Evidence map›Paper›PMID 32986690›Full record

ArticlePLoS computational biology2020

A bacterial size law revealed by a coarse-grained model of cell physiology.

François Bertaux, Julius von Kügelgen, Samuel Marguerat, Vahid Shahrezaei

Abstract read
In one paragraph

Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing 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

26 citing papers in PubMed.

  1. Article
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  10. A GeneralizedbioRxiv : the preprint server for biology · 2024
    Article
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  18. Phenotypic and metabolic adaptations ofFrontiers in microbiology · 2023
    Article
  19. Review
  20. Review
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.

François BertauxDepartment of Mathematics, Imperial College London, London, United Kingdom.
Julius von KügelgenDepartment of Mathematics, Imperial College London, London, United Kingdom.ORCID 0000-0001-6469-4118
Samuel MargueratMRC London Institute of Medical Sciences (LMS), London, United Kingdom.ORCID 0000-0002-2402-3165
Vahid ShahrezaeiDepartment of Mathematics, Imperial College London, London, United Kingdom.ORCID 0000-0002-4013-5458

Funding

Medical Research Council MC_UP_1102/8
6 · The paper itself

Abstract

Universal observations in Biology are sometimes described as "laws". In E. coli, experimental studies performed over the past six decades have revealed major growth laws relating ribosomal mass fraction and cell size to the growth rate. Because they formalize complex emerging principles in biology, growth laws have been instrumental in shaping our understanding of bacterial physiology. Here, we discovered a novel size law that connects cell size to the inverse of the metabolic proteome mass fraction and the active fraction of ribosomes. We used a simple whole-cell coarse-grained model of cell physiology that combines the proteome allocation theory and the structural model of cell division. This integrated model captures all available experimental data connecting the cell proteome composition, ribosome activity, division size and growth rate in response to nutrient quality, antibiotic treatment and increased protein burden. Finally, a stochastic extension of the model explains non-trivial correlations observed in single cell experiments including the adder principle. This work provides a simple and robust theoretical framework for studying the fundamental principles of cell size determination in unicellular organisms.

Indexed as

Bacterial Physiological PhenomenaEscherichia coliModels, BiologicalModels, MolecularAnti-Bacterial AgentsCulture MediaProteomeRibosomesSystems BiologyAnti-Bacterial AgentsCulture MediaProteome

Identifiers

PMID32986690
PMCPMC7553314

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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.