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Cited by 303

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Imaging the traction stresses exerted by locomoting cells with the elastic substratum method

Open access
Dembo, M., Oliver, T., Ishihara, A., Jacobson, K.·1996·Biophysical Journal

Abstract

The cytoskeletal activity of motile or adherent cells is frequently seen to induce detectable displacements of sufficiently compliant substrata. The physics of this phenomenon is discussed in terms of the classical theory of small-strain, plane-stress elasticity. The main results of such analysis is a transform expressing the displacement field of the elastic substrate as an integral over the traction field. The existence of this transform is used to derive a Bayesian method for converting noisy measurements of substratum displacement into "images" of the actual traction forces exerted by adherent or locomoting cells. Finally, the Monte Carlo validation of the statistical method is discussed, some new rheological studies of films are presented, and a practical application is given.

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Metadata

Authors
Dembo, M., Oliver, T., Ishihara, A., & Jacobson, K.
Venue
Biophysical Journal
Publisher
Elsevier BV
Published
1996-04
Volume
70
Issue
4
Pages
2008-2022
ISSN
0006-3495 (ISSN-L)
OpenAlex
W2035382078
Cited by
303
References
1
FWCI
1.77
Type
journal article
Language
en
Open access
hybrid

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CoChat. (2026). Citation check: Imaging the traction stresses exerted by locomoting cells with the elastic substratum method. https://app.cochat.ai/papers/10.1016/s0006-3495(96)79767-9
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Dembo, M., Oliver, T., Ishihara, A., & Jacobson, K. (1996). Imaging the traction stresses exerted by locomoting cells with the elastic substratum method. Biophysical Journal. https://doi.org/10.1016/s0006-3495(96)79767-9

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.