Flux analysis in plant metabolic networks: increasing throughput and coverage

Junker, Björn H.

doi:10.1016/j.copbio.2014.01.016

Cited by 26 publications

(12 citation statements)

References 39 publications

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“…Due to the generally low throughput of MFA, there have been efforts both to speed up flux analyses [72,111], to use 13 C labeling patterns in metabolite profile datasets without flux mapping to identify pathway activities [112] and to correlate differences in steady state labeling of biomass (protein amino acids) with alterations in particular fluxes among knockout mutants or different substrate use [113]. Here, we examined whether relatedness of strains in their overall labeling patterns was linked to relatedness in flux maps, which would be particularly valuable in flux phenotype screening of multiple strains.…”

Section: C Amino Acid Fingerprinting and Mfamentioning

confidence: 99%

Metabolic flux analyses of Pseudomonas aeruginosa cystic fibrosis isolates

Opperman

Shachar‐Hill

2016

Metabolic Engineering

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Section: C Amino Acid Fingerprinting and Mfamentioning

confidence: 99%

Metabolic flux analyses of Pseudomonas aeruginosa cystic fibrosis isolates

Opperman

Shachar‐Hill

2016

Metabolic Engineering

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“…The focus on the pattern of labeling dramatically simplifies the experimental work load (labeling of isotopomers needs to be measured only at a single time point rather than across a time course) and computational burden. The approach is known as steady-state MFA and, like INST-MFA, uses nonlinear fitting of labeling data (in this case to sets of equations describing the carbon transitions between metabolites) and measured input and output fluxes to scale and constrain the fluxes (O' Grady et al, 2012 Despite considerable methodological and computational advances, MFA, and particularly INST-MFA, remain low-to medium-throughput techniques (Junker, 2014). Therefore, researchers have increasingly turned to modeling approaches that allow fluxes to be predicted without the requirement for labor-intensive acquisition of isotope labeling data.…”

Section: General Principles Of Inference and The Prediction Of Metabomentioning

confidence: 99%

Inference and prediction of metabolic network fluxes

Nikoloski

Perez-Storey

2015

Plant Physiol.

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In this Update, we cover the basic principles of the estimation and prediction of the rates of the many interconnected biochemical reactions that constitute plant metabolic networks. This includes metabolic flux analysis approaches that utilize the rates or patterns of redistribution of stable isotopes of carbon and other atoms to estimate fluxes, as well as constraints-based optimization approaches such as flux balance analysis. Some of the major insights that have been gained from analysis of fluxes in plants are discussed, including the functioning of metabolic pathways in a network context, the robustness of the metabolic phenotype, the importance of cell maintenance costs, and the mechanisms that enable energy and redox balancing at steady state. We also discuss methodologies to exploit 'omic data sets for the construction of tissue-specific metabolic network models and to constrain the range of permissible fluxes in such models. Finally, we consider the future directions and challenges faced by the field of metabolic network flux phenotyping.The metabolic systems of plants utilize a continual energy stream (i.e. light in the case of photosynthetic tissues and chemical energy in the case of heterotrophic tissues) to drive a complex network of hundreds of chemical reactions away from equilibrium. Ultimately, this leads to the catalyzed biosynthesis of the ordered polymeric biomolecules that make up the biomass of the cells in each tissue and underpins the growth and development of the plant (Smith and Stitt, 2007). To operate on a time scale relevant for life, the system is dependent upon the acceleration of its chemistry by enzymes. Additionally, because of the highly compartmented nature of plant cells, transporter proteins are required to permit the movement of metabolites (i.e. the substrates and products of biochemical reactions) between subcellular compartments. Transporter proteins are also required to bring substrates into cells and to allow the excretion of waste products and other metabolites such as defense compounds. The sum total of expressed genes encoding enzymes and metabolite transporters determines the metabolic capabilities of a cell. In a growing tissue, the net output of this metabolism is anabolic (i.e. leading to the biosynthesis of biomass constituents such as starch, fructans, cell wall, lipid, and protein), but catabolic metabolism is also required. Most importantly, catabolism generates universal energy currencies such as ATP and NAD(P)H, whose turnover is used to provide the energetic driving force for anabolism. Catabolism is also important to allow the turnover of cellular components for regulatory and repair purposes (Linster et al., 2013;Ishihara et al., 2015). The turnover and resynthesis of cellular components, along with the maintenance of electrochemical potentials across membranes, are important facets of metabolism that need to be considered alongside the biosynthesis of macromolecules for growth (Stitt, 2013;Sweetlove et al., 2013).Metabolism, then, is the entirety of c...

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“…Metabolomic characterization of reference panel would enable using this information for identifying tissue specific MSCs as well as their ability to differentiate into a particular progeny. One commonly used metabolism assay is a specialized HALO assay to predict the activity through ATP levels [41]. A modification of this assay is to determine other metabolite products such as NAD and FAD.…”

Section: Other Limitationsmentioning

confidence: 99%

Research using Mesenchymal Stem/Stromal Cells: quality metric towards developing a reference material

Tanavde

Vaz

Rao³

et al. 2015

Cytotherapy

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Mesenchymal stem/stromal cells (MSCs) have been extensively investigated for their regenerative, immune-modulatory, and wound healing properties. While the laboratory studies have suggested that MSC’s have a unique potential for modulating the etiopathology of multiple diseases, the results from clinical trials have not been encouraging or reproducible. One of the explanations for such variability is explained by the “art” of isolating and propagating MSCs. Therefore, establishing more than minimal criteria to define MSC would help understand best protocols to isolate, propagate and deliver MSCs. Developing a calibration standard, a database and a set of functional tests would be a better quality metric for MSCs. In this review, we discuss the importance of selecting a standard, issues associated with coming up with such a standard and how these issues can be mitigated.

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Flux analysis in plant metabolic networks: increasing throughput and coverage

Cited by 26 publications

References 39 publications

Metabolic flux analyses of Pseudomonas aeruginosa cystic fibrosis isolates

Metabolic flux analyses of Pseudomonas aeruginosa cystic fibrosis isolates

Inference and prediction of metabolic network fluxes

Research using Mesenchymal Stem/Stromal Cells: quality metric towards developing a reference material

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