Evidence map›Paper›PMID 36377847›Full record

ArticleeLife2022

Patterns of interdivision time correlations reveal hidden cell cycle factors.

Fern A Hughes, Alexis R Barr, Philipp Thomas

Open access · goldAbstract read
In one paragraph

Article in eLife, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.8field-weighted citation impact, top 30% of its field
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

7 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
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  5. An efficient solution to Hidden Markov Models on trees with coupled branches.IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society · 2025
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  6. Review
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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

3 authors at 2 institutions in 1 country.

Fern A HughesDepartment of Mathematics, Imperial College London, London, United Kingdom.ORCID 0000-0002-4599-5027
Alexis R BarrMRC London Institute of Medical Sciences, London, United Kingdom.ORCID 0000-0002-6684-8114
Philipp ThomasDepartment of Mathematics, Imperial College London, London, United Kingdom.ORCID 0000-0003-4919-8452
MRC London Institute of Medical Sciences · GBImperial College London · GB

Funding

Cancer Research UK 25729Cancer Research UK C63833/A25729Medical Research Council MC-A658-5TY60Medical Research Council MC_UP_1605/8Medical Research Council MR/T018429/1
6 · The paper itself

Abstract

The time taken for cells to complete a round of cell division is a stochastic process controlled, in part, by intracellular factors. These factors can be inherited across cellular generations which gives rise to, often non-intuitive, correlation patterns in cell cycle timing between cells of different family relationships on lineage trees. Here, we formulate a framework of hidden inherited factors affecting the cell cycle that unifies known cell cycle control models and reveals three distinct interdivision time correlation patterns: aperiodic, alternator, and oscillator. We use Bayesian inference with single-cell datasets of cell division in bacteria, mammalian and cancer cells, to identify the inheritance motifs that underlie these datasets. From our inference, we find that interdivision time correlation patterns do not identify a single cell cycle model but generally admit a broad posterior distribution of possible mechanisms. Despite this unidentifiability, we observe that the inferred patterns reveal interpretable inheritance dynamics and hidden rhythmicity of cell cycle factors. This reveals that cell cycle factors are commonly driven by circadian rhythms, but their period may differ in cancer. Our quantitative analysis thus reveals that correlation patterns are an emergent phenomenon that impact cell proliferation and these patterns may be altered in disease.

Indexed as

Circadian RhythmMammalsAnimalsBayes TheoremCell CycleCell DivisionCell Proliferationcell biologycell cyclecellular noisecircadian rhythmcomputational biologyhumanlineage correlationmousepatternsstochastic modellingsystems biology

Identifiers

PMID36377847
PMCPMC9822260
OpenAlexW4309099578

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.