Evidence map›Paper›PMID 42223636›Full record

ReviewResults and problems in cell differentiation2026

Models of Cellular Mechanosensation.

David Dolgitzer, Pablo A Iglesias

Abstract readReview
PubMed Publisher
In one paragraph

Review in Results and problems in cell differentiation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

David DolgitzerDepartment of Physics and Astronomy, Johns Hopkins University, Baltimore, MD, USA.
Pablo A IglesiasDepartment of Electrical & Computer Engineering, Johns Hopkins University, Baltimore, MD, USA. pi@jhu.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Over the past decade, theoretical and experimental work in mechanobiology has become increasingly interdependent, with modeling now serving as an essential tool for interpreting experiments and guiding measurements. Cellular mechanosensation, the conversion of mechanical cues into biochemical signals, governs essential processes such as migration, polarization, lineage specification, and tissue homeostasis, and its dysregulation contributes to disease. Quantitative models of mechanosensation have been central to clarifying how cells detect and respond to force across multiple scales, from ion channels and molecular bonds to adhesions, cytoskeletal dynamics, nuclear deformation, and transcriptional regulators such as YAP/TAZ (Yes-associated protein and transcriptional coactivator with PDZ-binding motif). In this chapter, we survey both experimental and theoretical advances in mechanosensation, emphasizing how mathematical and computational models complement experiments, expose limitations, and generate testable hypotheses and conceptual frameworks.

Indexed as

Mechanotransduction, CellularModels, BiologicalAnimalsHumansCatch bondsCell-matrix interactionsComputational modelingMechanosensationMechanosensitive channels

Identifiers

What OpenQuestion holds

Textmetadata
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.