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Consequences of microbial interactions with hydrocarbons, oils, and lipids : production of fuels and chemicals / / edited by Sang Yup Lee
| Consequences of microbial interactions with hydrocarbons, oils, and lipids : production of fuels and chemicals / / edited by Sang Yup Lee |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource : 100 illus., 50 illus. in color |
| Disciplina | 579 |
| Collana | Handbook of Hydrocarbon and Lipid Microbiology |
| Soggetto topico |
Biomass energy
Industrial microbiology Microbial biotechnology |
| ISBN | 3-319-31421-1 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Hydrocarbon-lipid Microbiology and Poverty Reduction -- Hydrocarbons from Algae -- Industrial Isoprene Biosynthesis -- Lipid-Containing Secondary Metabolites from Algae -- Metagenomic Mining of Enzyme Diversity -- Microbial Conversion of Carbon Dioxide to Electrofuels -- Microbial Facilitation of Petroleum Recovery: An Introduction -- Microbial Production of Flavours and Fragrances -- Microbial Production of Isoprenoids -- Novel Sensors for Engineering Microbiology -- Production of Fatty Acids and Derivatives by Metabolic Engineering of Bacteria -- Protein Emulsifiers -- Rediscovering Biopolymers -- Rhamnolipids -- Screening for Enantioselective Enzymes -- Synthetic Biology for Biocatalysis -- Synthetic Biology for Biofuels in Saccharomyces cerevisiae -- Use of Biosurfactants in Oil Recovery -- Using Microorganisms as Prospecting Agents in Oil and Gas Exploration -- Yarrowia lipolytica as a Cell Factory for Oleochemical Biotechnology. |
| Record Nr. | UNINA-9910349265403321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Consequences of Microbial Interactions with Hydrocarbons, Oils, and Lipids: Production of Fuels and Chemicals / / edited by Sang Yup Lee
| Consequences of Microbial Interactions with Hydrocarbons, Oils, and Lipids: Production of Fuels and Chemicals / / edited by Sang Yup Lee |
| Edizione | [1st ed. 2017.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
| Descrizione fisica | 1 online resource (174 illus., 102 illus. in color. eReference.) |
| Disciplina | 665.501579 |
| Collana | Handbook of Hydrocarbon and Lipid Microbiology |
| Soggetto topico |
Microbiology
Industrial microbiology Environmental engineering Biotechnology Bioremediation Biochemistry Microbial ecology Industrial Microbiology Environmental Engineering/Biotechnology Microbial Ecology |
| ISBN | 3-319-50436-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Hydrocarbon-lipid Microbiology and Poverty Reduction -- Hydrocarbons from Algae -- Industrial Isoprene Biosynthesis -- Lipid-Containing Secondary Metabolites from Algae -- Metagenomic Mining of Enzyme Diversity -- Microbial Conversion of Carbon Dioxide to Electrofuels -- Microbial Facilitation of Petroleum Recovery: An Introduction -- Microbial Production of Flavours and Fragrances -- Microbial Production of Isoprenoids -- Novel Sensors for Engineering Microbiology -- Production of Fatty Acids and Derivatives by Metabolic Engineering of Bacteria -- Protein Emulsifiers -- Rediscovering Biopolymers -- Rhamnolipids -- Screening for Enantioselective Enzymes -- Synthetic Biology for Biocatalysis -- Synthetic Biology for Biofuels in Saccharomyces cerevisiae -- Use of Biosurfactants in Oil Recovery -- Using Microorganisms as Prospecting Agents in Oil and Gas Exploration -- Yarrowia lipolytica as a Cell Factory for Oleochemical Biotechnology. |
| Record Nr. | UNINA-9910279578503321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Genome informatics 2009 : proceedings of the 20th international conference : Pacifico Yokohama, Japan, 14-16 December 2009. / / editors: Shinichi Morishita, Sang Yup Lee, Yasubumi Sakakibara
| Genome informatics 2009 : proceedings of the 20th international conference : Pacifico Yokohama, Japan, 14-16 December 2009. / / editors: Shinichi Morishita, Sang Yup Lee, Yasubumi Sakakibara |
| Autore | Shinichi Morishita |
| Pubbl/distr/stampa | London, : Imperial College Press, 2009 |
| Descrizione fisica | xv, 224 p. : ill |
| Disciplina | 572.860285 |
| Altri autori (Persone) |
MorishitaShinichi <1960->
YiSang-yŏp <1964-> SakakibaraYasubumi |
| Collana | Genome informatics series |
| Soggetto topico |
Genomics - Data processing
Bioinformatics |
| Soggetto non controllato |
Computational Systems Biology
Genomics Computational Biology Proteomics Genome Informatics Bioinformatics |
| ISBN |
1-282-76016-5
9786612760167 1-84816-563-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Predicting protein-protein relationships from literature using latent topics / T. Aso & K. Eguchi -- Evaluation of DNA intramolecular interactions for nucleosome positioning in yeast / M. Fernandez ... [et al.] -- Quality control and reproducibility in DNA microarray experiments / A. Fujita ... [et al.] -- Comparative analysis of topological patterns in different mammalian networks / B. Goemann ... [et al.] -- Tools for investigating mechanisms of antigenic variation : new extensions to varDB / C. N. Hayes ... [et al.] -- Localized suffix array and its application to genome mapping problems for paired-end short reads / K. Kimura & A. Koike -- Comparative analysis of aerobic and anaerobic prokaryotes to identify correlation between oxygen requirement and gene-gene functional association patterns / Y. Lin & H. Wu -- Calculation of protein-ligand binding free energy using Smooth Reaction Path Generation (SRPG) method : a comparison of the explicit water model, GB/SA model and docking score function / D. Mitomo ... [et al.] -- Structural insights into the enzyme mechanism of a new family of D-2-hydroxyacid dehydrogenases, a close homolog of 2-Ketopantoate reductase / S. Mondal & K. Mizuguchi -- Comprehensive analysis of sequence-structure relationships in the loop regions of proteins / S. Nakamura & K. Shimizu -- The prediction of local modular structures in a co-expression network based on gene expression datasets / Y. Ogata ... [et al.] -- Gradient-based optimization of hyperparameters for base-pairing profile local alignment kernels / K. Sato, Y. Saito & Y. Sakakibara -- A method for efficient execution of bioinformatics workfiows / J. Seo ... [et al.] -- Development of a new meta-score for protein structure prediction from seven all-atom distance dependent potentials using support vector regression / M. Shirota, T. Ishida & K. Kinoshita -- Refining Markov clustering for protein complex prediction by incorporating core-attachment structure / S. Srihari, K. Ning & H. W. Leong -- An assessment of prediction algorithms for nucleosome positioning / Y. Tanaka & K. Nakai -- Cancer classification using single genes / X. Wang & O. Gotoh -- RECOUNT : expectation maximization based error correction tool for next generation sequencing data / E. Wijaya ... [et al.] -- A new generation of homology search tools based on probabilistic inference / S. R. Eddy -- Representation and analysis of molecular networks involving diseases and drugs / M. Kanehisa -- Systems biotechnology / S. Y. Lee -- Strategies toward CNS-regeneration using induced pluripotent stem cells / H. Okano -- Thinking laterally about genomes / M. A. Ragan. |
| Record Nr. | UNINA-9910346696103321 |
Shinichi Morishita
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| London, : Imperial College Press, 2009 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Metabolic engineering : concepts and applications / / edited by Sang Yup Lee, Jens Nielsen, Gregory Stephanopoulos
| Metabolic engineering : concepts and applications / / edited by Sang Yup Lee, Jens Nielsen, Gregory Stephanopoulos |
| Pubbl/distr/stampa | Weinheim, Germany : , : WILEY-VCH, , [2021] |
| Descrizione fisica | 1 online resource (962 pages) |
| Disciplina | 660.62 |
| Collana | Advanced Biotechnology |
| Soggetto topico |
Microbial biotechnology
Microbial genetic engineering |
| Soggetto genere / forma | Electronic books. |
| ISBN |
3-527-82345-X
3-527-82344-1 3-527-82346-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright -- Contents -- Preface -- Part 1 Concepts -- Chapter 1 Metabolic Engineering Perspectives -- 1.1 History and Overview of Metabolic Engineering -- 1.2 Understanding Cellular Metabolism and Physiology -- 1.2.1 Computational Methods in Understanding Metabolism -- 1.2.2 Experimental Methods in Understanding Metabolism -- 1.3 General Approaches to Metabolic Engineering -- 1.3.1 Rational Metabolic Engineering -- 1.3.2 Combinatorial Metabolic Engineering -- 1.3.3 Systems 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 -- Chapter 2 Genome‐Scale Models: Two Decades of Progress and a 2020 Vision -- 2.1 Introduction -- 2.2 Flux Balance Analysis -- 2.2.1 Dynamic Mass Balances -- 2.2.2 Analogy to Deriving Enzymatic Rate Equations -- 2.2.3 Formulating Flux Balances at the Genome‐Scale -- 2.2.4 Constrained Optimization -- 2.2.5 Principles -- 2.2.6 Additional Constraints -- 2.2.7 Flux-Concentration Duality -- 2.2.8 Recap -- 2.3 Network Reconstruction -- 2.3.1 Assembling the Reactome -- 2.3.2 Basic Principles of Network Reconstruction -- 2.3.3 Curation -- 2.3.4 GEMs Have a Genomic Basis -- 2.3.5 Computational Queries -- 2.3.6 Scope Expansion -- 2.3.7 Knowledge Bases -- 2.3.8 Availability of GEMs -- 2.3.9 Recap -- 2.4 Brief History of the GEM for E. coli -- 2.4.1 Origin -- 2.4.2 Model Organism -- 2.4.3 Key Predictions -- 2.4.4 Design Algorithms -- 2.4.5 Scope Expansions -- 2.4.6 Recap -- 2.5 From Metabolism to the Proteome -- 2.5.1 ME Models -- 2.5.2 Capabilities of ME Models -- 2.5.2.1 Growth‐Coupled Metabolic Designs Can Be Reproduced in GEMs -- 2.5.2.2 ME Models Can Reflect Properties of the Metalloproteome -- 2.5.2.3 ME Models Can Compute the Biomass Objective Function.
2.5.2.4 Computing Stresses -- 2.5.3 Recapitulation -- 2.6 Current Developments -- 2.6.1 Kinetics -- 2.6.2 Transcriptional Regulation -- 2.6.2.1 iModulons -- 2.6.2.2 Activities -- 2.6.3 Protein Structures -- 2.7 Broader Perspectives -- 2.7.1 Distal Causation -- 2.7.2 Contextualization of GEMs Within Workflows -- 2.8 What Does the Future Look Like for GEMs? -- Disclaimer -- Acknowledgments -- References -- Chapter 3 Quantitative Metabolic Flux Analysis Based on Isotope Labeling -- 3.1 Introduction -- 3.1.1 What Metabolic Flux Analysis Is About -- 3.1.2 The Variants of 13C‐MFA -- 3.2 A Toy Example Illustrates the Basic Principles -- 3.2.1 Fluxomics: More Than Just a Branch of Metabolomics -- 3.2.2 Isotope Labeling: The Key to Metabolic Fluxes -- 3.2.3 From the Data to the Intracellular Fluxes -- 3.2.4 INST‐13C‐MFA: Metabolic Stationary, but Isotopically Nonstationary -- 3.2.5 From Measurements to Flux Estimates: Parameter Fitting -- 3.2.6 Flux Estimates Have Confidence Bounds: Statistical Analysis -- 3.2.7 The Classical Approach at Metabolic and Isotopic Stationary State -- 3.2.8 An Additional Source of Information: Carbon Atom Transitions -- 3.2.9 Input Labeling Design: How Informative Can an Experiment Be Made? -- 3.2.10 The Isotopomers of a Single Metabolite can be a Rich Source of Information -- 3.2.11 Bidirectional Reaction Steps: More Than Just Nuisance Factors -- 3.2.12 Isotopomer Fractions Cannot Be Measured Comprehensively -- 3.3 Lessons Learned from the Example -- 3.3.1 Definition of 13C‐MFA Revisited -- 3.3.2 Statistical Evaluation and Optimal Experimental Design -- 3.4 How to Configure an Isotope Labeling Experiment -- 3.4.1 Modeling and Simulation of Isotope Labeling Experiments -- 3.4.2 Metabolic Network Specification -- 3.4.3 Atom Transition Network Specification -- 3.4.4 Input Labeling Composition -- 3.4.5 Measurement Specification. 3.4.6 Flux Constraints -- 3.4.7 In Silico Experimental ILE Design -- 3.5 Putting Theory into Practice -- 3.5.1 A Recipe How to Start -- 3.5.2 Metabolic and Isotopic Stationarity -- 3.5.3 Measuring Extracellular Fluxes -- 3.5.4 Administering Labeled Substrate(s) -- 3.5.5 Metabolomics: Sampling, Sample Preparation, and Analytical Procedures -- 3.5.6 Adjusting Labeling Enrichments for Isotopic Steady State Approximation -- 3.5.7 Correcting Labeling Enrichments for Natural Isotope Abundance -- 3.5.8 Simulation of Labeling Data and Flux Estimation -- 3.5.9 Delicacies of INST‐13C‐MFA -- 3.6 Future Challenges of 13C‐MFA -- Acknowledgments -- Abbreviations -- References -- Chapter 4 Proteome Constraints in Genome‐Scale Models -- 4.1 Introduction -- 4.2 Cellular Constraints -- 4.3 Formulation of Proteome Constraints -- 4.3.1 Coarse‐Grained Integration of Proteome Constraints -- 4.3.2 Fine‐Tuned Integration of Proteome Constraints -- 4.4 Perspectives -- References -- Chapter 5 Kinetic Models of Metabolism -- 5.1 Introduction -- 5.2 Definition of Enzyme Kinetics -- 5.2.1 Michaelis-Menten Formula -- 5.3 Factors Affecting Intracellular Enzyme Kinetics -- 5.4 Kinetic Model: Definition and Scope -- 5.4.1 What Is a Kinetic Model? -- 5.4.2 Scope of Kinetic Models -- 5.4.3 How to Build a Functional Kinetic Model? -- 5.5 Main Mathematical Expressions in Description of Reaction Rates -- 5.5.1 Mechanistic Rate Expressions -- 5.6 Approximative Rate Expressions -- 5.7 Approaches to Assign Parameters in the Rate Expressions -- 5.7.1 Direct Measurements of Kinetic Parameters in Enzyme Assays -- 5.7.2 Querying Databases -- 5.7.3 Inferring from Measured Fluxes -- 5.7.4 Parameters Inference Using the Statistical Analysis -- 5.8 Applications -- 5.9 Perspectives -- References -- Chapter 6 Metabolic Control Analysis. 6.1 The Metabolic Engineering Context of Metabolic Control Analysis -- 6.2 MCA Theory -- 6.2.1 Metabolic Steady State -- 6.2.2 Flux Control Coefficients -- 6.2.3 Examples of the Flux-Enzyme Relationship -- 6.2.4 Flux Summation Theorem -- 6.2.5 Concentration Control Coefficients -- 6.2.6 Linking Control Coefficients to Enzyme Properties -- 6.2.6.1 Enzyme Rate Equations and Elasticity Coefficients -- 6.2.6.2 Elasticities and Control Coefficients -- 6.2.6.3 Block Coefficients and Top‐Down Analysis -- 6.2.7 Feedback Inhibition -- 6.2.8 Large Alterations of Enzyme Activity -- 6.3 Implications of MCA for Metabolic Engineering Strategies -- 6.3.1 Abolishing Feedback Inhibition -- 6.3.2 Increasing Demand for Product -- 6.3.3 Inhibition of Competing Pathways -- 6.3.4 Designing Large Changes in Metabolic Flux -- 6.3.4.1 Yeast Tryptophan Synthesis -- 6.3.4.2 The Universal Method -- 6.3.4.3 Bacterial Production of Aromatic Amino Acids -- 6.3.4.4 Penicillin and Other Instances -- 6.3.5 Impacts on Yield from a Growing System -- 6.4 Conclusion -- Appendix 6.A: Feedback Inhibition Simulation -- References -- Chapter 7 Thermodynamics of Metabolic Pathways -- 7.1 Bioenergetics in Life and in Metabolic Engineering -- 7.2 Thermodynamics‐Based Flux Analysis Workflow -- 7.2.1 Thermodynamic Model Curation -- 7.2.1.1 Estimation of the Standard Free Energies of Formation -- 7.2.1.2 Compensating for Compartment‐Specific Ionic Strength and pH -- 7.2.1.3 Compensating the Free Energy of Formation for Isomer Distributions -- 7.2.1.4 Computing the Transformed Free Energies of Reaction -- 7.2.2 Mathematical Formulation -- 7.3 Thermodynamics‐Based Flux Analysis Applications -- 7.3.1 Constraining the Flux Space with Metabolomics Data -- 7.3.2 Characterizing the Feasible Concentration Space -- 7.4 Conclusion and Future Perspectives -- References -- Chapter 8 Pathway Design. Definition -- 8.1 De Novo Design of Metabolic Pathways -- 8.1.1 Manual Versus Computational Design -- 8.2 Pathway Design Workflow -- 8.2.1 Biochemical Search Space -- 8.2.1.1 Reaction Prediction -- 8.2.1.2 Retrobiosynthesis -- 8.2.1.3 Network Data Representation -- 8.2.2 Pathway Search -- 8.2.2.1 Stoichiometric Matrix‐Based Search -- 8.2.2.2 Graph‐Based Search -- 8.2.2.3 Pathway Ranking -- 8.2.3 Enzyme Assignment -- 8.2.3.1 Enzyme Prediction for Orphan and Novel Reactions -- 8.2.3.2 Choice of Protein Sequence -- 8.2.4 Pathway Feasibility -- 8.2.4.1 Chassis Metabolic Model -- 8.2.4.2 Stoichiometric Feasibility -- 8.2.4.3 Thermodynamic Feasibility -- 8.2.4.4 Kinetic Feasibility -- 8.2.4.5 Toxicity of Intermediates -- 8.3 Applications -- 8.3.1 Available Tools for Pathway Design -- 8.3.2 Successful Applications of Pathway Design Tools -- 8.3.3 Practical Example of Pathway Design -- 8.3.3.1 Creating a Biochemical Network Around BDO -- 8.3.3.2 Search for Biosynthetic Pathways -- 8.3.3.3 Finding Enzymes for Novel Reactions -- 8.3.3.4 Stoichiometric and Thermodynamic Pathway Evaluation -- 8.3.3.5 Overall Ranking of Pathways -- 8.4 Conclusions and Future Perspectives -- References -- Chapter 9 Metabolomics -- 9.1 Introduction -- 9.2 Fundamentals -- 9.2.1 Experimental Design -- 9.2.2 Targeted and Untargeted Metabolomics -- 9.2.3 Sequences and Standards -- 9.3 Analytical Techniques -- 9.3.1 Sample Preparation -- 9.3.2 Separation Techniques -- 9.3.2.1 Liquid Chromatography -- 9.3.2.2 Gas Chromatography -- 9.3.2.3 Alternative Separation Techniques -- 9.3.3 Mass Spectrometry -- 9.3.3.1 Ionization Techniques -- 9.3.3.2 Low‐Resolution MS -- 9.3.3.3 High‐Resolution MS -- 9.3.3.4 Acquisition Modes for Targeted MS -- 9.3.3.5 Acquisition Modes for Untargeted Metabolomics -- 9.4 Data Analysis -- 9.4.1 Data Processing in Untargeted Metabolomics. 9.4.1.1 Preprocessing of Individual MS Runs. |
| Record Nr. | UNINA-9910554813403321 |
| Weinheim, Germany : , : WILEY-VCH, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Metabolic engineering : concepts and applications / / edited by Sang Yup Lee, Jens Nielsen, Gregory Stephanopoulos
| Metabolic engineering : concepts and applications / / edited by Sang Yup Lee, Jens Nielsen, Gregory Stephanopoulos |
| Pubbl/distr/stampa | Weinheim, Germany : , : WILEY-VCH, , [2021] |
| Descrizione fisica | 1 online resource (962 pages) |
| Disciplina | 660.62 |
| Collana | Advanced Biotechnology |
| Soggetto topico |
Microbial biotechnology
Microbial genetic engineering |
| ISBN |
3-527-82345-X
3-527-82344-1 3-527-82346-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright -- Contents -- Preface -- Part 1 Concepts -- Chapter 1 Metabolic Engineering Perspectives -- 1.1 History and Overview of Metabolic Engineering -- 1.2 Understanding Cellular Metabolism and Physiology -- 1.2.1 Computational Methods in Understanding Metabolism -- 1.2.2 Experimental Methods in Understanding Metabolism -- 1.3 General Approaches to Metabolic Engineering -- 1.3.1 Rational Metabolic Engineering -- 1.3.2 Combinatorial Metabolic Engineering -- 1.3.3 Systems 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 -- Chapter 2 Genome‐Scale Models: Two Decades of Progress and a 2020 Vision -- 2.1 Introduction -- 2.2 Flux Balance Analysis -- 2.2.1 Dynamic Mass Balances -- 2.2.2 Analogy to Deriving Enzymatic Rate Equations -- 2.2.3 Formulating Flux Balances at the Genome‐Scale -- 2.2.4 Constrained Optimization -- 2.2.5 Principles -- 2.2.6 Additional Constraints -- 2.2.7 Flux-Concentration Duality -- 2.2.8 Recap -- 2.3 Network Reconstruction -- 2.3.1 Assembling the Reactome -- 2.3.2 Basic Principles of Network Reconstruction -- 2.3.3 Curation -- 2.3.4 GEMs Have a Genomic Basis -- 2.3.5 Computational Queries -- 2.3.6 Scope Expansion -- 2.3.7 Knowledge Bases -- 2.3.8 Availability of GEMs -- 2.3.9 Recap -- 2.4 Brief History of the GEM for E. coli -- 2.4.1 Origin -- 2.4.2 Model Organism -- 2.4.3 Key Predictions -- 2.4.4 Design Algorithms -- 2.4.5 Scope Expansions -- 2.4.6 Recap -- 2.5 From Metabolism to the Proteome -- 2.5.1 ME Models -- 2.5.2 Capabilities of ME Models -- 2.5.2.1 Growth‐Coupled Metabolic Designs Can Be Reproduced in GEMs -- 2.5.2.2 ME Models Can Reflect Properties of the Metalloproteome -- 2.5.2.3 ME Models Can Compute the Biomass Objective Function.
2.5.2.4 Computing Stresses -- 2.5.3 Recapitulation -- 2.6 Current Developments -- 2.6.1 Kinetics -- 2.6.2 Transcriptional Regulation -- 2.6.2.1 iModulons -- 2.6.2.2 Activities -- 2.6.3 Protein Structures -- 2.7 Broader Perspectives -- 2.7.1 Distal Causation -- 2.7.2 Contextualization of GEMs Within Workflows -- 2.8 What Does the Future Look Like for GEMs? -- Disclaimer -- Acknowledgments -- References -- Chapter 3 Quantitative Metabolic Flux Analysis Based on Isotope Labeling -- 3.1 Introduction -- 3.1.1 What Metabolic Flux Analysis Is About -- 3.1.2 The Variants of 13C‐MFA -- 3.2 A Toy Example Illustrates the Basic Principles -- 3.2.1 Fluxomics: More Than Just a Branch of Metabolomics -- 3.2.2 Isotope Labeling: The Key to Metabolic Fluxes -- 3.2.3 From the Data to the Intracellular Fluxes -- 3.2.4 INST‐13C‐MFA: Metabolic Stationary, but Isotopically Nonstationary -- 3.2.5 From Measurements to Flux Estimates: Parameter Fitting -- 3.2.6 Flux Estimates Have Confidence Bounds: Statistical Analysis -- 3.2.7 The Classical Approach at Metabolic and Isotopic Stationary State -- 3.2.8 An Additional Source of Information: Carbon Atom Transitions -- 3.2.9 Input Labeling Design: How Informative Can an Experiment Be Made? -- 3.2.10 The Isotopomers of a Single Metabolite can be a Rich Source of Information -- 3.2.11 Bidirectional Reaction Steps: More Than Just Nuisance Factors -- 3.2.12 Isotopomer Fractions Cannot Be Measured Comprehensively -- 3.3 Lessons Learned from the Example -- 3.3.1 Definition of 13C‐MFA Revisited -- 3.3.2 Statistical Evaluation and Optimal Experimental Design -- 3.4 How to Configure an Isotope Labeling Experiment -- 3.4.1 Modeling and Simulation of Isotope Labeling Experiments -- 3.4.2 Metabolic Network Specification -- 3.4.3 Atom Transition Network Specification -- 3.4.4 Input Labeling Composition -- 3.4.5 Measurement Specification. 3.4.6 Flux Constraints -- 3.4.7 In Silico Experimental ILE Design -- 3.5 Putting Theory into Practice -- 3.5.1 A Recipe How to Start -- 3.5.2 Metabolic and Isotopic Stationarity -- 3.5.3 Measuring Extracellular Fluxes -- 3.5.4 Administering Labeled Substrate(s) -- 3.5.5 Metabolomics: Sampling, Sample Preparation, and Analytical Procedures -- 3.5.6 Adjusting Labeling Enrichments for Isotopic Steady State Approximation -- 3.5.7 Correcting Labeling Enrichments for Natural Isotope Abundance -- 3.5.8 Simulation of Labeling Data and Flux Estimation -- 3.5.9 Delicacies of INST‐13C‐MFA -- 3.6 Future Challenges of 13C‐MFA -- Acknowledgments -- Abbreviations -- References -- Chapter 4 Proteome Constraints in Genome‐Scale Models -- 4.1 Introduction -- 4.2 Cellular Constraints -- 4.3 Formulation of Proteome Constraints -- 4.3.1 Coarse‐Grained Integration of Proteome Constraints -- 4.3.2 Fine‐Tuned Integration of Proteome Constraints -- 4.4 Perspectives -- References -- Chapter 5 Kinetic Models of Metabolism -- 5.1 Introduction -- 5.2 Definition of Enzyme Kinetics -- 5.2.1 Michaelis-Menten Formula -- 5.3 Factors Affecting Intracellular Enzyme Kinetics -- 5.4 Kinetic Model: Definition and Scope -- 5.4.1 What Is a Kinetic Model? -- 5.4.2 Scope of Kinetic Models -- 5.4.3 How to Build a Functional Kinetic Model? -- 5.5 Main Mathematical Expressions in Description of Reaction Rates -- 5.5.1 Mechanistic Rate Expressions -- 5.6 Approximative Rate Expressions -- 5.7 Approaches to Assign Parameters in the Rate Expressions -- 5.7.1 Direct Measurements of Kinetic Parameters in Enzyme Assays -- 5.7.2 Querying Databases -- 5.7.3 Inferring from Measured Fluxes -- 5.7.4 Parameters Inference Using the Statistical Analysis -- 5.8 Applications -- 5.9 Perspectives -- References -- Chapter 6 Metabolic Control Analysis. 6.1 The Metabolic Engineering Context of Metabolic Control Analysis -- 6.2 MCA Theory -- 6.2.1 Metabolic Steady State -- 6.2.2 Flux Control Coefficients -- 6.2.3 Examples of the Flux-Enzyme Relationship -- 6.2.4 Flux Summation Theorem -- 6.2.5 Concentration Control Coefficients -- 6.2.6 Linking Control Coefficients to Enzyme Properties -- 6.2.6.1 Enzyme Rate Equations and Elasticity Coefficients -- 6.2.6.2 Elasticities and Control Coefficients -- 6.2.6.3 Block Coefficients and Top‐Down Analysis -- 6.2.7 Feedback Inhibition -- 6.2.8 Large Alterations of Enzyme Activity -- 6.3 Implications of MCA for Metabolic Engineering Strategies -- 6.3.1 Abolishing Feedback Inhibition -- 6.3.2 Increasing Demand for Product -- 6.3.3 Inhibition of Competing Pathways -- 6.3.4 Designing Large Changes in Metabolic Flux -- 6.3.4.1 Yeast Tryptophan Synthesis -- 6.3.4.2 The Universal Method -- 6.3.4.3 Bacterial Production of Aromatic Amino Acids -- 6.3.4.4 Penicillin and Other Instances -- 6.3.5 Impacts on Yield from a Growing System -- 6.4 Conclusion -- Appendix 6.A: Feedback Inhibition Simulation -- References -- Chapter 7 Thermodynamics of Metabolic Pathways -- 7.1 Bioenergetics in Life and in Metabolic Engineering -- 7.2 Thermodynamics‐Based Flux Analysis Workflow -- 7.2.1 Thermodynamic Model Curation -- 7.2.1.1 Estimation of the Standard Free Energies of Formation -- 7.2.1.2 Compensating for Compartment‐Specific Ionic Strength and pH -- 7.2.1.3 Compensating the Free Energy of Formation for Isomer Distributions -- 7.2.1.4 Computing the Transformed Free Energies of Reaction -- 7.2.2 Mathematical Formulation -- 7.3 Thermodynamics‐Based Flux Analysis Applications -- 7.3.1 Constraining the Flux Space with Metabolomics Data -- 7.3.2 Characterizing the Feasible Concentration Space -- 7.4 Conclusion and Future Perspectives -- References -- Chapter 8 Pathway Design. Definition -- 8.1 De Novo Design of Metabolic Pathways -- 8.1.1 Manual Versus Computational Design -- 8.2 Pathway Design Workflow -- 8.2.1 Biochemical Search Space -- 8.2.1.1 Reaction Prediction -- 8.2.1.2 Retrobiosynthesis -- 8.2.1.3 Network Data Representation -- 8.2.2 Pathway Search -- 8.2.2.1 Stoichiometric Matrix‐Based Search -- 8.2.2.2 Graph‐Based Search -- 8.2.2.3 Pathway Ranking -- 8.2.3 Enzyme Assignment -- 8.2.3.1 Enzyme Prediction for Orphan and Novel Reactions -- 8.2.3.2 Choice of Protein Sequence -- 8.2.4 Pathway Feasibility -- 8.2.4.1 Chassis Metabolic Model -- 8.2.4.2 Stoichiometric Feasibility -- 8.2.4.3 Thermodynamic Feasibility -- 8.2.4.4 Kinetic Feasibility -- 8.2.4.5 Toxicity of Intermediates -- 8.3 Applications -- 8.3.1 Available Tools for Pathway Design -- 8.3.2 Successful Applications of Pathway Design Tools -- 8.3.3 Practical Example of Pathway Design -- 8.3.3.1 Creating a Biochemical Network Around BDO -- 8.3.3.2 Search for Biosynthetic Pathways -- 8.3.3.3 Finding Enzymes for Novel Reactions -- 8.3.3.4 Stoichiometric and Thermodynamic Pathway Evaluation -- 8.3.3.5 Overall Ranking of Pathways -- 8.4 Conclusions and Future Perspectives -- References -- Chapter 9 Metabolomics -- 9.1 Introduction -- 9.2 Fundamentals -- 9.2.1 Experimental Design -- 9.2.2 Targeted and Untargeted Metabolomics -- 9.2.3 Sequences and Standards -- 9.3 Analytical Techniques -- 9.3.1 Sample Preparation -- 9.3.2 Separation Techniques -- 9.3.2.1 Liquid Chromatography -- 9.3.2.2 Gas Chromatography -- 9.3.2.3 Alternative Separation Techniques -- 9.3.3 Mass Spectrometry -- 9.3.3.1 Ionization Techniques -- 9.3.3.2 Low‐Resolution MS -- 9.3.3.3 High‐Resolution MS -- 9.3.3.4 Acquisition Modes for Targeted MS -- 9.3.3.5 Acquisition Modes for Untargeted Metabolomics -- 9.4 Data Analysis -- 9.4.1 Data Processing in Untargeted Metabolomics. 9.4.1.1 Preprocessing of Individual MS Runs. |
| Record Nr. | UNINA-9910829921803321 |
| Weinheim, Germany : , : WILEY-VCH, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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