Pinchuck2010 - Genome-scale metabolic network of Shewanella oneidensis (iSO783)

Pinchuck2010 - Genome-scale metabolic network
of Shewanella oneidensis (iSO783)

This model is described in the article:

Constraint-based model of
Shewanella oneidensis MR-1 metabolism: a tool for data analysis
and hypothesis generation.

Pinchuk GE, Hill EA, Geydebrekht OV,
De Ingeniis J, Zhang X, Osterman A, Scott JH, Reed SB, Romine MF,
Konopka AE, Beliaev AS, Fredrickson JK, Reed JL.
PLoS Comput. Biol. 2010 Jun; 6(6):
e1000822
Abstract:

Shewanellae are gram-negative facultatively anaerobic
metal-reducing bacteria commonly found in chemically (i.e.,
redox) stratified environments. Occupying such niches requires
the ability to rapidly acclimate to changes in electron
donor/acceptor type and availability; hence, the ability to
compete and thrive in such environments must ultimately be
reflected in the organization and utilization of electron
transfer networks, as well as central and peripheral carbon
metabolism. To understand how Shewanella oneidensis MR-1
utilizes its resources, the metabolic network was
reconstructed. The resulting network consists of 774 reactions,
783 genes, and 634 unique metabolites and contains biosynthesis
pathways for all cell constituents. Using constraint-based
modeling, we investigated aerobic growth of S. oneidensis MR-1
on numerous carbon sources. To achieve this, we (i) used
experimental data to formulate a biomass equation and estimate
cellular ATP requirements, (ii) developed an approach to
identify cycles (such as futile cycles and circulations), (iii)
classified how reaction usage affects cellular growth, (iv)
predicted cellular biomass yields on different carbon sources
and compared model predictions to experimental measurements,
and (v) used experimental results to refine metabolic fluxes
for growth on lactate. The results revealed that aerobic
lactate-grown cells of S. oneidensis MR-1 used less efficient
enzymes to couple electron transport to proton motive force
generation, and possibly operated at least one futile cycle
involving malic enzymes. Several examples are provided whereby
model predictions were validated by experimental data, in
particular the role of serine hydroxymethyltransferase and
glycine cleavage system in the metabolism of one-carbon units,
and growth on different sources of carbon and energy. This work
illustrates how integration of computational and experimental
efforts facilitates the understanding of microbial metabolism
at a systems level.

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