A Rho-GTPase based model explains group advantage in collective chemotaxis of neural crest cells
Bibliographic record
Abstract
A cluster of neural crest cells (NCCs) may chemotax up a shallow external gradient to which a single cell is unresponsive. To explain this intriguing 'group advantage', we propose a chemo-mechanical model based on the signaling proteins Rac1 and RhoA. We represent each cell as a polygon with nodes connected by elastic membranes. Via reaction-diffusion on the membrane and exchange with their cytosolic pools, Rac1 and RhoA interact to produce cell polarization and repolarization subject to random noise. Mechanically, we represent cell motility via overdamped nodal motion subject to passive elastic membrane forces and active protrusive or contractile forces where Rac1 or RhoA dominates. The model reproduces the random walk of a single cell in a weak gradient and two modes of cell-cell interaction: contact inhibition of locomotion and co-attraction. The simultaneous action of contact inhibition and co-attraction suppresses random Rac1 bursts on the membrane and serves to preserve existing protrusions. This amounts to an emergent persistence of polarity that markedly enhances the ability of a cluster of NCCs to chemotax in a weak gradient against random noise, thereby giving rise to the group advantage.
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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 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 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".