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J Exp Bot. 2014 Oct;65(19):5657-71. doi: 10.1093/jxb/eru227. Epub 2014 May 26.

Nitrogen-use efficiency in maize (Zea mays L.): from 'omics' studies to metabolic modelling.

Author information

1
Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802, USA.
2
Adaptation des Plantes à leur Environnement, Unité Mixte de Recherche 1318, INRA-Agro-ParisTech, Institut Jean-Pierre Bourgin, Institut National de la Recherche Agronomique (INRA), Centre de Versailles-Grignon, RD 10, 78026 Versailles cedex, France.
3
Plateau Technique Spécifique de Chimie du Végétal, Institut National de la Recherche Agronomique (INRA), Centre de Versailles-Grignon, Unité Mixte de Recherche 1318, INRA-Agro-ParisTech, Route de St Cyr, F-78026 Versailles Cedex, France.
4
Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, UK.
5
Adaptation des Plantes à leur Environnement, Unité Mixte de Recherche 1318, INRA-Agro-ParisTech, Institut Jean-Pierre Bourgin, Institut National de la Recherche Agronomique (INRA), Centre de Versailles-Grignon, RD 10, 78026 Versailles cedex, France hirel@versailles.inra.fr.

Abstract

In this review, we will present the latest developments in systems biology with particular emphasis on improving nitrogen-use efficiency (NUE) in crops such as maize and demonstrating the application of metabolic models. The review highlights the importance of improving NUE in crops and provides an overview of the transcriptome, proteome, and metabolome datasets available, focusing on a comprehensive understanding of nitrogen regulation. 'Omics' data are hard to interpret in the absence of metabolic flux information within genome-scale models. These models, when integrated with 'omics' data, can serve as a basis for generating predictions that focus and guide further experimental studies. By simulating different nitrogen (N) conditions at a pseudo-steady state, the reactions affecting NUE and additional gene regulations can be determined. Such models thus provide a framework for improving our understanding of the metabolic processes underlying the more efficient use of N-based fertilizers.

KEYWORDS:

Maize; metabolic modelling; metabolome; proteome; systems biology; transcriptome.

PMID:
24863438
DOI:
10.1093/jxb/eru227
[Indexed for MEDLINE]
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