| Issue |
Math. Model. Nat. Phenom.
Volume 20, 2025
|
|
|---|---|---|
| Article Number | 28 | |
| Number of page(s) | 38 | |
| Section | Mathematical methods | |
| DOI | https://doi.org/10.1051/mmnp/2025030 | |
| Published online | 24 December 2025 | |
Mathematical and Numerical Methods for Understanding Immune Cell Motion During Wound Healing
1
Department of Mathematics, Slovak University of Technology, Radlinského 11, 81005 Bratislava, Slovakia
2
Laboratory of Pathogens and Host Immunity (LPHI), CNRS/Université de Montpellier, Place Eugéne Bataillon, 34095 Montpellier cedex 5, France
* Corresponding author: karol.mikula@stuba.sk
Received:
4
February
2025
Accepted:
15
November
2025
In this paper, we propose a new workflow to analyze macrophage motion during wound healing. These immune cells are attracted to the wound after an injury and they move showing both directional and random motion. Thus, first, we smooth the trajectories and we separate the random from the directional parts of the motion. The smoothing model is based on curve evolution where the curve motion is influenced by the smoothing term and the attracting term. Once we obtain the random sub-trajectories, we analyze them using the mean squared displacement to characterize the type of diffusion. Finally, we compute the velocities on the smoothed trajectories and use them as sparse samples to reconstruct the wound attractant field. To do that, we consider a minimization problem for the vector components and lengths, which leads to solving the Laplace equation with Dirichlet conditions for the sparse samples and zero Neumann boundary conditions on the domain boundary.
Mathematics Subject Classification: 35Q68 / 92C55 / 65N08 / 65D10 / 92C37 / 60J60
Key words: Partial differential equations / evolving curves / trajectory smoothing / flowing finite volume method / anomalous diffusion / mean squared displacement / vector field reconstruction / Laplace operator / sparse samples
© The authors. Published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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