Computational modelling of cell motility modes emerging from cell-matrix adhesion dynamics

Lymphocytes exhibit a range of motility patterns, including Brownian random walks, persistent random walks, and Lévy walks. These patterns vary depending on environmental factors such as confinement and target cell distribution. While some motility types enhance interaction efficiency, the underlying mechanisms remain unclear. Prior research has shown that lymphocytes can switch between floating, sliding, stepping, and pivoting motility based on extracellular matrix (ECM) composition. However, the precise relationship between ECM properties and lymphocyte movement is not fully understood. This gap motivated the development of a computational framework to explore how ECM interactions influence motility. The need for a mechanistic model that integrates adhesion dynamics and propulsion remains unmet in current literature. Understanding how adhesion formation and rupture affect movement is critical for explaining lymphocyte behavior. The diversity of motility modes suggests a need to explore how ECM composition dictates movement patterns. This paper aims to address the lack of a unified computational model for lymphocyte motility.

The purpose of this study is to develop a computational model that simulates lymphocyte motility by integrating adhesion dynamics and cell propulsion. The researchers aim to determine how changes in cell-matrix adhesion affect movement patterns across different lymphocyte subsets and substrates. By incorporating adhesion formation, growth, shrinkage, and rupture, the model seeks to replicate observed motility behaviors. The study focuses on how adhesion strength and area influence speed and persistence. The goal is to identify a minimal set of rules that can explain the plasticity of lymphocyte movement. The model is designed to capture both short-term persistence and long-term subdiffusive properties. The researchers also aim to explore how spatial adhesion distribution affects motility outcomes. This approach allows for a systematic investigation of ECM-dependent motility plasticity.

The researchers employed a Cellular Potts model (CPM) to simulate lymphocyte motility. The model incorporates adhesion formation, growth, shrinkage, and rupture as key processes. Adhesion dynamics are linked to cell propulsion through feedback mechanisms. The model is applied to different lymphocyte subsets and substrates to test its generality. Parameters such as adhesion area and strength are varied to observe effects on movement. The model simulates both persistent and Brownian-like movement patterns. Adhesion spatial distribution is treated as a variable influencing motility outcomes. The model is validated against known motility behaviors such as pivoting and stepping.

The model successfully recapitulates multiple lymphocyte motility patterns. As adhesion area and strength increase, cell speed and persistence decrease. The model predicts random walks with short-term persistence and long-term subdiffusive properties. These patterns resemble pivoting motility observed in experiments. For small adhesion areas, the spatial distribution of adhesions becomes a key factor. Adhesions at the front of the cell allow for more persistent movement than those at the back. Despite equal total adhesion area, front-localized adhesions enhance motility persistence. The model reveals that adhesion feedback mechanisms are essential for simulating realistic motility.

The study presents a computational framework that explains how ECM interactions influence lymphocyte motility. The model demonstrates that adhesion dynamics alone can account for diverse motility patterns. The findings suggest that adhesion strength and area are sufficient to explain speed and persistence changes. The model also highlights the importance of adhesion spatial distribution in determining movement efficiency. The results support the hypothesis that adhesion feedback mechanisms are central to motility plasticity. The study does not propose that adhesion is the only factor affecting motility. The model provides a minimal set of rules that can reproduce experimentally observed behaviors. The authors suggest that the framework can be extended to other cell types and ECM conditions.