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Metabolic Flux Analysis

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Cover of 'Metabolic Flux Analysis'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Stoichiometric Modelling of Microbial Metabolism
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    Chapter 2 Tapping the wealth of microbial data in high-throughput metabolic model reconstruction.
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    Chapter 3 Constraining the Flux Space Using Thermodynamics and Integration of Metabolomics Data
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    Chapter 4 NExT : Integration of Thermodynamic Constraints and Metabolomics Data into a Metabolic Network
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    Chapter 5 Customization of 13C-MFA Strategy According to Cell Culture System
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    Chapter 6 Quantitative Metabolomics Using ID-MS
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    Chapter 7 Determining the biomass composition of a sponge holobiont for flux analysis.
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    Chapter 8 Successful Downsizing for High-Throughput (13)C-MFA Applications.
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    Chapter 9 Labelling Analysis for 13 C MFA Using NMR Spectroscopy
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    Chapter 10 GC-MS-Based 13 C Metabolic Flux Analysis
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    Chapter 11 Dynamic Analysis of CO 2 Labeling and Cell Respiration Using Membrane-Inlet Mass Spectrometry
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    Chapter 12 Correction of MS Data for Naturally Occurring Isotopes in Isotope Labelling Experiments
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    Chapter 13 Steady-State 13C Fluxomics Using OpenFLUX
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    Chapter 14 Flux Visualization Using VANTED/FluxMap
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    Chapter 15 Metabolic Flux Analysis for Escherichia coli by Flux Balance Analysis
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    Chapter 16 13 C-Metabolic Flux Analysis for Escherichia coli
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    Chapter 17 (13)C-Based Metabolic Flux Analysis of Recombinant Pichia pastoris.
Attention for Chapter 17: (13)C-Based Metabolic Flux Analysis of Recombinant Pichia pastoris.
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Chapter title
(13)C-Based Metabolic Flux Analysis of Recombinant Pichia pastoris.
Chapter number 17
Book title
Metabolic Flux Analysis
Published in
Methods in molecular biology, January 2014
DOI 10.1007/978-1-4939-1170-7_17
Pubmed ID
Book ISBNs
978-1-4939-1169-1, 978-1-4939-1170-7
Authors

Pau Ferrer, Joan Albiol

Abstract

Overexpression of a foreign protein may negatively affect several cell growth parameters, as well as cause cellular stress. Central (or core) metabolism plays a crucial role since it supplies energy, reduction equivalents, and precursor molecules for the recombinant product, cell's maintenance, and growth needs. However, the number of quantitative physiology studies of the impact of recombinant protein production on the central metabolic pathways of yeast cell factories has been traditionally rather limited, thereby hampering the application of rational strain engineering strategies targeting central metabolism.The development and application of quantitative physiology and modelling tools and methodologies is allowing for a systems-level understanding of the effect of bioprocess parameters such as growth rate, temperature, oxygen availability, and substrate(s) choice on metabolism, and its subsequent interactions with recombinant protein synthesis, folding, and secretion.Here, we review the recent developments and applications of (13)C-based metabolic flux analysis ((13)C-MFA) of Pichia pastoris and the gained understanding of the metabolic behavior of this yeast in recombinant protein production bioprocesses. We also discuss the potential of multilevel studies integrating (13)C-MFA with other omics analyses, as well as future perspectives on the metabolic modelling approaches to study and design metabolic engineering strategies for improved protein production.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 4%
Unknown 22 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 26%
Researcher 5 22%
Professor > Associate Professor 3 13%
Student > Bachelor 2 9%
Student > Doctoral Student 1 4%
Other 4 17%
Unknown 2 9%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 9 39%
Agricultural and Biological Sciences 5 22%
Engineering 2 9%
Social Sciences 1 4%
Energy 1 4%
Other 0 0%
Unknown 5 22%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 September 2014.
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#20,236,620
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Outputs from Methods in molecular biology
#9,864
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#264,835
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Outputs of similar age from Methods in molecular biology
#404
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