790 research outputs found
Cell mechanics and its applications in biomedical engineering
Biophysics and mechanobiology of biological cells are important subjects to not only cellular physiology but also to biomedical engineering, such as nanoparticle-based drug delivery, nanomaterial cytotoxicity, and tissue engineering. Two main load-bearing structures of biological cells are cell membrane and cytoskeleton. These macromolecular structures are regulated by biochemical events when fulfilling their mechanical roles. On the other hand, they also act as mechanosensors to enable the mechanical forces to regulate the biochemical reactions in the cell. Therefore, mechanics and biochemistry are closely coupled for the cell. A better understanding of the complexity of cell mechanics and mechanobiology can help achieve rational design in bioengineering where mechanical properties of engineered materials can be tailored to yield optimal outcomes. In this talk, I will present our recent and ongoing work on mechanobiochemical modeling of several different problems including (1) the endocytosis of nanoparticles, (2) the molecular dynamics modeling of cell membranes, and (3) development of a mechanobiochemical virtual-cell simulation program
Stochastic reconstruction of snow microstructure from x-ray microtomography images
Thesis (M.S.) University of Alaska Fairbanks, 2007The three-dimensional (3D) high-resolution digitized snow microstructure (pixel size 6 micron) was obtained by X-ray microtomography. The experimental result was verified by measuring the density of the snow sample. Statistical characteristics (porosity, local porosity, two-point correlation function) were extracted from cross-sectional images. The one-level-cut Gaussian random field model was used to stochastically reconstruct snow microstructure from X-ray microtomography images. Efficient computer programs were developed in MATLAB for the whole stochastic reconstruction procedure, including the numerical inversion of the correlation function and the generation of 3D large-scale Gaussian random fields by 3D inverse fast Fourier transform. The quality of the reconstruction was assessed by comparing the two-point correlation function and cross-sectional images.1. Introduction -- 2. Snow images by X-ray microtomography -- 2.1. Introduction of computed tomography -- 2.2. Acquisition and reconstruction -- 2.3. Binary images and 3D visualization -- 2.4. Statistical characteristics of the snow sample -- 3. Stochastic reconstruction of porous materials -- 3.1. Weak sense stationary gaussian random fields -- 3.2. The power spectral density function -- 3.3. The one-level-cut gaussian random field model -- 3.4. Generation of gaussian random fields -- 3.5. Stochastic reconstruction procedure -- 4. Reconstruction results -- 5. Conclusions -- References -- Appendices
A full contraction-reaction-diffusion model for pattern formation in geometrically confined microtissues
The reaction-diffusion models have been extensively applied to explain the
mechanism of pattern formations in early embryogenesis based on geometrically
confined microtissues consisting of human pluripotent stem cells. Recently,
mechanical cues, such as the cellular stresses and strains, have been found to
dictate the pattern formation in human stem cell differentiation. As a result,
the traditional reaction-diffusion models are modified by adding mechanically
related terms to consider the role played by the mechanical cues. However,
these models either do not consider the activeness of the cellular tissues or
neglect their poroelastic nature that biological tissues are made by both cells
and interstitial fluid. Hence, the current models suffer from the lacks of
biophysical relevance. Here we propose a modified reaction-diffusion model that
couples with the active contraction of cellular tissues. The cellular tissue is
modelled as a piece of biphasic poroelastic material, where mechanical forces
naturally regulate the transport of chemical cues. Such chemical cues direct
cell fate and hence yield certain types of pattern formations observed in
previous experiments
The effect of viscous force on the prediction of muscle contractility in biohybrid cantilever-based experiments
The biohybrid cantilevers have been recently reported for high-throughput measurement of muscle contractility. In previous works, mechanical models were used to predict the contractile stress from the cantilever bending curvature. To derive those models, the cantilever bending process was considered as quasi-static and the viscous force was neglected. To ascertain the effect of the viscous force on the prediction of the muscle contractility in biohybrid cantilever-based experiments, we extend the modified Stoney’s equation to a dynamic model that takes into account both the viscous force and the inertia force. Parametric studies show that, because the viscous force hinders the movement of the cantilever, use of static models result in a system error between the calculated and true contractile stresses. When using static models, the diastolic stress will be over-estimated while the peak systolic stress will be under-estimated. The present work suggests that dynamic models can be used in biohybrid cantilever assays to calculate the muscle contractility with higher accuracy, or can be used to optimize the experimental parameters such that the error due to the use of static models is minimized
The Effect of Thermal Fluctuation on the Receptor-Mediated Adhesion of a Cell Membrane to an Elastic Substrate
Mechanics of the bilayer membrane play an important role in many biological and bioengineering problems such as cell–substrate and cell–nanomaterial interactions. In this work, we study the effect of thermal fluctuation and the substrate elasticity on the cell membrane–substrate adhesion. We model the adhesion of a fluctuating membrane on an elastic substrate as a two-step reaction comprised of the out-of-plane membrane fluctuation and the receptor–ligand binding. The equilibrium closed bond ratio as a function of substrate rigidity was computed by developing a coupled Fourier space Brownian dynamics and Monte Carlo method. The simulation results show that there exists a crossover value of the substrate rigidity at which the closed bond ratio is maximal
Long Term Spectral Evolution of Tidal Disruption Candidates Selected by Strong Coronal Lines
We present results of follow-up optical spectroscopic observations of seven
rare, extreme coronal line emitting galaxies reported by Wang et al. (2012)
with Multi-Mirror Telescope (MMT). Large variations in coronal lines are found
in four objects, making them strong candidates of tidal disruption events
(TDE). For the four TDE candidates, all the coronal lines with ionization
status higher than [Fe VII] disappear within 5-9 years. The [Fe VII] faded by a
factor of about five in one object (J0952+2143) within 4 years, whereas emerged
in other two without them previously. A strong increment in the [O III] flux is
observed, shifting the line ratios towards the loci of active galactic nucleus
on the BPT diagrams. Surprisingly, we detect a non-canonical [O III]5007/[O
III]4959 2 in two objects, indicating a large column density of O and
thus probably optical thick gas. This also requires a very large ionization
parameter and relatively soft ionizing spectral energy distribution (e.g.
blackbody with K). Our observations can be explained as
echoing of a strong ultraviolet to soft X-ray flare caused by tidal disruption
events, on molecular clouds in the inner parsecs of the galactic nuclei.
Re-analyzing the SDSS spectra reveals double-peaked or strongly blue-shouldered
broad lines in three of the objects, which disappeared in the MMT spectra in
two objects, and faded by a factor of ten in 8 years in the remaining object
with a decrease in both the line width and centroid offset. We interpret these
broad lines as arising from decelerating biconical outflows. Our results
demonstrate that the signatures of echoing can persist for as long as ten
years, and can be used to probe the gas environment in the quiescent galactic
nuclei.Comment: 30 Pages, 10 Figures, 2 Tables, Accepted for Publication in Ap
A minimal mechanics model for mechanosensing of substrate rigidity gradient in durotaxis
Durotaxis refers to the phenomenon in which cells can sense the spatial gradient of the substrate rigidity in the process of cell migration. A conceptual two-part theory consisting of the focal adhesion force generation and mechanotransduction has been proposed previously by Lo et al. to explain the mechanism underlying durotaxis. In the present work, we are concerned with the first part of the theory: how exactly is the larger focal adhesion force generated in the part of the cell adhering to the stiffer region of the substrate? Using a simple elasticity model and by assuming the cell adheres to the substrate continuously underneath the whole cell body, we show that the mechanics principle of static equilibrium alone is sufficient to account for the generation of the larger traction stress on the stiffer region of the substrate. We believe that our model presents a simple mechanistic understanding of mechanosensing of substrate stiffness gradient at the cellular scale, which can be incorporated in more sophisticated mechanobiochemical models to address complex problems in mechanobiology and bioengineering
A structure-preserving integrator for incompressible finite elastodynamics based on a grad-div stabilized mixed formulation with particular emphasis on stretch-based material models
We present a structure-preserving scheme based on a recently-proposed mixed
formulation for incompressible hyperelasticity formulated in principal
stretches. Although there exist Hamiltonians introduced for
quasi-incompressible elastodynamics based on different variational
formulations, the one in the fully incompressible regime has yet been
identified in the literature. The adopted mixed formulation naturally provides
a new Hamiltonian for fully incompressible elastodynamics. Invoking the
discrete gradient formula, we are able to design fully-discrete schemes that
preserve the Hamiltonian and momenta. The scaled mid-point formula, another
popular option for constructing algorithmic stresses, is analyzed and
demonstrated to be non-robust numerically. The generalized Taylor-Hood element
based on the spline technology conveniently provides a higher-order, robust,
and inf-sup stable spatial discretization option for finite strain analysis. To
enhance the element performance in volume conservation, the grad-div
stabilization, a technique initially developed in computational fluid dynamics,
is introduced here for elastodynamics. It is shown that the stabilization term
does not impose additional restrictions for the algorithmic stress to respect
the invariants, leading to an energy-decaying and momentum-conserving fully
discrete scheme. A set of numerical examples is provided to justify the claimed
properties. The grad-div stabilization is found to enhance the discrete mass
conservation effectively. Furthermore, in contrast to conventional algorithms
based on Cardano's formula and perturbation techniques, the spectral
decomposition algorithm developed by Scherzinger and Dohrmann is robust and
accurate to ensure the discrete conservation laws and is thus recommended for
stretch-based material modeling
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