Evidence map›Paper›PMID 42678999›Full record

ArticlePLoS computational biology2026

Cross-bridge model for predicting muscle short-range stiffness during movement.

Tim J van der Zee, Surabhi N Simha, Gregory N Milburn, Kenneth S Campbell, Lena H Ting, Friedl De Groote

Abstract read
In one paragraph

Article in PLoS computational biology, 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

6 authors.

Tim J van der ZeeDepartment of Movement Sciences, KU Leuven, Leuven, Belgium.ORCID 0000-0001-9037-2366
Surabhi N SimhaWallace H. Coulter Department of Biomedical Engineering, Emory University and The Georgia Institute of Technology, Atlanta, Georgia, United States of America.
Gregory N MilburnDivision of Cardiovascular Medicine, University of Kentucky, Lexington, Kentucky, United States of America.ORCID 0000-0001-5547-2441
Kenneth S CampbellDivision of Cardiovascular Medicine, University of Kentucky, Lexington, Kentucky, United States of America.
Lena H TingWallace H. Coulter Department of Biomedical Engineering, Emory University and The Georgia Institute of Technology, Atlanta, Georgia, United States of America.ORCID 0000-0001-6854-9444
Friedl De GrooteDepartment of Movement Sciences, KU Leuven, Leuven, Belgium.

Funding

Multiscale models of proprioceptive encoding to reveal mechanisms of impaired sensorimotor controlR01HD090642 · NICHD · EMORY UNIVERSITY · PI COPE, TIMOTHY C, SAWICKI, GREGORY STEPHEN · 2016 to 2025
$4.9M
NICHD NIH HHS R01 HD090642
6 · The paper itself

Abstract

Musculoskeletal simulations can offer valuable insight into how the properties of our musculoskeletal system influence the biomechanics of our daily movements. One such property is muscle's initial resistance to stretch, also known as short-range stiffness, which is key to stabilizing movements in response to external perturbations. Short-range stiffness is poorly captured by existing musculoskeletal simulations since they employ phenomenological Hill-type models lacking activation-dependent stiffness properties. Existing simulations also do not capture the history-dependent reduction in short-range stiffness after muscle shortening, known as muscle thixotropy. While cross-bridge models can reproduce muscle short-range stiffness, it remains unclear which model properties are necessary to capture its history dependence. Here, we tested the ability of various cross-bridge models to reproduce empirical short-range stiffness and its history-dependent changes across a broad range of behaviorally relevant length changes and activation levels, using an existing dataset on 11 permeabilized rat soleus muscle fibers. We quantified muscle thixotropy using the ratio between the observed short-range stiffnesses after and before shortening. We computed the root-mean-square deviation (σSRS) between the predicted short-range stiffness ratio of various muscle models and the measured stiffness ratio. We found that cross-bridge models captured short-range stiffness changes across conditions with both small and large history-dependent stiffness reductions (σSRS ≤ 0.1), but only when including cooperative activation of both thin and thick myofilaments. In contrast, Hill-type models and a cross-bridge model without cooperative myofilament activation underestimated short-range stiffness and did not capture its change across conditions with large history-dependent stiffness reductions (σSRS > 0.2). Similar results were obtained when using a Gaussian-approximated solution method to simulate the cross-bridge distribution, but at an approximately eightfold lower computational cost. We therefore propose to implement Gaussian-approximated cross-bridge models with cooperative myofilament activation into musculoskeletal simulations to improve the prediction of short-range stiffness during movements.

Indexed as

Models, BiologicalMuscle ContractionMuscle, SkeletalAnimalsBiomechanical PhenomenaComputational BiologyComputer SimulationMovementRats

Identifiers

PMID42678999
PMCPMC13561430

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.