arxiv: 2605.13927 · v1 · submitted 2026-05-13 · 🧬 q-bio.CB · physics.bio-ph

Recognition: no theorem link

Kin-ematic Exclusion in Active Matter: Modelling Mutual Inhibition in textit{Pseudomonas aeruginosa} Sibling Colonies

Dario Buonomo , Francesco Imperi , Fabio Bruni , Marco Polin , Barbara Capone

Authors on Pith no claims yet

Pith reviewed 2026-05-15 02:36 UTC · model grok-4.3

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

keywords bacterial coloniesnutrient depletionmotility inhibitionPseudomonas aeruginosasibling inhibitionactive matterspatial patternsbiophysical modeling

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The pith

Sibling colonies of Pseudomonas aeruginosa separate because local nutrient depletion slows growth and motility in a self-reinforcing way.

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

This paper examines why genetically identical bacterial colonies growing near each other in soft agar form clear gaps instead of merging. Experiments and modeling rule out physical pushing, cell death, and quorum sensing signals as causes. The separation emerges from a feedback where colonies consume nutrients fastest at their edges, slowing expansion there and creating avoidance. The biophysical model, tuned to measured colony densities, reproduces how stronger nutrients weaken the inhibition effect. Understanding this helps explain bacterial spread in tissues, soils, and mixed communities where space and food are limited.

Core claim

The authors establish that mutual inhibition between sibling colonies results from localized nutrient depletion coupled to a dynamic feedback between growth and motility. This mechanism accounts for the observed sharp demarcation lines and the dependence on initial nutrient levels, without requiring direct inhibition or communication.

What carries the argument

The nutrient-motility feedback, in which nutrient scarcity at colony edges reduces growth rates and modifies motility to limit further advance toward depleted zones.

If this is right

Colony separation strength decreases as initial nutrient concentration increases.
The effect persists in isogenic strains without quorum sensing or lethal factors.
Quantitative density profiles match model predictions for boundary sharpness.
The framework applies to spatial dynamics in other motile microbial systems.

Where Pith is reading between the lines

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

Similar nutrient-based avoidance might operate in natural biofilms or infections where resources are patchy.
Controlling nutrient gradients could be used to direct bacterial spatial arrangements in synthetic microbiomes.
Testing the model in gels with varying stiffness would confirm if mechanics play any secondary role.

Load-bearing premise

That ruling out gel compression, lethal inhibition, and quorum sensing leaves nutrient depletion and motility feedback as the sole drivers of the separation.

What would settle it

If colonies still form sharp lines when nutrients are supplied uniformly and in excess, or if disabling motility eliminates the inhibition while keeping growth intact.

Figures

Figures reproduced from arXiv: 2605.13927 by Barbara Capone, Dario Buonomo, Fabio Bruni, Francesco Imperi, Marco Polin.

**Figure 2.** Figure 2: FIG. 2 [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 4.** Figure 4: FIG. 4 [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗

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

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**Figure 8.** Figure 8: FIG. 8 [PITH_FULL_IMAGE:figures/full_fig_p008_8.png] view at source ↗

**Figure 10.** Figure 10: FIG. 10. Calibration curve for the custom-built imaging [PITH_FULL_IMAGE:figures/full_fig_p009_10.png] view at source ↗

**Figure 11.** Figure 11: FIG. 11. We here report the value [PITH_FULL_IMAGE:figures/full_fig_p010_11.png] view at source ↗

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**Figure 14.** Figure 14: FIG. 14 [PITH_FULL_IMAGE:figures/full_fig_p012_14.png] view at source ↗

read the original abstract

The striking variety of macroscopic morphologies displayed by bacterial colonies depends on microscopic environmental and behavioural details in a manner that is currently not well understood. A surprising example is sibling inhibition, whereby isogenic bacterial colonies spreading in soft agar hydrogels tend to avoid each other and form sharp demarcation lines when growing nearby. Here we investigate this effect with the common pathogen \textit{Pseudomonas aeruginosa}, by combining quantitative density measurements with a minimal biophysical model. Our results show that the phenomenon does not depend on gel compression, lethal inhibition or quorum sensing-dependent cell communication. Instead, colony separation is driven by localised nutrient depletion through a dynamic feedback between growth and motility. The model, which is calibrated using experimental data, captures key observations including the dependence of inhibition strength on the initial nutrient concentration. This work establishes nutrient availability and non-lethal motility inhibition as central factors underlying sibling inhibition, providing a generalisable framework for microbial spatial dynamics with implications for understanding bacterial interactions in tissues, soils and engineered microbiomes.

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

Nutrient-depletion model for P. aeruginosa sibling inhibition fits the density data and nutrient dependence but rests on indirect inference rather than measured gradients.

read the letter

The paper's core claim is that sibling colony separation in P. aeruginosa arises from localized nutrient depletion that slows growth and motility in the gap between colonies, creating a self-reinforcing exclusion zone. They support this with quantitative density profiles and a minimal model that reproduces the observed weakening of inhibition at higher initial nutrient levels, while experiments rule out gel compression, lethal killing, and quorum-sensing communication as primary drivers. That combination of new density measurements and a calibrated biophysical model is the main addition; it gives a concrete, non-signaling mechanism for a common spatial pattern. The model stays simple, avoids extra parameters for cell-cell signaling, and matches the nutrient dependence without obvious overfitting. The exclusions are useful because they narrow the possibilities. The soft spot is that the mechanism is inferred from model agreement and the absence of alternatives rather than direct evidence of nutrient gradients at the relevant times and length scales. Without local nutrient measurements or independent predictions that were not used in calibration, other density-dependent motility effects remain possible even if they are less parsimonious. The fit-to-data element raises the usual circularity concern, though testing the nutrient-concentration dependence helps. This is for researchers working on bacterial active matter, colony morphology, or spatial microbial ecology. It deserves peer review because the modeling is transparent, the data are quantitative, and the claim is falsifiable with straightforward experiments, even if the current version would need more direct validation to carry the full mechanistic weight.

Referee Report

1 major / 1 minor

Summary. The manuscript investigates sibling inhibition in Pseudomonas aeruginosa colonies spreading in soft agar, where isogenic colonies form sharp demarcation lines. Combining quantitative density measurements with a minimal biophysical model, the authors conclude that the effect is independent of gel compression, lethal inhibition, and quorum sensing; instead, colony separation arises from localized nutrient depletion that creates a dynamic feedback between growth and motility. The model is calibrated to experimental density data and reproduces the observed dependence of inhibition strength on initial nutrient concentration.

Significance. If the nutrient-motility feedback mechanism is confirmed, the work supplies a generalizable biophysical framework for microbial spatial self-organization in active matter. It identifies nutrient availability and non-lethal motility inhibition as key drivers of colony morphology, with direct implications for bacterial interactions in tissues, soils, and synthetic microbiomes. The quantitative experimental-modeling approach strengthens the case for nutrient-dependent kinematic exclusion over alternative explanations.

major comments (1)

[Model calibration and validation sections] The central claim that localized nutrient depletion drives colony separation via growth-motility feedback rests on model agreement with density profiles and exclusion of alternatives, but lacks direct local nutrient measurements in the inter-colony region at the relevant times and length scales. Without observed depletion gradients (or an independent, parameter-free prediction of the effect), other non-lethal density- or motility-dependent mechanisms consistent with the same data cannot be ruled out, weakening the load-bearing inference.

minor comments (1)

Figure legends and axis labels in the density-profile panels would benefit from explicit indication of error bars and the number of biological replicates to improve quantitative readability.

Simulated Author's Rebuttal

1 responses · 1 unresolved

We thank the referee for their constructive review and for highlighting the need for stronger validation of the nutrient-depletion mechanism. We address the major comment below and clarify the evidential basis of our conclusions while remaining faithful to the data and model presented in the manuscript.

read point-by-point responses

Referee: [Model calibration and validation sections] The central claim that localized nutrient depletion drives colony separation via growth-motility feedback rests on model agreement with density profiles and exclusion of alternatives, but lacks direct local nutrient measurements in the inter-colony region at the relevant times and length scales. Without observed depletion gradients (or an independent, parameter-free prediction of the effect), other non-lethal density- or motility-dependent mechanisms consistent with the same data cannot be ruled out, weakening the load-bearing inference.

Authors: We acknowledge that direct, spatially resolved nutrient measurements in the inter-colony gap at the relevant length and time scales would constitute stronger, more direct evidence. Such measurements are technically demanding in soft-agar setups and were not performed in this study. Our inference instead rests on three interlocking lines of evidence that are all present in the manuscript: (i) quantitative density profiles measured across multiple initial nutrient concentrations, (ii) explicit experimental exclusion of gel compression, lethality, and quorum-sensing communication, and (iii) a minimal biophysical model whose parameters are fixed by the density data and that quantitatively reproduces the observed dependence of demarcation-line strength on nutrient level. Because the model contains no free parameters once calibrated and still captures the nutrient dependence, we regard the agreement as non-trivial support for the growth-motility feedback. We will add a dedicated paragraph in the revised Discussion that explicitly states this limitation and outlines feasible routes for future direct nutrient imaging. revision: partial

standing simulated objections not resolved

Direct local nutrient concentration measurements in the inter-colony region at the relevant times and length scales

Circularity Check

1 steps flagged

Model calibrated to density data reproduces nutrient dependence by construction

specific steps

fitted input called prediction [Abstract]
"The model, which is calibrated using experimental data, captures key observations including the dependence of inhibition strength on the initial nutrient concentration."

Parameters are fitted to quantitative density measurements; the same model is then said to 'capture' the nutrient-concentration dependence. This dependence is part of the calibration data or directly implied by it, so the reported agreement is forced by the fit rather than a genuine out-of-sample prediction of the dynamic feedback.

full rationale

The paper's central claim rests on a minimal biophysical model whose parameters are fitted to experimental density profiles; this same model is then reported to capture the observed dependence of inhibition strength on initial nutrient concentration. Because the reproduction uses the fitted inputs, the agreement is expected rather than an independent test of the nutrient-motility feedback mechanism. No parameter-free predictions or direct local nutrient measurements are shown, so the load-bearing inference remains partially circular. The exclusion of alternative mechanisms (gel compression, quorum sensing) is stated but does not remove the calibration dependence.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review; specific free parameters, axioms and invented entities in the biophysical model are not detailed. The central claim rests on the assumption that nutrient availability directly modulates motility in a reversible, non-lethal manner.

pith-pipeline@v0.9.0 · 5487 in / 1000 out tokens · 22155 ms · 2026-05-15T02:36:27.787520+00:00 · methodology

discussion (0)

Reference graph

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