Evidence map›Paper›PMID 27626798›Full record

ArticlePLoS computational biology2016

SUMOFLUX: A Generalized Method for Targeted 13C Metabolic Flux Ratio Analysis.

Maria Kogadeeva, Nicola Zamboni

Abstract read
In one paragraph

Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

18 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Frontiers in molecular neuroscience · 2022
    Review
  7. Review
  8. Stable Isotopes for Tracing Cardiac Metabolism in Diseases.Frontiers in cardiovascular medicine · 2021
    Review
  9. Review
  10. Methods and protocols · 2020
    Article
  11. Review
  12. DynamiciScience · 2020
    Article
  13. The era of big data: Genome-scale modelling meets machine learning.Computational and structural biotechnology journal · 2020
    Review
  14. Article
  15. Article
  16. Review
  17. Article
  18. PeerJ · 2017
    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.

Maria KogadeevaInstitute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland.
Nicola ZamboniInstitute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland.ORCID 0000-0003-1271-1021

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Metabolic fluxes are a cornerstone of cellular physiology that emerge from a complex interplay of enzymes, carriers, and nutrients. The experimental assessment of in vivo intracellular fluxes using stable isotopic tracers is essential if we are to understand metabolic function and regulation. Flux estimation based on 13C or 2H labeling relies on complex simulation and iterative fitting; processes that necessitate a level of expertise that ordinarily preclude the non-expert user. To overcome this, we have developed SUMOFLUX, a methodology that is broadly applicable to the targeted analysis of 13C-metabolic fluxes. By combining surrogate modeling and machine learning, we trained a predictor to specialize in estimating flux ratios from measurable 13C-data. SUMOFLUX targets specific flux features individually, which makes it fast, user-friendly, applicable to experimental design and robust in terms of experimental noise and exchange flux magnitude. Collectively, we predict that SUMOFLUX's properties realistically pave the way to high-throughput flux analyses.

Indexed as

Models, BiologicalAlgorithmsCarbon IsotopesComputational BiologyEscherichia coliMetabolic Flux AnalysisCarbon Isotopes

Identifiers

PMID27626798
PMCPMC5023139

What OpenQuestion holds

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LicenceCC BY
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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.