arxiv: 2604.18851 · v1 · submitted 2026-04-20 · 🧬 q-bio.CB · physics.bio-ph

Recognition: unknown

Intrinsic stochasticity in cell polarity and contact inhibition of locomotion

Mariia Kryvoruchko , Brian A. Camley

Authors on Pith no claims yet

Pith reviewed 2026-05-10 03:25 UTC · model grok-4.3

classification 🧬 q-bio.CB physics.bio-ph

keywords cell polaritycontact inhibition of locomotionstochastic modelRho GTPasemolecular noisecell migrationrotational diffusion

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The pith

At low Rho GTPase copy numbers molecular noise masks weak cell contacts during contact inhibition of locomotion, while high numbers make variability arise mainly from differences in contact geometry and duration.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper builds a minimal stochastic model that tracks individual Rho GTPase molecules diffusing and switching between membrane and cytosol states to set cell polarity. Without contact the polarity direction performs rotational diffusion whose rate falls as molecule number increases. Modeling contact as local inhibition of GTPase activation shows that low copy numbers let fluctuations override brief, narrow, or weak contacts so that repolarization often fails. At high copy numbers the intrinsic noise shrinks, leaving randomness in CIL outcomes to reflect collision-to-collision differences in actual contact properties. This accounts for the high experimental variability seen when cells collide.

Core claim

The authors establish that intrinsic stochasticity from finite Rho GTPase copy numbers drives rotational diffusion of the polarity axis in isolation. When cell-cell contact inhibits GTPase activation over a defined region, successful contact inhibition of locomotion occurs only if the contact overcomes the polarity noise. Low copy numbers render marginal contacts ineffective; high copy numbers stabilize polarity so that observed CIL randomness must originate in stochastic variation of contact geometry, strength, or duration across collisions.

What carries the argument

Stochastic diffusion and state-switching of discrete Rho GTPase molecules that set local membrane concentration and thereby orient the cell's polarity axis.

If this is right

Weak, brief, or narrow contacts fail to trigger reliable repolarization when Rho GTPase numbers are low.
High Rho GTPase numbers suppress intrinsic polarity noise, shifting the dominant source of CIL randomness to differences in contact properties between collisions.
The rotational diffusion constant of polarity decreases as copy number rises.
CIL decision reliability becomes more sensitive to contact geometry and duration once copy numbers are large.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Titrating Rho GTPase expression while holding contact geometry fixed could separate molecular-noise effects from contact-variability effects.
The same noise-masking logic may apply to other contact-dependent polarity decisions such as collective migration or wound closure.
Cells could adjust GTPase abundance to control how sensitively they respond to neighbor contacts in tissues.

Load-bearing premise

Cell-cell contact inhibits Rho GTPase activation in the region of contact.

What would settle it

Measure CIL success rates for controlled weak contacts while varying Rho GTPase copy number; if low numbers produce more failed repolarizations even when contact parameters are fixed, the noise-masking claim is supported.

Figures

Figures reproduced from arXiv: 2604.18851 by Brian A. Camley, Mariia Kryvoruchko.

**Figure 2.** Figure 2: FIG. 2. Kymographs of the normalized membrane concen [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

**Figure 3.** Figure 3: FIG. 3. Kymographs of the normalized membrane concentra [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

**Figure 4.** Figure 4: FIG. 4. Log-log plot of the diffusion coefficient [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗

**Figure 5.** Figure 5: FIG. 5. Illustration of a CIL event initiated at [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗

**Figure 6.** Figure 6: FIG. 6. Probability distributions [PITH_FULL_IMAGE:figures/full_fig_p006_6.png] view at source ↗

**Figure 7.** Figure 7: FIG. 7. Probability distributions [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗

**Figure 8.** Figure 8: FIG. 8. Angular shifts [PITH_FULL_IMAGE:figures/full_fig_p007_8.png] view at source ↗

**Figure 10.** Figure 10: FIG. 10 [PITH_FULL_IMAGE:figures/full_fig_p008_10.png] view at source ↗

**Figure 9.** Figure 9: FIG. 9. Signal-to-noise ratio (SNR) of the CIL response as a [PITH_FULL_IMAGE:figures/full_fig_p008_9.png] view at source ↗

**Figure 11.** Figure 11: FIG. 11. Comparison of acceleration vectors from simulations with experimental neural crest cell CIL [ [PITH_FULL_IMAGE:figures/full_fig_p010_11.png] view at source ↗

**Figure 12.** Figure 12: FIG. 12. Histogram of [PITH_FULL_IMAGE:figures/full_fig_p010_12.png] view at source ↗

read the original abstract

When cells collide, they often exhibit "contact inhibition of locomotion" (CIL), a behavior in which cells repolarize and migrate away from the site of contact. Experimental CIL outcomes are highly variable - why? Here, we develop a minimal stochastic model to quantify how intrinsic noise in cell polarity, arising from the finite number of signaling molecules, influences CIL decision-making. We simulate polarization dynamics by tracking individual Rho GTPase proteins that diffuse and switch stochastically between the cell membrane and cytosol. In the absence of cell-cell contact, the polarity axis diffuses rotationally - the cell's orientation wanders - with a diffusion coefficient that decreases as Rho GTPase copy number increases. Assuming that cell-cell contact inhibits Rho GTPase activation, we investigate how contact geometry, duration, and strength affect CIL sensitivity. At low protein copy number, weak, brief, or spatially narrow contacts are masked by molecular noise. In contrast, at high protein copy number, intrinsic polarity noise is negligible, and randomness in CIL response is more likely to reflect the variability from collision to collision in the cell-cell contact properties.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

This paper shows that low Rho GTPase copy numbers let molecular noise mask weak contacts during CIL, while high numbers make contact variability the main source of randomness.

read the letter

The main takeaway is that intrinsic noise from small numbers of signaling molecules can override weak or brief cell contacts in contact inhibition of locomotion, but once copy numbers rise the noise drops and randomness comes mostly from differences in how the cells actually touch each other each time they collide. That distinction follows directly from tracking finite molecule counts in a stochastic simulation. The model treats Rho GTPases as individual particles that diffuse and switch between membrane and cytosol, which produces rotational diffusion of the polarity axis whose strength falls with higher copy number. They then add contacts that inhibit activation and test how contact geometry, duration, and strength interact with that noise. At low numbers weak contacts get lost in the fluctuations; at high numbers the polarity is stable enough that only the contact details set the outcome. This is a clean way to connect molecule count to decision reliability without mean-field approximations. The simulations are run directly from the rules rather than tuned to match specific data, which keeps the logic transparent and avoids circularity. The low-to-high copy number crossover is a useful framing for why CIL looks so variable across experiments. The main soft spots are the contact rule itself and the lack of parameter robustness checks. Everything rests on the assumption that contact inhibits Rho activation, and the reported trends could shift if the inhibition is implemented differently or if diffusion and switching rates change. The abstract gives no equations or exact implementation details, so it is hard to judge how sensitive the masking effect is to those choices. The work is also purely simulation-based, so direct comparison to measured polarity diffusion or CIL statistics would make the claims stronger. This is for people who build models of cell polarity and collective migration, especially anyone thinking about how stochasticity scales with system size in development or invasion. A reader working on wound healing or cancer motility could use the copy-number regimes to interpret noisy experimental outcomes. It deserves peer review because the core mapping from molecule number to noise strength is clear and the simulations support the stated distinction, even if more validation and sensitivity tests would help.

Referee Report

0 major / 3 minor

Summary. The manuscript develops a minimal stochastic model of Rho GTPase dynamics to investigate the role of intrinsic molecular noise in contact inhibition of locomotion (CIL). By simulating diffusion and stochastic switching of individual proteins between membrane and cytosol, the authors show that the polarity axis undergoes rotational diffusion whose coefficient decreases with increasing copy number. Under the assumption that cell-cell contact inhibits Rho GTPase activation, they demonstrate that at low copy numbers, weak or brief contacts are masked by noise, whereas at high copy numbers, CIL variability arises primarily from collision-to-collision differences in contact properties.

Significance. If the results hold, this provides a mechanistic explanation for CIL variability grounded in finite-number fluctuation scaling, a standard feature of stochastic chemical kinetics. The minimal model and direct stochastic simulation are strengths, allowing the distinction between intrinsic polarity noise and contact variability to be attributed cleanly to copy-number effects without extraneous parameters. This offers falsifiable predictions for experiments that vary Rho GTPase expression levels while controlling contact geometry.

minor comments (3)

[Abstract] The abstract states that 'we simulate polarization dynamics by tracking individual Rho GTPase proteins that diffuse and switch stochastically' but does not name the numerical integrator or time-stepping scheme; adding this (e.g., in the Model section) would strengthen reproducibility claims.
[Model] The implementation of spatially narrow contacts and the precise functional form of inhibition (e.g., local reduction in activation rate) should be stated explicitly, perhaps with a supplementary equation or schematic, to confirm that the reported masking effect at low copy number is not an artifact of discretization.
[Results] Parameter values for diffusion coefficients, switching rates, and contact duration are listed as free but not tabulated; a short table of baseline values used for the figures would allow readers to assess sensitivity.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive assessment of the manuscript and for recommending minor revision. The referee's summary accurately describes our minimal stochastic model of Rho GTPase dynamics and its implications for CIL variability at different copy numbers. As no specific major comments were raised in the report, we have no points requiring rebuttal or revision at this stage.

Circularity Check

0 steps flagged

No significant circularity; results follow from direct stochastic simulation

full rationale

The paper builds a minimal stochastic model tracking individual Rho GTPase proteins that diffuse and switch between membrane and cytosol, then runs direct simulations of polarization dynamics and CIL responses under the explicit input assumption that cell-cell contact inhibits Rho GTPase activation. The central claims about noise masking at low copy number versus contact variability at high copy number emerge from standard finite-number fluctuation scaling in the described dynamics, without any fitting of parameters to target CIL statistics, self-definitional equations, or load-bearing self-citations. The derivation chain is self-contained against external benchmarks and does not reduce any prediction to its inputs by construction.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 0 invented entities

The model rests on a small set of modeling choices whose justification is not visible from the abstract alone.

free parameters (2)

Rho GTPase copy number
Varied parametrically to demonstrate the transition from noise-dominated to geometry-dominated regimes.
contact inhibition strength and duration
Treated as controllable inputs to explore sensitivity.

axioms (2)

domain assumption Cell-cell contact inhibits Rho GTPase activation
Invoked to translate geometric contact into a change in polarity dynamics.
standard math Rho GTPases diffuse and switch stochastically between membrane and cytosol
Standard stochastic particle model for membrane-cytosol exchange.

pith-pipeline@v0.9.0 · 5494 in / 1320 out tokens · 64140 ms · 2026-05-10T03:25:02.858594+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

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Polarity peak stability as a function of copy number N The emergence and long-term persistence of a stable polarity to the cell depend strongly on the total copy number N (Fig. 3). At low copy number, N ∼ 2 × 103 molecules, no stable polarization peak persists once the initial cue c(i)(x) is removed; instead, multiple transient peaks appear along the memb...
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Diffusion of the polarity peak at finite copy number We expect that, in the absence of CIL, the persistence of motion in migrating cells is governed by the protein concentration dynamics shown in the kymographs in Fig
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These fluctuations would then be captured by the diffusion coefficient of the polarization vector, DP [17]

For sufficiently large N, the system forms a clearly po- larized state — a single peak of high protein concentration whose center of mass performs an unbiased random walk along the cell perimeter. These fluctuations would then be captured by the diffusion coefficient of the polarization vector, DP [17]. To compute DP, we evaluate the mean-squared angu- la...
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We therefore quantify the CIL response by comparing the distributions P (∆θ) with and without CIL across different contact parameters (Fig

Distribution of cell reorientation angles in the stochastic model Given a cell–cell contact, how much does a cell reorient? Unlike the deterministic model, which predicts a single rotation angle, stochastic simulations at finite N yield a distribution of reorientation angles P (∆θ). We therefore quantify the CIL response by comparing the distributions P (...
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Mean angular response across contact parameters How much, on average, does a cell–cell contact reorient a cell? How does this depend on the parameters control- ling cell–cell contact, and is the average response simply given by the original deterministic wave-pinning model? We compute the ensemble-averaged reorientation angle 7 FIG. 7. Probability distrib...
[6]

(20) Here ⟨∆θ⟩CIL and ⟨∆θ⟩free are the ensemble-averaged reorientation angles with and without CIL, respectively (Eq

Cell sensitivity to CIL stimuli How reliably can a cell distinguish a contact event from spontaneous reorientation? We quantify this contact sen- sitivity using a signal-to-noise ratio (SNR) as a heuristic separability metric, SNR = ⟨∆θ⟩free − ⟨∆θ⟩CIL 2 δ 2 free + δ 2 CIL . (20) Here ⟨∆θ⟩CIL and ⟨∆θ⟩free are the ensemble-averaged reorientation angles with...
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We test this by computing ⟨∆θ⟩ for N = 10 3–105 at fixed contact parameters

Convergence of the stochastic model to the deterministic limit ( N → ∞) We expect that the average CIL response of our stochas- tic model should approach the deterministic prediction in the limit of a large number of proteins. We test this by computing ⟨∆θ⟩ for N = 10 3–105 at fixed contact parameters. As shown in Fig. 10, the mean angular re- orentation ...
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