Stochastic dynamics of substrate non-uniform stiffness affecting molecular adhesion in cell–substrate interface subjected to tensile loading
Bibliographic record
Abstract
The mechanically heterogeneous extracellular matrix (ECM) or tissues widely exist in biological systems and are capable of significantly regulating directional cell migration. However, prior to whole cell movement, how the cell senses these cues from mechanical heterogeneities of the ECM or substrate remains unclear at the molecular bond level. To address this issue, we theoretically investigate interface adhesion between a non-uniform stiffness substrate and a rigid plate via a series of receptor–ligand bonds subjected to a tensile loading by integrating substrate surface deformation described by continuum mechanics approach into the stochastic events of bond dissociation and association govern by Markov processes. Interestingly, it is found that, during stretching adhesion interface, due to the large collective contact forces near the stiff edge of the adhesion area, the crack first develops at this stiff edge and then grows to another relatively soft adhesion edge until the completed detachment achieved, which is distinct from the cracks growing from both two edges to center of adhesion area in the case of uniformly elastic solid–solid or solid–fluid interface. Moreover, the lifetime of the bond cluster, interface adhesion strength, and the effect of inter-bond distance are examined, respectively. The corresponding mechanism of dependence of the lifetime and adhesion strength on the non-uniform stiffness of the substrate and inter-bond distance is also analyzed. These findings provide a detailed mechanistic understanding of the adhesion interface responding to the mechanical heterogeneities of the substrate at the molecular bond level.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".