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Biblioteca

MARC Lista (tabellare)
Development of lactic fermentation bacteria for the production of bioactive food ingredients ... annual report
Development of lactic fermentation bacteria for the production of bioactive food ingredients ... annual report
Pubbl/distr/stampa [Washington, D.C.], : U.S. Dept. of Agriculture, Agricultural Research Service
Descrizione fisica : HTML files
Disciplina 338.1
Soggetto topico Dairy microbiology - United States
Bioactive compounds - United States
Microbial genetic engineering - United States
Bioactive compounds
Dairy microbiology
Microbial genetic engineering
Soggetto genere / forma Periodicals.
ISSN 1948-4887
Formato Materiale a stampa
Livello bibliografico Periodico
Lingua di pubblicazione eng
Record Nr. UNINA-9910698666003321
[Washington, D.C.], : U.S. Dept. of Agriculture, Agricultural Research Service
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Engineering microbiology
Engineering microbiology
Pubbl/distr/stampa Amsterdam : , : Elsevier B.V., , [2021]-
Disciplina 570
Soggetto topico Microbiology
Microbial genetic engineering
Genetic engineering
Medical microbiology
Synthetic biology
Synthetic Biology
Cell Engineering
Computational Biology
Génie génétique microbien
Microbiologie médicale
Microbiologie
Biologie synthétique
Bio-informatique
microbiology
Soggetto genere / forma Periodical
ISSN 2667-3703
Formato Materiale a stampa
Livello bibliografico Periodico
Lingua di pubblicazione eng
Altri titoli varianti EM
Record Nr. UNINA-9910489950703321
Amsterdam : , : Elsevier B.V., , [2021]-
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Field testing genetically modified organisms [[electronic resource] ] : framework for decisions / / Committee on Scientific Evaluation of the Introduction of Genetically Modified Microorganisms and Plants into the Environment, Board on Biology, Commission on Life Sciences, National Research Council
Field testing genetically modified organisms [[electronic resource] ] : framework for decisions / / Committee on Scientific Evaluation of the Introduction of Genetically Modified Microorganisms and Plants into the Environment, Board on Biology, Commission on Life Sciences, National Research Council
Pubbl/distr/stampa Washington, D.C., : National Academy Press, 1989
Descrizione fisica 1 online resource (184 p.)
Disciplina 660/.65
Altri autori (Persone) BurrisRobert H <1914-> (Robert Harza)
Soggetto topico Microbial genetic engineering
Biotechnology
Genetic engineering
Plant genetic engineering
Soggetto genere / forma Electronic books.
ISBN 1-280-21448-1
9786610214488
0-309-56420-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910455921703321
Washington, D.C., : National Academy Press, 1989
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Field testing genetically modified organisms : framework for decisions / / Committee on Scientific Evaluation of the Introduction of Genetically Modified Microorganisms and Plants into the Environment, Board on Biology, Commission on Life Sciences, National Research Council
Field testing genetically modified organisms : framework for decisions / / Committee on Scientific Evaluation of the Introduction of Genetically Modified Microorganisms and Plants into the Environment, Board on Biology, Commission on Life Sciences, National Research Council
Pubbl/distr/stampa Washington, D.C. : , : National Academy Press, , 1989
Descrizione fisica 1 online resource (184 pages) : illustrations
Disciplina 660/.65
Altri autori (Persone) BurrisRobert H <1914-> (Robert Harza)
Soggetto topico Microbial genetic engineering
Biotechnology
Genetic engineering
Plant genetic engineering
ISBN 1-280-21448-1
9786610214488
0-309-56420-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 1 Front Matter; 2 1 Executive Summary; 3 2 Introduction; 4 3 Past Experience with Genetic Modification of Plants and Their Introduction into the Environment; 5 4 Enhanced Weediness: A Major Environmental Issue; 6 5 Past Experience with the Introduction of Modified Plants: Molecular Genetic Techniques; 7 6 Conclusions and Recommendations: Plants; 8 7 Past Experience with the Introduction of Microorganisms into the Environment; 9 8 Properties of the Genetic Modification; 10 9 Phenotypic Properties of Source Microorganisms and Their Genetically Modified Derivatives; 11 10 Properties of the Environment Relevant to the Introduction of Genetically Modified Microorganisms; 12 11 Conclusions and Recommendations: Microorganisms; 13 Appendix - Historical Overview of Nucleic Acid Biotechnology: 1973 to 1989; 14 Literature Cited; 15 Information on Committee Members
Record Nr. UNINA-9910778789803321
Washington, D.C. : , : National Academy Press, , 1989
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Field testing genetically modified organisms : framework for decisions / / Committee on Scientific Evaluation of the Introduction of Genetically Modified Microorganisms and Plants into the Environment, Board on Biology, Commission on Life Sciences, National Research Council
Field testing genetically modified organisms : framework for decisions / / Committee on Scientific Evaluation of the Introduction of Genetically Modified Microorganisms and Plants into the Environment, Board on Biology, Commission on Life Sciences, National Research Council
Edizione [1st ed.]
Pubbl/distr/stampa Washington, D.C., : National Academy Press, 1989
Descrizione fisica 1 online resource (184 pages) : illustrations
Disciplina 660/.65
Altri autori (Persone) BurrisRobert H <1914-> (Robert Harza)
Soggetto topico Microbial genetic engineering
Biotechnology
Genetic engineering
Plant genetic engineering
ISBN 1-280-21448-1
9786610214488
0-309-56420-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 1 Front Matter; 2 1 Executive Summary; 3 2 Introduction; 4 3 Past Experience with Genetic Modification of Plants and Their Introduction into the Environment; 5 4 Enhanced Weediness: A Major Environmental Issue; 6 5 Past Experience with the Introduction of Modified Plants: Molecular Genetic Techniques; 7 6 Conclusions and Recommendations: Plants; 8 7 Past Experience with the Introduction of Microorganisms into the Environment; 9 8 Properties of the Genetic Modification; 10 9 Phenotypic Properties of Source Microorganisms and Their Genetically Modified Derivatives; 11 10 Properties of the Environment Relevant to the Introduction of Genetically Modified Microorganisms; 12 11 Conclusions and Recommendations: Microorganisms; 13 Appendix - Historical Overview of Nucleic Acid Biotechnology: 1973 to 1989; 14 Literature Cited; 15 Information on Committee Members
Record Nr. UNINA-9910828098603321
Washington, D.C., : National Academy Press, 1989
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
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
Opac: Controlla la disponibilità qui
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
Opac: Controlla la disponibilità qui