Mechanisms of Cell Motion in Confined Geometries

Hawkins, Rhoda J.; Voituriez, Raphaël

doi:10.1051/mmnp/20105104

Cited by 9 publications

(4 citation statements)

References 36 publications

(65 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The more focused problem of finding the detailed mechanism of motility initiation, is most commonly addressed in the framework of theories emphasizing polymerization-driven protrusion (Mogilner and Edelstein-Keshet, 2002;Dawes et al, 2006;Bernheim-Groswasser et al, 2005;Schreiber et al, 2010;Campas et al, 2012;Hodge and Papadopoulos, 2012). With such emphasis on 'pushers', spontaneous polarization was studied by Kozlov and Mogilner (2007); Callan-Jones et al (2008); John et al (2008); ;Hawkins and Voituriez (2010); Doubrovinski and Kruse (2011);Blanch-Mercader and Casademunt (2013). In Banerjee and Marchetti (2011);Ziebert et al (2012) and Ziebert and Aranson (2013), polarization was interpreted as a result of an inhomogeneity of adhesive interactions.…”

Section: Introductionmentioning

confidence: 99%

Mechanics of motility initiation and motility arrest in crawling cells

Recho

Putelat

Truskinovsky

2015

Journal of the Mechanics and Physics of Solids

View full text Add to dashboard Cite

Motility initiation in crawling cells requires transformation of a symmetric state into a polarized state. In contrast, motility arrest is associated with re-symmetrization of the internal configuration of a cell. Experiments on keratocytes suggest that polarization is triggered by the increased contractility of motor proteins but the conditions of re-symmetrization remain unknown. In this paper we show that if adhesion with the extra-cellular substrate is sufficiently low, the progressive intensification of motor-induced contraction may be responsible for both transitions: from static (symmetric) to motile (polarized) at a lower contractility threshold and from motile (polarized) back to static (symmetric) at a higher contractility threshold. Our model of lamellipodial cell motility is based on a 1D projection of the complex intra-cellular dynamics on the direction of locomotion. In the interest of analytical transparency we also neglect active protrusion and view adhesion as passive. Despite the unavoidable oversimplifications associated with these assumptions, the model reproduces quantitatively the motility initiation pattern in fish keratocytes and reveals a crucial role played in cell motility by the nonlocal feedback between the mechanics and the transport of active agents. A prediction of the model that a crawling cell can stop and re-symmetrize when contractility increases sufficiently far beyond the motility initiation threshold still awaits experimental verification.

show abstract

Section: Introductionmentioning

confidence: 99%

Mechanics of motility initiation and motility arrest in crawling cells

Recho

Putelat

Truskinovsky

2015

Journal of the Mechanics and Physics of Solids

View full text Add to dashboard Cite

show abstract

“…First, actin polymerisation and retrograde flow have been described using continuum theories [43][44][45][46][47][48]50], which may be coupled to advectiondiffusion models of polarity cue concentrations [13,[51][52][53][54] (figure 8(D)). Such models have also been extended to account for adhesion-independent cell migration in structured systems where cells actively use friction with the walls or the local topography of the environment to self-propel [44,140,224]. Secondly, the molecular clutch model [40] describes the stochastic binding and unbinding of adhesions and their coupling to actin flows (figure 8(E)).…”

Section: Bottom-up Models For Cell Migrationmentioning

confidence: 99%

Learning dynamical models of single and collective cell migration: a review

Brückner,

Broedersz

2024

Rep. Prog. Phys.

View full text Add to dashboard Cite

Single and collective cell migration are fundamental processes critical for physiological phenomena ranging from embryonic development and immune response to wound healing and cancer metastasis. To understand cell migration from a physical perspective, a broad variety of models for the underlying physical mechanisms that govern cell motility have been developed. A key challenge in the development of such models is how to connect them to experimental observations, which often exhibit complex stochastic behaviours. In this review, we discuss recent advances in data-driven theoretical approaches that directly connect with experimental data to infer dynamical models of stochastic cell migration. Leveraging advances in nanofabrication, image analysis, and tracking technology, experimental studies now provide unprecedented large datasets on cellular dynamics. In parallel, theoretical efforts have been directed towards integrating such datasets into physical models from the single cell to the tissue scale with the aim of conceptualizing the emergent behaviour of cells. We first review how this inference problem has been addressed in both freely migrating and confined cells. Next, we discuss why these dynamics typically take the form of underdamped stochastic equations of motion, and how such equations can be inferred from data. We then review applications of data-driven inference and machine learning approaches to heterogeneity in cell behaviour, subcellular degrees of freedom, and to the collective dynamics of multicellular systems. Across these applications, we emphasize how data-driven methods can be integrated with physical active matter models of migrating cells, and help reveal how underlying molecular mechanisms control cell behaviour. Together, these data-driven approaches are a promising avenue for building physical models of cell migration directly from experimental data, and for providing conceptual links between different length-scales of description.

show abstract

“…We express this force as F wall = F rep + F f r , where F rep and F f r are, respectively, repulsion and friction forces [24,25]. Repulsion forces are orthogonal to the solid obstacle, while friction forces are tangential.…”

Section: The Modelmentioning

confidence: 99%

Computational model for amoeboid motion: Coupling membrane and cytosol dynamics

Moure

Gómez

2016

Phys. Rev. E

View full text Add to dashboard Cite

A distinguishing feature of amoeboid motion is that the migrating cell undergoes large deformations, caused by the emergence and retraction of actin-rich protrusions, called pseudopods. Here, we propose a cell motility model that represents pseudopod dynamics, as well as its interaction with membrane signaling molecules. The model accounts for internal and external forces, such as protrusion, contraction, adhesion, surface tension, or those arising from cell-obstacle contacts. By coupling the membrane and cytosol interactions we are able to reproduce a realistic picture of amoeboid motion. The model results are in quantitative agreement with experiments and show how cells may take advantage of the geometry of their microenvironment to migrate more efficiently.

show abstract

Mechanisms of Cell Motion in Confined Geometries

Cited by 9 publications

References 36 publications

Mechanics of motility initiation and motility arrest in crawling cells

Mechanics of motility initiation and motility arrest in crawling cells

Learning dynamical models of single and collective cell migration: a review

Computational model for amoeboid motion: Coupling membrane and cytosol dynamics

Contact Info

Product

Resources

About