How to measure metabolic fluxes: a taxonomic guide for 13 C fluxomics

Niedenführ, Sebastian; Wiechert, Wolfgang; Nöh, Katharina

doi:10.1016/j.copbio.2014.12.003

Cited by 113 publications

(100 citation statements)

References 73 publications

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“…Unlike conventional mass spectrometric methods, IRMS cannot be used to determine MIDs. However, MIDs can provide interesting insights into metabolism and are the basis for 13 C-metabolic flux analysis ( 13 C-MFA) [20,21].…”

Section: Introductionmentioning

confidence: 99%

Non-targeted Tracer Fate Detection

Weindl¹,

Wegner²,

Hiller³

2015

Methods in Enzymology

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Stable isotopes have been used to trace atoms through metabolism and quantify metabolic fluxes for several decades. Only recently non-targeted stable isotope labeling approaches have emerged as a powerful tool to gain biological insights into metabolism. However, the manual detection of isotopic enrichment for a non-targeted analysis is tedious and time consuming. To overcome this limitation, the non-targeted tracer fate detection (NTFD) algorithm for the automated metabolome-wide detection of isotopic enrichment has been developed. NTFD detects and quantifies isotopic enrichment in the form of mass isotopomer distributions (MIDs) in an automated manner, providing the means to trace functional groups, determine MIDs for metabolic flux analysis, or detect tracer-derived molecules in general. Here, we describe the algorithmic background of NTFD, discuss practical considerations for the freely available NTFD software package, and present potential applications of non-targeted stable isotope labeling analysis.

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Section: Introductionmentioning

confidence: 99%

Non-targeted Tracer Fate Detection

Weindl¹,

Wegner²,

Hiller³

2015

Methods in Enzymology

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“…Hence, data incorporated in this network were re-used to generate an independent large-scale metabolic network [43] using a bottom-up approach with CARMEN software [199]. Following manual revision, this large-scale metabolic network was suitable for obtaining metabolic models for the simulation of metabolic fluxes by flux balance analysis (FBA) or 13 C metabolic flux analysis (see below) (Fig. 3e).…”

Section: Reconstruction Of the Metabolic Networkmentioning

confidence: 99%

“…While the biosynthetic pathway for arginine was available from a previous experimental study [125], biosynthetic pathways for 16 proteinogenic amino acids were determined by employing systematic and thorough interpretation of results from the bioinformatic tools (Table 1). For three remaining amino acids, 13 C NMR measurements were required to resolve those metabolic routes actually used for their synthesis (Fig. 3c) [107].…”

Section: Reconstruction Of the Metabolic Networkmentioning

confidence: 99%

“…This became possible with the development of high-throughput techniques associated with diverse computational methods and the availability of faster computing. Fundamental highthroughput methods are now routinely available in the fields of genomics, transcriptomics, metabolomics and proteomics, while additional specialized branches of omics disciplines have been developed, such as lipidomics [6][7][8], glycomics [9][10][11] and 13 C fluxomics [12,13]. …”

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confidence: 99%

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Systems and synthetic biology perspective of the versatile plant-pathogenic and polysaccharide-producing bacterium Xanthomonas campestris

et al. 2017

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Bacteria of the genus Xanthomonas are a major group of plant pathogens. They are hazardous to important crops and closely related to human pathogens. Being collectively a major focus of molecular phytopathology, an increasing number of diverse and intricate mechanisms are emerging by which they communicate, interfere with host signalling and keep competition at bay. Interestingly, they are also biotechnologically relevant polysaccharide producers. Systems biotechnology techniques have revealed their central metabolism and a growing number of remarkable features. Traditional analyses of Xanthomonas metabolism missed the Embden-Meyerhof-Parnas pathway (glycolysis) as being a route by which energy and molecular building blocks are derived from glucose. As a consequence of the emerging full picture of their metabolism process, xanthomonads were discovered to have three alternative catabolic pathways and they use an unusual and reversible phosphofructokinase as a key enzyme. In this review, we summarize the synthetic and systems biology methods and the bioinformatics tools applied to reconstruct their metabolic network and reveal the dynamic fluxes within their complex carbohydrate metabolism. This is based on insights from omics disciplines; in particular, genomics, transcriptomics, proteomics and metabolomics. Analysis of high-throughput omics data facilitates the reconstruction of organism-specific large-and genome-scale metabolic networks. Reconstructed metabolic networks are fundamental to the formulation of metabolic models that facilitate the simulation of actual metabolic activities under specific environmental conditions. SYSTEMS BIOLOGYIn recent years, systems and synthetic biology have substantially increased our understanding of many processes in life sciences at molecular and cellular levels [1][2][3]. Systems biology intends to understand the cell from a system-wide perspective. For over 100 years, life sciences have aimed at elucidating the details of molecular processes in organisms. In systems biology, scientists have started to inter-relate this data on a large scale in order to analyse the interactions of all elements [4,5], and thus initiate the decoding of entire systems, aiming at their quantitative understanding. This became possible with the development of high-throughput techniques associated with diverse computational methods and the availability of faster computing. Fundamental highthroughput methods are now routinely available in the fields of genomics, transcriptomics, metabolomics and proteomics, while additional specialized branches of omics disciplines have been developed, such as lipidomics [6][7][8], glycomics [9][10][11] and 13 C fluxomics [12,13].

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“…Although untargeted metabolomics still does not capture the entirety of the metabolome, this approach renders detectable ions across a wide m/z range using mass-spectrometry platforms [33]. Bioinformatic tools enable alignment, integration, and comparison of all m/z ion intensities between different biological samples and identify differentially changing ions.…”

Section: Metabolism and The Fluxome As A Phenotypic Signaturementioning

confidence: 99%

Systems Biology of the Fluxome

Aon

Cortassa

2015

Processes

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Abstract:The advent of high throughput -omics has made the accumulation of comprehensive data sets possible, consisting of changes in genes, transcripts, proteins and metabolites. Systems biology-inspired computational methods for translating metabolomics data into fluxomics provide a direct functional, dynamic readout of metabolic networks. When combined with appropriate experimental design, these methods deliver insightful knowledge about cellular function under diverse conditions. The use of computational models accounting for detailed kinetics and regulatory mechanisms allow us to unravel the control and regulatory properties of the fluxome under steady and time-dependent behaviors. This approach extends the analysis of complex systems from description to prediction, including control of complex dynamic behavior ranging from biological rhythms to catastrophic lethal arrhythmias. The powerful quantitative metabolomics-fluxomics approach will help our ability to engineer unicellular and multicellular organisms evolve from trial-and-error to a more predictable process, and from cells to organ and organisms.

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How to measure metabolic fluxes: a taxonomic guide for 13 C fluxomics

Cited by 113 publications

References 73 publications

Non-targeted Tracer Fate Detection

Non-targeted Tracer Fate Detection

Systems and synthetic biology perspective of the versatile plant-pathogenic and polysaccharide-producing bacterium Xanthomonas campestris

Systems Biology of the Fluxome

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