Academic literature on the topic 'Cell Mechanics -Stochastic Simulation'
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Journal articles on the topic "Cell Mechanics -Stochastic Simulation"
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Hanjalić, K., and S. Kenjereš. "RANS-Based Very Large Eddy Simulation of Thermal and Magnetic Convection at Extreme Conditions." Journal of Applied Mechanics 73, no. 3 (October 2, 2005): 430–40. http://dx.doi.org/10.1115/1.2150499.
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For thermal and magnetic convection at very high Rayleigh and Hartman numbers, which are inaccessible to the conventional large eddy simulation, we propose a time-dependent Reynolds-average-Navier-Stokes (T-RANS) approach in which the large-scale deterministic motion is fully resolved by time and space solution, whereas the unresolved stochastic motion is modeled by a “subscale” model for which an one-point RANS closure is used. The resolved and modeled contributions to the turbulence moments are of the same order of magnitude and in the near-wall regions the modeled heat transport becomes dominant, emphasizing the role of the subscale model. This T-RANS approach, with an algebraic stress/flux subscale model, verified earlier in comparison with direct numerical simulation and experiments in classic Rayleigh-Bénard convection, is now expanded to simulate Rayleigh-Bénard (RB) convection at very high Ra numbers—at present up to O(1016)—and to magnetic convection in strong uniform magnetic fields. The simulations reproduce the convective cell structure and its reorganization caused by an increase in Ra number and effects of the magnetic field. The T-RANS simulations of classic RB indicate expected thinning of both the thermal and hydraulic wall boundary layer with an increase in the Ra number and an increase in the exponent of the Nu∝Ran correlation in accord with recent experimental findings and Kraichnan asymptotic theory.
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Gao, Huajian, Jin Qian, and Bin Chen. "Probing mechanical principles of focal contacts in cell–matrix adhesion with a coupled stochastic–elastic modelling framework." Journal of The Royal Society Interface 8, no. 62 (June 2011): 1217–32. http://dx.doi.org/10.1098/rsif.2011.0157.
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Cell–matrix adhesion depends on the collective behaviours of clusters of receptor–ligand bonds called focal contacts between cell and extracellular matrix. While the behaviour of a single molecular bond is governed by statistical mechanics at the molecular scale, continuum mechanics should be valid at a larger scale. This paper presents an overview of a series of recent theoretical studies aimed at probing the basic mechanical principles of focal contacts in cell–matrix adhesion via stochastic–elastic models in which stochastic descriptions of molecular bonds and elastic descriptions of interfacial traction–separation are unified in a single modelling framework. The intention here is to illustrate these principles using simple analytical and numerical models. The aim of the discussions is to provide possible clues to the following questions: why does the size of focal adhesions (FAs) fall into a narrow range around the micrometre scale? How can cells sense and respond to substrates of varied stiffness via FAs? How do the magnitude and orientation of mechanical forces affect the binding dynamics of FAs? The effects of cluster size, cell–matrix elastic modulus, loading direction and cytoskeletal pretension on the lifetime of FA clusters have been investigated by theoretical arguments as well as Monte Carlo numerical simulations, with results showing that intermediate adhesion size, stiff substrate, cytoskeleton stiffening, low-angle pulling and moderate cytoskeletal pretension are factors that contribute to stable FAs. From a mechanistic point of view, these results provide possible explanations for a wide range of experimental observations and suggest multiple mechanisms by which cells can actively control adhesion and de-adhesion via cytoskeletal contractile machinery in response to mechanical properties of their surroundings.
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Li, Long, Wei Kang, and Jizeng Wang. "Mechanical Model for Catch-Bond-Mediated Cell Adhesion in Shear Flow." International Journal of Molecular Sciences 21, no. 2 (January 16, 2020): 584. http://dx.doi.org/10.3390/ijms21020584.
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Catch bond, whose lifetime increases with applied tensile force, can often mediate rolling adhesion of cells in a hydrodynamic environment. However, the mechanical mechanism governing the kinetics of rolling adhesion of cells through catch-bond under shear flow is not yet clear. In this study, a mechanical model is proposed for catch-bond-mediated cell adhesion in shear flow. The stochastic reaction of bond formation and dissociation is described as a Markovian process, whereas the dynamic motion of cells follows classical analytical mechanics. The steady state of cells significantly depends on the shear rate of flow. The upper and lower critical shear rates required for cell detachment and attachment are extracted, respectively. When the shear rate increases from the lower threshold to the upper threshold, cell rolling became slower and more regular, implying the flow-enhanced adhesion phenomenon. Our results suggest that this flow-enhanced stability of rolling adhesion is attributed to the competition between stochastic reactions of bonds and dynamics of cell rolling, instead of force lengthening the lifetime of catch bonds, thereby challenging the current view in understanding the mechanism behind this flow-enhanced adhesion phenomenon. Moreover, the loading history of flow defining bistability of cell adhesion in shear flow is predicted. These theoretical predictions are verified by Monte Carlo simulations and are related to the experimental observations reported in literature.
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Sadikin, Indera, Djoko Suharto, Bangkit Meliana, Kemal Supelli, and Abdul Arya. "Probabilistic Fracture Mechanics Analysis for Optimization of High-Pressure Vessel Inspection." Advanced Materials Research 33-37 (March 2008): 79–84. http://dx.doi.org/10.4028/www.scientific.net/amr.33-37.79.
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The use of High-pressure Vessel in eco-friendly Natural Gas Vehicles (NGV) is technologically feasible nowadays. Common applications of High-pressure Vessel are to carry Compressed Natural Gas (CNG), hydrogen for fuel-cell vehicle, and high-compression air in the new air-car technology. High-pressure Vessel is subjected to extreme compression-decompression cycles that could cause fatigue failure. Therefore, vessel shall be inspected regularly to detect if there is crack inside. The objective of this paper is to optimize the inspection interval of CNG Highpressure Vessel by means of Probabilistic Fracture Mechanics Analysis. Vessel is made of highalloy steel and assumed to have distributed elliptical cracks. Three length-to-depth crack ratios (a/c), i.e. 3, 8, and 15, are simulated. Crack is assumed to propagate in fixed ratio. Stress Intensity Factors at each crack tip are calculated by Finite Element Analysis and Crack Closure Technique. Fatigue crack growth is simulated by Cycle-by-Cycle Integration Technique. The Fracture Mechanics Analysis is then expanded to probabilistic analysis by considering stochastic nature of analysis parameters. Probability of failure is computed by Guided Direct Simulation Method using software which is specially written for this project [1]. Based on simulation result, High-pressure Vessel is recommended to be inspected every 3 years.
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Sun, J. Q., and C. S. Hsu. "The Generalized Cell Mapping Method in Nonlinear Random Vibration Based Upon Short-Time Gaussian Approximation." Journal of Applied Mechanics 57, no. 4 (December 1, 1990): 1018–25. http://dx.doi.org/10.1115/1.2897620.
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A short-time Gaussian approximation scheme is proposed in the paper. This scheme provides a very efficient and accurate way of computing the one-step transition probability matrix of the previously developed generalized cell mapping (GCM) method in nonlinear random vibration. The GCM method based upon this scheme is applied to some very challenging nonlinear systems under external and parametric Gaussian white noise excitations in order to show its power and efficiency. Certain transient and steady-state solutions such as the first-passage time probability, steady-state mean square response, and the steady-state probability density function have been obtained. Some of the solutions are compared with either the simulation results or the available exact solutions, and are found to be very accurate. The computed steady-state mean square response values are found to be of error less than 1 percent when compared with the available exact solutions. The efficiency of the GCM method based upon the short-time Gaussian approximation is also examined. The short-time Gaussian approximation renders the overhead of computing the one-step transition probability matrix to be very small. It is found that in a comprehensive study of nonlinear stochastic systems, in which various transient and steady-state solutions are obtained in one computer program execution, the GCM method can have very large computational advantages over Monte Carlo simulation.
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Fritzsche, Marco, Christoph Erlenkämper, Emad Moeendarbary, Guillaume Charras, and Karsten Kruse. "Actin kinetics shapes cortical network structure and mechanics." Science Advances 2, no. 4 (April 2016): e1501337. http://dx.doi.org/10.1126/sciadv.1501337.
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The actin cortex of animal cells is the main determinant of cellular mechanics. The continuous turnover of cortical actin filaments enables cells to quickly respond to stimuli. Recent work has shown that most of the cortical actin is generated by only two actin nucleators, the Arp2/3 complex and the formin Diaph1. However, our understanding of their interplay, their kinetics, and the length distribution of the filaments that they nucleate within living cells is poor. Such knowledge is necessary for a thorough comprehension of cellular processes and cell mechanics from basic polymer physics principles. We determined cortical assembly rates in living cells by using single-molecule fluorescence imaging in combination with stochastic simulations. We find that formin-nucleated filaments are, on average, 10 times longer than Arp2/3-nucleated filaments. Although formin-generated filaments represent less than 10% of all actin filaments, mechanical measurements indicate that they are important determinants of cortical elasticity. Tuning the activity of actin nucleators to alter filament length distribution may thus be a mechanism allowing cells to adjust their macroscopic mechanical properties to their physiological needs.
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Burini, D., and N. Chouhad. "A multiscale view of nonlinear diffusion in biology: From cells to tissues." Mathematical Models and Methods in Applied Sciences 29, no. 04 (April 2019): 791–823. http://dx.doi.org/10.1142/s0218202519400062.
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This paper presents a review on the mathematical tools for the derivation of macroscopic models in biology from the underlying description at the scale of cells as it is delivered by a kinetic theory model. The survey is followed by an overview of research perspectives. The derivation is inspired by the Hilbert’s method, known in classic kinetic theory, which is here applied to a broad class of kinetic equations modeling multicellular dynamics. The main difference between this class of equations with respect to the classical kinetic theory consists in the modeling of cell interactions which is developed by theoretical tools of stochastic game theory rather than by laws of classical mechanics. The survey is focused on the study of nonlinear diffusion and source terms.
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Canela-Xandri, Oriol, Samira Anbari, and Javier Buceta. "TiFoSi: an efficient tool for mechanobiology simulations of epithelia." Bioinformatics 36, no. 16 (June 26, 2020): 4525–26. http://dx.doi.org/10.1093/bioinformatics/btaa592.
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Abstract Motivation Emerging phenomena in developmental biology and tissue engineering are the result of feedbacks between gene expression and cell biomechanics. In that context, in silico experiments are a powerful tool to understand fundamental mechanisms and to formulate and test hypotheses. Results Here, we present TiFoSi, a computational tool to simulate the cellular dynamics of planar epithelia. TiFoSi allows to model feedbacks between cellular mechanics and gene expression (either in a deterministic or a stochastic way), the interaction between different cell populations, the custom design of the cell cycle and cleavage properties, the protein number partitioning upon cell division, and the modeling of cell communication (juxtacrine and paracrine signaling). Availability and implementation http://tifosi.thesimbiosys.com. Supplementary information Supplementary data are available at Bioinformatics online.
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Vermolen, F. J., and A. Gefen. "A semi-stochastic cell-based formalism to model the dynamics of migration of cells in colonies." Biomechanics and Modeling in Mechanobiology 11, no. 1-2 (March 26, 2011): 183–95. http://dx.doi.org/10.1007/s10237-011-0302-6.
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Chen, Jian, Xiongfei Li, Wei Li, Cong Li, Baoshan Xie, Shuowei Dai, Jian-Jun He, and Yanjie Ren. "Research on energy absorption properties of open-cell copper foam for current collector of Li-ions." Materials Science-Poland 37, no. 1 (March 1, 2019): 8–15. http://dx.doi.org/10.2478/msp-2019-0011.
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AbstractQuasi-static uniaxial compressive tests of open-cell copper (Cu) foams (OCCF) were carried out on an in-situ bi-direction tension/compress testing machine (IBTC 2000). The effects of strain rate, porosity and pore size on the energy absorption of open-cell copper foams were investigated to reveal the energy absorption mechanism. The results show that three performance parameters of open-cell copper foams (OCCF), involving compressive strength, Young modulus and yield stress, increase simultaneously with an increase of strain rate and reduce with increasing porosity and pore size. Furthermore, the energy absorption capacity of OCCF increases with an increase of porosity and pore size. However, energy absorption efficiency increases with increasing porosity and decreasing pore size. The finite element simulation results show that the two-dimensional stochastic model can predict the energy absorption performance of the foam during the compressive process. The large permanent plastic deformation at the weak edge hole is the main factor that affects the energy absorption.
Dissertations / Theses on the topic "Cell Mechanics -Stochastic Simulation"
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Morton-Firth, Carl Jason. "Stochastic simulation of cell signalling pathways." Thesis, University of Cambridge, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.625063.
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Szekely, Tamas. "Stochastic modelling and simulation in cell biology." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:f9b8dbe6-d96d-414c-ac06-909cff639f8c.
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Modelling and simulation are essential to modern research in cell biology. This thesis follows a journey starting from the construction of new stochastic methods for discrete biochemical systems to using them to simulate a population of interacting haematopoietic stem cell lineages. The first part of this thesis is on discrete stochastic methods. We develop two new methods, the stochastic extrapolation framework and the Stochastic Bulirsch-Stoer methods. These are based on the Richardson extrapolation technique, which is widely used in ordinary differential equation solvers. We believed that it would also be useful in the stochastic regime, and this turned out to be true. The stochastic extrapolation framework is a scheme that admits any stochastic method with a fixed stepsize and known global error expansion. It can improve the weak order of the moments of these methods by cancelling the leading terms in the global error. Using numerical simulations, we demonstrate that this is the case up to second order, and postulate that this also follows for higher order. Our simulations show that extrapolation can greatly improve the accuracy of a numerical method. The Stochastic Bulirsch-Stoer method is another highly accurate stochastic solver. Furthermore, using numerical simulations we find that it is able to better retain its high accuracy for larger timesteps than competing methods, meaning it remains accurate even when simulation time is speeded up. This is a useful property for simulating the complex systems that researchers are often interested in today. The second part of the thesis is concerned with modelling a haematopoietic stem cell system, which consists of many interacting niche lineages. We use a vectorised tau-leap method to examine the differences between a deterministic and a stochastic model of the system, and investigate how coupling niche lineages affects the dynamics of the system at the homeostatic state as well as after a perturbation. We find that larger coupling allows the system to find the optimal steady state blood cell levels. In addition, when the perturbation is applied randomly to the entire system, larger coupling also results in smaller post-perturbation cell fluctuations compared to non-coupled cells. In brief, this thesis contains four main sets of contributions: two new high-accuracy discrete stochastic methods that have been numerically tested, an improvement that can be used with any leaping method that introduces vectorisation as well as how to use a common stepsize adapting scheme, and an investigation of the effects of coupling lineages in a heterogeneous population of haematopoietic stem cell niche lineages.
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Chen, Minghan. "Stochastic Modeling and Simulation of Multiscale Biochemical Systems." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/90898.
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Numerous challenges arise in modeling and simulation as biochemical networks are discovered with increasing complexities and unknown mechanisms. With the improvement in experimental techniques, biologists are able to quantify genes and proteins and their dynamics in a single cell, which calls for quantitative stochastic models for gene and protein networks at cellular levels that match well with the data and account for cellular noise.
This dissertation studies a stochastic spatiotemporal model of the Caulobacter crescentus cell cycle. A two-dimensional model based on a Turing mechanism is investigated to illustrate the bipolar localization of the protein PopZ. However, stochastic simulations are often impeded by expensive computational cost for large and complex biochemical networks. The hybrid stochastic simulation algorithm is a combination of differential equations for traditional deterministic models and Gillespie's algorithm (SSA) for stochastic models. The hybrid method can significantly improve the efficiency of stochastic simulations for biochemical networks with multiscale features, which contain both species populations and reaction rates with widely varying magnitude. The populations of some reactant species might be driven negative if they are involved in both deterministic and stochastic systems. This dissertation investigates the negativity problem of the hybrid method, proposes several remedies, and tests them with several models including a realistic biological system.
As a key factor that affects the quality of biological models, parameter estimation in stochastic models is challenging because the amount of empirical data must be large enough to obtain statistically valid parameter estimates. To optimize system parameters, a quasi-Newton algorithm for stochastic optimization (QNSTOP) was studied and applied to a stochastic budding yeast cell cycle model by matching multivariate probability distributions between simulated results and empirical data. Furthermore, to reduce model complexity, this dissertation simplifies the fundamental cooperative binding mechanism by a stochastic Hill equation model with optimized system parameters. Considering that many parameter vectors generate similar system dynamics and results, this dissertation proposes a general α-β-γ rule to return an acceptable parameter region of the stochastic Hill equation based on QNSTOP. Different objective functions are explored targeting different features of the empirical data.Doctor of Philosophy
Modeling and simulation of biochemical networks faces numerous challenges as biochemical networks are discovered with increased complexity and unknown mechanisms. With improvement in experimental techniques, biologists are able to quantify genes and proteins and their dynamics in a single cell, which calls for quantitative stochastic models, or numerical models based on probability distributions, for gene and protein networks at cellular levels that match well with the data and account for randomness. This dissertation studies a stochastic model in space and time of a bacterium’s life cycle— Caulobacter. A two-dimensional model based on a natural pattern mechanism is investigated to illustrate the changes in space and time of a key protein population. However, stochastic simulations are often complicated by the expensive computational cost for large and sophisticated biochemical networks. The hybrid stochastic simulation algorithm is a combination of traditional deterministic models, or analytical models with a single output for a given input, and stochastic models. The hybrid method can significantly improve the efficiency of stochastic simulations for biochemical networks that contain both species populations and reaction rates with widely varying magnitude. The populations of some species may become negative in the simulation under some circumstances. This dissertation investigates negative population estimates from the hybrid method, proposes several remedies, and tests them with several cases including a realistic biological system. As a key factor that affects the quality of biological models, parameter estimation in stochastic models is challenging because the amount of observed data must be large enough to obtain valid results. To optimize system parameters, the quasi-Newton algorithm for stochastic optimization (QNSTOP) was studied and applied to a stochastic (budding) yeast life cycle model by matching different distributions between simulated results and observed data. Furthermore, to reduce model complexity, this dissertation simplifies the fundamental molecular binding mechanism by the stochastic Hill equation model with optimized system parameters. Considering that many parameter vectors generate similar system dynamics and results, this dissertation proposes a general α-β-γ rule to return an acceptable parameter region of the stochastic Hill equation based on QNSTOP. Different optimization strategies are explored targeting different features of the observed data.
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Staber, Brian. "Stochastic analysis, simulation and identification of hyperelastic constitutive equations." Thesis, Paris Est, 2018. http://www.theses.fr/2018PESC1042/document.
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Le projet de thèse concerne la construction, la génération et l'identification de modèles continus stochastiques, pour des milieux hétérogènes exhibant des comportements non linéaires. Le domaine d'application principal visé est la biomécanique, notamment au travers du développement d'outils de modélisation multi-échelles et stochastiques, afin de quantifier les grandes incertitudes exhibées par les tissus mous. Deux aspects sont particulièrement mis en exergue. Le premier point a trait à la prise en compte des incertitudes en mécanique non linéaire, et leurs incidences sur les prédictions des quantités d'intérêt. Le second aspect concerne la construction, la génération (en grandes dimensions) et l'identification multi-échelle de représentations continues à partir de résultats expérimentaux limitésThis work is concerned with the construction, generation and identification of stochastic continuum models, for heterogeneous materials exhibiting nonlinear behaviors. The main covered domains of applications are biomechanics, through the development of multiscale methods and stochastic models, in order to quantify the great variabilities exhibited by soft tissues. Two aspects are particularly highlighted. The first one is related to the uncertainty quantification in non linear mechanics, and its implications on the quantities of interest. The second aspect is concerned with the construction, the generation in high dimension and multiscale identification based on limited experimental data
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Ahmadian, Mansooreh. "Hybrid Modeling and Simulation of Stochastic Effects on Biochemical Regulatory Networks." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/99481.
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A complex network of genes and proteins governs the robust progression through cell cycles in the presence of inevitable noise. Stochastic modeling is viewed as a key paradigm to study the effects of intrinsic and extrinsic noise on the dynamics of biochemical networks. A detailed quantitative description of such complex and multiscale networks via stochastic modeling poses several challenges. First, stochastic models generally require extensive computations, particularly when applied to large networks. Second, the accuracy of stochastic models is highly dependent on the quality of the parameter estimation based on experimental observations. The goal of this dissertation is to address these problems by developing new efficient methods for modeling and simulation of stochastic effects in biochemical systems. Particularly, a hybrid stochastic model is developed to represent a detailed molecular mechanism of cell cycle control in budding yeast cells. In a single multiscale model, the proposed hybrid approach combines the advantages of two regimes: 1) the computational efficiency of a deterministic approach, and 2) the accuracy of stochastic simulations. The results show that this hybrid stochastic model achieves high computational efficiency while generating simulation results that match very well with published experimental measurements. Furthermore, a new hierarchical deep classification (HDC) algorithm is developed to address the parameter estimation problem in a monomolecular system. The HDC algorithm adopts a neural network that, via multiple hierarchical search steps, finds reasonably accurate ranges for the model parameters. To train the neural network in the presence of experimental data scarcity, the proposed method leverages the domain knowledge from stochastic simulations to generate labeled training data. The results show that the proposed HDC algorithm yields accurate ranges for the model parameters and highlight the potentials of model-free learning for parameter estimation in stochastic modeling of complex biochemical networks.Doctor of Philosophy
Cell cycle is a process in which a growing cell replicates its DNA and divides into two cells. Progression through the cell cycle is regulated by complex interactions between networks of genes, transcripts, and proteins. These interactions inside the confined volume of a cell are subject to inherent noise. To provide a quantitative description of the cell cycle, several deterministic and stochastic models have been developed. However, deterministic models cannot capture the intrinsic noise. In addition, stochastic modeling poses the following challenges. First, stochastic models generally require extensive computations, particularly when applied to large networks. Second, the accuracy of stochastic models is highly dependent on the accuracy of the estimated model parameters. The goal of this dissertation is to address these challenges by developing new efficient methods for modeling and simulation of stochastic effects in biochemical networks. The results show that the proposed hybrid model that combines stochastic and deterministic modeling approaches can achieve high computational efficiency while generating accurate simulation results. Moreover, a new machine learning-based method is developed to address the parameter estimation problem in biochemical systems. The results show that the proposed method yields accurate ranges for the model parameters and highlight the potentials of model-free learning for parameter estimation in stochastic modeling of complex biochemical networks.
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Hohenegger, Christel. "Small Scale Stochastic Dynamics For Particle Image Velocimetry Applications." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/10464.
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Fluid velocities and Brownian effects at nanoscales in the near-wall region of microchannels can be experimentally measured in an image plane parallel to the wall using, for example, evanescent wave illumination technique combined with particle image velocimetry [R. Sadr extit{et al.}, J. Fluid. Mech. 506, 357-367 (2004)]. The depth of field of this technique being difficult to modify, reconstruction of the out-of-plane dependence of the in-plane velocity profile remains extremely challenging. Tracer particles are not only carried by the flow, but they undergo random fluctuation imposed by the proximity of the wall. We study such a system under a particle based stochastic approach (Langevin) and a probabilistic approach (Fokker-Planck). The Langevin description leads to a coupled system of stochastic differential equations. Because the simulated data will be used to test a statistical hypothesis, we pay particular attention to the strong order of convergence of the scheme developing an appropriate Milstein scheme of strong order of convergence 1. Based on the probability density function of mean in-plane displacements, a statistical solution to the problem of the reconstruction of the out-of-plane dependence of the velocity profile is proposed. We developed a maximum likelihood algorithm which determines the most likely values for the velocity profile based on simulated perfect particle position, simulated perfect mean displacements and simulated observed mean displacements. Effects of Brownian motion on the approximation of the mean displacements are briefly discussed. A matched particle is a particle that starts and ends in the same image window after a measurement time. AS soon as the computation and observation domain are not the same, the distribution of the out-of-plane distances sampled by matched particles during the measurement time is not uniform. The combination of a forward and a backward solution of the one dimensional Fokker-Planck equation is used to determine this probability density function. The non-uniformity of the resulting distribution is believed to induce a bias in the determination of slip length and is quantified for relevant experimental parameters.
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Charlebois, Daniel A. "An algorithm for the stochastic simulation of gene expression and cell population dynamics." Thesis, University of Ottawa (Canada), 2010. http://hdl.handle.net/10393/28755.
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Over the past few years, it has been increasingly recognized that stochastic mechanisms play a key role in the dynamics of biological systems. Genetic networks are one example where molecular-level fluctuations are of particular importance. Here stochasticity in the expression of gene products can result in genetically identical cells in the same environment displaying significant variation in biochemical or physical attributes. This variation can influence individual and population-level fitness.
In this thesis we first explore the background required to obtain analytical solutions and perform simulations of stochastic models of gene expression. Then we develop an algorithm for the stochastic simulation of gene expression and heterogeneous cell population dynamics. The algorithm combines an exact method to simulate molecular-level fluctuations in single cells and a constant-number Monte Carlo approach to simulate the statistical characteristics of growing cell populations. This approach permits biologically realistic and computationally feasible simulations of environment and time-dependent cell population dynamics. The algorithm is benchmarked against steady-state and time-dependent analytical solutions of gene expression models, including scenarios when cell growth, division, and DNA replication are incorporated into the modelling framework. Furthermore, using the algorithm we obtain the steady-state cell size distribution of a large cell population, grown from a small initial cell population undergoing stochastic and asymmetric division, to the size distribution of a small representative sample of this population simulated to steady-state. These comparisons demonstrate that the algorithm provides an accurate and efficient approach to modelling the effects of complex biological features on gene expression dynamics. The algorithm is also employed to simulate expression dynamics within 'bet-hedging' cell populations during their adaption to environmental stress. These simulations indicate that the cell population dynamics algorithm provides a framework suitable for simulating and analyzing realistic models of heterogeneous population dynamics combining molecular-level stochastic reaction kinetics, relevant physiological details, and phenotypic variability and fitness.
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Liu, Haipei, and 刘海培. "AFM-based experimental investigation, numerical simulation and theoretical modeling of mechanics of cell adhesion." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2014. http://hdl.handle.net/10722/208565.
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Cell-extracellular matrix and cell-cell adhesion are essential for biological processes such as cell motility, signaling, proliferation, cytoskeletal organization and gene expression. For this reason, extensive effort has been devoted in the past few decades to measure cell adhesion as well as identify key molecules involved. This thesis focuses on two outstanding problems in this area, namely, how to quantitatively characterize the adhesion between neural cells and the substrate and how to model the turnover of adhesions in the intriguing phenomenon of stretch-induced cell realignment.
First of all, using a combined atomic force (AFM) and total internal reflection fluorescence microscope (TIRFM) system a novel method was developed to systematically and quantitatively examine the adhesion between neurite branches and the extracellular matrix. Specifically, a tipless AFM cantilever was used to penetrate between a well-developed neurite and the functionalized substrate and then gradually peel the neurite from the surface. At the same time, a laser TIRFM was utilized to monitor the activities of different adhesion molecules during the detaching process. This approach provides a solution to the long-standing problem of how to quantitatively measure neuron-extracellular matrix interactions while, simultaneously, identify the roles of various adhesion proteins in the process. Besides heathy neurons, testes have also been conducted on cells affected by the Alzheimer's disease (AD) where the influence of such disease on the mechanical response of neural cells was demonstrated.
Secondly, to better understand the observed peeling response of the neurite, as well as extract key information from it, finite element (FEM) simulation was carried out using ABAQUS. It was shown that a good fit between the simulation results and experimental data can be achieved by representing the adhesion between two surfaces with simple cohesive interactions. In particular, it was found that the apparent adhesion energy density, a quantity of central interest in cell adhesion studies, is in the range of 0.2-0.8 mj/m^2.
Last but not the least, a mechanochemical modeling framework was developed to investigate the mechanism of cell reorientation induced by cyclic stretching on the substrate. It was shown that the final alignment of cells reflects the competition between stress fiber assembly or disassembly, focal adhesion growth or disruption, substrate stiffening and whole-cell rotation. Predictions from the model are consistent with a variety of experimental observations, suggesting that the main physics of this intriguing phenomenon may have been well captured.published_or_final_version
Mechanical Engineering
Doctoral
Doctor of Philosophy
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Wang, Shuo. "Analysis and Application of Haseltine and Rawlings's Hybrid Stochastic Simulation Algorithm." Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/82717.
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Stochastic effects in cellular systems are usually modeled and simulated with Gillespie's stochastic simulation algorithm (SSA), which follows the same theoretical derivation as the chemical master equation (CME), but the low efficiency of SSA limits its application to large chemical networks.
To improve efficiency of stochastic simulations, Haseltine and Rawlings proposed a hybrid of ODE and SSA algorithm, which combines ordinary differential equations (ODEs) for traditional deterministic models and SSA for stochastic models. In this dissertation, accuracy analysis, efficient implementation strategies, and application of of Haseltine and Rawlings's hybrid method (HR) to a budding yeast cell cycle model are discussed.
Accuracy of the hybrid method HR is studied based on a linear chain reaction system, motivated from the modeling practice used for the budding yeast cell cycle control mechanism. Mathematical analysis and numerical results both show that the hybrid method HR is accurate if either numbers of molecules of reactants in fast reactions are above certain thresholds, or rate constants of fast reactions are much larger than rate constants of slow reactions. Our analysis also shows that the hybrid method HR allows for a much greater region in system parameter space than those for the slow scale SSA (ssSSA) and the stochastic quasi steady state assumption (SQSSA) method.
Implementation of the hybrid method HR requires a stiff ODE solver for numerical integration and an efficient event-handling strategy for slow reaction firings. In this dissertation, an event-handling strategy is developed based on inverse interpolation. Performances of five wildly used stiff ODE solvers are measured in three numerical experiments.
Furthermore, inspired by the strategy of the hybrid method HR, a hybrid of ODE and SSA stochastic models for the budding yeast cell cycle is developed, based on a deterministic model in the literature. Simulation results of this hybrid model match very well with biological experimental data, and this model is the first to do so with these recently available experimental data. This study demonstrates that the hybrid method HR has great potential for stochastic modeling and simulation of large biochemical networks.Ph. D.
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Wijanto, Florent. "Multiscale mechanics of soft tissues." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLX093.
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Les réseaux de fibre sont une structure omniprésente dans les tissus biologiques, aussi bien au niveau macroscopique, où ils sont l'ingrédient principal des tissus mous, qu'au niveau microscopique, en tant que constituants des structures collagèniques ou du cytosquelette. L'objectif de ce travail de thèse est de proposer un modèle basé sur la microstructure physique des réseaux de fibres afin d'obtenir une compréhension du comportement mécanique des réseaux de fibres biologiques. Le modèle est basé sur une description de fibres glissant les unes par rapport aux autres et interagissant via des ponts qui se comportent comme des ressorts. Ces ponts peuvent s'attacher et se détacher de manière stochastique avec un taux de détachement qui dépend de la force subie. Comparé aux modélisations existantes, notre travail met en jeu une configuration en glissement dynamique des fibres, ainsi que des sites d'attachement discrets ne permettant l'attachement qu'à des endroits localisés de la fibre. Le détachement des ponts est basé sur la diffusion thermique hors d'un puit de potentiel suivant la théorie de Kramers. Cette théorie donne un contexte physique à la dynamique du détachement ainsi qu'une dépendance naturelle du détachement au chargement via l'inclinaison du paysage énergétique par la force de chargement. Le modèle donne deux modes de contrôle du système : un contrôle à vitesse imposée, appelé système dur, et un contrôle à force imposée, appelé système mou. Notre travail permet également de visualiser le comportement du système à travers une simulation stochastique. Les simulations offrent deux algorithmes, chacun adapté à la méthode de contrôle du système, en système dur ou mou et respectant la causalité dans chacun des modes. Les résultats de la simulation sont explorés via la visualisation des données sortantes de la simulation, qui servent de support pour l'investigation paramétrique du comportement du modèle et ancrent l'interprétation physique des résultats. En particulier, l'influence de l'espacement des sites d'attachement du système, un point caractéristique du modèle, est examiné. De même, nous explorons l'effet de chargements complexes (transitoires, cycliques, etc.) qui représentent les chargements physiologiques des tissus fibreuxFibre networks are ubiquitous structures in biological tissues, both at the macroscopic level being the main ingredient in soft tissues and at the microscopic level, as constituents of collagen structures or the cytoskeleton. The goal of this work is to propose a model based on the physical microstructure of fibre networks in order to provide an understanding of the mechanical behaviour of biological fibre networks. The current model starts from fibres sliding with respect to one another and interacting via spring-like cross-bridges. These cross-bridges can attach and detach stochastically with a load-dependent detachment rate. Compared to existing modelling approaches, this work features a dynamic sliding configuration for the interacting fibres and discrete binding sites which permit attachment on localised spaces of the fibre. The detachment of cross-bridges is based on thermal diffusion out of an energy well, following the Kramers rate theory. This theory provides a physical background to the detachment dynamics as well as a natural load dependency in the tilting of the energy landscape by the load force. The model provides two modes by which the depicted system may be driven: an imposed velocity driving, called a hard device and an imposed load driving, called a soft device. The work also provides a way of visualising the behaviour of the model by performing a stochastic simulation. The simulations provided present two algorithms, each tailored to represent the driving of the system, whether in hard or soft device, respecting the causality in each of the driving mode. Simulation results are explored via data visualisation of simulation output. These visualisation serve as an entry point into parametric investigation of the model behaviour and anchor the interpretation of the results into physical systems. In particular, the influence of binding site spacing, one of the key features of the model, is investigated. We also investigate the effects of complex loading paths (transitory, cyclic, etc.) which can be associated to the physiological loadings fibrous tissues
Books on the topic "Cell Mechanics -Stochastic Simulation"
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Advances in cell mechanics. Heidelberg: Springer, 2011.
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Arnaud, Chauvière, Preziosi Luigi, and Verdier Claude 1962-, eds. Cell mechanics: From single scale-based models to multiscale modeling. Boca Raton: Chapman & Hall/CRC, 2009.
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Arnaud, Chauvière, Preziosi Luigi, and Verdier Claude, eds. Cell mechanics: From single scale-based models to multiscale modeling. Boca Raton: Chapman & Hall/CRC, 2009.
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Luigi, Preziosi, and Verdier Claude, eds. Cell mechanics: From single scale-based models to multiscale modeling. Boca Raton: Chapman & Hall/CRC, 2009.
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Chauvière, Arnaud. Cell mechanics: From single scale-based models to multiscale modeling. Boca Raton: Chapman & Hall/CRC, 2009.
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Chauvière, Arnaud. Cell mechanics: From single scale-based models to multiscale modeling. Boca Raton: Chapman & Hall/CRC, 2009.
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Jakubowski, Jacek. Stochastyczna symulacja stateczności wyrobisk w nieciągłym masywie skalnym. Kraków: Wydawnictwa AGH, 2010.
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Bris, Claude. Systèmes multi-échelles: Modélisation et simulation. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005.
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Verdier, Claude, Luigi Preziosi, and Arnaud Chauvière. Cell Mechanics. Taylor & Francis Group, 2019.
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Bris, Claude Le. Systèmes multi-èchelles: Modélisation et simulation (Mathématiques et Applications). Springer, 2005.
Find full textBook chapters on the topic "Cell Mechanics -Stochastic Simulation"
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de Simone, P., A. Ghersi, and R. Mauro. "Monte Carlo Simulation of Beams on Winkler Foundation." In Computational Stochastic Mechanics, 523–32. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3692-1_44.
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Wedig, Walter V. "Simulation and Analysis of Mechanical Systems with Parameter Fluctuation." In Nonlinear Stochastic Mechanics, 523–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-84789-9_45.
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Bielewicz, E., J. Górski, and H. Walukiewicz. "Random Fields. Digital Simulation and Applications in Structural Mechanics." In Computational Stochastic Mechanics, 557–68. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3692-1_47.
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Kareem, A., and Y. Li. "Simulation of Multi-Variate Stationary and Nonstationary Random Processes: A Recent Development." In Computational Stochastic Mechanics, 533–44. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3692-1_45.
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Cheng, A. H.-D., K. Hackl, and C. Y. Yang. "Chaos, Stochasticity, and Stability of a Nonlinear Oscillator with Control Part II: Simulation." In Computational Stochastic Mechanics, 239–52. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3692-1_21.
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Seya, H., H. H. M. Hwang, and M. Shinozuka. "Probabilistic Seismic Response Analysis of a Steel Frame Structure Using Monte Carlo Simulation." In Computational Stochastic Mechanics, 499–510. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3692-1_42.
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De López La Cruz, J., M. A. Gutiérrez, and L. Koene. "Stochastic simulation of pitting corrosion." In III European Conference on Computational Mechanics, 665. Dordrecht: Springer Netherlands, 2006. http://dx.doi.org/10.1007/1-4020-5370-3_665.
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Giesa, Tristan, Graham Bratzel, and Markus J. Buehler. "Modeling and Simulation of Hierarchical Protein Materials." In Nano and Cell Mechanics, 389–409. Chichester, UK: John Wiley & Sons, Ltd, 2012. http://dx.doi.org/10.1002/9781118482568.ch15.
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Jacobs, Christopher R., and Daniel J. Kelly. "Cell mechanics: The role of simulation." In Computational Methods in Applied Sciences, 1–14. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-1254-6_1.
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Zhu, Dong. "Numerical Simulation of Surface Contact and Mixed Lubrication — Deterministic Approach vs. Stochastic Approach." In Computational Mechanics, 394. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-75999-7_194.
Full textConference papers on the topic "Cell Mechanics -Stochastic Simulation"
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Lin, Chan-Chiao, Huei Peng, Min Joong Kim, and Jessy W. Grizzle. "Integrated Dynamic Simulation Model With Supervisory Control Strategy for a PEM Fuel Cell Hybrid Vehicle." In ASME 2004 International Mechanical Engineering Congress and Exposition. ASMEDC, 2004. http://dx.doi.org/10.1115/imece2004-61775.
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System-level modeling and control strategy development for a hybrid fuel cell vehicle (HFCV) are presented in this paper. A reduced-order fuel cell model is created to accurately predict the fuel cell system efficiency while retaining dynamic effects of important reactant variables. The fuel cell system model is then integrated with a DC/DC converter, a Li-Ion battery, an electric drive and tire/vehicle dynamics to form a HFCV. The supervisory-level control problem of the HFCV is subsequently investigated. A stochastic dynamic programming (SDP) based approach is applied to obtain an optimal power management strategy. Simulations over different driving cycles showed that the SDP control strategy not only saved a significant amount of hydrogen but also produced smoother load for the fuel cell stack—both of which help the long term viability of the fuel cell technology for automotive applications.
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Pappu, Vijay, and Prosenjit Bagchi. "Capture, Deformation, Rolling and Detachment of a Cell on an Adhesive Surface in a Shear Flow." In ASME 2008 International Mechanical Engineering Congress and Exposition. ASMEDC, 2008. http://dx.doi.org/10.1115/imece2008-67742.
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Three-dimensional computational modeling and simulation using front tracking method are presented on the motion of a deformable cell over an adhesive surface in a shear flow. The numerical method couples a Navier-Stokes flow solver with cell membrane mechanics, and a Monte Carlo simulation to capture stochastic formation and breakage of receptor/ligand bonds. The entire range of events during cell adhesion, namely, initial arrest of a free-flowing cell, slow rolling of an adherent cell, and detachment off the surface is simulated. Simulations are conducted to signify the role of hydrodynamic lift force that exists for a deformable particle in a wall-bounded flow. Three sets of numerical experiments are presented. In the first set, we consider the initial arrest of the cell, and show that the time needed for the cell to arrest increases with increasing Ca, but rapidly drops and saturates for higher bond strength. In the second set, we consider quasi-steady rolling motion of the cell, and predict the experimentally observed “stop and go” motion of the rolling leukocytes which is characterized by intermittent pauses and sudden jumps in cell velocity. In the third set we consider the detachment of the cell from the surface upon breakage of bonds. The bond strength needed to prevent the detachment of an adherent cell is computed and shown to be maximum for an intermediate Ca.
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Johnston, Joel, and Aditi Chattopadhyay. "Stochastic Multiscale Modeling and Damage Progression for Composite Materials." In ASME 2013 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/imece2013-66566.
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Modeling and characterization of complex composite structures is challenging due to uncertainties inherent in these materials. Uncertainty is present at each length scale in composites and must be quantified in order to accurately model the mechanical response and damage progression of this material. The ability to pass information between length scales permits multiscale models to transport uncertainties from one scale to the next. Limitations in the physics and errors in numerical methods pose additional challenges for composite models. By replacing deterministic inputs with random inputs, stochastic methods can be implemented within these multiscale models making them more robust. This work focuses on understanding the sensitivity of multiscale models and damage progression variations to stochastic input parameters as well as quantifying these uncertainties within a modeling framework. A multiscale, sectional model is used due to its efficiency and capacity to incorporate stochastic methods with little difficulty. The sectional micromechanics in this model are similar to that of the Generalized Method of Cells with the difference being the discretization techniques of the unit cell and the continuity conditions. A Latin Hypercube sampling technique is used due to its reported computational savings over other methods such as a fully random Monte Carlo simulation. Specifically in the sectional model, the Latin Hypercube sampling method provides an approximate 35 % reduction in computations compared to the fully random Monte Carlo method. The Latin Hypercube sampling is a stratified technique which discretizes the distribution function and randomizes the input parameters within those discretized fields. Within this multiscale modeling framework, a progressive failure theory is implemented using these stochastic methods and a modified Hashin failure theory. With a combined stochastic method and progressive failure theory, this multiscale model is capable of modeling the uncertainty and material property variations for composite materials.
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Amirpourabasi, Arezoo, Mohammad Pourgol-Mohammad, and Hanieh Niroomand-Oscuii. "Reliability Evaluation for Biomechanics Transient Stresses: Case Study of Biological Cell Vitality in Freezing Process." In ASME 2014 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/imece2014-39468.
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This research proposes reliability evaluation for performance of biological transient mechanical stresses scenarios. This is part of a broader research done by the authors for comprehensive analysis of the biological reliability analysis [1]. A literature review is conducted in area of biomechanics phenomenological processes in order to classify the approaches for success criteria determination and reliability metrics calculation based on their merits and limitations. A limited failure mode and effect analysis is performed as a pre-processor for identification of the corresponding figure of merits. Biological environment is complex in correlated occurrence of microscopic and macroscopic phenomena. Therefore the modeling of this complex medium, in context of mechanical stresses, requires numerical solution of conservation equation and inclusion of corresponding constitution models. Determination of success criteria (first phase for reliability calculation in this research) is a challenging object, and requires consideration of several dependent figures of merits (e.g. temperature, mass and etc.). The developed success criteria matrix is based on the approaches of representation of the figures of merit. A multi-objective criteria is developed according to the phenomena occurrence in the intended study and the selected proper figure(s) of merit. The matrix determines the region of acceptance as well as the rejection area. The reliability index is proposed to estimate the probability of the success based on the calculated system performance in a non-deterministic (stochastic parameters) approach. By augmentation of developed success criteria and the system analysis calculation, a decision is made on the success and rejection of the system performance the methodology is applied to the case of cell cryopreservation phenomenon. The process of freezing in living cells is considerably more complicated than in a solution, primarily due to the presence of cellular structure. The process is considered a transient mechanical stress on the cell structure including the thermal and mass transport. The success criteria is determined based on two figures of merit of temperature and mass and their rate of change. Numerical calculation is completed for study of thermal and mass behavior for the transient of the cell. The uncertain parameters are considered random and Monte Carlo simulation is conducted for inclusion of their variation in the calculation. The situation are specified for the observation of the success criteria and occurrence of the failure.
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de Carvalho, Thiago P., Hervé P. Morvan, David Hargreaves, Hatem Oun, and Andrew Kennedy. "Experimental and Tomography-Based CFD Investigations of the Flow in Open Cell Metal Foams With Application to Aero Engine Separators." In ASME Turbo Expo 2015: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/gt2015-43509.
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Oil-air separation is a key function in aero engines with closed-loop oil systems. Typically, aero engine air/oil separators employ the use of a porous medium such as open cell metal foams, as a secondary separation mechanism. Assessing its impact on overall separation is important since non-captured oil is released overboard. Computational fluid dynamics offers a possibility to evaluate the metal foam separation effectiveness. A pore scale numerical modelling methodology is applied to determine the transport properties of fluid flow through open cell metal foams. Microcomputer tomography scans were used to generate a 3D digital representation of commercial open cell metal foams of different grades. Foam structural properties such as porosity, specific surface, pore size distribution and the minimum size of a representative elementary volume are directly extracted from the CT scans. Subsequently, conventional finite volume simulations are carried out on the realistic tomography-based foam samples. Simulations were performed for a wide range of Reynolds numbers. The feasibility of using standard Reynolds-averaged Navier-Stokes (RANS) turbulence models is investigated here. As part of the method validation, samples with varying lengths were simulated. Pressure drop values were compared on a length-normalized basis against in-house experimental data. The oil phase was modelled using a Lagrangian particle tracking approach. Boundary conditions for the oil phase were extracted from a previous CFD simulation of a full breather device in the ground idle regime (worst separation effectiveness). Steady state particle tracking simulations were run for droplet diameters ranging from 0.5–15 μm, and for flow inlet velocities ranging from 10–60 m/s. Stochastic tracking was taken into account in order to model the effects of turbulence on the particle trajectories. Simulations were run on different types of foam and the results are compared qualitatively. The procedure has shown that pore scale modelling is a valid tool to capture the flow field and model oil separation inside open cell metal foams. However, at the moment there is no experimental data available for validation of the oil phase modelling.
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Yan, Karen Chang, Aren Moy, and Michael Sebok. "Modeling of Diffusive Behavior of Macromolecules Encapsulated in Electrospun Fibers." In ASME 2016 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/imece2016-67770.
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Electrospun (ES) fibers made of biocompatible polymers have been used as scaffolds in tissue engineering due to the potential to mimic the fibrous environment found in the extracellular matrix of biological tissue. Bioactive macromolecules such as growth factors have also been incorporated in the electrospun fibers to promote cell growth and differentiation. Therefore, it is critical to understand and control the release rate of the bioactive molecules. This paper presents the development of a stochastic simulation method to model the diffusive behaviors of macromolecules encapsulated in electrospun fibers. Specifically, a given ES fiber sample is represented by a set of random fibers with total fiber number denoted as N. Each fiber in the set is assumed as a cylinder and has a randomly assigned diameter and length, these parameters are based on statistical distributions determined from physical fiber samples. The Fick’s diffusion equation is used to solve the concentration of encapsulated macromolecules in the fiber due to diffusion. Upon obtaining the solution of the concentration of molecules in individual fibers, one can determine the overall diffusion behavior for a given sample with random fibers distributed. A subsequent statistical characterization can be performed based on the results of a set of random generated samples. Moreover, the developed method can be applied to the diffusion of macromolecules encapsulated in microspheres. The developed method was implemented in MATHEMATICA. As an example, the ES fiber samples were generated via electrospinning alginate and poly(ethylene oxide) (PEO) blend polymer aqueous solution (1:1 ratio, 3% w/v), and FITC–dextran was mixed in the polymer solution to enable fluorescent image analysis. The fiber diameter, length and number of fibers were determined based on the fluorescent images of fiber samples. Parametric study was conducted to examine how the diffusive behavior of encapsulated macromolecules is affected by the fiber diameters, total number of fibers, diffusion constants, and boundary conditions. Furthermore, the stochastic analyses were conducted for cases of the diffusion of macromolecules encapsulated in microspheres. The model predictions agree well with the experimental data obtained from the literature.
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"Numerical simulation of stochastic process as a model of technical object state changes." In Engineering Mechanics 2018. Institute of Theoretical and Applied Mechanics of the Czech Academy of Sciences, 2018. http://dx.doi.org/10.21495/91-8-485.
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Wielgos, Piotr, Tomasz Lipecki, and Andrzej Flaga. "Simulation of stochastic wind action on transmission power lines." In COMPUTER METHODS IN MECHANICS (CMM2017): Proceedings of the 22nd International Conference on Computer Methods in Mechanics. Author(s), 2018. http://dx.doi.org/10.1063/1.5019114.
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Naik, Pranjal, and Sayan Gupta. "Parallel Computing in Stochastic Finite Element Analysis." In 5th International Congress on Computational Mechanics and Simulation. Singapore: Research Publishing Services, 2014. http://dx.doi.org/10.3850/978-981-09-1139-3_446.
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Bocchini, Paolo, Dan M. Frangopol, and George Deodatis. "Computationally Efficient Simulation Techniques for Bridge Network Maintenance Optimization under Uncertainty." In 6th International Conference on Computational Stochastic Mechanics. Singapore: Research Publishing Services, 2011. http://dx.doi.org/10.3850/978-981-08-7619-7_p010.
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