pith. machine review for the scientific record. sign in

arxiv: 2604.15283 · v1 · submitted 2026-04-16 · 🧬 q-bio.CB · nlin.AO

Recognition: unknown

Cell-cell adhesion cannot sustain extended follower streams in a minimal non-local model of leader-follower migration

Philip K. Maini, Ruth E. Baker, Samuel W.S. Johnson, Thomas Jun Jewell

Pith reviewed 2026-05-10 09:21 UTC · model grok-4.3

classification 🧬 q-bio.CB nlin.AO
keywords cell-cell adhesionleader-follower migrationnon-local modelscollective cell migrationadvection-diffusionstream formationneural crest migration
0
0 comments X

The pith

A minimal non-local adhesion model sustains only short follower cohorts whose length is bounded by the interaction range.

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

The paper tests whether cell-cell adhesion alone can hold together the long chains of follower cells that trail leaders during collective migration in processes such as neural crest movement or cancer invasion. It constructs a simple continuum model in which leaders move at fixed speed and followers are pulled toward leaders and other followers by non-local attraction over a finite distance. Numerical simulations show that small travelling groups of followers can form and persist, yet their size stays tightly limited by the attraction range and never reaches the extended streams seen in living tissues. This result indicates that the standard mathematical treatment of adhesion is structurally unable to produce long coherent migration without extra mechanisms.

Core claim

Numerical simulations of the minimal non-local advection-diffusion model reveal that follower cells can form small travelling cohorts behind constant-velocity leaders, but the maximum cohort size is strictly limited by the finite adhesive interaction lengthscale and remains far below the extended streams observed in vivo.

What carries the argument

The minimal non-local advection-diffusion equation for followers, driven by constant-velocity leaders and finite-range attraction kernels between cells.

If this is right

  • Small cohorts of followers can travel coherently with leaders through adhesion alone.
  • Cohort size cannot exceed the spatial scale of the adhesive interaction.
  • Extended, long-distance follower streams cannot arise from mass-conserving non-local adhesion in this framework.
  • New continuum models with additional mechanisms are required to reproduce the long migratory streams seen biologically.

Where Pith is reading between the lines

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

  • The limitation may be general to any finite-range non-local attraction term and could persist even with more complex kernels.
  • Mechanisms such as cell proliferation, contact inhibition, or long-range chemical signalling may be necessary to overcome the lengthscale bound.
  • The result suggests that purely adhesive explanations for stream cohesion need re-examination in other collective migration contexts.

Load-bearing premise

The chosen minimal non-local advection-diffusion framework with constant leader velocity and finite-range attraction represents the essential features of adhesive cell interactions.

What would settle it

A numerical simulation or experiment in which the interaction range is systematically increased while all other parameters remain fixed, and the resulting follower stream length is measured to see whether it grows without bound or stays capped.

Figures

Figures reproduced from arXiv: 2604.15283 by Philip K. Maini, Ruth E. Baker, Samuel W.S. Johnson, Thomas Jun Jewell.

Figure 1
Figure 1. Figure 1: Mathematical model schematic. The model represents leader and follower populations as continu [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: (a) Model simulations for (ξ, µf f , µf l) = (50 µm, 40 µm min−1 , 40 µm min−1 ) (upper) and (50 µm, 40 µm min−1 , 400 µm min−1 ) (lower), shown at 0 h, 6 h, and 12 h. In the upper panel, fol￾lower–follower and leader–follower attraction strengths are equal. Under these conditions, follower cells (red) remain tightly-bound, and the attraction exerted by leader cells (green) is insufficient to detach fol￾lo… view at source ↗
Figure 3
Figure 3. Figure 3: (a) Heatmaps of Mf /M0 for ξ = 10 µm, 50 µm, 100 µm for a range of interaction strengths. Heatmaps show the existence of two regimes in (µf f , µf l) space; a regime in which follower–follower attraction is sufficiently strong to maintain cluster integrity but followers do not migrate, and a regime in which strong leader–follower attraction advects only those followers within the leaders’ initial interacti… view at source ↗
read the original abstract

Cell-cell adhesion is widely hypothesised to maintain cohesion within the long streams of follower cells that trail leader subpopulations during collective migration, including in neural crest cell migration, angiogenesis, and cancer cell invasion. Mathematically, non-local advection-diffusion equations provide the canonical continuum framework within which to study such adhesive cell-cell interactions. Here, we study a minimal model of leader-follower migration within this framework, in which leaders migrate at constant velocity while followers are attracted to leaders and to one another over a finite spatial interaction range. Numerical simulations reveal that, although the model can maintain small cohorts of travelling follower cells, the size of these cohorts is limited by the adhesive interaction lengthscale, and is far below what is needed to reproduce the extended streams observed in vivo. This points to a structural limitation of the standard non-local adhesion formulation and highlights the need for the development of extended continuum models capable of sustaining long, coherent migratory streams through purely mass-conserving collective cell movement.

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.

Referee Report

0 major / 3 minor

Summary. The manuscript studies a minimal non-local advection-diffusion model of leader-follower cell migration in which leaders advance at constant velocity while followers experience finite-range attraction to both leaders and other followers. Numerical simulations show that stable travelling follower cohorts form but remain bounded in size by the adhesive interaction lengthscale, with maximum sizes far smaller than the extended streams observed in vivo (e.g., neural crest, angiogenesis). The authors interpret this as evidence of a structural limitation in the standard continuum formulation of cell-cell adhesion and call for extended models that can sustain long coherent streams under mass conservation.

Significance. If the reported scaling holds, the result is significant because it supplies a clear negative finding for the canonical minimal non-local adhesion model that is widely used to explain collective migration. By restricting to constant leader speed and finite-range interactions, the work isolates the limitation without additional mechanisms, thereby directing future modelling efforts toward non-local kernels with longer effective range, dynamic adhesion, or additional transport terms. The explicit framing as a limitation of the minimal setup rather than a universal claim increases the utility of the negative result for the field.

minor comments (3)
  1. [Numerical methods] The numerical methods section should include a brief convergence study (grid size, time step, and interaction kernel discretisation) to confirm that the reported cohort-size bound is not an artifact of the discretisation scheme.
  2. [Results] A table listing all parameter values (including the range of adhesive interaction lengths tested and the corresponding steady-state cohort sizes) would improve reproducibility and allow readers to judge how far below in-vivo lengths the model cohorts remain.
  3. [Discussion] The discussion could briefly contrast the finite-range kernel used here with alternative non-local formulations (e.g., those with decaying but infinite-range kernels) to clarify why the size limitation is specific to the standard finite-range choice.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive assessment of the manuscript and their recommendation to accept. The referee's summary accurately reflects the central result: that the minimal non-local adhesion model produces only bounded follower cohorts whose size is limited by the interaction lengthscale, far below the extended streams seen in vivo.

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper defines a minimal non-local advection-diffusion model with constant leader velocity and finite-range attraction, then reports direct numerical simulation outcomes showing that follower cohort sizes are bounded by the interaction lengthscale. This negative result is obtained by integrating the stated PDE system and observing the emergent scaling; it does not reduce to a fitted parameter renamed as a prediction, a self-definitional loop, or any load-bearing self-citation. The authors explicitly frame the work as exposing a limitation of the standard formulation rather than claiming a first-principles derivation that collapses to its inputs.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 0 invented entities

The model assumes a standard non-local adhesion term with finite interaction range and constant leader speed; these are domain-standard assumptions rather than new inventions.

free parameters (2)
  • adhesive interaction lengthscale
    Sets the maximum cohort size; its value is chosen to match biological scales but directly limits the result.
  • leader velocity
    Held constant; affects stream coherence but not derived from the model.
axioms (2)
  • standard math Mass conservation in the advection-diffusion system
    Standard for continuum cell models; invoked implicitly by the non-local framework.
  • domain assumption Finite-range non-local attraction between followers and leaders
    Core modeling choice for adhesive interactions; stated in the abstract.

pith-pipeline@v0.9.0 · 5483 in / 1332 out tokens · 34743 ms · 2026-05-10T09:21:44.190910+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

34 extracted references · 2 canonical work pages

  1. [1]

    Collective action in birds.Current Biology, 32(20):R1140–R1144, 2022

    Damien R Farine. Collective action in birds.Current Biology, 32(20):R1140–R1144, 2022

  2. [2]

    From behavioural analyses to models of collective motion in fish schools.Interface Focus, 2(6):693–707, 2012

    Ugo Lopez, Jacques Gautrais, Iain D Couzin, and Guy Theraulaz. From behavioural analyses to models of collective motion in fish schools.Interface Focus, 2(6):693–707, 2012

  3. [3]

    Collective cell migration.Annual Review of Cell and Developmental Biology, 25:407–429, 2009

    Pernille Rørth. Collective cell migration.Annual Review of Cell and Developmental Biology, 25:407–429, 2009

  4. [4]

    Collective cell migration in development.Journal of Cell Biology, 212(2):143–155, 2016

    Elena Scarpa and Roberto Mayor. Collective cell migration in development.Journal of Cell Biology, 212(2):143–155, 2016. 12

  5. [5]

    Collective cell migration: Implications for wound healing and cancer invasion.Burns & Trauma, 1(1):21–26, 2013

    Li Li, Yong He, Min Zhao, and Jianxin Jiang. Collective cell migration: Implications for wound healing and cancer invasion.Burns & Trauma, 1(1):21–26, 2013

  6. [6]

    Collective cell migration in morphogenesis and cancer

    Peter Friedl, Yael Hegerfeldt, and Miriam Tusch. Collective cell migration in morphogenesis and cancer. International Journal of Developmental Biology, 48(5-6):441–449, 2004

  7. [7]

    The principles of directed cell migration

    Shuvasree SenGupta, Carole A Parent, and James E Bear. The principles of directed cell migration. Nature Reviews Molecular Cell Biology, 22(8):529–547, 2021

  8. [8]

    Cell interactions in collective cell migration.Development, 146(23):dev172056, 2019

    Abhinava K Mishra, Joseph P Campanale, James A Mondo, and Denise J Montell. Cell interactions in collective cell migration.Development, 146(23):dev172056, 2019

  9. [9]

    Cranial neural crest migration: new rules for an old road.Developmental Biology, 344(2):543–554, 2010

    Paul M Kulesa, Caleb M Bailey, Jennifer C Kasemeier-Kulesa, and Rebecca McLennan. Cranial neural crest migration: new rules for an old road.Developmental Biology, 344(2):543–554, 2010

  10. [10]

    Biological waves: single species models

    James D Murray. Biological waves: single species models. InMathematical Biology, pages 437–482. Springer, 2004

  11. [11]

    Matthew J Simpson and Scott W McCue. Fisher–KPP-type models of biological invasion: open source computational tools, key concepts and analysis.Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 480(2294), 2024

  12. [12]

    Looking inside an invasion wave of cells using continuum models: proliferation is the key.Journal of Theoretical Biology, 243(3):343–360, 2006

    Matthew J Simpson, Kerry A Landman, Barry D Hughes, and Donald F Newgreen. Looking inside an invasion wave of cells using continuum models: proliferation is the key.Journal of Theoretical Biology, 243(3):343–360, 2006

  13. [13]

    Cell proliferation drives neural crest cell invasion of the intestine.Developmental Biology, 302(2):553–568, 2007

    Matthew J Simpson, Dong C Zhang, Michael Mariani, Kerry A Landman, and Donald F Newgreen. Cell proliferation drives neural crest cell invasion of the intestine.Developmental Biology, 302(2):553–568, 2007

  14. [14]

    The neural crest cell cycle is related to phases of migration in the head.Development, 141(5):1095–1103, 2014

    Dennis A Ridenour, Rebecca McLennan, Jessica M Teddy, Craig L Semerad, Jeffrey S Haug, and Paul M Kulesa. The neural crest cell cycle is related to phases of migration in the head.Development, 141(5):1095–1103, 2014. 13

  15. [15]

    Self- generated chemotaxis of mixed cell populations.Proceedings of the National Academy of Sciences, 122(34):e2504064122, 2025

    Mehmet Can U¸ car, Zane Alsberga, Jonna Alanko, Michael Sixt, and Edouard Hannezo. Self- generated chemotaxis of mixed cell populations.Proceedings of the National Academy of Sciences, 122(34):e2504064122, 2025

  16. [16]

    A continuum approach to modelling cell–cell adhesion.Journal of Theoretical Biology, 243(1):98–113, 2006

    Nicola J Armstrong, Kevin J Painter, and Jonathan A Sherratt. A continuum approach to modelling cell–cell adhesion.Journal of Theoretical Biology, 243(1):98–113, 2006

  17. [17]

    A population dynamics model of cell-cell adhesion incorporating population pressure and density saturation.Journal of Theoretical Biology, 474:14–24, 2019

    Jose A Carrillo, Hideki Murakawa, Makoto Sato, Hideru Togashi, and Olena Trush. A population dynamics model of cell-cell adhesion incorporating population pressure and density saturation.Journal of Theoretical Biology, 474:14–24, 2019

  18. [18]

    Biological modeling with nonlocal advection– diffusion equations.Mathematical Models and Methods in Applied Sciences, 34(1):57–107, 2024

    Kevin J Painter, Thomas Hillen, and Jonathan R Potts. Biological modeling with nonlocal advection– diffusion equations.Mathematical Models and Methods in Applied Sciences, 34(1):57–107, 2024

  19. [19]

    A phylogeny of biological patterns formed by nonlocal advection.arXiv preprint arXiv:2506.00489, 2025

    Valeria Giunta, Thomas Hillen, Mark A Lewis, and Jonathan R Potts. A phylogeny of biological patterns formed by nonlocal advection.arXiv preprint arXiv:2506.00489, 2025

  20. [20]

    Synergistic action of nectins and cadherins generates the mosaic cellular pattern of the olfactory epithelium.Journal of Cell Biology, 212(5):561–575, 2016

    Sayaka Katsunuma, Hisao Honda, Tomoyasu Shinoda, Yukitaka Ishimoto, Takaki Miyata, Hiroshi Kiy- onari, Takaya Abe, Ken-ichi Nibu, Yoshimi Takai, and Hideru Togashi. Synergistic action of nectins and cadherins generates the mosaic cellular pattern of the olfactory epithelium.Journal of Cell Biology, 212(5):561–575, 2016

  21. [21]

    Directed cell migration is a versatile mechanism for rapid developmental pattern formation.bioRxiv, 2025

    Chengyou Yu, Malte Mederacke, Roman Vetter, and Dagmar Iber. Directed cell migration is a versatile mechanism for rapid developmental pattern formation.bioRxiv, 2025

  22. [22]

    Patterning of nonlocal transport models in biology: the impact of spatial dimension.Mathematical Biosciences, 366:109093, 2023

    Thomas Jun Jewell, Andrew L Krause, Philip K Maini, and Eamonn A Gaffney. Patterning of nonlocal transport models in biology: the impact of spatial dimension.Mathematical Biosciences, 366:109093, 2023

  23. [23]

    A nonlocal model for contact attraction and repulsion in heterogeneous cell populations.Bulletin of Mathematical Biology, 77(6):1132–1165, 2015

    Kevin J Painter, Jennifer M Bloomfield, Jonathan A Sherratt, and Alf Gerisch. A nonlocal model for contact attraction and repulsion in heterogeneous cell populations.Bulletin of Mathematical Biology, 77(6):1132–1165, 2015. 14

  24. [24]

    Mathematical modelling of cancer invasion: implications of cell adhesion variability for tumour infiltrative growth patterns.Journal of Theoretical Biology, 361:41–60, 2014

    Pia Domschke, Dumitru Trucu, Alf Gerisch, and Mark AJ Chaplain. Mathematical modelling of cancer invasion: implications of cell adhesion variability for tumour infiltrative growth patterns.Journal of Theoretical Biology, 361:41–60, 2014

  25. [25]

    Mathematical modelling of cancer cell invasion of tissue: local and non-local models and the effect of adhesion.Journal of Theoretical Biology, 250(4):684–704, 2008

    Alf Gerisch and Mark AJ Chaplain. Mathematical modelling of cancer cell invasion of tissue: local and non-local models and the effect of adhesion.Journal of Theoretical Biology, 250(4):684–704, 2008

  26. [26]

    The impact of adhesion on cellular invasion processes in cancer and development.Journal of Theoretical Biology, 264(3):1057–1067, 2010

    Kevin J Painter, Nicola J Armstrong, and Jonathan A Sherratt. The impact of adhesion on cellular invasion processes in cancer and development.Journal of Theoretical Biology, 264(3):1057–1067, 2010

  27. [27]

    Chiara Villa, Alf Gerisch, and Mark AJ Chaplain. A novel nonlocal partial differential equation model of endothelial progenitor cell cluster formation during the early stages of vasculogenesis.Journal of Theoretical Biology, 534:110963, 2022

  28. [28]

    Modeling the morphodynamic galectin pat- terning network of the developing avian limb skeleton.Journal of Theoretical Biology, 346:86–108, 2014

    Tilmann Glimm, Ramray Bhat, and Stuart A Newman. Modeling the morphodynamic galectin pat- terning network of the developing avian limb skeleton.Journal of Theoretical Biology, 346:86–108, 2014

  29. [29]

    Distinguishing between long-transient and asymptotic states in a biological aggregation model.Bulletin of Mathematical Biology, 86(3):28, 2024

    Jonathan R Potts and Kevin J Painter. Distinguishing between long-transient and asymptotic states in a biological aggregation model.Bulletin of Mathematical Biology, 86(3):28, 2024

  30. [30]

    Chase-and-run and chirality in nonlocal models of pattern formation.arXiv preprint arXiv:2505.17372, 2025

    Thomas Jun Jewell, Andrew L Krause, Philip K Maini, and Eamonn A Gaffney. Chase-and-run and chirality in nonlocal models of pattern formation.arXiv preprint arXiv:2505.17372, 2025

  31. [31]

    Zoology of a nonlocal cross-diffusion model for two species.SIAM Journal on Applied Mathematics, 78(2):1078–1104, 2018

    Jos´ e A Carrillo, Yanghong Huang, and Markus Schmidtchen. Zoology of a nonlocal cross-diffusion model for two species.SIAM Journal on Applied Mathematics, 78(2):1078–1104, 2018

  32. [32]

    Variations in non-local interac- tion range lead to emergent chase-and-run in heterogeneous populations.Journal of the Royal Society Interface, 21(219):20240409, 2024

    Kevin J Painter, Valeria Giunta, Jonathan R Potts, and Sara Bernardi. Variations in non-local interac- tion range lead to emergent chase-and-run in heterogeneous populations.Journal of the Royal Society Interface, 21(219):20240409, 2024

  33. [33]

    Aggregation–diffusion in heterogeneous environments.Journal of Mathematical Biology, 90(6):59, 2025

    Jonathan R Potts. Aggregation–diffusion in heterogeneous environments.Journal of Mathematical Biology, 90(6):59, 2025. 15

  34. [34]

    The impact of different degrees of leadership on collective navigation in follower-leader systems.Bulletin of Mathematical Biology, 87(5):1–31, 2025

    Sara Bernardi and Kevin J Painter. The impact of different degrees of leadership on collective navigation in follower-leader systems.Bulletin of Mathematical Biology, 87(5):1–31, 2025. 16 Appendix A: Model Formulation and Numerical Scheme A.1 Numerical scheme We discretise the domain intoN= 10 4 mesh points,{x i}N i=1 with uniform spacing,h=L/(N−1). The s...