Rhizobium etli strain:R22G
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA330443 | ENA |
REPOSITORIES: ENA
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Project description:Resendis-Antonio2007 - Genome-scale metabolic
network of Rhizobium etli (iOR363)
This model is described in the article:
Metabolic reconstruction and
modeling of nitrogen fixation in Rhizobium etli.
Resendis-Antonio O, Reed JL,
Encarnación S, Collado-Vides J, Palsson BØ.
PLoS Comput. Biol. 2007 Oct; 3(10):
1887-1895
Abstract:
Rhizobiaceas are bacteria that fix nitrogen during symbiosis
with plants. This symbiotic relationship is crucial for the
nitrogen cycle, and understanding symbiotic mechanisms is a
scientific challenge with direct applications in agronomy and
plant development. Rhizobium etli is a bacteria which provides
legumes with ammonia (among other chemical compounds), thereby
stimulating plant growth. A genome-scale approach, integrating
the biochemical information available for R. etli, constitutes
an important step toward understanding the symbiotic
relationship and its possible improvement. In this work we
present a genome-scale metabolic reconstruction (iOR363) for R.
etli CFN42, which includes 387 metabolic and transport
reactions across 26 metabolic pathways. This model was used to
analyze the physiological capabilities of R. etli during stages
of nitrogen fixation. To study the physiological capacities in
silico, an objective function was formulated to simulate
symbiotic nitrogen fixation. Flux balance analysis (FBA) was
performed, and the predicted active metabolic pathways agreed
qualitatively with experimental observations. In addition,
predictions for the effects of gene deletions during nitrogen
fixation in Rhizobia in silico also agreed with reported
experimental data. Overall, we present some evidence supporting
that FBA of the reconstructed metabolic network for R. etli
provides results that are in agreement with physiological
observations. Thus, as for other organisms, the reconstructed
genome-scale metabolic network provides an important framework
which allows us to compare model predictions with experimental
measurements and eventually generate hypotheses on ways to
improve nitrogen fixation.
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2015-07-30 | MODEL1507180006 | BioModels