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Jamey D Young - One of the best experts on this subject based on the ideXlab platform.
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Isotopically nonstationary metabolic Flux Analysis (INST-MFA): putting theory into practice.
Current opinion in biotechnology, 2018Co-Authors: Yi Ern Cheah, Jamey D YoungAbstract:Typically, 13C Flux Analysis relies on assumptions of both metabolic and isotopic steady state. If metabolism is steady but isotope labeling is not allowed to fully equilibrate, isotopically nonstationary metabolic Flux Analysis (INST-MFA) can be used to estimate Fluxes. This requires solution of differential equations that describe the time-dependent labeling of network metabolites, while iteratively adjusting the Flux and pool size parameters to match the transient labeling measurements. INST-MFA holds a number of unique advantages over approaches that rely solely upon steady-state isotope enrichments. First, INST-MFA can be applied to estimate Fluxes in autotrophic systems, which consume only single-carbon substrates. Second, INST-MFA is ideally suited to systems that label slowly due to the presence of large intermediate pools or pathway bottlenecks. Finally, INST-MFA provides increased measurement sensitivity to estimate reversible exchange Fluxes and metabolite pool sizes, which represents a potential framework for integrating metabolomic Analysis with 13C Flux Analysis. This review highlights the unique capabilities of INST-MFA, describes newly available software tools that automate INST-MFA calculations, presents several practical examples of recent INST-MFA applications, and discusses the technical challenges that lie ahead.
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Isotopically nonstationary 13C Flux Analysis of cyanobacterial isobutyraldehyde production.
Metabolic engineering, 2017Co-Authors: Lara J. Jazmin, Yi Ern Cheah, Adeola O. Adebiyi, Carl Hirschie Johnson, Jamey D YoungAbstract:We applied isotopically nonstationary 13C metabolic Flux Analysis (INST-MFA) to compare the pathway Fluxes of wild-type (WT) Synechococcus elongatus PCC 7942 to an engineered strain (SA590) that produces isobutyraldehyde (IBA). The Flux maps revealed a potential bottleneck at the pyruvate kinase (PK) reaction step that was associated with diversion of Flux into a three-step PK bypass pathway involving the enzymes PEP carboxylase (PEPC), malate dehydrogenase (MDH), and malic enzyme (ME). Overexpression of pk in SA590 led to a significant improvement in IBA specific productivity. Single-gene overexpression of the three enzymes in the proposed PK bypass pathway also led to improvements in IBA production, although to a lesser extent than pk overexpression. Combinatorial overexpression of two of the three genes in the proposed PK bypass pathway (mdh and me) led to improvements in specific productivity that were similar to those achieved by single-gene pk overexpression. Our work demonstrates how 13C Flux Analysis can be used to identify potential metabolic bottlenecks and novel metabolic routes, and how these findings can guide rational metabolic engineering of cyanobacteria for increased production of desired molecules.
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application of isotope labeling experiments and 13 c Flux Analysis to enable rational pathway engineering
Current Opinion in Biotechnology, 2015Co-Authors: Allison G Mcatee, Lara J. Jazmin, Jamey D YoungAbstract:Isotope labeling experiments (ILEs) and 13 C Flux Analysis provide actionable information for metabolic engineers to identify knockout, overexpression, and/or media optimization targets. ILEs have been used in both academic and industrial labs to increase product formation, discover novel metabolic functions in previously uncharacterized organisms, and enhance the metabolic efficiency of host cell factories. This review highlights specific examples of how ILEs have been used in conjunction with enzyme or metabolic engineering to elucidate host cell metabolism and improve product titer, rate, or yield in a directed manner. We discuss recent progress and future opportunities involving the use of ILEs and 13 C Flux Analysis to characterize non-model host organisms and to identify and subsequently eliminate wasteful byproduct pathways or metabolic bottlenecks.
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inca a computational platform for isotopically non stationary metabolic Flux Analysis
Bioinformatics, 2014Co-Authors: Jamey D YoungAbstract: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.
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Isotopically Nonstationary 13 C Metabolic Flux Analysis
Methods of Molecular Biology, 2013Co-Authors: Lara J. Jazmin, Jamey D YoungAbstract:: (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.
Maciek R. Antoniewicz - One of the best experts on this subject based on the ideXlab platform.
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A guide to metabolic Flux Analysis in metabolic engineering: Methods, tools and applications.
Metabolic engineering, 2020Co-Authors: Maciek R. AntoniewiczAbstract: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.
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High-resolution 13C metabolic Flux Analysis.
Nature protocols, 2019Co-Authors: Christopher P. Long, Maciek R. AntoniewiczAbstract: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.
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Methods and advances in metabolic Flux Analysis: a mini-review
Journal of Industrial Microbiology & Biotechnology, 2015Co-Authors: Maciek R. AntoniewiczAbstract: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.
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Towards dynamic metabolic Flux Analysis in CHO cell cultures
Biotechnology journal, 2011Co-Authors: Woo Suk Ahn, Maciek R. AntoniewiczAbstract:Chinese hamster ovary (CHO) cells are the most widely used mammalian cell line for biopharmaceutical production, with a total global market approaching $100 billion per year. In the pharmaceutical industry CHO cells are grown in fed-batch culture, where cellular metabolism is characterized by high glucose and glutamine uptake rates combined with high rates of ammonium and lactate secretion. The metabolism of CHO cells changes dramatically during a fed-batch culture as the cells adapt to a changing environment and transition from exponential growth phase to stationary phase. Thus far, it has been challenging to study metabolic Flux dynamics in CHO cell cultures using conventional metabolic Flux Analysis techniques that were developed for systems at metabolic steady state. In this paper we review progress on Flux Analysis in CHO cells and techniques for dynamic metabolic Flux Analysis. Application of these new tools may allow identification of intracellular metabolic bottlenecks at specific stages in CHO cell cultures and eventually lead to novel strategies for improving CHO cell metabolism and optimizing biopharmaceutical process performance.
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accurate assessment of amino acid mass isotopomer distributions for metabolic Flux Analysis
Analytical Chemistry, 2007Co-Authors: Maciek R. Antoniewicz, And Joanne K Kelleher, Gregory StephanopoulosAbstract:Metabolic Flux Analysis based on stable-isotope labeling experiments and Analysis of mass isotopomer distributions (MID) of cellular metabolites is a tool of great significance for metabolic engineering and study of human disease. This method relies on accurate and precise measurements of mass isotopomers by gas chromatography/mass spectrometry. To improve Flux estimates, we assessed potential errors in determining MID of tert-butyldimethylsilyl-derivatized amino acids, which were attributed to (i) the choice of integration algorithm, (ii) concentration effects, and (iii) overlapping fragments. We report 29 amino acid fragments that are useful for Flux Analysis and another 18 fragments that should be rejected, most importantly Val-302, Leu-200, Leu-302, Ile-302, Ser-302, and Asp-316. In addition, we provide a protocol to minimize errors for determining MID to less than 0.4 mol % for accepted fragments.
Brigitte Thomasset - One of the best experts on this subject based on the ideXlab platform.
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13C labeling Analysis of sugars by high resolution-mass spectrometry for metabolic Flux Analysis.
Analytical biochemistry, 2017Co-Authors: Sébastien Acket, Anthony Degournay, Franck Merlier, Brigitte ThomassetAbstract: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].
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13C labeling Analysis of sugars by high resolution-mass spectrometry for metabolic Flux Analysis
Analytical Biochemistry, 2017Co-Authors: Sébastien Acket, Anthony Degournay, Franck Merlier, Brigitte ThomassetAbstract: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.
Björn H. Junker - One of the best experts on this subject based on the ideXlab platform.
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Flux Analysis in plant metabolic networks: increasing throughput and coverage.
Current opinion in biotechnology, 2014Co-Authors: Björn H. JunkerAbstract:Quantitative information about metabolic networks has been mainly obtained at the level of metabolite contents, transcript abundance, and enzyme activities. However, the active process of metabolism is represented by the flow of matter through the pathways. These metabolic Fluxes can be predicted by Flux Balance Analysis or determined experimentally by 13C-Metabolic Flux Analysis. These relatively complicated and time-consuming methods have recently seen significant improvements at the level of coverage and throughput. Metabolic models have developed from single cell models into whole-organism dynamic models. Advances in lab automation and data handling have significantly increased the throughput of Flux measurements. This review summarizes advances to increase coverage and throughput of metabolic Flux Analysis in plants.
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High-Throughput Data Pipelines for Metabolic Flux Analysis in Plants
Methods in molecular biology (Clifton N.J.), 2013Co-Authors: C. Hart Poskar, Jan Huege, Christian Krach, Yair Shachar-hill, Björn H. JunkerAbstract:In this chapter we illustrate the methodology for high-throughput metabolic Flux Analysis. Central to this is developing an end to end data pipeline, crucial for integrating the wet lab experiments and analytics, combining hardware and software automation, and standardizing data representation providing importers and exporters to support third party tools. The use of existing software at the start, data extraction from the chromatogram, and the end, MFA Analysis, allows for the most flexibility in this workflow. Developing iMS2Flux provided a standard, extensible, platform independent tool to act as the "glue" between these end points. Most importantly this tool can be easily adapted to support different data formats, data verification and data correction steps allowing it to be central to managing the data necessary for high-throughput MFA. An additional tool was needed to automate the MFA software and in particular to take advantage of the course grained parallel nature of high-throughput Analysis and available high performance computing facilities.In combination these methods show the development of high-throughput pipelines that allow metabolic Flux Analysis to join as a full member of the omics family.
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Towards high throughput metabolic Flux Analysis in plants
Molecular bioSystems, 2012Co-Authors: Jan Huege, C. Hart Poskar, Mathias Franke, Björn H. JunkerAbstract:Research on plant metabolism is currently experiencing the common use of various omics methods creating valuable information on the concentrations of the cell's constituents. However, little is known about in vivo reaction rates, which can be determined by Metabolic Flux Analysis (MFA), a combination of isotope labeling experiments and computer modeling of the metabolic network. Large-scale applications of this method so far have been hampered by tedious procedures of tissue culture, analytics, modeling and simulation. By streamlining the workflow of MFA, the throughput of the method could be significantly increased. We propose strategies for these improvements on various sub-steps which will move Flux Analysis to the medium-throughput range and closer to established methods such as metabolite profiling. Furthermore, this may enable novel applications of MFA, for example screening plant populations for traits related to the Flux phenotype.
Wolfgang Wiechert - One of the best experts on this subject based on the ideXlab platform.
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The benefits of being transient: isotope-based metabolic Flux Analysis at the short time scale
Applied Microbiology and Biotechnology, 2011Co-Authors: Wolfgang WiechertAbstract:Metabolic Fluxes are the manifestations of the co-operating actions in a complex network of genes, transcripts, proteins, and metabolites. As a final quantitative endpoint of all cellular interactions, the intracellular Fluxes are of immense interest in fundamental as well as applied research. Unlike the quantities of interest in most omics levels, in vivo Fluxes are, however, not directly measureable. In the last decade, ^13C-based metabolic Flux Analysis emerged as the state-of-the-art technique to infer steady-state Fluxes by data from labeling experiments and the use of mathematical models. A very promising new area in systems metabolic engineering research is non-stationary ^13C-metabolic Flux Analysis at metabolic steady-state conditions. Several studies have demonstrated an information surplus contained in transient labeling data compared to those taken at the isotopic equilibrium, as it is classically done. Enabled by recent, fairly multi-disciplinary progress, the new method opens several attractive options to (1) generate new insights, e.g., in cellular storage metabolism or the dilution of tracer by endogenous pools and (2) shift limits, inherent in the classical approach, towards enhanced applicability with respect to cultivation conditions and biological systems. We review the new developments in metabolome-based non-stationary ^13C Flux Analysis and outline future prospects for accurate in vivo Flux measurement.
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eScience - Metabolic Flux Analysis in the Cloud
2010 IEEE Sixth International Conference on e-Science, 2010Co-Authors: Tolga Dalman, Wolfgang Wiechert, Katharina Nöh, Tim Doernemann, Ernst Juhnke, Michael Weitzel, Matthew Smith, Bernd FreislebenAbstract:The MapReduce pattern popularized by Google has successfully been utilized in several scientific applications. In this paper, it is investigated whether a MapReduce approach utilizing on-demand resources from a Cloud is beneficial to perform simulation tasks in the area of Systems Biology and whether it can be seamlessly integrated into a service-oriented scientific workflow framework. In particular, an Amazon Elastic Map Reduce Cloud implementation of the 13C-MFA (Metabolix Flux Analysis) Monte Carlo bootstrap approach aimed at the integration into an existing BPEL-based scientific workflow system is presented. A comparison of a 64 node MapReduce cluster with a single node computation approach reveals a total performance gain up to a factor of 14, with a total cost for on-demand resources of $11. The most critical factor in terms of performance is I/O, i.e. our application suffers from the fact that I/O operations on many small files are expensive using Amazon S3 and the Hadoop DFS.
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From stationary to instationary metabolic Flux Analysis.
Advances in biochemical engineering biotechnology, 2005Co-Authors: Wolfgang Wiechert, Katharina NöhAbstract:Metabolic Flux Analysis using 13 C labeled substrates is an important tool for metabolic engineering. Although it has now been evolving for more than ten years, metabolic Flux Analysis has still not reached the limits of its application. Current developments aim at extending to instationary industrial production conditions like batch and fed-batch fermentations on the one hand, and at miniaturization and high throughput Flux Analysis on the other. In both cases, reducing the duration of the labeling experiment is of major interest. For that reason, there is now interest in the instationary transient of the 13 C enrichment during a labeling experiment. This paper presents some recent developments in the field of instationary metabolic Flux Analysis and discusses some critical aspects and limitations using some simulation examples.
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An introduction to 13C metabolic Flux Analysis.
Genetic engineering, 2002Co-Authors: Wolfgang WiechertAbstract:In recent years, metabolic Flux Analysis has become one of the major tools in metabolic engineering (1, 2, 3). The aim of MFA is the detailed quantification of all metabolic Fluxes in the central metabolic pathways of a microorganism. The result is a Flux map that shows the distribution of anabolic and catabolic Fluxes over the metabolic network. Figure 1 shows such a Flux distribution for the simple example discussed throughout the text. More complex examples for quite different microorganisms can be found in (4, 5, 6, 7, 8, 9). Based on such a Flux Analysis, the result of a genetic manipulation can be judged or possible targets for future genetic modifications might be identified. Thus MFA is also an important tool for genetic engineering.
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13C metabolic Flux Analysis.
Metabolic engineering, 2001Co-Authors: Wolfgang WiechertAbstract:Metabolic Flux Analysis using 13C-labeled substrates has become an important tool in metabolic engineering. It allows the detailed quantification of all intracellular Fluxes in the central metabolism of a microorganism. The method has strongly evolved in recent years by the introduction of new experimental procedures, measurement techniques, and mathematical data evaluation methods. Many of these improvements require advanced skills in the application of nuclear magnetic resonance and mass spectrometry techniques on the one hand and computational and statistical experience on the other hand. This minireview summarizes these recent developments and sketches the major practical problems. An outlook to possible future developments concludes the text.
