Efficiency of Initiating Cell Adhesion in Hydrodynamic Flow

Korn, Christian; Schwarz, Ulrich S.

doi:10.1103/physrevlett.97.138103

Cited by 40 publications

(44 citation statements)

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“…At a given set of parameter values we let the cell start at a height z = R + r 0 and subsequently simulate its motion for 20 s. As a result of the downward acting buoyant force, which drives the cell even closer to the wall, the wall ligands will be immediately within the capture range of the cell-receptors. Then, as shown in [21], the mean time for receptor-ligand encounter is close to zero for typical All five states can be identified in Fig. 8 and in Fig.…”

Section: State Diagram Of Leukocyte Motionmentioning

confidence: 96%

“…Thus, the more encounter occur per time the more bonds will be formed at a given rate π. As the encounter rate increases with increasing number of receptor patches [21], this also increases the average number of bonds. Then, single tethers slow the cell down (depending on the off-rate they may either arrest the cell some while, resulting in state 'transient II', or just decelerate them resulting in state 'transient I'), but after dissociation it is unlikely that the current state of motion is supported by further bonds.…”

Section: Receptor Numbermentioning

confidence: 99%

“…Using this approach, we were able to predict the efficiency of initiating cell adhesion in shear flow as a function of the density and geometry of the receptor and ligand patches [21,22]. A large modeling effort has also been spent on the role of cell deformability [23,24,25,26,27] and the interaction between multiple particles [27,28,29].…”

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Dynamic states of cells adhering in shear flow: From slipping to rolling

2008

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Motivated by rolling adhesion of white blood cells in the vasculature, we study how cells move in linear shear flow above a wall to which they can adhere via specific receptor-ligand bonds. Our We also investigate the effect of non-molecular parameters. In particular, we find that an increase in viscosity of the medium leads to a characteristic expansion of the region of stable rolling to the expense of the region of firm adhesion, but not to the expense of the regions of free or transient motion. Our results can be used in an inverse approach to determine single bond parameters from flow chamber data on rolling adhesion.

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Section: State Diagram Of Leukocyte Motionmentioning

confidence: 96%

Section: Receptor Numbermentioning

confidence: 99%

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Dynamic states of cells adhering in shear flow: From slipping to rolling

2008

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“…Another example of a complex transport process of large biological relevance is the receptor-mediated adhesion of cells which are carried over a ligand-coated substrate by hydrodynamic flow [6]. Here the mean encounter time between receptors and ligands is a measure for the efficiency of cell adhesion under the conditions of hydrodynamic flow [7]. For this system, additional complications arise from the presence of multiple length scales.…”

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confidence: 99%

“…The efficiency with which cells or beads in flow can bind to a substrate depends crucially on the spatial distribution of receptors and ligands [12]. Previously, we proposed a model based on a Langevin equation for a spherical particle in linear shear flow above a wall which allows to numerically compute MFPTs for different ligand and receptor distributions and flow parameters both for two-dimensional (2D) and three-dimensional (3D) movements [7,13]. Due to the complex geometry arising from the receptor and p-1 ligand distributions and the complexity of the positiondependent mobility functions arising from the hydrodynamic equations, exact analytic results for the MFPT cannot be obtained in this general case.…”

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Mean encounter times for cell adhesion in hydrodynamic flow: Analytical progress by dimensional reduction

Korn

Schwarz

2008

Europhys. Lett.

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in biological systems PACS 47.15.G--fluid dynamics, low-Reynolds-number flows PACS 05.10.Gg -stochastic analysis methods (FokkerPlanck, Langevin, etc.) Abstract. -For a cell moving in hydrodynamic flow above a wall, translational and rotational degrees of freedom are coupled by the Stokes equation. In addition, there is a close coupling of convection and diffusion due to the position-dependent mobility. These couplings render calculation of the mean encounter time between cell surface receptors and ligands on the substrate very difficult. Here we show for a two-dimensional model system how analytical progress can be achieved by treating motion in the vertical direction by an effective reaction term in the mean first passage time equation for the rotational degree of freedom. The strength of this reaction term can either be estimated from equilibrium considerations or used as a fit parameter. Our analytical results are confirmed by computer simulations and allow to assess the relative roles of convection and diffusion for different scaling regimes of interest.Introduction. -Biological function is often based on the formation of a specific binding complex between receptor and ligand [1]. However, in order for binding to occur, a physical transport process must exist which brings the binding partners to sufficiently close proximity [2]. In many cases of interest, this transport process is rather complex. Usually it contains several coupled degrees of freedom, like a cell surface receptor moving laterally on a membrane which fluctuates in the vertical direction [3]. The efficiency of biological transport processes often can be framed as mean first passage time (MFPT) problems, for example for the gating of ion channels [4] or the arrival of a virus at the nucleus [5]. Another example of a complex transport process of large biological relevance is the receptor-mediated adhesion of cells which are carried over a ligand-coated substrate by hydrodynamic flow [6]. Here the mean encounter time between receptors and ligands is a measure for the efficiency of cell adhesion under the conditions of hydrodynamic flow [7]. For this system, additional complications arise from the presence of multiple length scales. For the micron-sized cell, the hydrodynamic equations result in coupling of the translational and rotational degrees of freedom. Even for high shear rates, Brownian motion is relevant because receptors and ligands are nanometer-sized objects, thus even small movements for the cell result in a large effect on the molecular level.

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Modeling cytoadhesion of Plasmodium falciparum‐infected erythrocytes and leukocytes—common principles and distinctive features

et al. 2016

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Cytoadhesion of Plasmodium falciparum‐infected erythrocytes to the microvascular endothelial lining shares striking similarities to cytoadhesion of leukocytes. In both cases, adhesins are presented in structures that raise them above the cell surface. Another similarity is the enhancement of adhesion under physical force (catch bonding). Here, we review recent advances in our understanding of the molecular and biophysical mechanisms underlying cytoadherence in both cellular systems. We describe how imaging, flow chamber experiments, single‐molecule measurements, and computational modeling have been used to decipher the relevant processes. We conclude that although the parasite seems to induce processes that resemble the cytoadherence of leukocytes, the mechanics of erythrocytes is such that the resulting behavior in shear flow is fundamentally different.

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Efficiency of Initiating Cell Adhesion in Hydrodynamic Flow

Cited by 40 publications

References 18 publications

Dynamic states of cells adhering in shear flow: From slipping to rolling

Dynamic states of cells adhering in shear flow: From slipping to rolling

Mean encounter times for cell adhesion in hydrodynamic flow: Analytical progress by dimensional reduction

Modeling cytoadhesion of Plasmodium falciparum‐infected erythrocytes and leukocytes—common principles and distinctive features

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