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Comparative Analysis of Yeast Metabolic Network Models Highlights Progress, Opportunities for Metabolic Reconstruction.

Heavner BD, Price ND - PLoS Comput. Biol. (2015)

Bottom Line: We have also compared pairwise gene knockout essentiality predictions for 10 of these models.We found that varying approaches to model scope and annotation reflected the involvement of multiple research groups in model development; that single-gene essentiality predictions were affected by simulated medium, objective function, and the reference list of essential genes; and that predictive ability for single-gene essentiality did not correlate well with predictive ability for our reference list of synthetic lethal gene interactions (R = 0.159).We conclude that the reconstruction of the yeast metabolic network is indeed gradually improving through the iterative process of model development, and there remains great opportunity for advancing our understanding of biology through continued efforts to reconstruct the full biochemical reaction network that constitutes yeast metabolism.

View Article: PubMed Central - PubMed

Affiliation: Institute for Systems Biology, Seattle, Washington, United States of America.

ABSTRACT
We have compared 12 genome-scale models of the Saccharomyces cerevisiae metabolic network published since 2003 to evaluate progress in reconstruction of the yeast metabolic network. We compared the genomic coverage, overlap of annotated metabolites, predictive ability for single gene essentiality with a selection of model parameters, and biomass production predictions in simulated nutrient-limited conditions. We have also compared pairwise gene knockout essentiality predictions for 10 of these models. We found that varying approaches to model scope and annotation reflected the involvement of multiple research groups in model development; that single-gene essentiality predictions were affected by simulated medium, objective function, and the reference list of essential genes; and that predictive ability for single-gene essentiality did not correlate well with predictive ability for our reference list of synthetic lethal gene interactions (R = 0.159). We conclude that the reconstruction of the yeast metabolic network is indeed gradually improving through the iterative process of model development, and there remains great opportunity for advancing our understanding of biology through continued efforts to reconstruct the full biochemical reaction network that constitutes yeast metabolism. Additionally, we suggest that there is opportunity for refining the process of deriving a metabolic model from a metabolic network reconstruction to facilitate mechanistic investigation and discovery. This comparative study lays the groundwork for developing improved tools and formalized methods to quantitatively assess metabolic network reconstructions independently of any particular model application, which will facilitate ongoing efforts to advance our understanding of the relationship between genotype and cellular phenotype.

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Comparing model genomic coverage and metabolite annotation.Models clustered differently when compared using genomic coverage (A and B) or the subset of metabolites in each model that are annotated with reference to an external database such as the Chemical Entities of Biological Interest (ChEBI) database (C and D). Results are presented as heatmaps with dendrograms (A and C) and scatterplots of the normalized pairwise distance between models (B and D). Yellow bands in the heat map signify inclusion of a particular open reading frame in that model, and dendrogram clustering is based upon similarity.
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pcbi.1004530.g002: Comparing model genomic coverage and metabolite annotation.Models clustered differently when compared using genomic coverage (A and B) or the subset of metabolites in each model that are annotated with reference to an external database such as the Chemical Entities of Biological Interest (ChEBI) database (C and D). Results are presented as heatmaps with dendrograms (A and C) and scatterplots of the normalized pairwise distance between models (B and D). Yellow bands in the heat map signify inclusion of a particular open reading frame in that model, and dendrogram clustering is based upon similarity.

Mentions: Next, we evaluated model scope by comparing genomic coverage and the metabolites that could be cross-identified with Chemical Entities of Biological Interest (ChEBI) identifiers with the annotation included with the models. We were unable to directly compare model reactions because of the lack of standardized reaction identification between models, and the lack of an external reaction reference database identifier in any yeast metabolic model, a current limitation for interoperability and comparison in our field. We found that models clustered in groups that reflect their historical development [10], but these clusters differ between gene and metabolite comparisons (Fig 2). Models clustered in 4 groups when comparing genomic coverage: 1) Versions 4–7 of the Consensus Reconstruction; 2) iMM904, iMM904bs, iAZ900, and iTO977; 3) a looser cluster of iFF708, iIN800, and iND750; and 4) the Biomodels.db model. A row-aligned comparative table of genes in each model is included as S1 Table.


Comparative Analysis of Yeast Metabolic Network Models Highlights Progress, Opportunities for Metabolic Reconstruction.

Heavner BD, Price ND - PLoS Comput. Biol. (2015)

Comparing model genomic coverage and metabolite annotation.Models clustered differently when compared using genomic coverage (A and B) or the subset of metabolites in each model that are annotated with reference to an external database such as the Chemical Entities of Biological Interest (ChEBI) database (C and D). Results are presented as heatmaps with dendrograms (A and C) and scatterplots of the normalized pairwise distance between models (B and D). Yellow bands in the heat map signify inclusion of a particular open reading frame in that model, and dendrogram clustering is based upon similarity.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4643975&req=5

pcbi.1004530.g002: Comparing model genomic coverage and metabolite annotation.Models clustered differently when compared using genomic coverage (A and B) or the subset of metabolites in each model that are annotated with reference to an external database such as the Chemical Entities of Biological Interest (ChEBI) database (C and D). Results are presented as heatmaps with dendrograms (A and C) and scatterplots of the normalized pairwise distance between models (B and D). Yellow bands in the heat map signify inclusion of a particular open reading frame in that model, and dendrogram clustering is based upon similarity.
Mentions: Next, we evaluated model scope by comparing genomic coverage and the metabolites that could be cross-identified with Chemical Entities of Biological Interest (ChEBI) identifiers with the annotation included with the models. We were unable to directly compare model reactions because of the lack of standardized reaction identification between models, and the lack of an external reaction reference database identifier in any yeast metabolic model, a current limitation for interoperability and comparison in our field. We found that models clustered in groups that reflect their historical development [10], but these clusters differ between gene and metabolite comparisons (Fig 2). Models clustered in 4 groups when comparing genomic coverage: 1) Versions 4–7 of the Consensus Reconstruction; 2) iMM904, iMM904bs, iAZ900, and iTO977; 3) a looser cluster of iFF708, iIN800, and iND750; and 4) the Biomodels.db model. A row-aligned comparative table of genes in each model is included as S1 Table.

Bottom Line: We have also compared pairwise gene knockout essentiality predictions for 10 of these models.We found that varying approaches to model scope and annotation reflected the involvement of multiple research groups in model development; that single-gene essentiality predictions were affected by simulated medium, objective function, and the reference list of essential genes; and that predictive ability for single-gene essentiality did not correlate well with predictive ability for our reference list of synthetic lethal gene interactions (R = 0.159).We conclude that the reconstruction of the yeast metabolic network is indeed gradually improving through the iterative process of model development, and there remains great opportunity for advancing our understanding of biology through continued efforts to reconstruct the full biochemical reaction network that constitutes yeast metabolism.

View Article: PubMed Central - PubMed

Affiliation: Institute for Systems Biology, Seattle, Washington, United States of America.

ABSTRACT
We have compared 12 genome-scale models of the Saccharomyces cerevisiae metabolic network published since 2003 to evaluate progress in reconstruction of the yeast metabolic network. We compared the genomic coverage, overlap of annotated metabolites, predictive ability for single gene essentiality with a selection of model parameters, and biomass production predictions in simulated nutrient-limited conditions. We have also compared pairwise gene knockout essentiality predictions for 10 of these models. We found that varying approaches to model scope and annotation reflected the involvement of multiple research groups in model development; that single-gene essentiality predictions were affected by simulated medium, objective function, and the reference list of essential genes; and that predictive ability for single-gene essentiality did not correlate well with predictive ability for our reference list of synthetic lethal gene interactions (R = 0.159). We conclude that the reconstruction of the yeast metabolic network is indeed gradually improving through the iterative process of model development, and there remains great opportunity for advancing our understanding of biology through continued efforts to reconstruct the full biochemical reaction network that constitutes yeast metabolism. Additionally, we suggest that there is opportunity for refining the process of deriving a metabolic model from a metabolic network reconstruction to facilitate mechanistic investigation and discovery. This comparative study lays the groundwork for developing improved tools and formalized methods to quantitatively assess metabolic network reconstructions independently of any particular model application, which will facilitate ongoing efforts to advance our understanding of the relationship between genotype and cellular phenotype.

Show MeSH
Related in: MedlinePlus