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Maciek R. Antoniewicz - One of the best experts on this subject based on the ideXlab platform.

  • A guide to Metabolic Flux Analysis in Metabolic engineering: Methods, tools and applications.
    Metabolic engineering, 2020
    Co-Authors: Maciek R. Antoniewicz
    Abstract:

    Abstract The field of Metabolic engineering is primarily concerned with improving the biological production of value-added chemicals, fuels and pharmaceuticals through the design, construction and optimization of Metabolic pathways, redirection of intracellular Fluxes, and refinement of cellular properties relevant for industrial bioprocess implementation. Metabolic network models and Metabolic Fluxes are central concepts in Metabolic engineering, as was emphasized in the first paper published in this journal, “Metabolic Fluxes and Metabolic engineering” (Metabolic Engineering, 1: 1–11, 1999). In the past two decades, a wide range of computational, analytical and experimental approaches have been developed to interrogate the capabilities of biological systems through Analysis of Metabolic network models using techniques such as Flux balance Analysis (FBA), and quantify Metabolic Fluxes using constrained-based modeling approaches such as Metabolic Flux Analysis (MFA) and more advanced experimental techniques based on the use of stable-isotope tracers, i.e. 13C-Metabolic Flux Analysis (13C-MFA). In this review, we describe the basic principles of Metabolic Flux Analysis, discuss current best practices in Flux quantification, highlight potential pitfalls and alternative approaches in the application of these tools, and give a broad overview of pragmatic applications of Flux Analysis in Metabolic engineering practice.

  • High-resolution 13C Metabolic Flux Analysis.
    Nature protocols, 2019
    Co-Authors: Christopher P. Long, Maciek R. Antoniewicz
    Abstract:

    Precise quantification of Metabolic pathway Fluxes in biological systems is of major importance in guiding efforts in Metabolic engineering, biotechnology, microbiology, human health, and cell culture. 13C Metabolic Flux Analysis (13C-MFA) is the predominant technique used for determining intracellular Fluxes. Here, we present a protocol for 13C-MFA that incorporates recent advances in parallel labeling experiments, isotopic labeling measurements, and statistical Analysis, as well as best practices developed through decades of experience. Experimental design to ensure that Fluxes are estimated with the highest precision is an integral part of the protocol. The protocol is based on growing microbes in two (or more) parallel cultures with 13C-labeled glucose tracers, followed by gas chromatography–mass spectrometry (GC–MS) measurements of isotopic labeling of protein-bound amino acids, glycogen-bound glucose, and RNA-bound ribose. Fluxes are then estimated using software for 13C-MFA, such as Metran, followed by comprehensive statistical Analysis to determine the goodness of fit and calculate confidence intervals of Fluxes. The presented protocol can be completed in 4 d and quantifies Metabolic Fluxes with a standard deviation of ≤2%, a substantial improvement over previous implementations. The presented protocol is exemplified using an Escherichia coli ΔtpiA case study with full supporting data, providing a hands-on opportunity to step through a complex troubleshooting scenario. Although applications to prokaryotic microbial systems are emphasized, this protocol can be easily adjusted for application to eukaryotic organisms. Precise quantification of Metabolic pathway Fluxes is needed in many applications, e.g., microbiological engineering. The authors describe a GC–MS method for 13C Metabolic Flux Analysis with data Analysis using Metran software.

  • Methods and advances in Metabolic Flux Analysis: a mini-review
    Journal of Industrial Microbiology & Biotechnology, 2015
    Co-Authors: Maciek R. Antoniewicz
    Abstract:

    Metabolic Flux Analysis (MFA) is one of the pillars of Metabolic engineering. Over the past three decades, it has been widely used to quantify intracellular Metabolic Fluxes in both native (wild type) and engineered biological systems. Through MFA, changes in Metabolic pathway Fluxes are quantified that result from genetic and/or environmental interventions. This information, in turn, provides insights into the regulation of Metabolic pathways and may suggest new targets for further Metabolic engineering of the strains. In this mini-review, we discuss and classify the various methods of MFA that have been developed, which include stoichiometric MFA, ^13C Metabolic Flux Analysis, isotopic non-stationary ^13C Metabolic Flux Analysis, dynamic Metabolic Flux Analysis, and ^13C dynamic Metabolic Flux Analysis. For each method, we discuss key advantages and limitations and conclude by highlighting important recent advances in Flux Analysis approaches.

  • 13C Metabolic Flux Analysis: optimal design of isotopic labeling experiments
    Current Opinion in Biotechnology, 2013
    Co-Authors: Maciek R. Antoniewicz
    Abstract:

    Measuring Fluxes by 13C Metabolic Flux Analysis (13C-MFA) has become a key activity in chemical and pharmaceutical biotechnology. Optimal design of isotopic labeling experiments is of central importance to 13C-MFA as it determines the precision with which Fluxes can be estimated. Traditional methods for selecting isotopic tracers and labeling measurements did not fully utilize the power of 13C-MFA. Recently, new approaches were developed for optimal design of isotopic labeling experiments based on parallel labeling experiments and algorithms for rational selection of tracers. In addition, advanced isotopic labeling measurements were developed based on tandem mass spectrometry. Combined, these approaches can dramatically improve the quality of 13C-MFA results with important applications in Metabolic engineering and biotechnology.

  • rational design of 13c labeling experiments for Metabolic Flux Analysis in mammalian cells
    BMC Systems Biology, 2012
    Co-Authors: Scott B Crown, Maciek R. Antoniewicz
    Abstract:

    Background 13C-Metabolic Flux Analysis (13C-MFA) is a standard technique to probe cellular metabolism and elucidate in vivo Metabolic Fluxes. 13C-Tracer selection is an important step in conducting 13C-MFA, however, current methods are restricted to trial-and-error approaches, which commonly focus on an arbitrary subset of the tracer design space. To systematically probe the complete tracer design space, especially for complex systems such as mammalian cells, there is a pressing need for new rational approaches to identify optimal tracers.

Christoph Wittmann - One of the best experts on this subject based on the ideXlab platform.

  • GC-MS-Based 13 C Metabolic Flux Analysis
    Methods in molecular biology (Clifton N.J.), 2014
    Co-Authors: Judith Becker, Christoph Wittmann
    Abstract:

    The in vivo Analysis of Metabolic Fluxes has become a valuable method for the investigation of microorganisms. It turned out especially useful in industrial biotechnology for the prediction of beneficial genetic targets for rational strain optimization. Here, we describe in detail the procedure for the state-of-the-art approach of (13)C Metabolic Flux Analysis comprising steady-state cultivations in a mineral salt medium with (13)C-labeled substrates, GC-MS measurement for labeling Analysis, as well as Metabolic modeling using the open-source software OpenFlux.

  • openFlux efficient modelling software for 13c based Metabolic Flux Analysis
    Microbial Cell Factories, 2009
    Co-Authors: Lake-ee Quek, Christoph Wittmann, Lars K. Nielsen, Jens O. Krömer
    Abstract:

    The quantitative Analysis of Metabolic Fluxes, i.e., in vivo activities of intracellular enzymes and pathways, provides key information on biological systems in systems biology and Metabolic engineering. It is based on a comprehensive approach combining (i) tracer cultivation on 13C substrates, (ii) 13C labelling Analysis by mass spectrometry and (iii) mathematical modelling for experimental design, data processing, Flux calculation and statistics. Whereas the cultivation and the analytical part is fairly advanced, a lack of appropriate modelling software solutions for all modelling aspects in Flux studies is limiting the application of Metabolic Flux Analysis. We have developed OpenFlux as a user friendly, yet flexible software application for small and large scale 13C Metabolic Flux Analysis. The application is based on the new Elementary Metabolite Unit (EMU) framework, significantly enhancing computation speed for Flux calculation. From simple notation of Metabolic reaction networks defined in a spreadsheet, the OpenFlux parser automatically generates MATLAB-readable metabolite and isotopomer balances, thus strongly facilitating model creation. The model can be used to perform experimental design, parameter estimation and sensitivity Analysis either using the built-in gradient-based search or Monte Carlo algorithms or in user-defined algorithms. Exemplified for a microbial Flux study with 71 reactions, 8 free Flux parameters and mass isotopomer distribution of 10 metabolites, OpenFlux allowed to automatically compile the EMU-based model from an Excel file containing Metabolic reactions and carbon transfer mechanisms, showing it's user-friendliness. It reliably reproduced the published data and optimum Flux distributions for the network under study were found quickly (<20 sec). We have developed a fast, accurate application to perform steady-state 13C Metabolic Flux Analysis. OpenFlux will strongly facilitate and enhance the design, calculation and interpretation of Metabolic Flux studies. By providing the software open source, we hope it will evolve with the rapidly growing field of Fluxomics.

  • OpenFlux: efficient modelling software for 13C-based Metabolic Flux Analysis.
    Microbial cell factories, 2009
    Co-Authors: Lake-ee Quek, Christoph Wittmann, Lars K. Nielsen, Jens O. Krömer
    Abstract:

    The quantitative Analysis of Metabolic Fluxes, i.e., in vivo activities of intracellular enzymes and pathways, provides key information on biological systems in systems biology and Metabolic engineering. It is based on a comprehensive approach combining (i) tracer cultivation on 13C substrates, (ii) 13C labelling Analysis by mass spectrometry and (iii) mathematical modelling for experimental design, data processing, Flux calculation and statistics. Whereas the cultivation and the analytical part is fairly advanced, a lack of appropriate modelling software solutions for all modelling aspects in Flux studies is limiting the application of Metabolic Flux Analysis. We have developed OpenFlux as a user friendly, yet flexible software application for small and large scale 13C Metabolic Flux Analysis. The application is based on the new Elementary Metabolite Unit (EMU) framework, significantly enhancing computation speed for Flux calculation. From simple notation of Metabolic reaction networks defined in a spreadsheet, the OpenFlux parser automatically generates MATLAB-readable metabolite and isotopomer balances, thus strongly facilitating model creation. The model can be used to perform experimental design, parameter estimation and sensitivity Analysis either using the built-in gradient-based search or Monte Carlo algorithms or in user-defined algorithms. Exemplified for a microbial Flux study with 71 reactions, 8 free Flux parameters and mass isotopomer distribution of 10 metabolites, OpenFlux allowed to automatically compile the EMU-based model from an Excel file containing Metabolic reactions and carbon transfer mechanisms, showing it's user-friendliness. It reliably reproduced the published data and optimum Flux distributions for the network under study were found quickly (

  • Theoretical aspects of 13C Metabolic Flux Analysis with sole quantification of carbon dioxide labeling
    Computational biology and chemistry, 2005
    Co-Authors: Tae Hoon Yang, Elmar Heinzle, Christoph Wittmann
    Abstract:

    The potential of using sole respirometric CO"2 labeling measurement for ^1^3C Metabolic Flux Analysis was investigated by Metabolic simulations. For this purpose a model was created, considering all CO"2 forming and consuming reactions in the central catabolic and anabolic pathways. To facilitate the interpretation of the simulation results, the underlying Metabolic network was parameterized by physiologically meaningful Flux parameters such as Flux partitioning ratios at Metabolic branch points and reaction reversibilities. For real case Flux scenarios of the industrial amino acid producer Corynebacterium glutamicum and different commercially available ^1^3C-labeled tracer substrates, observability and output sensitivity towards key Flux parameters was investigated. Metabolic net Fluxes in the central metabolism, involving, e.g. glycolysis, pentose phosphate pathway, tricarboxylic acid cycle, anaplerotic carboxylation, and glyoxylate pathway were found to be determinable by the respirometric approach using a combination of [1-^1^3C] and [6-^1^3C] glucose in two parallel studies. The reversibilities of bidirectional reactions influence the isotopic labeling of CO"2 only to a negligible degree. On one hand, they therefore cannot be determined. On the other hand, their precise values are not required for the quantification of net Fluxes. Computer-aided optimal experimental design was carried out to predict the quality of the information from the respirometric tracer experiments and identify suitable tracer substrates. A combination of [1-^1^3C] and [6-^1^3C] glucose in two parallel studies was found to yield a similar quality of information as compared to an approach with mass spectrometric labeling Analysis of secreted products. The quality of information can be further increased by additional studies with [1,2-^1^3C"2] or [1,6-^1^3C"2] glucose. Respirometric tracer studies with sole labeling Analysis of CO"2 are therefore promising for ^1^3C Metabolic Flux Analysis. for ^1^3C Metabolic Flux Analysis.

  • Metabolic Flux Analysis Using Mass Spectrometry
    Advances in biochemical engineering biotechnology, 2002
    Co-Authors: Christoph Wittmann
    Abstract:

    Detailed knowledge on carbon Flux distributions is crucial for the understanding and targeted optimization of cellular systems. Analytical methods to identify the topology of Metabolic networks and to quantify Fluxes through its different pathways are therefore in the core of Metabolic engineering. An elegant approach for Metabolic Flux Analysis is provided by tracer experiments. In such studies tracer substrates with stable isotopes such as 13C are applied and the labeling pattern of metabolites is subsequently measured. Detailed Flux distributions can be obtained by a combination of tracer experiments and stoichiometric balancing. In recent years, mass spectrometry (MS) has emerged as an interesting method for labeling measurements in Metabolic Flux Analysis and provided valuable insights into the cellular metabolism. The present review provides an overview on current experimental and modeling tools for Metabolic Flux Analysis by MS. The application of MS for Flux Analysis is illustrated by examples from the literature for various biological systems, including bacteria, fungi, tissue cultures and in vivo studies in humans.

Elmar Heinzle - One of the best experts on this subject based on the ideXlab platform.

  • Investigation of glutamine metabolism in CHO cells by dynamic Metabolic Flux Analysis
    BMC Proceedings, 2013
    Co-Authors: Judith Wahrheit, Averina Nicolae, Elmar Heinzle
    Abstract:

    Background Glutamine metabolism represents one of the major targets in Metabolic engineering and process optimization due to its importance as cellular energy, carbon and nitrogen source. Metabolic Flux Analysis represents a powerful method to investigate the physiology and metabolism of cells [1]. Classical Metabolic Flux Analysis methods require steady state conditions. However, industrially relevant cultivation conditions, i.e. batch and fed-batch cultivations, are characterized by changing environmental conditions and Metabolic shifts [2]. We used dynamic Metabolic Flux Analysis to study the impact of glutamine availability or limitation on the physiology of CHO K1 cells capturing Metabolic dynamics during batchand fed-batch cultivations.

  • Metabolic Flux Analysis in eukaryotes.
    Current opinion in biotechnology, 2010
    Co-Authors: Jens Niklas, Konstantin Schneider, Elmar Heinzle
    Abstract:

    Metabolic Flux Analysis (MFA) represents a powerful tool for systems biology research on eukaryotic cells. This review describes recent advances, the challenges as well as applications of Metabolic Flux Analysis comprising fungi, mammalian cells and plants. While MFA is widely established and applied in microorganisms, it remains still a challenge to adapt these methods to eukaryotic cell systems having a higher complexity particularly concerning compartmentation or media composition. In fungi MFA was used in the past few years to analyze a variety of conditions and factors and their effects on cellular metabolism. In mammalian cells MFA was applied mainly in cell culture technology and in medical and toxicological research. (13)C Metabolic studies on native whole plants are additionally challenging by the fact that CO(2) is usually the only carbon source.

  • Hybrid optimization for 13C Metabolic Flux Analysis using systems parametrized by compactification.
    BMC systems biology, 2008
    Co-Authors: Tae Hoon Yang, Oliver Frick, Elmar Heinzle
    Abstract:

    Background The importance and power of isotope-based Metabolic Flux Analysis and its contribution to understanding the Metabolic network is increasingly recognized. Its application is, however, still limited partly due to computational inefficiency. 13C Metabolic Flux Analysis aims to compute in vivo Metabolic Fluxes in terms of metabolite balancing extended by carbon isotopomer balances and involves a nonlinear least-squares problem. To solve the problem more efficiently, improved numerical optimization techniques are necessary.

  • Theoretical aspects of 13C Metabolic Flux Analysis with sole quantification of carbon dioxide labeling
    Computational biology and chemistry, 2005
    Co-Authors: Tae Hoon Yang, Elmar Heinzle, Christoph Wittmann
    Abstract:

    The potential of using sole respirometric CO"2 labeling measurement for ^1^3C Metabolic Flux Analysis was investigated by Metabolic simulations. For this purpose a model was created, considering all CO"2 forming and consuming reactions in the central catabolic and anabolic pathways. To facilitate the interpretation of the simulation results, the underlying Metabolic network was parameterized by physiologically meaningful Flux parameters such as Flux partitioning ratios at Metabolic branch points and reaction reversibilities. For real case Flux scenarios of the industrial amino acid producer Corynebacterium glutamicum and different commercially available ^1^3C-labeled tracer substrates, observability and output sensitivity towards key Flux parameters was investigated. Metabolic net Fluxes in the central metabolism, involving, e.g. glycolysis, pentose phosphate pathway, tricarboxylic acid cycle, anaplerotic carboxylation, and glyoxylate pathway were found to be determinable by the respirometric approach using a combination of [1-^1^3C] and [6-^1^3C] glucose in two parallel studies. The reversibilities of bidirectional reactions influence the isotopic labeling of CO"2 only to a negligible degree. On one hand, they therefore cannot be determined. On the other hand, their precise values are not required for the quantification of net Fluxes. Computer-aided optimal experimental design was carried out to predict the quality of the information from the respirometric tracer experiments and identify suitable tracer substrates. A combination of [1-^1^3C] and [6-^1^3C] glucose in two parallel studies was found to yield a similar quality of information as compared to an approach with mass spectrometric labeling Analysis of secreted products. The quality of information can be further increased by additional studies with [1,2-^1^3C"2] or [1,6-^1^3C"2] glucose. Respirometric tracer studies with sole labeling Analysis of CO"2 are therefore promising for ^1^3C Metabolic Flux Analysis. for ^1^3C Metabolic Flux Analysis.

  • mass spectrometry for Metabolic Flux Analysis
    Biotechnology and Bioengineering, 1999
    Co-Authors: Christoph Wittmann, Elmar Heinzle
    Abstract:

    Mass spectrometry in combination with tracer experiments based on 13C substrates can serve as a powerful tool for the modeling and Analysis of intracellular Fluxes and the investigation of biochemical networks. The theoretical background for the application of mass spectrometry to Metabolic Flux Analysis is discussed. Mass spectrometry methods are especially useful to determine mass distribution of metabolites. Additional information gained from fragmentation of metabolites, e.g., by electron impact ionization, allows further localization of labeling positions, up to complete resolution of isotopomer pools. To effectively handle mass distributions in simulation experiments, a matrix based general methodology is formulated. The natural isotope distribution of carbon, oxygen, hydrogen and nitrogen in the target metabolites is considered by introduction of correction matrices. It is shown by simulation results for the central carbon metabolism that neglecting natural isotope distributions causes significant errors in intracellular Flux distributions. By varying relative Fluxes into pentosephosphate pathway and pyruvate carboxylation reaction, marked changes in the mass distributions of metabolites result, which are illustrated for pyruvate, oxaloacetate, and α-ketoglutarate. In addition mass distributions of metabolites are significantly influenced over a broad range by the degree of reversibility of transaldolase and transketolase reactions in the pentosephosphate pathway. The mass distribution of metabolites is very sensitive towards intracellular Flux patterns and can be measured with high accuracy by routine mass spectrometry methods. © 1999 John Wiley & Sons, Inc. Biotechnol Bioeng 62: 739–750, 1999.

Brigitte Thomasset - One of the best experts on this subject based on the ideXlab platform.

  • 13C-Metabolic Flux Analysis in Developing Flaxseed Embryos to Understand Storage Lipid Biosynthesis
    2019
    Co-Authors: Sébastien Acket, Anthony Degournay, Yannick Rossez, Stéphane Mottelet, Pierre Villon, Adrián Troncoso-ponce, Brigitte Thomasset
    Abstract:

    Flaxseed (Linum usitatissinum L.) oil is an important source of α-linolenic (C18:3 ω-3), this polyunsaturated fatty acid is well known for its nutritional role in human and animal diet. Understanding storage lipid biosynthesis in developing flaxseed embryos can lead to an increase in seed yield. While a tremendous amount of work has been done on different plant species to highlight their metabolism during embryos development, flaxseed Metabolic Flux Analysis is still lacking. In this context, we have developed an in vitro cultured developing embryos of flaxseed and determined net Fluxes by performing three complementary parallel labeling experiments with 13C-labeled glucose and glutamine. Metabolic Fluxes were estimated by computer-aided modeling of the central Metabolic network including 11 cofactors of 118 reactions of the central metabolism, 12 pseudo Fluxes. A focus on lipid storage biosynthesis and the associated pathways was done in comparison with rapeseed, arabidopsis, maize and sunflower embryos. In our conditions, glucose was the main source of carbone of flaxseed embryos, leading to the conversion of phosphoenolpyruvate to pyruvate. The oxidative pentose phosphate pathway (OPPP) was identified as the producer of NADPH for fatty acid biosynthesis. Overall, the use of 13C-Metabolic Flux Analysis provided new insight into flaxseed embryos Metabolic processes involved in storage lipids biosynthesis. The elucidation of the Metabolic network of this important crop plant reinforces the relevance of the application of this technique to the Analysis of complex plant Metabolic systems.

  • 13C labeling Analysis of sugars by high resolution-mass spectrometry for Metabolic Flux Analysis.
    Analytical biochemistry, 2017
    Co-Authors: Sébastien Acket, Anthony Degournay, Franck Merlier, Brigitte Thomasset
    Abstract:

    Abstract Metabolic Flux Analysis is particularly complex in plant cells because of highly compartmented metabolism. Analysis of free sugars is interesting because it provides data to define Fluxes around hexose, pentose, and triose phosphate pools in different compartment. In this work, we present a method to analyze the isotopomer distribution of free sugars labeled with carbon 13 using a liquid chromatography–high resolution mass spectrometry, without derivatized procedure, adapted for Metabolic Flux Analysis. Our results showed a good sensitivity, reproducibility and better accuracy to determine isotopic enrichments of free sugars compared to our previous methods [5, 6].

  • 13C labeling Analysis of sugars by high resolution-mass spectrometry for Metabolic Flux Analysis
    Analytical Biochemistry, 2017
    Co-Authors: Sébastien Acket, Anthony Degournay, Franck Merlier, Brigitte Thomasset
    Abstract:

    Metabolic Flux Analysis is particularly complex in plant cells because of highly compartmented metabolism. Analysis of free sugars is interesting because it provides data to define Fluxes around hexose, pentose, and triose phosphate pools in different compartment. In this work, we present a method to analyze the isotopomer distribution of free sugars labeled with carbon 13 using a liquid chromatography ehigh resolution mass spectrometry, without derivatized procedure, adapted for Metabolic Flux Analysis. Our results showed a good sensitivity, reproducibility and better accuracy to determine isotopic en-richments of free sugars compared to our previous methods.

Jamey D Young - One of the best experts on this subject based on the ideXlab platform.

  • 13C Metabolic Flux Analysis of recombinant expression hosts
    Current opinion in biotechnology, 2014
    Co-Authors: Jamey D Young
    Abstract:

    Identifying host cell Metabolic phenotypes that promote high recombinant protein titer is a major goal of the biotech industry. 13C Metabolic Flux Analysis (MFA) provides a rigorous approach to quantify these Metabolic phenotypes by applying isotope tracers to map the flow of carbon through intracellular Metabolic pathways. Recent advances in tracer theory and measurements are enabling more information to be extracted from 13C labeling experiments. Sustained development of publicly available software tools and standardization of experimental workflows is simultaneously encouraging increased adoption of 13C MFA within the biotech research community. A number of recent 13C MFA studies have identified increased citric acid cycle and pentose phosphate pathway Fluxes as consistent markers of high recombinant protein expression, both in mammalian and microbial hosts. Further work is needed to determine whether redirecting Flux into these pathways can effectively enhance protein titers while maintaining acceptable glycan profiles.

  • inca a computational platform for isotopically non stationary Metabolic Flux Analysis
    Bioinformatics, 2014
    Co-Authors: Jamey D Young
    Abstract:

    13C Flux Analysis studies have become an essential component of Metabolic engineering research. The scope of these studies has gradually expanded to include both isotopically steady-state and transient labeling experiments, the latter of which are uniquely applicable to photosynthetic organisms and slow-to-label mammalian cell cultures. Isotopomer network compartmental Analysis (INCA) is the first publicly available software package that can perform both steady-state Metabolic Flux Analysis and isotopically non-stationary Metabolic Flux Analysis. The software provides a framework for comprehensive Analysis of Metabolic networks using mass balances and elementary metabolite unit balances. The generation of balance equations and their computational solution is completely automated and can be performed on networks of arbitrary complexity.

  • Isotopically Nonstationary 13 C Metabolic Flux Analysis
    Methods of Molecular Biology, 2013
    Co-Authors: Lara J. Jazmin, Jamey D Young
    Abstract:

    : (13)C Metabolic Flux Analysis (MFA) is a powerful approach for quantifying cell physiology based upon a combination of extracellular Flux measurements and intracellular isotope labeling measurements. In this chapter, we present the method of isotopically nonstationary (13)C MFA (INST-MFA), which is applicable to systems that are at Metabolic steady state, but are sampled during the transient period prior to achieving isotopic steady state following the introduction of a (13)C tracer. We describe protocols for performing the necessary isotope labeling experiments, for quenching and extraction of intracellular metabolites, for mass spectrometry (MS) Analysis of metabolite labeling, and for computational Flux estimation using INST-MFA. By combining several recently developed experimental and computational techniques, INST-MFA provides an important new platform for mapping carbon Fluxes that is especially applicable to animal cell cultures, autotrophic organisms, industrial bioprocesses, high-throughput experiments, and other systems that are not amenable to steady-state (13)C MFA experiments.

  • Isotopically nonstationary 13C Metabolic Flux Analysis.
    Methods in molecular biology (Clifton N.J.), 2013
    Co-Authors: Lara J. Jazmin, Jamey D Young
    Abstract:

    (13)C Metabolic Flux Analysis (MFA) is a powerful approach for quantifying cell physiology based upon a combination of extracellular Flux measurements and intracellular isotope labeling measurements. In this chapter, we present the method of isotopically nonstationary (13)C MFA (INST-MFA), which is applicable to systems that are at Metabolic steady state, but are sampled during the transient period prior to achieving isotopic steady state following the introduction of a (13)C tracer. We describe protocols for performing the necessary isotope labeling experiments, for quenching and extraction of intracellular metabolites, for mass spectrometry (MS) Analysis of metabolite labeling, and for computational Flux estimation using INST-MFA. By combining several recently developed experimental and computational techniques, INST-MFA provides an important new platform for mapping carbon Fluxes that is especially applicable to animal cell cultures, autotrophic organisms, industrial bioprocesses, high-throughput experiments, and other systems that are not amenable to steady-state (13)C MFA experiments.

  • Fluxomers: a new approach for 13C Metabolic Flux Analysis.
    BMC systems biology, 2011
    Co-Authors: Orr Srour, Jamey D Young, Yonina C. Eldar
    Abstract:

    Background The ability to perform quantitative studies using isotope tracers and Metabolic Flux Analysis (MFA) is critical for detecting pathway bottlenecks and elucidating network regulation in biological systems, especially those that have been engineered to alter their native Metabolic capacities. Mathematically, MFA models are traditionally formulated using separate state variables for reaction Fluxes and isotopomer abundances. Analysis of isotope labeling experiments using this set of variables results in a non-convex optimization problem that suffers from both implementation complexity and convergence problems.