Mathematical and Numerical Methods for Understanding Immune Cell Motion During Wound Healing

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.

Publication Details

Journal
Springer Link (Chiba Institute of Technology)
Published
2025-12-24
DOI
https://doi.org/10.1051/mmnp/2025030/pdf
Primary Topic
Mathematical Biology Tumor Growth
Type
article
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article

Mathematical and Numerical Methods for Understanding Immune Cell Motion During Wound Healing

Springer Link (Chiba Institute of Technology)
Mathematical Biology Tumor Growth
article

Mathematical and Numerical Methods for Understanding Immune Cell Motion During Wound Healing

article en

Abstract

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.

Springer Link (Chiba Institute of Technology)
Openalex Percentile: Top 38%
Mathematical Biology Tumor Growth
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