Macroscopic Model of Substrate-Based Cell Motility

Ziebert, Falko; Löber, Jakob; Aranson, Igor S.

doi:10.1007/978-3-319-24448-8_1

Cited by 8 publications

(6 citation statements)

References 176 publications

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“…In Eq. (1), D ρ determines the width of the diffuse interface (and the cell's surface tension 47 , see also ref. 48 for modeling more realistic membrane features).…”

Section: Resultsmentioning

confidence: 99%

Confinement and substrate topography control cell migration in a 3D computational model

2019

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Cell movement in vivo is typically characterized by strong confinement and heterogeneous, three-dimensional environments. Such external constraints on cell motility are known to play important roles in many vital processes e.g. during development, differentiation, and the immune response, as well as in pathologies like cancer metastasis. Here we develop a physics-driven three-dimensional computational modeling framework that describes lamellipodium-based motion of cells in arbitrarily shaped and topographically structured surroundings. We use it to investigate the primary in vitro model scenarios currently studied experimentally: motion in vertical confinement, confinement in microchannels, as well as motion on fibers and on imposed modulations of surface topography. We find that confinement, substrate curvature and topography modulate the cell's speed, shape and actin organization and can induce changes in the direction of motion along axes defined by the constraints. Our model serves as a benchmark to systematically explore lamellipodium-based motility and its interaction with the environment.

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“…In Eq. (1), D ρ determines the width of the diffuse interface (and the cell's surface tension 47 , see also ref. 48 for modeling more realistic membrane features).…”

Section: Resultsmentioning

confidence: 99%

Confinement and substrate topography control cell migration in a 3D computational model

2019

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“…As already mentioned, cell motion requires protrusions, retractions, and friction and thus rely on a balance of forces. Therefore, describing the motion of cells in a quantitative manner requires solving equations that incorporate these forces [21, 22]. In addition, some of these forces are generated through the action of signaling molecules which are part of signaling networks [23].…”

Section: Introductionmentioning

confidence: 99%

Physical models of collective cell motility: from cell to tissue

Camley

Rappel

2017

J. Phys. D: Appl. Phys.

186

147

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In this article, we review physics-based models of collective cell motility. We discuss a range of techniques at different scales, ranging from models that represent cells as simple self-propelled particles to phase field models that can represent a cell’s shape and dynamics in great detail. We also extensively review the ways in which cells within a tissue choose their direction, the statistics of cell motion, and some simple examples of how cell-cell signaling can interact with collective cell motility. This review also covers in more detail selected recent works on collective cell motion of small numbers of cells on micropatterns, in wound healing, and the chemotaxis of clusters of cells.

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“…Finally, the last term models excluded volume interaction with the particle; it can be derived from an interaction potential λ 2 ´ρ2 Φ 2 d 3 r with excluded volume strength λ. For more information on these contributions see [25].…”

Section: Basic Model Frameworkmentioning

confidence: 99%

Physical phase field model for phagocytosis

Winkler,

Abu Hamed,

Nepomnyashchy

et al. 2024

New J. Phys.

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We propose and study a simple, physical model for phagocytosis, i.e. the active, actin-mediated uptake of micron-sized particles by biological cells. The cell is described by the phase field method and the driving mechanisms of uptake are actin ratcheting, modeled by a dynamic vector field, as well as cell-particle adhesion due to receptor-ligand binding. We first test the modeling framework for the symmetricsituation of a spherical cell engulfing a fixed spherical particle. We then exemplify its versatility by studying various asymmetric situations like different particle shapes and orientations, as well as the simultaneous uptake of two particles. In addition, we perform a perturbation theory of a slightly modified model version in the symmetric setting, allowing to derive a reduced model, shedding light on the effective driving forcesand being easier to solve. This work is meant as a first step in describing phagocytosis and we discuss several effects that are amenable to future modeling within the same framework.

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Macroscopic Model of Substrate-Based Cell Motility

Cited by 8 publications

References 176 publications

Confinement and substrate topography control cell migration in a 3D computational model

Confinement and substrate topography control cell migration in a 3D computational model

Physical models of collective cell motility: from cell to tissue

Physical phase field model for phagocytosis

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