Scollr summary
What this paper is about
This framework provides a multiscale representation of cell migration, from single-cell dynamics to population-level behaviors, as it starts with a microscopic model that describes the dynamics of single cells in terms of stochastic processes.
Full abstract
Read the full abstract
Abstract. The capability of cells to form surface extensions to nonlocally probe the surrounding environment plays a key role in cell migration. The existing mathematical models for migration of cell populations driven by this nonlocal 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 modeling 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 behaviors, 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 nonlocality. 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.
Direct answer
What can I do from this paper page?
Use this page to scan "Phenotype-Structuring of Nonlocal Kinetic Models of Cell Migration Driven by Environmental Sensing" quickly: start with the summary and abstract, then check the authors, source, topics, and related papers. From here, open Scollr to follow Mathematical Biology Tumor Growth research, save the paper, or map adjacent work.
Research areas
Follow related topics
Citation
BibTeX
@article{Lorenzi2026Phenotype,
title = {Phenotype-Structuring of Nonlocal Kinetic Models of Cell Migration Driven by Environmental Sensing},
author = {Tommaso Lorenzi and Nadia Loy and Chiara Villa},
journal = {Multiscale Modeling and Simulation},
year = {2026},
doi = {10.1137/24m1719566},
url = {https://doi.org/10.1137/24m1719566}
}
FAQ
Using this paper in a discovery workflow
How do I find related work for this paper?
Use the related papers and topic links on this page as starting points. In Scollr, you can also open the paper and build a literature map around its references, citing papers, and related work.
How can I keep up with new Mathematical Biology Tumor Growth research papers?
Follow Mathematical Biology Tumor Growth research in Scollr. New papers from the topic flow into a personalized feed, and you can save useful studies to revisit later.
Can I cite this paper from this page?
This page includes a static BibTeX block for Phenotype-Structuring of Nonlocal Kinetic Models of Cell Migration Driven by Environmental Sensing. Always verify the DOI, source, and publication details against the publisher record before submitting a manuscript.
Follow this research in Scollr
Follow the topics and authors behind this paper, save useful studies, and build a literature map when you are ready to go deeper.
Get the app