Ketogulonicigenium vulgare Y25
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA51787 | ENA |
REPOSITORIES: ENA
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Project description:Zou2012 - Genome-scale metabolic network of
Ketogulonicigenium vulgare (iWZ663)
This model is described in the article:
Genome-scale metabolic model
of Helicobacter pylori 26695.
Schilling CH, Covert MW, Famili I,
Church GM, Edwards JS, Palsson BO.
J. Bacteriol. 2002 Aug; 184(16):
4582-4593
Abstract:
A genome-scale metabolic model of Helicobacter pylori 26695
was constructed from genome sequence annotation, biochemical,
and physiological data. This represents an in silico model
largely derived from genomic information for an organism for
which there is substantially less biochemical information
available relative to previously modeled organisms such as
Escherichia coli. The reconstructed metabolic network contains
388 enzymatic and transport reactions and accounts for 291 open
reading frames. Within the paradigm of constraint-based
modeling, extreme-pathway analysis and flux balance analysis
were used to explore the metabolic capabilities of the in
silico model. General network properties were analyzed and
compared to similar results previously generated for
Haemophilus influenzae. A minimal medium required by the model
to generate required biomass constituents was calculated,
indicating the requirement of eight amino acids, six of which
correspond to essential human amino acids. In addition a list
of potential substrates capable of fulfilling the bulk carbon
requirements of H. pylori were identified. A deletion study was
performed wherein reactions and associated genes in central
metabolism were deleted and their effects were simulated under
a variety of substrate availability conditions, yielding a
number of reactions that are deemed essential. Deletion results
were compared to recently published in vitro essentiality
determinations for 17 genes. The in silico model accurately
predicted 10 of 17 deletion cases, with partial support for
additional cases. Collectively, the results presented herein
suggest an effective strategy of combining in silico modeling
with experimental technologies to enhance biological discovery
for less characterized organisms and their genomes.
This model is hosted on
BioModels Database
and identified by:
MODEL1507180039.
To cite BioModels Database, please use:
BioModels Database:
An enhanced, curated and annotated resource for published
quantitative kinetic models.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to
the public domain worldwide. Please refer to
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2015-07-30 | MODEL1507180039 | BioModels
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