Montagud2010 - Genome-scale metabolic network of Synechocystis sp. PCC6803 (iSyn669)
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ABSTRACT:
Montagud2010 - Genome-scale metabolic network
of Synechocystis sp. PCC6803 (iSyn669)
This model is described in the article:
Reconstruction and analysis
of genome-scale metabolic model of a photosynthetic
bacterium.
Montagud A, Navarro E,
Fernández de Córdoba P, Urchueguía JF, Patil
KR.
BMC Syst Biol 2010; 4: 156
Abstract:
BACKGROUND: Synechocystis sp. PCC6803 is a cyanobacterium
considered as a candidate photo-biological production
platform--an attractive cell factory capable of using CO2 and
light as carbon and energy source, respectively. In order to
enable efficient use of metabolic potential of Synechocystis
sp. PCC6803, it is of importance to develop tools for
uncovering stoichiometric and regulatory principles in the
Synechocystis metabolic network. RESULTS: We report the most
comprehensive metabolic model of Synechocystis sp. PCC6803
available, iSyn669, which includes 882 reactions, associated
with 669 genes, and 790 metabolites. The model includes a
detailed biomass equation which encompasses elementary building
blocks that are needed for cell growth, as well as a detailed
stoichiometric representation of photosynthesis. We demonstrate
applicability of iSyn669 for stoichiometric analysis by
simulating three physiologically relevant growth conditions of
Synechocystis sp. PCC6803, and through in silico metabolic
engineering simulations that allowed identification of a set of
gene knock-out candidates towards enhanced succinate
production. Gene essentiality and hydrogen production potential
have also been assessed. Furthermore, iSyn669 was used as a
transcriptomic data integration scaffold and thereby we found
metabolic hot-spots around which gene regulation is dominant
during light-shifting growth regimes. CONCLUSIONS: iSyn669
provides a platform for facilitating the development of
cyanobacteria as microbial cell factories.
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SUBMITTER: 
Nicolas Le Novère
PROVIDER: MODEL1507180008 | BioModels | 2015-07-30
REPOSITORIES: BioModels
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