Literature DB >> 16441345

Measuring multiple fluxes through plant metabolic networks.

R G Ratcliffe1, Y Shachar-Hill.   

Abstract

Fluxes through metabolic networks are crucial for cell function, and a knowledge of these fluxes is essential for understanding and manipulating metabolic phenotypes. Labeling provides the key to flux measurement, and in network flux analysis the measurement of multiple fluxes allows a flux map to be superimposed on the metabolic network. The principles and practice of two complementary methods, dynamic and steady-state labeling, are described, emphasizing best practice and illustrating their contribution to network flux analysis with examples taken from the plant and microbial literature. The principal analytical methods for the detection of stable isotopes are also described, as well as the procedures for obtaining flux maps from labeling data. A series of boxes summarizing the key concepts of network flux analysis is provided for convenience.

Mesh:

Year:  2006        PMID: 16441345     DOI: 10.1111/j.1365-313X.2005.02649.x

Source DB:  PubMed          Journal:  Plant J        ISSN: 0960-7412            Impact factor:   6.417


  55 in total

Review 1.  Fluxomics: mass spectrometry versus quantitative imaging.

Authors:  Wolfgang Wiechert; Oliver Schweissgut; Hitomi Takanaga; Wolf B Frommer
Journal:  Curr Opin Plant Biol       Date:  2007-05-03       Impact factor: 7.834

Review 2.  Systems approaches to identifying gene regulatory networks in plants.

Authors:  Terri A Long; Siobhan M Brady; Philip N Benfey
Journal:  Annu Rev Cell Dev Biol       Date:  2008       Impact factor: 13.827

3.  Capturing metabolite channeling in metabolic flux phenotypes.

Authors:  Thomas C R Williams; Lee J Sweetlove; R George Ratcliffe
Journal:  Plant Physiol       Date:  2011-09-06       Impact factor: 8.340

4.  Quantifying the labeling and the levels of plant cell wall precursors using ion chromatography tandem mass spectrometry.

Authors:  Ana P Alonso; Rebecca J Piasecki; Yan Wang; Russell W LaClair; Yair Shachar-Hill
Journal:  Plant Physiol       Date:  2010-05-04       Impact factor: 8.340

5.  Flux profiling of photosynthetic carbon metabolism in intact plants.

Authors:  Robert Heise; Stéphanie Arrivault; Marek Szecowka; Takayuki Tohge; Adriano Nunes-Nesi; Mark Stitt; Zoran Nikoloski; Alisdair R Fernie
Journal:  Nat Protoc       Date:  2014-07-03       Impact factor: 13.491

6.  Metabolic network fluxes in heterotrophic Arabidopsis cells: stability of the flux distribution under different oxygenation conditions.

Authors:  Thomas C R Williams; Laurent Miguet; Shyam K Masakapalli; Nicholas J Kruger; Lee J Sweetlove; R George Ratcliffe
Journal:  Plant Physiol       Date:  2008-07-30       Impact factor: 8.340

7.  Metabolic fluxes in an illuminated Arabidopsis rosette.

Authors:  Marek Szecowka; Robert Heise; Takayuki Tohge; Adriano Nunes-Nesi; Daniel Vosloh; Jan Huege; Regina Feil; John Lunn; Zoran Nikoloski; Mark Stitt; Alisdair R Fernie; Stéphanie Arrivault
Journal:  Plant Cell       Date:  2013-02-26       Impact factor: 11.277

8.  Subcellular flux analysis of central metabolism in a heterotrophic Arabidopsis cell suspension using steady-state stable isotope labeling.

Authors:  Shyam K Masakapalli; Pascaline Le Lay; Joanna E Huddleston; Naomi L Pollock; Nicholas J Kruger; R George Ratcliffe
Journal:  Plant Physiol       Date:  2009-11-25       Impact factor: 8.340

Review 9.  Bidirectionality and compartmentation of metabolic fluxes are revealed in the dynamics of isotopomer networks.

Authors:  David W Schryer; Pearu Peterson; Toomas Paalme; Marko Vendelin
Journal:  Int J Mol Sci       Date:  2009-04-17       Impact factor: 6.208

10.  Sulphur flux through the sulphate assimilation pathway is differently controlled by adenosine 5'-phosphosulphate reductase under stress and in transgenic poplar plants overexpressing gamma-ECS, SO, or APR.

Authors:  Ursula Scheerer; Robert Haensch; Ralf R Mendel; Stanislav Kopriva; Heinz Rennenberg; Cornelia Herschbach
Journal:  J Exp Bot       Date:  2009-11-18       Impact factor: 6.992

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