Sun2009 - Genome-scale metabolic network of Geobacter metallireducens (iJS747)

Sun2009 - Genome-scale metabolic network of
Geobacter metallireducens (iJS747)

This model is described in the article:

Genome-scale
constraint-based modeling of Geobacter metallireducens.

Sun J, Sayyar B, Butler JE, Pharkya
P, Fahland TR, Famili I, Schilling CH, Lovley DR, Mahadevan
R.
BMC Syst Biol 2009; 3: 15
Abstract:

BACKGROUND: Geobacter metallireducens was the first organism
that can be grown in pure culture to completely oxidize organic
compounds with Fe(III) oxide serving as electron acceptor.
Geobacter species, including G. sulfurreducens and G.
metallireducens, are used for bioremediation and electricity
generation from waste organic matter and renewable biomass. The
constraint-based modeling approach enables the development of
genome-scale in silico models that can predict the behavior of
complex biological systems and their responses to the
environments. Such a modeling approach was applied to provide
physiological and ecological insights on the metabolism of G.
metallireducens. RESULTS: The genome-scale metabolic model of
G. metallireducens was constructed to include 747 genes and 697
reactions. Compared to the G. sulfurreducens model, the G.
metallireducens metabolic model contains 118 unique reactions
that reflect many of G. metallireducens' specific metabolic
capabilities. Detailed examination of the G. metallireducens
model suggests that its central metabolism contains several
energy-inefficient reactions that are not present in the G.
sulfurreducens model. Experimental biomass yield of G.
metallireducens growing on pyruvate was lower than the
predicted optimal biomass yield. Microarray data of G.
metallireducens growing with benzoate and acetate indicated
that genes encoding these energy-inefficient reactions were
up-regulated by benzoate. These results suggested that the
energy-inefficient reactions were likely turned off during G.
metallireducens growth with acetate for optimal biomass yield,
but were up-regulated during growth with complex electron
donors such as benzoate for rapid energy generation.
Furthermore, several computational modeling approaches were
applied to accelerate G. metallireducens research. For example,
growth of G. metallireducens with different electron donors and
electron acceptors were studied using the genome-scale
metabolic model, which provided a fast and cost-effective way
to understand the metabolism of G. metallireducens. CONCLUSION:
We have developed a genome-scale metabolic model for G.
metallireducens that features both metabolic similarities and
differences to the published model for its close relative, G.
sulfurreducens. Together these metabolic models provide an
important resource for improving strategies on bioremediation
and bioenergy generation.

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