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ABSTRACT:
PROVIDER: PRJNA1289394 | ENA |
REPOSITORIES: ENA
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Project description:Kim2007 - Genome-scale metabolic network of
Mannheimia succiniciproducens (iTY425)
This model is described in the article:
Genome-scale analysis of
Mannheimia succiniciproducens metabolism.
Kim TY, Kim HU, Park JM, Song H, Kim
JS, Lee SY.
Biotechnol. Bioeng. 2007 Jul; 97(4):
657-671
Abstract:
Mannheimia succiniciproducens MBEL55E isolated from bovine
rumen is a capnophilic gram-negative bacterium that efficiently
produces succinic acid, an industrially important four carbon
dicarboxylic acid. In order to design a metabolically
engineered strain which is capable of producing succinic acid
with high yield and productivity, it is essential to optimize
the whole metabolism at the systems level. Consequently, in
silico modeling and simulation of the genome-scale metabolic
network was employed for genome-scale analysis and efficient
design of metabolic engineering experiments. The genome-scale
metabolic network of M. succiniciproducens consisting of 686
reactions and 519 metabolites was constructed based on
reannotation and validation experiments. With the reconstructed
model, the network structure and key metabolic characteristics
allowing highly efficient production of succinic acid were
deciphered; these include strong PEP carboxylation, branched
TCA cycle, relative weak pyruvate formation, the lack of
glyoxylate shunt, and non-PTS for glucose uptake.
Constraints-based flux analyses were then carried out under
various environmental and genetic conditions to validate the
genome-scale metabolic model and to decipher the altered
metabolic characteristics. Predictions based on
constraints-based flux analysis were mostly in excellent
agreement with the experimental data. In silico knockout
studies allowed prediction of new metabolic engineering
strategies for the enhanced production of succinic acid. This
genome-scale in silico model can serve as a platform for the
systematic prediction of physiological responses of M.
succiniciproducens to various environmental and genetic
perturbations and consequently for designing rational
strategies for strain improvement.
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2015-07-30 | MODEL1507180062 | BioModels