Arabidopsis thaliana
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PROVIDER: PRJNA316694 | ENA |
REPOSITORIES: ENA
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Project description:deOliveiraDalMolin2010 - Genome-scale
metabolic network of Arabidopsis thaliana (AraGEM)
This model is described in the article:
AraGEM, a genome-scale
reconstruction of the primary metabolic network in
Arabidopsis.
de Oliveira Dal'Molin CG, Quek LE,
Palfreyman RW, Brumbley SM, Nielsen LK.
Plant Physiol. 2010 Feb; 152(2):
579-589
Abstract:
Genome-scale metabolic network models have been successfully
used to describe metabolism in a variety of microbial organisms
as well as specific mammalian cell types and organelles. This
systems-based framework enables the exploration of global
phenotypic effects of gene knockouts, gene insertion, and
up-regulation of gene expression. We have developed a
genome-scale metabolic network model (AraGEM) covering primary
metabolism for a compartmentalized plant cell based on the
Arabidopsis (Arabidopsis thaliana) genome. AraGEM is a
comprehensive literature-based, genome-scale metabolic
reconstruction that accounts for the functions of 1,419 unique
open reading frames, 1,748 metabolites, 5,253 gene-enzyme
reaction-association entries, and 1,567 unique reactions
compartmentalized into the cytoplasm, mitochondrion, plastid,
peroxisome, and vacuole. The curation process identified 75
essential reactions with respective enzyme associations not
assigned to any particular gene in the Kyoto Encyclopedia of
Genes and Genomes or AraCyc. With the addition of these
reactions, AraGEM describes a functional primary metabolism of
Arabidopsis. The reconstructed network was transformed into an
in silico metabolic flux model of plant metabolism and
validated through the simulation of plant metabolic functions
inferred from the literature. Using efficient resource
utilization as the optimality criterion, AraGEM predicted the
classical photorespiratory cycle as well as known key
differences between redox metabolism in photosynthetic and
nonphotosynthetic plant cells. AraGEM is a viable framework for
in silico functional analysis and can be used to derive new,
nontrivial hypotheses for exploring plant metabolism.
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