Evidence map›Paper›PMID 27381431›Full record

ArticleScientific reports2016

Evolutionary pressures on microbial metabolic strategies in the chemostat.

Meike T Wortel, Evert Bosdriesz, Bas Teusink, Frank J Bruggeman

Abstract read
In one paragraph

Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing 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

21 citing papers in PubMed.

  1. Adaptive Laboratory Evolution inMicroorganisms · 2026
    Review
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  12. Fungal evolution: cellular, genomic and metabolic complexity.Biological reviews of the Cambridge Philosophical Society · 2020
    Article
  13. Article
  14. Article
  15. Continual evolution through coupled fast and slow feedbacks.Proceedings of the National Academy of Sciences of the United States of America · 2020
    Article
  16. Article
  17. Article
  18. Article
  19. Article
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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

4 authors.

Meike T WortelSystems Bioinformatics, VU University, Amsterdam, De Boelelaan 1087, 1081 HV, The Netherlands.
Evert BosdrieszSystems Bioinformatics, VU University, Amsterdam, De Boelelaan 1087, 1081 HV, The Netherlands.
Bas TeusinkSystems Bioinformatics, VU University, Amsterdam, De Boelelaan 1087, 1081 HV, The Netherlands.
Frank J BruggemanSystems Bioinformatics, VU University, Amsterdam, De Boelelaan 1087, 1081 HV, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Protein expression is shaped by evolutionary processes that tune microbial fitness. The limited biosynthetic capacity of a cell constrains protein expression and forces the cell to carefully manage its protein economy. In a chemostat, the physiology of the cell feeds back on the growth conditions, hindering intuitive understanding of how changes in protein concentration affect fitness. Here, we aim to provide a theoretical framework that addresses the selective pressures and optimal evolutionary-strategies in the chemostat. We show that the optimal enzyme levels are the result of a trade-off between the cost of their production and the benefit of their catalytic function. We also show that deviations from optimal enzyme levels are directly related to selection coefficients. The maximal fitness strategy for an organism in the chemostat is to express a well-defined metabolic subsystem known as an elementary flux mode. Using a coarse-grained, kinetic model of Saccharomyces cerevisiae's metabolism and growth, we illustrate that the dynamics and outcome of evolution in a chemostat can be very counter-intuitive: Strictly-respiring and strictly-fermenting strains can evolve from a common ancestor. This work provides a theoretical framework that relates a kinetic, mechanistic view on metabolism with cellular physiology and evolutionary dynamics in the chemostat.

Indexed as

Evolution, MolecularBiochemical PhenomenaBiological EvolutionBiological TransportBiomassBioreactorsComputer SimulationFermentationGlucoseGlycolysisMetabolismMutationPhenotypeProtein ConformationSaccharomyces cerevisiaeSelection, GeneticGlucose

Identifiers

PMID27381431
PMCPMC4933952

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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.