Evidence map›Paper›PMID 21186354›Full record

ArticleNature genetics2011

Non-genetic heterogeneity from stochastic partitioning at cell division.

Dann Huh, Johan Paulsson

Abstract read
In one paragraph

Article in Nature genetics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 197 papers.

0numbers the graph read from it
0cells of the map it votes in
197citing 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

197 citing papers in PubMed.

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  6. 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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  14. Inheritance of extraordinary metabolic activity from parental bacteria individuals.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  15. Review
  16. Relative Distribution of DnaA and DNA inInternational journal of molecular sciences · 2025
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  17. Review
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  20. Review

137 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

2 authors.

Dann HuhDepartment of Systems Biology, Harvard University, Boston, Massachusetts, USA.
Johan Paulsson

Funding

Limits and Trade-offs of Feedback ControlR01GM081563 · NIGMS · HARVARD MEDICAL SCHOOL · PI Johan Paulsson · 2008 to 2026
$4.9M
NIGMS NIH HHS GM081563-02NIGMS NIH HHS R01 GM081563
6 · The paper itself

Abstract

Gene expression involves inherently probabilistic steps that create fluctuations in protein abundances. The results from many in-depth analyses and genome-scale surveys have suggested how such fluctuations arise and spread, often in ways consistent with stochastic models of transcription and translation. But fluctuations also arise during cell division when molecules are partitioned stochastically between the two daughters. Here we mathematically demonstrate how stochastic partitioning contributes to the non-genetic heterogeneity. Our results show that partitioning errors are hard to correct, and that the resulting noise profiles are remarkably difficult to separate from gene expression noise. By applying these results to common experimental strategies and distinguishing between creation versus transmission of noise, we hypothesize that much of the cell-to-cell heterogeneity that has been attributed to various aspects of gene expression instead comes from random segregation at cell division. We propose experiments to separate between these two types of fluctuations and discuss future directions.

Indexed as

Gene Expression RegulationStochastic ProcessesCell DivisionEscherichia coliGene ExpressionGene Expression ProfilingGreen Fluorescent ProteinsModels, BiologicalModels, StatisticalModels, TheoreticalTranscription, GeneticGreen Fluorescent Proteins

Identifiers

PMID21186354
PMCPMC3208402

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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.