PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
Logo of bmcsysbioBioMed Centralsearchsubmit a manuscriptregisterthis articleBMC Systems Biology
 
BMC Syst Biol. 2012; 6: 49.
Published online Jul 5, 2012. doi:  10.1186/1752-0509-6-49
PMCID: PMC3390277
Genome-scale metabolic model of the fission yeast Schizosaccharomyces pombe and the reconciliation of in silico/in vivo mutant growth
Seung Bum Sohn,1,2 Tae Yong Kim,1,2 Jay H Lee,4 and Sang Yup Leecorresponding author1,2,3
1Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 program), Center for Systems and Synthetic Biotechnology, Institute for the BioCentury, KAIST, Daejeon, Republic of Korea
2Bioinformatics Research Center, KAIST, Daejeon, Republic of Korea
3Department of Bio and Brain Engineering and Bioinformatics Research Center, KAIST, Daejeon, Republic of Korea
4Department of Chemical and Biomolecular Engineering (WCU Program), KAIST, Daejeon, Republic of Korea
corresponding authorCorresponding author.
Seung Bum Sohn: sleepingforest/at/kaist.ac.kr; Tae Yong Kim: kimty/at/kaist.ac.kr; Jay H Lee: jayhlee/at/kaist.ac.kr; Sang Yup Lee: leesy/at/kaist.ac.kr
Received October 24, 2011; Accepted May 25, 2012.
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.
Keywords: Schizosaccharomyces pombe, Genome-scale metabolic model, Single-gene mutant growth, Essentiality
Articles from BMC Systems Biology are provided here courtesy of
BioMed Central