Simulation of Cell Adhesion to Bioreactive Surfaces in Shear:  The Effect of Cell Size

Tees, David F. J.; Chang, Kai-Chien; Rodgers, Stephen D.; Hammer, Daniel A.

doi:10.1021/ie010383p

Cited by 13 publications

(9 citation statements)

References 39 publications

(79 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is seen that a larger diameter cell decreases the peeling time and increases the cell rolling velocity. This result is consistent with the simulation of Tees et al (2002) and experiments of Shinde Patil et al (2001). A larger cell causes a greater blockage of the vessel, which results in larger local hydrodynamic forces.…”

Section: Compound Drop Modelsupporting

confidence: 92%

Computational Modeling of Cell Adhesion and Movement Using a Continuum-Kinetics Approach

N’Dri

Shyy

Tran‐Son‐Tay

2003

Biophysical Journal

136

View full text Add to dashboard Cite

Adhesion of leukocytes to substrate involves the coupling of disparate length and timescales between molecular mechanics and macroscopic transport, and existing models of cell adhesion do not use full cellular information. To address these challenges, a multiscale computational approach for studying the adhesion of a cell on a substrate is developed and assessed. The cellular level model consists of a continuum representation of the field equations and a moving boundary tracking capability to allow the cell to change its shape continuously. At the receptor-ligand level, a bond molecule is mechanically represented by a spring. Communication between the macro/micro- and nanoscale models is facilitated interactively during the computation. The computational model is assessed using an adherent cell, rolling and deforming along the vessel wall under imposed shear flows. Using this approach, we first confirm existing numerical and experimental results. In this study, the intracellular viscosity and interfacial tension are found to directly affect the rolling of a cell. Our results also show that the presence of a nucleus increases the bond lifetime, and decreases the cell rolling velocity. Furthermore, it is found that a cell with a larger diameter rolls faster, and decreases the bond lifetime. This study shows that cell rheological properties have significant effects on the adhesion process contrary to what has been hypothesized in most literature.

show abstract

Section: Compound Drop Modelsupporting

confidence: 92%

Computational Modeling of Cell Adhesion and Movement Using a Continuum-Kinetics Approach

N’Dri

Shyy

Tran‐Son‐Tay

2003

Biophysical Journal

136

View full text Add to dashboard Cite

show abstract

“…For example, microbubbles designed to adhere to a post-capillary venule (lower shear rate) might be designed to be larger than those which would adhere to endothelial cells in the arterial circulation (higher shear rate) to prevent unwanted binding to these areas. This conclusion was previously suggested by Tees et al (Tees et al 2002), who examined the effects of microparticle size and applied shear rate on particle targeting to different targeting receptors(selectin-like, antibody-like, or streptavidin-like). Their results demonstrated that the types of adhesion that can be expected (rolling, transient, and firm) are a function of the type of targeting receptor (each of which has specific kinetic rate, bond compliance, and bond stiffness), particle radius, and shear rate.…”

Section: Discussionsupporting

confidence: 71%

“…We chose our design space to independently sample the kinetic rates based on the assumption that these types of molecules will likely have a narrow range of bond stiffness (σ) and compliance (γ). This assumption is not outside of convention as reported values for γ and σ have been within an order of magnitude independent of the bond type (Chang et al 2000b; Smith et al 1999; Tees et al 2002). To further validate this premise, we have performed a series of simulations where these parameters were varied independently of each other as well as the targeting receptor density on the surface of the microbubble (see Supplemental Data) and found that their effects are below the firm adhesion threshold for the receptor densities and ligand densities relevant to their clinical application.…”

Section: Discussionmentioning

confidence: 98%

Optimization of ultrasound contrast agents with computational models to improve selection of ligands and binding strength

Maul

Dudgeon

Beste

et al. 2010

Biotech & Bioengineering

Self Cite

View full text Add to dashboard Cite

Diagnosis of cardiovascular disease is currently limited by the testing modality. Serum tests for biomarkers can provide quantification of severity but lack the ability to localize the source of the cardiovascular disease, while imaging technology such as angiography and ultrasound can only determine areas of reduced flow but not the severity of tissue ischemia. Targeted imaging with ultrasound contrast agents offers the ability to locally image as well as determine the degree of ischemia by utilizing agents that will cause the contrast agent to home to the affected tissue. Ultrasound molecular imaging via targeted microbubbles (MB) is currently limited by its sensitivity to molecular markers of disease relative to other techniques (e.g. radiolabeling). We hypothesize that computational modeling may provide a useful first approach to maximize microbubble binding by defining key parameters governing adhesion. Adhesive Dynamics was used to simulate the fluid dynamic and stochastic molecular binding of microbubbles to inflamed endothelial cells. Sialyl LewisX (sLex), P-selectin aptamer (PSA), and ICAM-1 antibody (abICAM) were modeled as the targeting receptors on the microbubble surface in both single-and dual-targeted arrangements. Microbubble properties (radius [Rc], kinetics [kf, kr] and densities of targeting receptors) and the physical environment (shear rate and target ligand densities) were modeled. The kinetics for sLex and PSA were measured with surface plasmon resonance. Rc, shear rate, and densities of sLex, PSA or abICAM were varied independently to assess model sensitivity. Firm adhesion was defined as MB velocity < 2% of the free stream velocity. Adhesive Dynamics simulations revealed an optimal microbubble radius of 1–2 μm and thresholds for kinf (>102 sec−1) and kor (<10−3 sec−1) for firm adhesion in a multi-targeted system. State diagrams for multi-targeted microbubbles suggest sLex and abICAM microbubbles may require 10-fold more ligand to achieve firm adhesion at higher shear rates than sLex and PSA microbubbles. The Adhesive Dynamics model gives useful insight into the key parameters for stable microbubble binding, and may allow flexible, prospective design and optimization of microbubbles to enhance clinical translation of ultrasound molecular imaging.

show abstract

“…Simulation of cell adhesion to bioactive surfaces has been reported (10). Leukocyte adhesion during flow in the microvasculature is known to be a multistep process.…”

Section: Discussionmentioning

confidence: 99%

Measurement of plaque‐forming macrophages activated by lipopolysaccharide in a micro‐channel chip

Isoda

Tsutsumi

Yamazaki

et al. 2009

J of Periodontal Research

View full text Add to dashboard Cite

show abstract

Simulation of Cell Adhesion to Bioreactive Surfaces in Shear: The Effect of Cell Size

Cited by 13 publications

References 39 publications

Computational Modeling of Cell Adhesion and Movement Using a Continuum-Kinetics Approach

Computational Modeling of Cell Adhesion and Movement Using a Continuum-Kinetics Approach

Optimization of ultrasound contrast agents with computational models to improve selection of ligands and binding strength

Measurement of plaque‐forming macrophages activated by lipopolysaccharide in a micro‐channel chip

Contact Info

Product

Resources

About