Kim2009 - Genome-scale metabolic network of Acinetobacter baumannii (AbyMBEL891)

Kim2009 - Genome-scale metabolic network of
Acinetobacter baumannii (AbyMBEL891)

This model is described in the article:

Genome-scale metabolic
network analysis and drug targeting of multi-drug resistant
pathogen Acinetobacter baumannii AYE.

Kim HU, Kim TY, Lee SY.
Mol Biosyst 2010 Feb; 6(2):
339-348
Abstract:

Acinetobacter baumannii has emerged as a new clinical threat
to human health, particularly to ill patients in the hospital
environment. Current lack of effective clinical solutions to
treat this pathogen urges us to carry out systems-level studies
that could contribute to the development of an effective
therapy. Here we report the development of a strategy for
identifying drug targets by combined genome-scale metabolic
network and essentiality analyses. First, a genome-scale
metabolic network of A. baumannii AYE, a drug-resistant strain,
was reconstructed based on its genome annotation data, and
biochemical knowledge from literatures and databases. In order
to evaluate the performance of the in silico model,
constraints-based flux analysis was carried out with
appropriate constraints. Simulations were performed from both
reaction (gene)- and metabolite-centric perspectives, each of
which identifies essential genes/reactions and metabolites
critical to the cell growth. The gene/reaction essentiality
enables validation of the model and its comparative study with
other known organisms' models. The metabolite essentiality
approach was undertaken to predict essential metabolites that
are critical to the cell growth. The EMFilter, a framework that
filters initially predicted essential metabolites to find the
most effective ones as drug targets, was also developed.
EMFilter considers metabolite types, number of total and
consuming reaction linkage with essential metabolites, and
presence of essential metabolites and their relevant enzymes in
human metabolism. Final drug target candidates obtained by this
system framework are presented along with implications of this
approach.

This model is hosted on
BioModels Database
and identified by:
MODEL1507180029.
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
CC0
Public Domain Dedication for more information.