Sohn2012 - Genome-scale metabolic network of Schizosaccharomyces pombe (SpoMBEL1693)
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ABSTRACT:
Sohn2012 - Genome-scale metabolic network of
Schizosaccharomyces pombe (SpoMBEL1693)
This model is described in the article:
Genome-scale metabolic model
of the fission yeast Schizosaccharomyces pombe and the
reconciliation of in silico/in vivo mutant growth.
Sohn SB, Kim TY, Lee JH, Lee
SY.
BMC Syst Biol 2012; 6: 49
Abstract:
BACKGROUND: Over the last decade, the genome-scale metabolic
models have been playing increasingly important roles in
elucidating metabolic characteristics of biological systems for
a wide range of applications including, but not limited to,
system-wide identification of drug targets and production of
high value biochemical compounds. However, these genome-scale
metabolic models must be able to first predict known in vivo
phenotypes before it is applied towards these applications with
high confidence. One benchmark for measuring the in silico
capability in predicting in vivo phenotypes is the use of
single-gene mutant libraries to measure the accuracy of
knockout simulations in predicting mutant growth phenotypes.
RESULTS: Here we employed a systematic and iterative process,
designated as Reconciling In silico/in vivo mutaNt Growth
(RING), to settle discrepancies between in silico prediction
and in vivo observations to a newly reconstructed genome-scale
metabolic model of the fission yeast, Schizosaccharomyces
pombe, SpoMBEL1693. The predictive capabilities of the
genome-scale metabolic model in predicting single-gene mutant
growth phenotypes were measured against the single-gene mutant
library of S. pombe. The use of RING resulted in improving the
overall predictive capability of SpoMBEL1693 by 21.5%, from
61.2% to 82.7% (92.5% of the negative predictions matched the
observed growth phenotype and 79.7% the positive predictions
matched the observed growth phenotype). CONCLUSION: This study
presents validation and refinement of a newly reconstructed
metabolic model of the yeast S. pombe, through improving the
metabolic model's predictive capabilities by reconciling the in
silico predicted growth phenotypes of single-gene knockout
mutants, with experimental in vivo growth data.
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MODEL1507180061.
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SUBMITTER: 
Nicolas Le Novère
PROVIDER: MODEL1507180061 | BioModels | 2015-07-30
REPOSITORIES: BioModels
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