Evidence map›Paper›PMID 41056994›Full record

ArticleJournal of the Royal Society, Interface2025

Physical traits of supercompetitors in cell competition.

Logan C Carpenter, Shiladitya Banerjee

Abstract read
In one paragraph

Article in Journal of the Royal Society, Interface, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Cell competition driven by secreted ligands: Modeling liver metastasis of colorectal cancer.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  2. Physical traits of supercompetitors in cell competition.Journal of the Royal Society, Interface · 2025
    Article
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.

Logan C CarpenterCarnegie Mellon University, Pittsburgh, PA, USA.
Shiladitya BanerjeeGeorgia Institute of Technology, Atlanta, GA, USA.ORCID 0000-0001-8000-2556

Funding

NIGMS NIH HHS
6 · The paper itself

Abstract

Cell competition is a fitness control mechanism in tissues, where less fit cells are eliminated to maintain tissue homeostasis. Two primary mechanisms of cell competition have been identified: contact-dependent apoptosis and mechanical stress-induced competition. While both operate in tissues, their combined impact on population dynamics is unclear. Here, we present a cell-based computational model that integrates cellular mechanics with proliferation, contact-induced apoptosis and mechanically triggered apoptosis to investigate competition between two distinct cell types. Using this framework, we systematically examine how differences in physical traits-such as stiffness, adhesion and crowding sensitivity-govern competitive outcomes. Our results show that apoptosis rates alone are insufficient to predict cell fate; differences in proliferation and contact inhibition play equally important, context-dependent roles. Notably, we find that increased cell stiffness can confer a fitness advantage, enabling stiffer cells to outcompete softer neighbours. However, cells with reduced stiffness can become 'soft' supercompetitors if they exhibit faster growth and lower sensitivity to crowding. We also demonstrate that colony size critically influences competition: a minimum size is required for mutant expansion, below which elimination becomes stochastic. This stochastic clearance is driven by a protrusive instability in the interface between two cells that promotes invasion of the supercompetitors.

Indexed as

ApoptosisCell CompetitionModels, BiologicalAnimalsCell AdhesionCell ProliferationComputer SimulationHumansapoptosiscancercell competitioncell growthcellular Potts modeltissue homeostasis

Identifiers

PMID41056994
PMCPMC12503947

What OpenQuestion holds

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LicenceCC BY
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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.