Modeling cell shape diversity arising from complex Rho GTPase dynamics
Bibliographic record
Abstract
ABSTRACT It is well known that cells exhibit a variety of morphologically distinct responses to their environments that manifest in their cell shape. Some protrude uniformly to increase substrate contacts, others are broadly contrac-tile, some polarize to facilitate migration, and yet others exhibit mixtures of these responses. Prior imaging studies have identified a discrete collection of shapes that the majority of cells display and have demonstrated links between those shapes and activity levels of the cytoskeletal regulators Rho GTPases. Here we use a novel computational modeling approach to demonstrate that well known Rho GTPase signaling dynamics naturally give rise to this diverse but discrete (rather than continuum) set of morphologies. Specifically, the combination of auto-activation and mutually-antagonistic crosstalk between GTPases along with the conservative membrane (un)binding dynamics readily explain at least 6 of the 7 commonly observed morphologies. We further use this methodology to map the entire parameter space of this model and show that in appropriate regimes, individual parameter sets give rise to a variety of different morphologies. This provides an explanation for how seemingly similar cells of the same fate derived from the same population can exhibit a diverse array of cell shapes in imaging studies. These results thus demonstrate that Rho GTPases form the core of a cytoskeletal regulatory system governing cell shape, further supporting the picture that they act as a central signaling hub determining how cells respond to their environmental context.
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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) | 0.001 | 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) | 0.001 | 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".