Raghunathan2010 - Genome-scale metabolic network of Francisella tularensis (iRS605)

Raghunathan2010 - Genome-scale metabolic
network of Francisella tularensis (iRS605)

This model is described in the article:

Systems approach to
investigating host-pathogen interactions in infections with the
biothreat agent Francisella. Constraints-based model of
Francisella tularensis.

Raghunathan A, Shin S, Daefler
S.
BMC Syst Biol 2010; 4: 118
Abstract:

BACKGROUND: Francisella tularensis is a prototypic example
of a pathogen for which few experimental datasets exist, but
for which copious high-throughout data are becoming available
because of its re-emerging significance as biothreat agent. The
virulence of Francisella tularensis depends on its growth
capabilities within a defined environmental niche of the host
cell. RESULTS: We reconstructed the metabolism of Francisella
as a stoichiometric matrix. This systems biology approach
demonstrated that changes in carbohydrate utilization and amino
acid metabolism play a pivotal role in growth, acid resistance,
and energy homeostasis during infection with Francisella. We
also show how varying the expression of certain metabolic genes
in different environments efficiently controls the metabolic
capacity of F. tularensis. Selective gene-expression analysis
showed modulation of sugar catabolism by switching from
oxidative metabolism (TCA cycle) in the initial stages of
infection to fatty acid oxidation and gluconeogenesis later on.
Computational analysis with constraints derived from
experimental data revealed a limited set of metabolic genes
that are operational during infection. CONCLUSIONS: This
integrated systems approach provides an important tool to
understand the pathogenesis of an ill-characterized biothreat
agent and to identify potential novel drug targets when rapid
target identification is required should such microbes be
intentionally released or become epidemic.

This model is hosted on
BioModels Database
and identified by:
MODEL1507180003.
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.