Evidence map›Paper›PMID 41939013›Full record

ArticlePRX life

Morphogen Patterning in Dynamic Tissues.

Alex M Plum, Mattia Serra

Abstract read
In one paragraph

Article in PRX life. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Review
  2. Positional information and information flows in dynamic tissues.bioRxiv : the preprint server for biology · 2026
    Article
  3. Review
  4. Dynamical systems of fate and form in development.Seminars in cell & developmental biology · 2025
    Review
  5. Article
  6. 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.

Alex M PlumDepartment of Physics, University of California San Diego, CA 92093, USA.
Mattia SerraDepartment of Physics, University of California San Diego, CA 92093, USA.

Funding

Training Program in Quantitative Integrative BiologyT32GM127235 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI HWA, TERENCE · 2018 to 2022
$1.3M
Flows, Fates and Forces: A Biophysical Framework for Data-Driven Discovery in DevelopmentR35GM156889 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Mattia Serra · 2025 to 2026
$654k
NIGMS NIH HHS R35 GM156889NIGMS NIH HHS T32 GM127235
6 · The paper itself

Abstract

Embryogenesis integrates morphogenesis-coordinated cell movements-with morphogen patterning and cell differentiation. While largely studied independently, morphogenesis and patterning often unfold simultaneously in early embryos. Yet, how cell movements affect morphogen transport and cells' exposure over time remains unclear, as most pattern formation models assume static tissues. Here, we develop a theoretical framework for morphogen patterning in dynamic tissues, recasting advection-reaction-diffusion equations in the cells' moving reference frames. This framework (i) elucidates how morphogenesis mediates morphogen transport and compartmentalization: cell-cell diffusive transport is enhanced at multicellular flow attractors, while repellers act as barriers, affecting cell fate induction and bifurcations. (ii) It formalizes cell-cell signaling ranges in dynamic tissues, deconfounding morphogenetic movements to identify which cells could communicate via morphogens. (iii) It provides two new nondimensional numbers to assess when and where morphogenesis affects morphogen transport. We demonstrate this framework by analyzing classical patterning models with common morphogenetic motifs as well as experimental tissue flows. Our work rationalizes dynamic tissue patterning in development, constraining candidate patterning mechanisms and parameters using accessible cell motion data.

Identifiers

PMID41939013
PMCPMC13045748

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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.