Evidence map›Paper›PMID 28779005›Full record

ArticleMolecular systems biology2017

Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints.

Benjamín J Sánchez, Cheng Zhang, Avlant Nilsson, Petri-Jaan Lahtvee, Eduard J Kerkhoven, Jens Nielsen

Abstract read
In one paragraph

Article in Molecular systems biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 219 papers.

0numbers the graph read from it
0cells of the map it votes in
219citing 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

219 citing papers in PubMed.

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  14. The return of metabolism: biochemistry and physiology of glycolysis.Biological reviews of the Cambridge Philosophical Society · 2026
    Review
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159 more citing papers are in PubMed but not listed here.

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

6 authors.

Benjamín J SánchezDepartment of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.ORCID 0000-0001-6093-4110
Cheng ZhangScience for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden.ORCID 0000-0002-3721-8586
Avlant NilssonDepartment of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.ORCID 0000-0002-9476-4516
Petri-Jaan LahtveeDepartment of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.ORCID 0000-0002-3327-3190
Eduard J KerkhovenDepartment of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.ORCID 0000-0002-3593-5792
Jens NielsenDepartment of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden nielsenj@chalmers.se.ORCID 0000-0002-9955-6003

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Genome-scale metabolic models (GEMs) are widely used to calculate metabolic phenotypes. They rely on defining a set of constraints, the most common of which is that the production of metabolites and/or growth are limited by the carbon source uptake rate. However, enzyme abundances and kinetics, which act as limitations on metabolic fluxes, are not taken into account. Here, we present GECKO, a method that enhances a GEM to account for enzymes as part of reactions, thereby ensuring that each metabolic flux does not exceed its maximum capacity, equal to the product of the enzyme's abundance and turnover number. We applied GECKO to a

Indexed as

Genome, FungalKineticsMetabolic EngineeringMetabolic Networks and PathwaysModels, BiologicalPhenotypeSaccharomyces cerevisiaeSystems Biologyenzyme kineticsflux balance analysismolecular crowdingproteomicsSaccharomyces cerevisiae

Identifiers

PMID28779005
PMCPMC5572397

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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.