A mathematical model of GTPase pattern formation during single-cell wound repair
Bibliographic record
Abstract
Rho GTPases are regulatory proteins whose patterns on the surface of a cell affect cell polarization, cell motility and repair of single-cell wounds. The stereotypical patterns formed by two such proteins, Rho and Cdc42, around laser-injured frog oocytes permit experimental analysis of GTPase activation, inactivation, segregation and crosstalk. Here, we review the development and analysis of a spatial model of GTPase dynamics that describe the formation of concentric zones of Rho and Cdc42 activity around wounds, and describe how this model has provided insights into the roles of the GTPase effector molecules protein kinase C (PKCβ and PKCη) and guanosine nucleotide dissociation inhibitor (GDI) in the wound response. We further demonstrate how the use of a 'sharp switch' model approximation in combination with bifurcation analysis can aid mapping the model behaviour in parameter space (approximate results confirmed with numerical simulation methods). Using these methods in combination with experimental manipulation of PKC activity (PKC overexpression (OE) and dominant negative conditions), we have shown that: (i) PKCβ most probably acts by enhancing existing positive feedbacks (from Rho to itself via the guanosine nucleotide exchange factor domain of Abr, and from Cdc42 to itself), (ii) PKCη most probably increases basal rates of inactivation (or possibly decreases basal rates of activation) of Rho and Cdc42, and (iii) the graded distribution of PKCη and its effect on initial Rho activity accounts for inversion of zones in a fraction (20%) of PKCη OE cells. Finally, we speculate that GDIs (which sequester GTPases) may have a critical role in defining the spatial domain, where the wound response may occur. This paper provides a more thorough exposition of the methods of analysis used in the investigation, whereas previous work on this topic was addressed to biologists and abbreviated such discussion.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".