Nogales2012 - Genome-scale metabolic network of Synechocystis sp. PCC6803 (iJN678)
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ABSTRACT:
Nogales2012 - Genome-scale metabolic network
of Synechocystis sp. (iJN678)
This model is described in the article:
Detailing the optimality of
photosynthesis in cyanobacteria through systems biology
analysis.
Nogales J, Gudmundsson S, Knight EM,
Palsson BO, Thiele I.
Proc. Natl. Acad. Sci. U.S.A. 2012 Feb;
109(7): 2678-2683
Abstract:
Photosynthesis has recently gained considerable attention
for its potential role in the development of renewable energy
sources. Optimizing photosynthetic organisms for biomass or
biofuel production will therefore require a systems
understanding of photosynthetic processes. We reconstructed a
high-quality genome-scale metabolic network for Synechocystis
sp. PCC6803 that describes key photosynthetic processes in
mechanistic detail. We performed an exhaustive in silico
analysis of the reconstructed photosynthetic process under
different light and inorganic carbon (Ci) conditions as well as
under genetic perturbations. Our key results include the
following. (i) We identified two main states of the
photosynthetic apparatus: a Ci-limited state and a
light-limited state. (ii) We discovered nine alternative
electron flow pathways that assist the photosynthetic linear
electron flow in optimizing the photosynthesis performance.
(iii) A high degree of cooperativity between alternative
pathways was found to be critical for optimal autotrophic
metabolism. Although pathways with high photosynthetic yield
exist for optimizing growth under suboptimal light conditions,
pathways with low photosynthetic yield guarantee optimal growth
under excessive light or Ci limitation. (iv) Photorespiration
was found to be essential for the optimal photosynthetic
process, clarifying its role in high-light acclimation.
Finally, (v) an extremely high photosynthetic robustness drives
the optimal autotrophic metabolism at the expense of metabolic
versatility and robustness. The results and modeling approach
presented here may promote a better understanding of the
photosynthetic process. They can also guide bioengineering
projects toward optimal biofuel production in photosynthetic
organisms.
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SUBMITTER: 
Nicolas Le Novère
PROVIDER: MODEL1507180046 | BioModels | 2015-07-30
REPOSITORIES: BioModels
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