Modeling collective behavior of cells in the presence of elastic forces
Bibliographic record
Abstract
Collective behavior has drawn much attention in recent years. Many models have been created to study the collective motion of cells. Nevertheless, the contribution of substrate deformation and its influence on the emergent properties of cells is less well known. One of the most successful models is the Vicsek model which uses the dynamical equations to describe the position and orientation of the cells. In this work, we have used the elastic free energy of cell pairs to and the repolarization ofthe cells due to soft and stiff substrates deformations. Various type of structures form as we change the substrate stiffness and cell-cell adhesion strength. Based on our simulations, cells exhibit highly correlated motions on stiff substrates, where the elastic forces are less dominant, and low correlated motions on the soft substrates. These results have been observed in the experiments as well. Furthermore, cells are known to be responsive to the gradients of stiffness in their environment. Such a phenomenon is called Durotaxis, which is a necessary element of the wound healing process. We show that our model can give rise to the directed migration of the cells towards rigid regions. Our results are in agreement with recent experimental work.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".