Evidence map›Paper›PMID 26566239›Full record

ArticlePLoS computational biology2015

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

Benjamin D Heavner, Nathan D Price

Abstract readComparative Study
In one paragraph

Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

38 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. The origins of the Guinness stout yeast.Communications biology · 2024
    Article
  6. Reconstruction of the genome-scale metabolic network model ofFrontiers in bioengineering and biotechnology · 2024
    Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Tolerance to NADH/NADiScience · 2021
    Article
  12. Article
  13. Article
  14. Review
  15. CEG 2.0: an updated database of clusters of essential genes including eukaryotic organisms.Database : the journal of biological databases and curation · 2020
    Article
  16. Genetic buffering and potentiation in metabolism.PLoS computational biology · 2020
    Article
  17. Review
  18. Review
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Benjamin D HeavnerInstitute for Systems Biology, Seattle, Washington, United States of America.
Nathan D PriceInstitute for Systems Biology, Seattle, Washington, United States of America.

Funding

NIGMS NIH HHS 2P50GM075647
6 · The paper itself

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.

Indexed as

Models, BiologicalGene Knockout TechniquesGenome, FungalMetabolic Networks and PathwaysSaccharomyces cerevisiaeSystems Biology

Identifiers

PMID26566239
PMCPMC4643975

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.