFA
Acknowledgments
Abbreviations
References
Notes
4 Proteome Constraints in Genome‐Scale Models
4.1 Introduction
4.2 Cellular Constraints
4.3 Formulation of Proteome Constraints
4.4 Perspectives
References
5 Kinetic Models of Metabolism
5.1 Introduction
5.2 Definition of Enzyme Kinetics
5.3 Factors Affecting Intracellular Enzyme Kinetics
5.4 Kinetic Model: Definition and Scope
5.5 Main Mathematical Expressions in Description of Reaction Rates
5.6 Approximative Rate Expressions
5.7 Approaches to Assign Parameters in the Rate Expressions
5.8 Applications
5.9 Perspectives
References
6 Metabolic Control Analysis
6.1 The Metabolic Engineering Context of Metabolic Control Analysis
6.2 MCA Theory
6.3 Implications of MCA for Metabolic Engineering Strategies
6.4 Conclusion
Appendix 6.A: Feedback Inhibition Simulation
References
Notes
7 Thermodynamics of Metabolic Pathways
7.1 Bioenergetics in Life and in Metabolic Engineering
7.2 Thermodynamics‐Based Flux Analysis Workflow
7.3 Thermodynamics‐Based Flux Analysis Applications
7.4 Conclusion and Future Perspectives
References
8 Pathway Design
Definition
8.1 De Novo Design of Metabolic Pathways
8.2 Pathway Design Workflow
8.3 Applications
8.4 Conclusions and Future Perspectives
References
9 Metabolomics
9.1 Introduction
9.2 Fundamentals
9.3 Analytical Techniques
9.4 Data Analysis
9.5 Emerging Trends for Cellular Analyses
9.6 Applications of Metabolomics in Metabolic Engineering
9.7 Final Remarks
References
10 Genome Editing of Eukarya
10.1 Basic Principles of Genome Editing
10.2 Endonucleases
10.3 Genome Editing of Industrially Relevant Eukaryotes
10.4 Outlook
References
Volume 13b
Part 2: Applications
11 Metabolic Engineering of
Escherichia coli
11.1 Introduction
11.2 Metabolic Engineering of E. coli for the Production of Fuels
11.3 Metabolic Engineering of E. coli for the Production of Chemicals
11.4 Metabolic Engineering of E. coli for the Production of Materials
11.5 Conclusions and Perspectives
Acknowledgment
References
12 Metabolic Engineering of
Corynebacterium glutamicum
12.1 Introduction
12.2 Systems Metabolic Engineering Strategies
12.3 Metabolic Engineering of the Substrate Spectrum
12.4 Industrial Products
12.5 Conclusions and Perspectives
References
13 Metabolic Engineering of
Bacillus
– New Tools, Strains, and Concepts
13.1 Introduction
13.2 The Determination of Essential Physiological Traits and Circuits
13.3 The Minimal Cell Concept
13.4 Tools for Genome Editing
13.5 Optimization, Standardization, and Modularity in Gene Expression
13.6 Activity‐Independent Screening of Target Molecule Synthesis
13.7 The Biotechnological Application of Metabolic Engineering Strategies
13.8 Concluding Remarks and Future Perspectives
References
14 Metabolic Engineering of
Pseudomonas
14.1 Introduction
14.2 Bacteria from the Genus Pseudomonas as Platforms for Metabolic Engineering
14.3 Examples of Metabolic Engineering of P. putida and Other Pseudomonas Species
14.4 Conclusions and Future Prospects
Acknowledgments
References
15 Metabolic Engineering of Lactic Acid Bacteria
15.1 Introduction
15.2 Genetic Engineering Strategies for LAB
15.3 Traditional Applications of LAB and Optimizing Performance
15.4 Metabolic Engineering of LAB for Production of Chemicals or Proteins
15.5 Conclusion and Prospects
References
16 Metabolic Engineering and the Synthetic Biology Toolbox for
Clostridium
16.1 Introduction
16.2 Aims of Metabolic Engineering in Clostridium
16.3 Genomic Editing in Clostridium
16.4 Genetic Parts in Clostridium
Author Contributions
References
17 Metabolic Engineering of Filamentous Actinomycetes
17.1 Definition, Subject, and Importance
17.2 Recently Developed Tools and Strategies to Find Novel Bioactive Natural Products
17.3 Natural Product Biosynthetic Pathways
17.4 Genome Mining for Biosynthetic Gene Clusters
17.5 Engineering Secondary Metabolite Biosynthesis
17.6 Systems Metabolic Engineering of Filamentous Actinomycetes
17.7 Outlook and Perspectives
Acknowledgments
References
18 Metabolic Engineering of Yeast
18.1 Introduction
18.2 Production of Biofuels
References
19 Harness
Yarrowia lipolytica
to Make Small Molecule Products
19.1 Introduction
19.2 Genetic Tools for Engineering Y. lipolytica
19.3 Production of Short‐Chain Organic Acids
19.4 Production of Triacylglycerol
19.5 Production of New Products
19.6 Opportunities and Challenges
References
20 Metabolic Engineering of Filamentous Fungi
20.1 Introduction
20.2 Development and Implementation of Genetic and Genome Tools
20.3 Metabolic and Regulatory Models
20.4 Engineering Strategies for Improved Substrate Utilization
20.5 Engineering Strategies for Enhanced Product Formation
20.6 Engineering Strategies for the Production of New‐to‐Nature Compounds
20.7 Engineering Strategies for Controlled Macromorphologies
20.8 Future of the Field
References
21 Metabolic Engineering of Photosynthetic Cells – in Collaboration with Nature
21.1 Plants for the Future
21.2 Photosynthetic Organisms
21.3 Plant Cell Wall
21.4 Plant Bioactive Natural Products
21.5 Chloroplasts as the Site of Production
21.6 Metabolic Engineered Production in Microalgae
21.7 Metabolons – Advantages Using Plants
21.8 Biocondensates
21.9 Conclusion: Metabolic Engineering of Plants in the Transition Toward a Biobased Society
Acknowledgments
References
22 Metabolic Engineering for Large‐Scale Environmental Bioremediation
22.1 Introduction
22.2 Metabolic Engineering for Bioremediation: From 2.0 to 3.0
22.3 Dealing with Global Environmental Waste
22.4 Beyond Bioremediation 3.0: From the Test Tube to Planet Earth
22.5 Bottlenecks in the Development of Environmental Biocatalysts
22.6
Chassis
for Delivery of Activities Beneficial for the Environment
22.7 Manufacturing Catalytic Consortia
22.8 Environmental Galenics
22.9 Toward HGT‐Based, Large‐Scale Bioremediation
22.10 Conclusions and Future Prospects: Towards
Bioremediation
4.0
Acknowledgments
References
Note
Index
End User License Agreement
List of Tables
Chapter 3
Table 3.1 Selection of contemporary software tools for
13
C‐MFA (free‐of‐charge...
Table 3.2 List prices of glucose tracers with isotopic purity of 99 atom%
13
C....
Table 3.3 Selection of NA correction tools (
free‐of‐charge for academic purpo
...
Chapter 4
Table 4.1 Reactions in upper glycolysis in
S. cerevisiae
.
Table 4.2 The synthesis reaction of the TPI enzyme.
Table 4.3 The ribosome assembly reaction.
Chapter 6
Table 6.1 Multiple overexpression of yeast tryptophan synthesis enzymes.
Chapter 7
Table 7.1 Thermodynamic information (aqueous standard Gibbs free energy and i...
Table 7.2 Thermodynamic information for the interaction groups as defined in ...
Table 7.3 Parameters A and B calculated for different temperatures according ...
Table 7.4 Metabolomics data from Park et al. [25] integrated into the IJO1355...
Chapter 8
Table 8.1 Selection of popular biochemical databases as a source of molecular...
Table 8.2 Overview on different enzyme prediction tools and methods.
Table 8.3 Available pathway design tools and their characteristics.
Table 8.4 Each reaction involved in the extracted showcase network is either ...
Table 8.5 List of extracted pathways with number of steps, stoichiometric and...
Table 8.6 TFA‐feasible pathways ranked by the three criteria length, enzyme a...
Chapter 11
Table 11.1 Representative biofuels, chemicals, and materials produced by meta...
Chapter 12
Table 12.1 Metabolic engineering of
Corynebacterium glutamicum
for the utiliza...
Chapter 13
Table 13.1 Selected CRISPR/Cas9 genome editing systems developed for
Bacillus
...
Chapter 14
Table 14.1
Pseudomonas
species as the source of genetic parts applied for regulat...
Chapter 16
Table 16.1 CRISPR‐based gene and gene repression in
Clostridium
spp.
Table 16.2 Inducible promoters in
Clostridium
.
Chapter 17
Table 17.1 A brief summary of the major CRISPR‐mediated gene manipulation sys...
Table 17.2 Biological parts used in actinomycete synthetic biology; for a mor...
Table 17.3 Examples of combinatorial biosynthesis/synthetic biology applicati...
Chapter 18
Table 18.1 Examples of virus like particles produced in
S. cerevisiae
.
Chapter 20
Table 20.1 List of some filamentous fungal cell factories and their products.
Table 20.2 Filamentous fungal cell factories with available genome sequence d...
Table 20.3 Filamentous fungal cell factories with curated genome‐scale metabo...
Table 20.4 Selected secondary metabolites from filamentous fungi and their ap...
Chapter 22
Table 22.1 Major global environmental problems caused by urban and industrial...
Table 22.2 Synthetic biology‐based technologies for large‐scale bioremediatio...
List of Illustrations
Chapter 1
Figure 1.1 A potential generalized metabolic engineering strategy to rapidly...
Chapter 2
Figure 2.1 Basics of flux balance analysis. (a) The types of fluxes that aff...
Figure 2.2 Conceptual depiction of the null space of
S
with capacity constra...
Figure 2.3 The basic steps of constraint‐based modeling.
Figure 2.4 The phylogenetic tree of COBRA methods as of 2012 showing the maj...
Figure 2.5 A workflow that has been developed to incorporate knowledge of ti...
Figure 2.6 Genomes and reactomes are reconstructed from their constituent pa...
Figure 2.7 Principles of network reconstruction. (a) The first few reactions...
Figure 2.8 Principles of network reconstruction. The representation of gene‐...
Figure 2.9 Predicting experimental outcomes of cellular growth screens. The ...
Figure 2.10 The alleleome for the metabolic genes in
E. coli
. Across 99...
Figure 2.11 Historical development of the reconstruction of the
E. coli
Figure 2.12 Histogram the new genes added to the iAF1260 reconstruction to f...
Figure 2.13 Growth of
E. coli
K‐12 on glycerol. a, Change in growth rat...
Figure 2.14 Integrated constraint‐based model of
E. coli
: the
E. coli
...
Figure 2.15 History of generated and potential future genome‐scale models of...
Figure 2.16 General formulation of the ME model and its application to the s...
Figure 2.17 The engineered fermentation pathways in
E. coli
. All the en...
Figure 2.18 ME models can be used to study properties of the metalloproteome...
Figure 2.19 Comparison of ME‐ and M‐model predicted amino acid growth‐normal...
Figure 2.20 Utilization of protein structural properties within a genome‐sca...
Figure 2.21 OxidizeME: a multiscale description of metabolism and macromolec...
Figure 2.22 Comparison of acidifyME simulations, accounting for the three ac...
Figure 2.23 Approach for obtaining
k
cat
in vivo
from metabolic specialists: K...
Figure 2.24 Estimates of
in vivo
turnover numbers are consistent between wil...
Figure 2.25 Characterization of iModulons. (a) Schematic illustration of the...
Figure 2.26 Tradeoffs in the bacterial transcriptome. (a) The growth vs. str...
Figure 2.27 Classification of structural systems biology studies into six us...
Figure 2.28 Multiscale characterization of mutational effects, from genotype...
Figure 2.29 Four basic layers of data analysis available for metabolic engin...
Figure 2.30 An early depiction of the use of systems level analysis, both st...
Figure 2.31 Synthetic biology and minimal cells: a historical perspective. E...
Chapter 3
Figure 3.1 Typical result of a
13
C‐MFA investigation. Flux map of central ca...
Figure 3.2 Taxonomy of
13
C‐isotope labeling techniques and related flux info...
Figure 3.3 Running example to illustrate the principles of
13
C‐MFA. Extracel...
Figure 3.4 Common isotopically labeled glucose tracers used for
13
C‐MFA.
Figure 3.5 Time course of an isotope labeling experiment (ILE) illustrated w...
Figure 3.6 General statistical parameter fitting scheme. By a systematic var...
Figure 3.7 Univariate confidence intervals such as commonly determined with ...
Figure 3.8 Carbon atom transitions for the 2‐dehydro‐3‐deoxy‐phosphogluconat...
Figure 3.9 Left: Atom transition network for the modified toy example shown ...
Figure 3.10 Example network from Figure 3.9 extended by two further intracel...
Figure 3.11 Principle of a tandem MS for isotope labeling analysis for a C3 ...
Figure 3.12 Snapshots from the Omix visual tool suite for specifying
13
C‐MFA...
Figure 3.13 Effect of natural isotope abundance correction for TBDMS‐derivat...
Chapter 4
Figure 4.1 External and internal constraints that may shape cellular phenoty...
Figure 4.2 Coarse‐grained integration of proteome constraints. (a) The GECKO...
Figure 4.3 Expansion of the stoichiometric matrix by coarse‐grained integrat...
Figure 4.4 Fine‐tuned integration of proteome constraints. (a) In pcModels, ...
Chapter 5
Figure 5.1 Relationship between parameters and reaction rates in Michaelis–M...
Figure 5.2 Framework to build a functional kinetic model.
Figure 5.3 An entry example from SABIO‐RK
13
(http://sabio.villa-bosch.de/SAB...
Figure 5.4 Procedure to calculate
in vivo k
cat
values. For each reaction, th...
Figure 5.5 A toy example to show how to build kinetic model.
Figure 5.6 Detailed steps to build a functional kinetic model which could pr...
Figure 5.7 Applications of kinetic models of metabolism. (a) In metabolic co...
Chapter 6
Figure 6.1 A schematic metabolic network. Upper case metabolite names corres...
Figure 6.2 Definition of the flux control coefficient. (a) The coefficient i...
Figure 6.3 Examples of hyperbolic flux
v
enzyme curves. (a) Dependence of ar...
Figure 6.4 The reversible Michaelis–Menten equation. Equation (6.9) illustra...
Figure 6.5 Elasticity of a reversible enzyme. Equation (6.11) is plotted for...
Figure 6.6 Feedback inhibition transfers control to the steps consuming the ...
Figure 6.7 Flux change for different degrees of overexpression. The curves a...
Figure 6.8 Flux change for different flux control coefficients. The curves a...
Figure 6.9 Synthetic pathways of the aspartate group of amino acids in bacte...
Figure 6.10 Pathways of aromatic amino acid synthesis. Gene designations fol...
Figure 6.11 Simple model to illustrate the Universal Method. N, metabolic in...
Figure 6.12 Changes in yield and flux to growth‐coupled product for differen...
Figure 6.A.1 The concentrations of metabolites A (solid line) and B (dashed ...
Chapter 7
Figure 7.1 Application of the group contribution method to metabolic compoun...
Figure 7.2 Flux ranges in the central carbon metabolism of
E. coli
(IJO...
Figure 7.3 Reaction network of the central carbon metabolism of
E. coli
Figure 7.4 Thermodynamically feasible concentration ranges in glycolysis and...
Chapter 8
Figure 8.1 Workflow for computational pathway design.
Figure 8.2 Schematic of retrobiosynthetic network generation.
Figure 8.3 Retrobiosynthetic network generation around BDO. (a) Visualizatio...
Chapter 10
Figure 10.1 DNA double‐strand break repair by homologous recombination resul...
Figure 10.2 HR‐mediated techniques for genome editing. The HR‐mediated techn...
Figure 10.3 Model of a pair of Cys
2
His
2
zinc fingers. Encircled letters indi...
Figure 10.4 Domain architecture of
Xanthomonas
transcription activator‐like ...
Figure 10.5 Components of the CRISPR/Cas9‐system. The Cas9 protein generates...
Figure 10.6 CRISPR/Cas‐strategies for genome editing and gene modulation. (a...
Chapter 11
Figure 11.1 Metabolic pathways and engineering strategies for the production...
Figure 11.2 Metabolic pathways for the production of representative bulk and...
Figure 11.3 Metabolic pathways and engineering strategies for the production...
Figure 11.4 Overall scheme for microbial production of recombinant proteins ...
Figure 11.5 Metabolic pathways and engineering strategies for the production...
Figure 11.6 Engineering strategies for the biosynthesis of nanomaterials in
Chapter 12
Figure 12.1 Metabolic engineering of
Corynebacterium glutamicum
for the util...
Figure 12.2 Systems‐wide metabolic engineering of
Corynebacterium glutamicum
Figure 12.3 Metabolic engineering of
Corynebacterium glutamicum
for valoriza...
Figure 12.4 Systems‐wide metabolic engineering of
Corynebacterium glutamicum
Figure 12.5 Systems metabolic engineering of
Corynebacterium glutamicum
for ...
Figure 12.6 Systems metabolic engineering of
Corynebacterium glutamicum
for ...
Figure 12.7 Systems‐wide metabolic engineering of
Corynebacterium glutamicum
Figure 12.8 Systems metabolic engineering of
Corynebacterium glutamicum
CgHA...
Chapter 13
Figure 13.1 Genome reduction in
B. subtilis
168. The timeline illustrat...
Figure 13.2 Generation of markerless deletion mutants using counter‐selectio...
Figure 13.3 Overview of industrially relevant
Bacilli
and their commercial f...
Figure 13.4 Metabolic engineering of the hyaluronic acid (HA) biosynthetic p...
Chapter 14
Figure 14.1
Pseudomonas putida
as a functional
chassis
for metabolic enginee...
Figure 14.2 Natural and engineered features of
Pseudomonas putida
useful for...
Figure 14.3 (a) General structure of the
Standard European Vector Architectu
...
Chapter 15
Figure 15.1 16s rRNA‐based unrooted phylogenetic tree of representative LAB ...
Figure 15.2 Sugar metabolism of LAB. Stoichiometry is not considered for sim...
Figure 15.3 Gene expression systems in LAB. (a) Nisin‐controlled gene expres...
Figure 15.4 Principle behind using suicide or conditionally replicating plas...
Figure 15.5 Principle behind recombineering based genetic engineering in LAB...
Figure 15.6 Principle behind Cas9‐mediated mutagenesis. It is required that ...
Figure 15.7 Methods for strain development applied for obtaining superior mu...
Figure 15.8 Metabolic engineering of
Lactococcus lactis
for over‐producing f...
Figure 15.9 Metabolic engineering of
Lactococcus lactis
for producing bulk c...
Figure 15.10 Reaction scheme of reducing a prochiral ketone to the correspon...
Figure 15.11 Glycerol metabolism in lactobacilli. Abbreviations as follows: ...
Chapter 16
Figure 16.1 Counterselection markers used in
Clostridium
spp. and their mech...
Chapter 17
Figure 17.1 Key events of
Streptomyces
metabolic engineering and systems bio...
Figure 17.2 Biosynthesis of the antibiotic kirromycin. The carbon skeleton o...
Figure 17.3 Genome mining for secondary metabolite biosynthetic pathways. (a...
Figure 17.4 An example of using CRISPR‐BEST to edit genes coding for the bio...
Figure 17.5 Examples of synthetic biology approaches to engineer natural pro...
Figure 17.6 Generalized systems metabolicengineering workflow for actinomyce...
Chapter 18
Figure 18.1 Timeline showing the use of the yeast
S. cerevisiae
as a ce...
Figure 18.2 Overview of key reactions involved in balancing NADH in yeast. T...
Figure 18.3 Illustration of pathways involved in the metabolism of the pento...
Figure 18.4 Overview of biosynthetic pathways of fatty acids and related der...
Figure 18.5 Overview of engineering strategies to optimize the production of...
Figure 18.6 Schematic illustration of coumarin and flavonoid biosynthesis. S...
Figure 18.7 P450‐mediated biosynthesis of benzylisoquinoline alkaloids (BIAs...
Figure 18.8 Schematic overview of the mevalonate (MVA) and sterol pathway in...
Figure 18.9 Overview of the protein secretory pathway in yeast. Components o...
Chapter 19
Figure 19.1
Y. lipolytica
diverged very early from other yeast genera. ...
Figure 19.2 Metabolic pathways of
Y. lipolytica
related to citrate prod...
Figure 19.3 Metabolic pathways of
Y. lipolytica
related to pyruvate and...
Figure 19.4 Metabolic pathways of
Y. lipolytica
related to α‐ketoglutar...
Figure 19.5 Metabolic pathways of
Y. lipolytica
related to succinate pr...
Figure 19.6 Metabolic pathways of
Y. lipolytica
related to triacylglycerol p...
Figure 19.7 Two strategies to use NADH to regenerate NADPH. (a) The glyceral...
Figure 19.8 Metabolic pathways of
Y. lipolytica
related to triacylglyce...
Figure 19.9 Engineering
Y. lipolytica
to utilize lactose as carbon sour...
Figure 19.10 Seven approaches to improve availability of cytosolic acetyl‐Co...
Figure 19.11 Biosynthetic pathway of eicosapentaenoic acid (EPA) used by the...
Figure 19.12 The metabolic pathway of producing triacetic acid lactone (TAL)...
Figure 19.13 The metabolic pathway of producing β‐carotene from glucose in
Y
...
Chapter 20
Figure 20.1 Morphologies adopted by filamentous fungi under submerged cultiv...
Figure 20.2 Gene coexpression networks uncover highly connected genes, which...
Figure 20.3 A simplified model of carbon catabolism in filamentous fungi whe...
Figure 20.4 Image analysis tools to quantify fungal macromorphologies. (a) T...
Chapter 21
Figure 21.1 An overview of different targets in plant metabolic engineering ...
Figure 21.2 Schematic illustration of a chloroplast thylakoid containing the...
Figure 21.3 Metabolic engineering of the entire process of photorespiration ...
Figure 21.4 Arabidopsis (
Arabidopsis thaliana
) mutants in the IRX7 glycosylt...
Figure 21.5 Metabolic engineered anthocyanin content in tomatoes regulated b...
Figure 21.6 The chloroplast as a platform for biosynthesis of plant metaboli...
Figure 21.7 The biosynthetic pathway of forskolin from Indian coleus (
Coleus
...
Figure 21.8 Schematic illustration of chloroplast thylakoid containing the i...
Figure 21.9 Metabolic engineering based on incorporation of the genes encodi...
Figure 21.10 Model of the dhurrin metabolon illustrating how the biosyntheti...
Figure 21.11 (a) Black flower petals of the gentiana “flower of death” (
Lisi
...
Figure 21.12 Vanillin glucoside synthesis and accumulation takes place in th...
Chapter 22
Figure 22.1 Types of microbiological agents for bioremediation of environmen...
Figure 22.2 Typical chemical emissions and parameters involved in the ease o...
Figure 22.3 Multiscale propagation of microbial activities. (a) Microorganis...
Figure 22.4 Exploration of the solution space for a given pathway through in...
Figure 22.5 Single‐strain multistep process vs. consortium‐based distributed...
Figure 22.6 Engineering bacterial consortia in flocks with predetermined 3D ...
Figure 22.7 Fortification of environmental microbiomes with beneficial trait...
Guide
Cover
Table of Contents
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Metabolic Engineering
Concepts and Applications
Edited by
Sang Yup LeeJens NielsenGregory Stephanopoulos
Volume 13a
Metabolic Engineering
Concepts and Applications
Edited by
Sang Yup LeeJens NielsenGregory Stephanopoulos
Volume 13b
Volume and Series Editors
Prof. Dr. Sang Yup Lee
KAIST
373‐1; Guseong‐Dong
291 Daehak‐ro, Yuseong‐gu
305‐701 Daejon
South Korea
Prof. Dr. Jens Nielsen
Chalmers University
Department of Biology and Biological Engineering
Kemivägen 10
412 96 Göteborg
Sweden
Prof. Dr. Gregory Stephanopoulos
Massachusetts Institute of Technology
Department of Chemical Engineering
Massachusetts Ave 77
Cambridge, MA 02139
USA
Cover
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Cover Design Adam‐Design, Weinheim, Germany
To the memory of Maria Flytzani Stephanopoulos, a brilliant engineer‐scientist, who believed in metabolic engineering as catalysis of the future, and Hye Jean Hwang and Dina Petranovic Nielsen for their inspiration and unwavering support.
Preface
We are facing unprecedented challenges of climate change, increasing and aging population that is placing increasing pressure on limited resources, environmental problems including waste plastics, and more recently the COVID‐19 pandemic. Metabolic engineering will play increasingly important roles in addressing many of these challenges. The central theme is sustainability and the ability of metabolic engineering to create sustainable solutions by efficiently utilizing renewable resources. This goal has motivated the many contributors to this volume. Even under the extremely difficult conditions of COVID‐19 pandemic, experts worldwide happily agreed to contribute to a book on metabolic engineering and completed their chapters on time. We are most grateful to the authors for their commitment, dedication, and quality of their work.
This book comprises two parts, concepts and applications. The concept part starts with Chapter 1 on the history and perspectives of metabolic engineering, which also provides directions of future metabolic engineering studies. This introductory chapter is followed by an insightful Chapter 2 on genome‐scale modeling and simulation, which over the last couple of decades have become an essential tool to understand metabolism and design metabolic engineering strategies. Metabolic flux analysis provides quantitative information on how metabolic fluxes are distributed in a metabolic network, and thus is essential in metabolic engineering. Chapter 3 describes how metabolic fluxes are determined from data collected following labeling with stable isotopes. Genome‐scale metabolic simulation can be much better performed and more realistically with proper constraints. Chapter 4 describes how constraints from proteome data can be implemented to achieve this goal. Chapter 5 covers kinetic models that allow analysis of pathway fluxes based on enzyme and substrate concentrations and general enzyme properties. This chapter is followed by Chapter 6 on metabolic control analysis, which is a theoretical framework for understanding how changes in the activity of one or multiple enzymes affect the fluxes of metabolic networks and metabolite concentrations. Chapter 7 describes thermodynamics of metabolic pathways, focusing on thermodynamic feasibility of metabolic pathway reactions, which is essential in pathway design. Chapter 8 then naturally follows to describe how to design metabolic pathways through a four‐step strategy involving defining biochemical search space, pathway search, enzyme assignment for each reaction step, and evaluation of pathway performance. Metabolic engineering cannot exist without fully understanding metabolites. Chapter 9 describes how to perform metabolome analysis and data processing together with emerging trends of metabolomics. As the technologies for genome engineering of eukaryotes is far behind those of prokaryotes, we decided to have a specific Chapter 10 on genome editing of eukaryotes as the last chapter of Part 1. The chapter also describes general tools that can be employed in any cell type.
Part 2 is devoted to applications of metabolic engineering in different organisms. Besides traditional workhorse strains such as Escherichia coli (Chapter 11), Corynebacteria (Chapter 12), Bacillus (Chapter 13), and yeasts (Chapter 18), emerging host strains including Pseudomonas (Chapter 14), lactic acid bacteria (Chapter 15), Clostridia (Chapter 16), actinomycetes (Chapter 17), Yarrowia lipolytica (Chapter 19), filamentous fungi (Chapter 20), and photosynthetic organisms (Chapter 21) are covered. These chapters showcase how metabolic engineering is performed for the production of a vast array of example products including chemicals, fuels, materials, drugs, and natural functional compounds in respective organisms. The metabolic engineering strategies employed in these organisms are often universal, yet some are uniquely developed and applied to the specific host cell described in these chapters. We believe that these actual metabolic engineering examples will be helpful to those working on these and similar topics. The final Chapter 22 covers the topic of bioremediation considering the importance of developing strategies to deal with increasing stresses on the environment. This chapter emphasizes that metabolic engineering is not only important for “producing something useful for humans,” but also essential for “improving our environment.”
We anticipate that this book will serve as a textbook for senior undergraduate and junior graduate metabolic engineering classes. Also, the book will be a useful resource for researchers working in the field of metabolic engineering. We want to thank again all the authors who contributed their expertise to this volume. Last but not least, we want to thank the Wiley team, who worked tirelessly in communicating with authors, copy editing, and finalizing the book in such a nice manner. We hope that you will enjoy reading this book as much as we did during the editing process.
November 2020
Sang Yup Lee, Jens Nielsen,
and Gregory Stephanopoulos
Part 1Concepts
1Metabolic Engineering Perspectives
Nian Liuand Gregory Stephanopoulos
Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
1.1 History and Overview of Metabolic Engineering
Metabolic engineering emerged in the late 1980s primarily to capitalize on the advent of recombinant DNA technologies that allowed, for the first time, the direct genetic modification of microbial cells. Its original manifestation was with the first Metabolic Engineering conference in 1996, which was renamed after the conference of “Recombinant DNA Biotechnology III: The Integration of Biological and Engineering Science.” The peer‐reviewed scientific journal, Metabolic Engineering, soon followed, and the book, Metabolic Engineering: Principles and Methodologies, completed the essentials of a new discipline. Even prior to its formal establishment, the ideas of metabolic engineering had already emerged. In a sense, the field was preceded by the study of mixed cultures: If a particular conversion from substrate A to a desired final product B could not be accomplished by a single organism, which could only convert A to an intermediate I, then it was logical to complement the organism with another species that completes the route from I to B. Recombinant technology essentially allowed the isolation and transfer of genes comprising the pathway from I to B so that the complete conversion could be accomplished in a single organism. Despite the declining interest in mixed cultures after that, many concepts of coexistence and stability as well as related mathematical methods of nonlinear dynamics and bifurcation theories found their way to the analysis of recombinant cultures in various configurations.
Of course, metabolic engineering would not be in its current position without molecular biology, which lies at the heart of modern biotechnology with numerous applications. In plant sciences, they enable the introduction of new, useful traits in crops such as draught and salinity resistance [1]; in the medical area, they facilitate the identification of genes underlying a disease and the development of gene therapy as a cure [2]; in environmental applications, they are used for the degradation of recalcitrant compounds [3]. In the microbial world, the central objective of metabolic engineering and associated industrial biotechnology applications is the overproduction of chemical and fuel products either native to an organism or newly synthesized through the introduction of a heterologous pathway. A microorganism is thus converted into a “chemical factory” to execute new biochemistry through its numerous native and non‐native enzymes. Many parallels can be drawn between this approach and conventional chemical processes. For instance, just like how chemical conversions are determined by the stoichiometry, kinetics, and thermodynamics of reactions, a microbial pathway is also defined by these physical parameters of the constituent enzymes. Similar to the necessity of identifying rate limiting steps in chemical processes, a central goal of metabolic engineering is also the analysis of bottlenecking steps in a biochemical reaction network. A key difference between the two is that, while methods to overcome limiting steps are limited in chemistry, there are molecular biology tools including gene deletion and overexpression that can specifically target bottlenecking enzymes to enhance the overall cell productivity.
At this point, one commonly asked question by many scientists and engineers is “Why should one use microbes instead of chemistry to carry out these reactions?” The answer lies in the unique ability of enzymes to conduct complex chemistry with high specificity. Thus, cell‐catalyzed processes will be the preferred methods for making more intricate molecules such as pharmaceuticals, vitamins, proteins, probiotics, and other similar compounds. While biotechnology can make most of these products in a few steps, chemical methods would require a much longer synthesis route including a series of unavoidable protection–deprotection steps to accomplish the same goal. The second class of applications where biotechnology is likely to be superior is sustainable production, which requires the use of renewable feedstocks. Sugars, as a prime example, tend to be highly reactive and attempts at modifying them using organic chemistry techniques will commonly induce many byproducts. On the other hand, these renewable compounds are the perfect substrate for most microorganisms. With the power of molecular biology and metabolic engineering, sugars can be converted to the target product (organic acids, alcohols, biopolymers, solvents, and many other chemical products) with high yield and specificity. Correspondingly, these applications have fueled much interest in metabolic and microbial cell engineering to achieve diverse goals.
Despite the focus of product biosynthesis, it should be noted that the methodology of metabolic engineering is applicable to nearly all areas of biotechnological activity. For example, judicious choice of isotopic tracers and analysis of labeled metabolites identified the function of a reverse TCA cycle in cancer cells under hypoxia [4]. This discovery had profound implications on our understanding of cancer metabolism and its treatment. In plant sciences, transferring genes with unknown functions into yeast cells and characterizing the metabolic steps in microbes has led to the elucidation of a new pathway responsible for cucurbitacin synthesis, which is used by plants for defense against pests [5]. A similar strategy has also been used to identify naturally synthesized herbicides that are highly effective [6]. These are just a few examples illustrating the broad application of metabolic engineering tools developed for the purpose of understanding and manipulating cell physiology, and there is no doubt that these tools will find further applications in the times to come.
Nevertheless, to maintain a tighter focus, in this book we will keep our discussion within processes that use microbial biocatalysts as the enabling element. Hence, this volume is mostly dedicated to microbial systems and reviews the issues and methods related to improving the capability of host cells to produce useful products. Host selection, pathway design and expression, assessment of pathway function, elimination of stoichiometric limitations and kinetic bottlenecks, and evaluation of cell performance in bioreactor environments are all core topics underlying the various chapters. Experimental and mathematical tools that help achieve strain optimization are also discussed.
For the remainder of this chapter in particular, we will briefly touch upon the central ideas of metabolic engineering. First and foremost, it is clear that metabolic engineering is related closely to microbial metabolism. This relationship is further addressed in the next section where computational and experimental methods that dissect cellular physiology are presented. Particular attention is given to methods probing cell‐wide and genome‐wide properties as they provide a holistic view of the entire cellular metabolism instead of a local one, and this is a hallmark of metabolic engineering. In Section 1.3, we examine the two general approaches to engineering a better cell catalyst, rational and combinatorial, along with systems metabolic engineering which combines the two. In the final few sections, we examine other important topics of metabolic engineering, such as host cell selection, substrate considerations, and synthetic biology, before closing with an assessment of the state of the field and its future directions.
1.2 Understanding Cellular Metabolism and Physiology
In metabolic engineering, cellular metabolism is viewed as a network of biochemical reactions that can be exploited to convert a starting substrate to the final product through a sequence of steps. The traditional approach to metabolic engineering relies on initially developing a systematic understanding of the metabolic network with an eye on kinetic bottlenecks and stoichiometric limitations, and then applying this knowledge to engineer pathways that funnel fluxes toward the desired substance. Earlier efforts that utilized this methodology oftentimes focused on a more “localized” view of metabolic pathways in that only the steps directly connecting the substrate to the product were considered. This paradigm, despite largely simplifying the complexity of biological systems, has seen great success in terms of improving the titer, productivity, and yield of several bioproducts, such as amino acids [7]. Since the number of reactions that needs to be considered is relatively small, it is possible to manually interrogate each enzyme to determine its kinetic and thermodynamic limitations, shedding light on how the properties of individual steps affect flux through the entire pathway. Once this is known, an overall engineering strategy can hence be formulated and subsequently carried out. However, successes in employing this “localized” view are limited to situations where only a few enzymatic steps are relevant, thereby restricting its range of applications. As the field progressed, the biosynthesis of complex molecules with more structural and functional diversity quickly became the focus of many researchers. Correspondingly, new tools and methods have been developed to better understand cellular metabolism and guide engineering on a “global” scale. Regardless of what approach a metabolic engineer decides to take in designing pathways, a basic understanding of cellular metabolism will be essential and thus is the focus of this section.
1.2.1 Computational Methods in Understanding Metabolism
As mentioned above, the first step in a strain engineering project is to understand how cells behave metabolically and many computational methods have been developed to accomplish this. Metabolic control analysis (MCA), originally developed in the 1970s, represents one of the earliest efforts [8]. This theoretical framework examines the effect of a single enzyme on a pathway flux and defines the metrics that describe this relationship. Of particular relevance to metabolic engineering is its focus on the rigidity or flexibility of enzymes in a metabolic network. The central idea here is that bottlenecking steps show minimal response to changes in other steps (i.e. rigid) and therefore kinetically limit the pathway as a whole. Quantitatively, this is captured by observing the changes in flux through the entire pathway resulting from perturbations of individual enzymes. Performing such an analysis will inform the researcher which individual targets in the network have greater global impact on a cell's overall metabolism. Hence, they deserve more attention when it comes to pathway optimization. Similar analyses can be applied to investigate the sensitivity of flux distribution at branch points of a metabolic network, with the idea that rigid branch points (or nodes) control the selectivity of a product by limiting the fraction of flux from a central pathway that can be diverted to it.
Besides identifying key control points, other computational methods focused on determining and visualizing flux distributions across a metabolic network: Metabolic flux analysis (MFA) [9] and flux balance analysis (FBA) [10]. The goal of both methods is to calculate the pathway flux values in a preconstructed model subject to several physical and biological constraints. Each method has its unique strengths and weaknesses. MFA uses experimentally determined extracellular fluxes (i.e. substrate consumption and product formation rates) and metabolite isotopic enrichment patterns (see Section 1.2.2) to determine a set of flux values that best satisfies these measurements, while simultaneously abiding to mass balance constraints. The results from this exercise are therefore experimentally supported and quite accurate. A critical aspect of MFA is the degree of redundancy that is achieved with the selected isotopic tracers and measured metabolite enrichments, where more degrees of redundancy leads to better refined flux values. In other words, MFA is a nonlinear regression algorithm with fewer parameters (i.e. pathway flux values) than experimental observations (i.e. extracellular fluxes and metabolite enrichment) and hence it can be challenging at times to obtain a good fit. This in turn highlights the importance of constructing the metabolic model to reflect the pathways within a cell as accurately as possible. As such, MFA typically considers only a small subset of all biochemical reactions, most notably the well‐characterized pathways such as central carbon metabolism, amino acid synthesis, and other secondary pathways. On the other hand, FBA is primarily based on computation and can be used on much larger models, even genome scale models (GSM), where nearly all biochemical reactions inferred from genome annotation are included. This results in greatly underdetermined systems, and to obtain a solution, an optimization criterion is superimposed, such as the maximization of growth yield. During the optimization process, the algorithm also attempts to satisfy mass balance and thermodynamic constraints. Since no experimental input is required, FBA can rapidly screen through a vast number of situations in silico, which is one of its major advantages. Nevertheless, while FBA operates on models that capture a more holistic view of the cell, the underconstrained nature of the system calls for suitable objective functions in order to pass experimental validation, which can sometimes be challenging to find. Regardless of whether MFA or FBA is used, both of them can be powerful tools in analyzing complex metabolic networks when correctly implemented. It is also worth noting that when carrying out these exercises, the most valuable information often resides not in the absolute values of fluxes but rather in their variations from a base case. In other words, determining flux variations resulting from genetic modulations or changes in organism type and culture conditions can generate valuable information that is masked when studying each condition in isolation. For instance, plots of fluxes against different enzyme levels can be elucidating, where a linear plot would suggest that the enzyme is likely a limiting step.
A key limitation of MFA and FBA is that they operate on stoichiometric models with no kinetic inputs. However, since enzyme kinetics are key determinants of pathway dynamics, genome‐scale kinetic models have also been developed in recent years [11]. As the name suggests, these models are parameterized with unknown rate constants associated with each enzyme that can be determined by fitting experimental flux data. For instance, if Michaelis–Menten kinetics are assumed, then the unknowns will be kcat and Km values for each enzymatic reaction, along with enzyme‐level regulations and their associated KI values. Enzyme as well as metabolite concentrations are also commonly included as partially determined parameters. Due to the nature of how these models are constructed, they tend to capture the intricacies of metabolism in vivo, with the ability to incorporate regulatory effects being a major advantage. Furthermore, once the rate parameters have been solved for, the model can be used to generate additional flux distributions in response to changing conditions. This generally allows for better predictability of flux results where there are no known experimental data. Using this information, one can simply test different engineering targets in silico and observe how the flux within a cell changes to determine the best engineering strategy prior to performing any wet‐lab experiments.
1.2.2 Experimental Methods in Understanding Metabolism
A key question that was raised in the early days of metabolic engineering was how it differed from genetic engineering. The simple answer to this question was that metabolic engineering concerned itself with the properties of metabolic networks viewed as a system instead of a collection of individual genes and enzymes. This introduced a new mind frame in research that was critically punctuated by the emergence of “omics” technologies. “Omics” refers to the profiling of individual cellular components enabled by state‐of‐the‐art analytical techniques and biological assays. These measurements can be applied to nearly all constituents of the cell, including DNA (genomics), RNA (transcriptomics), proteins (proteomics), metabolites (metabolomics), and lipids (lipidomics), and are invaluable to metabolic engineering as they provide holistic information about the cell state. Transcriptomics, for example, surveys the transcription level of nearly every gene and allows one to evaluate all physiological effects brought about by a particular genetic modulation or environmental change as opposed to only those around the small confines of a particular metabolic section [12]. In addition, “omics” data can also guide engineering efforts. With genomics data, one can design and assemble DNA fragments to upregulate or downregulate genes, redirecting metabolic flux into desired pathways. Similarly, transcriptomics and proteomics reveal specific elements that can be engineered to either take advantage of (e.g. inducible promoters) [13] or evade (e.g. mutation of allosteric site) biological regulation [13,14].
Metabolomics is of particular interest to metabolic engineers as it provides a snapshot of the numerous extra‐ and intracellular metabolites. Despite its complexity, this information is indispensable in assessing changes in the metabolic phenotype of cells in response to genetic or enzymatic changes, as pathway activity is ultimately reflected in the profiles of underlying metabolites. The ability to detect and quantify key metabolites, enabled by chromatographic separation techniques and mass spectrometry (MS), has seen major developments in the past several decades, allowing one to probe compounds within cells at concentrations as low as several nmol per gram cell dry weight (gCDW). Assessment of how these metabolites regulate pathway flux can then be carried out. For instance, changes in reduced‐to‐oxidized ratio of common electron carriers (e.g. NAD[P]+) globally affect the thermodynamics of many redox reactions [15]. As another example, high levels of a pathway intermediate can, in some cases, accelerate downstream mass‐action based enzymes or in other cases induce feedback inhibition in upstream enzymes. Closely related to these developments are methods that measure metabolite concentrations in intracellular compartments, such as the mitochondria and the endoplasmic reticulum (ER) [16]. Many important reactions that take place specifically in such compartments cannot be accessed presently with existing metabolite extraction methods. Therefore, compartment‐specific molecular sensors for metabolites have been constructed, which could provide valuable information in understanding the control of flux and improving the productivity of compounds synthesized in sub‐cellular locations [17]. With better accuracy and precision in metabolite quantification, the resulting data help researchers further appreciate the importance of metabolite pool sizes in determining the thermodynamic and kinetic control of associated enzymes. Therefore, it is common to see metabolomics being integrated into many strain engineering studies nowadays.
Another concept closely related to metabolomics is isotopic tracing, which provides information by tracking the flow of constituting atoms or groups of atoms as they transition from metabolite to metabolite. This is conducted by first providing the cells with an isotopically labeled tracer (replacing media components with isotopically labeled versions) and then analyzing the isotopic enrichment of the metabolome. Isotopic tracing requires deep knowledge of the atom transition mapping of all participating reactions as well as nearly all known metabolic pathways. Nevertheless, the outcomes can be very rewarding as such data has been instrumental in the discovery of new pathways [18], determination of flux ratios at key branch points [19], measurement of enzyme reversibility [20], quantification of pathway flux in conjunction with MFA (Section 1.2.1) [9], and analysis of individual pathway contribution to the final product [21].
At this point it is worthy to stress that all “omics” and isotopic tracing measurements are intimately related to one another. Metabolite pool sizes are determined by the upstream and downstream pathway strengths, which are in turn dependent on the transcriptional and translational profiles. Isotopic tracing depends on the reaction fluxes of associated enzymes, which are affected by the expression of associated genes. Due to such intricacies, interpreting specific “omics” data can be challenging and considerations should be put in place to avoid drawing misleading conclusions from their changes, especially those that are related to causality. Unfortunately, there are no generalized tools that can be used for the automated interpretation of the plethora of “omics” data in a mechanistic sense, which is certainly a limitation in realizing the full value of these measurements. Another point worth mentioning is that the “omics” profiles are dynamic and constantly changing with time, albeit at different time scales. Hence, taking “omics” measurements over time with frequent sampling along with sensor development for continuous monitoring will certainly help shed more light onto the dynamics of cellular processes.
1.3 General Approaches to Metabolic Engineering
When cellular metabolism is sufficiently understood in terms of kinetics and regulation, one can formulate strategies on rewiring the native metabolic network of an organism to achieve a certain objective. This paradigm of engineering cells based on a priori knowledge is referred to as rational metabolic engineering and has been broadly applied, especially in the early days of metabolic engineering. In recent years, with the advent of fast and automated DNA synthesis methods, robotics, and high‐throughput screening methods, a different approach has emerged which attempts to generate improved production phenotypes by broadly searching a space comprising numerous random genetic variants of a base strain. This approach, termed combinatorial metabolic engineering, relies less on a basic understanding of fundamental biochemistry and physiology, and is more of a systematic search process. Despite their differences in terms of pros and cons, both approaches have demonstrated considerable success in various applications. In fact, the most successful examples of metabolic engineering integrate ideas from both approaches as well as some other related disciplines. This resulted in the emergence of systems metabolic engineering as a more recent framework tailored toward strain design in industrial biotechnology.
