Evidence map›Paper›PMID 30375377›Full record

ArticleNature communications2018

Sources, propagation and consequences of stochasticity in cellular growth.

Philipp Thomas, Guillaume Terradot, Vincent Danos, Andrea Y Weiße

Abstract read
In one paragraph

Article in Nature communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.

0numbers the graph read from it
0cells of the map it votes in
53citing 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

53 citing papers in PubMed.

  1. Stochasticity in mammalian cell growth rates drives cell-to-cell variability independently of cell size and divisions.Proceedings of the National Academy of Sciences of the United States of America · 2026
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  7. Nonlinear memory in cell-division dynamics across species.Proceedings of the National Academy of Sciences of the United States of America · 2025
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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.

Philipp ThomasDepartment of Mathematics, Imperial College London, London, SW7 2AZ, UK. p.thomas@imperial.ac.uk.ORCID http://orcid.org/0000-0003-4919-8452
Guillaume TerradotSynthSys-Centre for Synthetic & Systems Biology, University of Edinburgh, Edinburgh, EH9 3BD, UK.
Vincent DanosCNRS, École Normale Supérieure, Paris, 75005, France.
Andrea Y WeißeSynthSys-Centre for Synthetic & Systems Biology, University of Edinburgh, Edinburgh, EH9 3BD, UK. andrea.weisse@imperial.ac.uk.ORCID http://orcid.org/0000-0001-7786-8252

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Growth impacts a range of phenotypic responses. Identifying the sources of growth variation and their propagation across the cellular machinery can thus unravel mechanisms that underpin cell decisions. We present a stochastic cell model linking gene expression, metabolism and replication to predict growth dynamics in single bacterial cells. Alongside we provide a theory to analyse stochastic chemical reactions coupled with cell divisions, enabling efficient parameter estimation, sensitivity analysis and hypothesis testing. The cell model recovers population-averaged data on growth-dependence of bacterial physiology and how growth variations in single cells change across conditions. We identify processes responsible for this variation and reconstruct the propagation of initial fluctuations to growth and other processes. Finally, we study drug-nutrient interactions and find that antibiotics can both enhance and suppress growth heterogeneity. Our results provide a predictive framework to integrate heterogeneous data and draw testable predictions with implications for antibiotic tolerance, evolutionary and synthetic biology.

Indexed as

Cell EnlargementGene ExpressionBacteriaCell DivisionModels, BiologicalStochastic Processes

Identifiers

PMID30375377
PMCPMC6207721

What OpenQuestion holds

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Registered trials

None linked

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.