Search arXivSearch

arXiv · 2412.16258

Phenotype-structuring of non-local kinetic models of cell migration driven by environmental sensing

Abstract

The capability of cells to form surface extensions to non-locally probe the surrounding environment plays a key role in cell migration. The existing mathematical models for migration of cell populations driven by this non-local form of environmental sensing rely on the simplifying assumption that cells in the population share the same cytoskeletal properties, and thus form surface extensions of the same size. To overcome this simplification, we develop a kinetic modelling framework wherein a population of migrating cells is structured by a continuous phenotypic variable that captures variability in structural properties of the cytoskeleton. This framework provides a multiscale representation of cell migration, from single-cell dynamics to population-level behaviours, as we start with a microscopic model that describes the dynamics of single cells in terms of stochastic processes. Next, we formally derive the mesoscopic counterpart of this model, which consists of a phenotype-structured kinetic equation that features a phenotype-dependent non-locality. Then, considering an appropriately rescaled version of this kinetic equation, we formally derive the corresponding macroscopic model, which takes the form of a partial differential equation for the cell number density. To validate the formal procedures employed to derive the macroscopic model from the microscopic model, through the mesoscopic one, we first compare the results of numerical simulations of the two models. We then compare numerical solutions of the macroscopic model with the results of cell locomotion assays, to test the ability of the model to recapitulate qualitative features of experimental observations.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Tommaso Lorenzi, Nadia Loy, Chiara Villa. 2025-12-23. Phenotype-structuring of non-local kinetic models of cell migration driven by environmental sensing. https://arxiv.org/abs/2412.16258

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Local intercellular coupling is sufficient for long-range calcium signaling

Long-range intercellular calcium (Ca2+) signaling coordinates biological processes ranging from fertilization to contraction and cell death. The classical model attributes this long-range propagation to rapid diffusion of inositol 1,4,5-trisphosphate (IP3) through gap junctions. However, recent evidence that IP3 diffuses far more slowly than previously believed, and that Ca2+ oscillations persist even when gap junctions are disassembled, indicates that an alternative mechanism must sustain long-range communication. Here we develop a computational model showing that local coupling between neighboring cells is sufficient to generate and propagate regenerative Ca2+ oscillations across a cell population without fast molecular diffusion. Each cell is treated as an oscillator whose intrinsic frequency is set by its local IP3 concentration through an IP3-dependent refractory period, and neighboring cells are coupled using a Kuramoto nearest-neighbor framework. In a dual-stiffness regime, cells on a stiff extracellular matrix entrain their soft-matrix neighbors, producing an offset traveling wave of Ca2+ release. This reproduces the finite spatial range of influence (~8 cell lengths) observed experimentally. Our findings propose a diffusion-independent paradigm for calcium signaling in which local intercellular coupling drives long-range communication, offering insight into how localized ECM stiffening in asthma and fibrosis may produce systemic effects.

q-bio.CB

Fragmented uptake drives lipid accumulation in macrophage cannibalistic efferocytosis

Efferocytosis, the clearance of dying cells typically by macrophages, is essential for tissue homeostasis and the resolution of inflammation. Previous experiments by Ford et al. (Proc. R. Soc. B, 2019) showed that cannibalistic efferocytosis redistributes endogenous lipid from dying macrophages into the surviving population, but existing mathematical models do not reproduce the observed population dynamics and lipid distributions. Here, fifteen candidate models are compared, combining three mechanisms of apoptotic material uptake with five forms of the macrophage death rate. Model comparison is guided by the Akaike Information Criterion and qualitative agreement with the observed lipid distributions. Numerical solutions show that whole-cell uptake models predict internal maxima that are absent from the data, whereas nibbling uptake produces distributions that are too concentrated about their means. By contrast, intermediate "fragmented" uptake models provide substantially improved agreement when combined with either linear lipid-dependent or exponential time-dependent death rates. The fitted models predict that smaller fragments from dying cells are ingested at higher frequency than larger ones. This analysis provides new insight into how efferocytosis shapes the distribution of lipid within macrophage populations and highlights the importance of distribution-level data for distinguishing between mechanistic models that reproduce similar population-average dynamics.

q-bio.CB

SpCAST enables scalable and interpretable integration of single-cell RNA sequencing and single-cell-resolved spatial transcriptomics

Single-cell-resolution spatial transcriptomics (scST) preserves tissue architecture but often provides targeted or sparse transcriptomic measurements, whereas scRNA-seq offers broader coverage without spatial context. We present SpCAST, a scalable and interpretable framework that uses scRNA-seq references to transfer cell identity, reconstruct expression and expose gene-level decision evidence in scST. SpCAST jointly learns reference-cell classification, reference--query alignment and query reconstruction in mini-batches, avoiding the need for a global reference-by-query correspondence matrix. Spatially Aware Gene Attribution (SAGA) approximates the learned decision function with a sparse additive Kolmogorov--Arnold network. Across 53 sections comprising 413,404 spatial cells from five technologies, SpCAST achieved the highest aggregate annotation rank among seven methods and scaled to ten million simulated cells. Controlled masking recovered cell-type-associated expression signals and improved spatial marker concordance. SAGA further resolved expression-dependent gene evidence and distinguished evidence retained or attenuated across intra- and cross-species reference settings.

q-bio.CB