Investigating local negative feedback of Rac activity by mathematical models and cell motility simulations
Bibliographic record
Abstract
For polarization and directed migration, cells use a combination of local positive feedback and long-range inhibition. We have previously used mathematical models to show the ability of this core circuit to regulate directed cell movement. However, this wave pinning model lacks important additional feedback circuits, including the recently demonstrated local negative feedback from Town and Weiner. Here we extend our models to investigate the consequences of this additional link on cell physiology. We model responses of neutrophil-like HL-60 cells to spatially-controlled optogenetic stimulation of PI3K, leading (via PIP3) to Rac activity. We sequentially build up and investigate partial differential equation (PDE) models of the key Rac, Rac-Inhibitor, and PIP3-Rac-Inhibitor circuits. We fit model parameters to temporal and spatial (cell trajectory) data. Cell shapes, motility, and responses to stimuli are modeled in 2D cell-based simulations, with PDEs for Rac and the other regulatory components solved along the cell edge. We demonstrate that the ability of modeled cells to respond to temporal as well as spatial features of guidance cues depends on the addition of the local negative feedback circuit. Furthermore, the local Rac inhibitor improves the ability of modeled cells to respond to noisy or dynamic extracellular gradients. Our work demonstrates how local negative feedback enhances dynamic polarity and gradient sensing in migratory cells.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".