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Статті в журналах з теми "Cell Mechanics -Stochastic Simulation"

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Автор: Grafiati

Опубліковано: 7 вересня 2023

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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.

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11

Kumar, Sandeep, Alakesh Das, and Shamik Sen. "Multicompartment cell-based modeling of confined migration: regulation by cell intrinsic and extrinsic factors." Molecular Biology of the Cell 29, no. 13 (July 2018): 1599–610. http://dx.doi.org/10.1091/mbc.e17-05-0313.

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Though cell and nuclear deformability are expected to influence efficiency of confined migration, their individual and collective influence on migration efficiency remains incompletely understood. In addition to cell intrinsic properties, the relevance of cell extrinsic factors on confined migration, if any, has not been adequately explored. Here we address these questions using a statistical mechanics-based stochastic modeling approach where cell/nuclear dimensions and their deformability are explicitly taken into consideration. In addition to demonstrating the importance of cell softness in sustaining confined migration, our results suggest that dynamic tuning of cell and nuclear properties at different stages of migration is essential for maximizing migration efficiency. Our simulations also implicate confinement shape and confinement history as two important cell extrinsic regulators of cell invasiveness. Together, our findings illustrate the strength of a multicompartment model in dissecting the contributions of multiple factors that collectively influence confined cell migration.

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12

Wu, Guofang, Yinlan Shen, Feng Fu, Juan Guo, and Haiqing Ren. "Study of the Mechanical Properties of Wood under Transverse Compression Using Monto Carlo Simulation-Based Stochastic FE Analysis." Forests 13, no. 1 (December 28, 2021): 32. http://dx.doi.org/10.3390/f13010032.

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Wood is an anisotropic material, the mechanical properties of which are strongly influenced by its microstructure. In wood, grain compression strength and modulus are the weakest perpendicular to the grain compared to other grain directions. FE (finite element) models have been developed to investigate the mechanical properties of wood under transverse compression. However, almost all existing models were deterministic. Thus, the variations of geometry of the cellular structure were not considered, and the statistical characteristic of the mechanical property was not involved. This study aimed to develop an approach to investigate the compression property of wood in a statistical sense by considering the irregular geometry of wood cells. First, the mechanical properties of wood under radial perpendicular to grain compression was experimentally investigated, then the statistical characteristic of cell geometry was extracted from test data. Finally, the mechanical property of wood was investigated using the finite element method in combination with the Monte Carlo Simulation (MCS) techniques using randomly generated FE models. By parameter sensitivity analysis, it was found that the occurrence of the yield points was caused by the bending or buckling of the earlywood axial tracheid cell wall in the tangential direction. The MCS-based stochastic FE analysis was revealed as an interesting approach for assessing the micro-mechanical performance of wood and in assisting in understanding the mechanical behavior of wood based on its hierarchical structure.

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13

Welf, Erik S., Heath E. Johnson, and Jason M. Haugh. "Bidirectional coupling between integrin-mediated signaling and actomyosin mechanics explains matrix-dependent intermittency of leading-edge motility." Molecular Biology of the Cell 24, no. 24 (December 15, 2013): 3945–55. http://dx.doi.org/10.1091/mbc.e13-06-0311.

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Animal cell migration is a complex process characterized by the coupling of adhesion, cytoskeletal, and signaling dynamics. Here we model local protrusion of the cell edge as a function of the load-bearing properties of integrin-based adhesions, actin polymerization fostered by adhesion-mediated signaling, and mechanosensitive activation of RhoA that promotes myosin II–generated stress on the lamellipodial F-actin network. Analysis of stochastic model simulations illustrates how these pleiotropic functions of nascent adhesions may be integrated to govern temporal persistence and frequency of protrusions. The simulations give mechanistic insight into the documented effects of extracellular matrix density and myosin abundance, and they show characteristic, nonnormal distributions of protrusion duration times that are similar to those extracted from live-cell imaging experiments. Analysis of the model further predicts relationships between measurable quantities that reflect the partitioning of stress between tension on F-actin–bound adhesions, which act as a molecular clutch, and dissipation by retrograde F-actin flow.

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14

Banavar, Samhita P., Michael Trogdon, Brian Drawert, Tau-Mu Yi, Linda R. Petzold, and Otger Campàs. "Coordinating cell polarization and morphogenesis through mechanical feedback." PLOS Computational Biology 17, no. 1 (January 28, 2021): e1007971. http://dx.doi.org/10.1371/journal.pcbi.1007971.

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Many cellular processes require cell polarization to be maintained as the cell changes shape, grows or moves. Without feedback mechanisms relaying information about cell shape to the polarity molecular machinery, the coordination between cell polarization and morphogenesis, movement or growth would not be possible. Here we theoretically and computationally study the role of a genetically-encoded mechanical feedback (in the Cell Wall Integrity pathway) as a potential coordination mechanism between cell morphogenesis and polarity during budding yeast mating projection growth. We developed a coarse-grained continuum description of the coupled dynamics of cell polarization and morphogenesis as well as 3D stochastic simulations of the molecular polarization machinery in the evolving cell shape. Both theoretical approaches show that in the absence of mechanical feedback (or in the presence of weak feedback), cell polarity cannot be maintained at the projection tip during growth, with the polarization cap wandering off the projection tip, arresting morphogenesis. In contrast, for mechanical feedback strengths above a threshold, cells can robustly maintain cell polarization at the tip and simultaneously sustain mating projection growth. These results indicate that the mechanical feedback encoded in the Cell Wall Integrity pathway can provide important positional information to the molecular machinery in the cell, thereby enabling the coordination of cell polarization and morphogenesis.

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15

Nuss, Dominik, Stephan Reuter, Markus Thom, Ting Yuan, Gunther Krehl, Michael Maile, Axel Gern, and Klaus Dietmayer. "A random finite set approach for dynamic occupancy grid maps with real-time application." International Journal of Robotics Research 37, no. 8 (July 2018): 841–66. http://dx.doi.org/10.1177/0278364918775523.

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Grid mapping is a well-established approach for environment perception in robotic and automotive applications. Early work suggests estimating the occupancy state of each grid cell in a robot’s environment using a Bayesian filter to recursively combine new measurements with the current posterior state estimate of each grid cell. This filter is often referred to as binary Bayes filter. A basic assumption of classical occupancy grid maps is a stationary environment. Recent publications describe bottom-up approaches using particles to represent the dynamic state of a grid cell and outline prediction-update recursions in a heuristic manner. This paper defines the state of multiple grid cells as a random finite set, which allows to model the environment as a stochastic, dynamic system with multiple obstacles, observed by a stochastic measurement system. It motivates an original filter called the probability hypothesis density / multi-instance Bernoulli (PHD/MIB) filter in a top-down manner. The paper presents a real-time application serving as a fusion layer for laser and radar sensor data and describes in detail a highly efficient parallel particle filter implementation. A quantitative evaluation shows that parameters of the stochastic process model affect the filter results as theoretically expected and that appropriate process and observation models provide consistent state estimation results.

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16

Hetmanski, Joseph H. R., Matthew C. Jones, Fatima Chunara, Jean-Marc Schwartz, and Patrick T. Caswell. "Combinatorial mathematical modelling approaches to interrogate rear retraction dynamics in 3D cell migration." PLOS Computational Biology 17, no. 3 (March 10, 2021): e1008213. http://dx.doi.org/10.1371/journal.pcbi.1008213.

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Cell migration in 3D microenvironments is a complex process which depends on the coordinated activity of leading edge protrusive force and rear retraction in a push-pull mechanism. While the potentiation of protrusions has been widely studied, the precise signalling and mechanical events that lead to retraction of the cell rear are much less well understood, particularly in physiological 3D extra-cellular matrix (ECM). We previously discovered that rear retraction in fast moving cells is a highly dynamic process involving the precise spatiotemporal interplay of mechanosensing by caveolae and signalling through RhoA. To further interrogate the dynamics of rear retraction, we have adopted three distinct mathematical modelling approaches here based on (i) Boolean logic, (ii) deterministic kinetic ordinary differential equations (ODEs) and (iii) stochastic simulations. The aims of this multi-faceted approach are twofold: firstly to derive new biological insight into cell rear dynamics via generation of testable hypotheses and predictions; and secondly to compare and contrast the distinct modelling approaches when used to describe the same, relatively under-studied system. Overall, our modelling approaches complement each other, suggesting that such a multi-faceted approach is more informative than methods based on a single modelling technique to interrogate biological systems. Whilst Boolean logic was not able to fully recapitulate the complexity of rear retraction signalling, an ODE model could make plausible population level predictions. Stochastic simulations added a further level of complexity by accurately mimicking previous experimental findings and acting as a single cell simulator. Our approach highlighted the unanticipated role for CDK1 in rear retraction, a prediction we confirmed experimentally. Moreover, our models led to a novel prediction regarding the potential existence of a ‘set point’ in local stiffness gradients that promotes polarisation and rapid rear retraction.

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17

Vermolen, F. J., M. M. Mul, and A. Gefen. "Semi-stochastic cell-level computational modeling of the immune system response to bacterial infections and the effects of antibiotics." Biomechanics and Modeling in Mechanobiology 13, no. 4 (September 26, 2013): 713–34. http://dx.doi.org/10.1007/s10237-013-0529-5.

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18

Winkle, James J., Bhargav R. Karamched, Matthew R. Bennett, William Ott, and Krešimir Josić. "Emergent spatiotemporal population dynamics with cell-length control of synthetic microbial consortia." PLOS Computational Biology 17, no. 9 (September 22, 2021): e1009381. http://dx.doi.org/10.1371/journal.pcbi.1009381.

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The increased complexity of synthetic microbial biocircuits highlights the need for distributed cell functionality due to concomitant increases in metabolic and regulatory burdens imposed on single-strain topologies. Distributed systems, however, introduce additional challenges since consortium composition and spatiotemporal dynamics of constituent strains must be robustly controlled to achieve desired circuit behaviors. Here, we address these challenges with a modeling-based investigation of emergent spatiotemporal population dynamics using cell-length control in monolayer, two-strain bacterial consortia. We demonstrate that with dynamic control of a strain’s division length, nematic cell alignment in close-packed monolayers can be destabilized. We find that this destabilization confers an emergent, competitive advantage to smaller-length strains—but by mechanisms that differ depending on the spatial patterns of the population. We used complementary modeling approaches to elucidate underlying mechanisms: an agent-based model to simulate detailed mechanical and signaling interactions between the competing strains, and a reductive, stochastic lattice model to represent cell-cell interactions with a single rotational parameter. Our modeling suggests that spatial strain-fraction oscillations can be generated when cell-length control is coupled to quorum-sensing signaling in negative feedback topologies. Our research employs novel methods of population control and points the way to programming strain fraction dynamics in consortial synthetic biology.

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19

Chen, Jiao, Daphne Weihs, and Fred J. Vermolen. "Computational modeling of therapy on pancreatic cancer in its early stages." Biomechanics and Modeling in Mechanobiology 19, no. 2 (September 9, 2019): 427–44. http://dx.doi.org/10.1007/s10237-019-01219-0.

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Abstract More than eighty percent of pancreatic cancer involves ductal adenocarcinoma with an abundant desmoplastic extracellular matrix surrounding the solid tumor entity. This aberrant tumor microenvironment facilitates a strong resistance of pancreatic cancer to medication. Although various therapeutic strategies have been reported to be effective in mice with pancreatic cancer, they still need to be tested quantitatively in wider animal-based experiments before being applied as therapies. To aid the design of experiments, we develop a cell-based mathematical model to describe cancer progression under therapy with a specific application to pancreatic cancer. The displacement of cells is simulated by solving a large system of stochastic differential equations with the Euler–Maruyama method. We consider treatment with the PEGylated drug PEGPH20 that breaks down hyaluronan in desmoplastic stroma followed by administration of the chemotherapy drug gemcitabine to inhibit the proliferation of cancer cells. Modeling the effects of PEGPH20 + gemcitabine concentrations is based on Green’s fundamental solutions of the reaction–diffusion equation. Moreover, Monte Carlo simulations are performed to quantitatively investigate uncertainties in the input parameters as well as predictions for the likelihood of success of cancer therapy. Our simplified model is able to simulate cancer progression and evaluate treatments to inhibit the progression of cancer.

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20

Lang, Tobias Georg, Mir Mohammad Badrul Hasan, Anwar Abdkader, Chokri Cherif, and Thomas Gereke. "Micro-Scale Model of rCF/PA6 Spun Yarn Composite." Journal of Composites Science 7, no. 2 (February 6, 2023): 66. http://dx.doi.org/10.3390/jcs7020066.

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Recycling carbon fibers (rCF) for reuse is one approach to improve the sustainability of CFRP. However, until now, recycled carbon fiber plastics (rCFRP) had limited composite properties due to the microgeometry of the fibers, which made it difficult to use in load-bearing components. The production of hybrid yarns from rCF and PA6 fibers allows the fibers to be aligned. The geometric properties of the yarn and the individual fibers influence the mechanical properties of the composite. An approach for the modeling and simulation of hybrid yarns consisting of recycled carbon fibers and thermoplastic fibers is presented. The yarn unit cell geometry is modeled in the form of a stochastic fiber network. The fiber trajectory is modeled in form of helical curves using the idealized yarn model of Hearle et al. The variability in the fiber geometry (e.g., length) is included in form of statistical distributions. An additional compaction step ensures a realistic composite geometry. The created model is validated geometrically and by comparison with tensile tests of manufactured composites. With the validated model, multiple parameter studies investigating the influence of fiber and yarn geometry are carried out.

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21

Ciocanel, Maria-Veronica, Aravind Chandrasekaran, Carli Mager, Qin Ni, Garegin A. Papoian, and Adriana Dawes. "Simulated actin reorganization mediated by motor proteins." PLOS Computational Biology 18, no. 4 (April 7, 2022): e1010026. http://dx.doi.org/10.1371/journal.pcbi.1010026.

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Cortical actin networks are highly dynamic and play critical roles in shaping the mechanical properties of cells. The actin cytoskeleton undergoes significant reorganization in many different contexts, including during directed cell migration and over the course of the cell cycle, when cortical actin can transition between different configurations such as open patched meshworks, homogeneous distributions, and aligned bundles. Several types of myosin motor proteins, characterized by different kinetic parameters, have been involved in this reorganization of actin filaments. Given the limitations in studying the interactions of actin with myosin in vivo, we propose stochastic agent-based models and develop a set of data analysis measures to assess how myosin motor proteins mediate various actin organizations. In particular, we identify individual motor parameters, such as motor binding rate and step size, that generate actin networks with different levels of contractility and different patterns of myosin motor localization, which have previously been observed experimentally. In simulations where two motor populations with distinct kinetic parameters interact with the same actin network, we find that motors may act in a complementary way, by tuning the actin network organization, or in an antagonistic way, where one motor emerges as dominant. This modeling and data analysis framework also uncovers parameter regimes where spatial segregation between motor populations is achieved. By allowing for changes in kinetic rates during the actin-myosin dynamic simulations, our work suggests that certain actin-myosin organizations may require additional regulation beyond mediation by motor proteins in order to reconfigure the cytoskeleton network on experimentally-observed timescales.

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22

Catterall, S. M., I. T. Drummond, and R. R. Horgan. "Stochastic simulation of quantum mechanics." Journal of Physics A: Mathematical and General 24, no. 17 (September 7, 1991): 4081–91. http://dx.doi.org/10.1088/0305-4470/24/17/025.

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23

Shin, Yong Cheol, Woojung Shin, Domin Koh, Alexander Wu, Yoko M. Ambrosini, Soyoun Min, S. Gail Eckhardt, et al. "Three-Dimensional Regeneration of Patient-Derived Intestinal Organoid Epithelium in a Physiodynamic Mucosal Interface-on-a-Chip." Micromachines 11, no. 7 (July 7, 2020): 663. http://dx.doi.org/10.3390/mi11070663.

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The regeneration of the mucosal interface of the human intestine is critical in the host–gut microbiome crosstalk associated with gastrointestinal diseases. The biopsy-derived intestinal organoids provide genetic information of patients with physiological cytodifferentiation. However, the enclosed lumen and static culture condition substantially limit the utility of patient-derived organoids for microbiome-associated disease modeling. Here, we report a patient-specific three-dimensional (3D) physiodynamic mucosal interface-on-a-chip (PMI Chip) that provides a microphysiological intestinal milieu under defined biomechanics. The real-time imaging and computational simulation of the PMI Chip verified the recapitulation of non-linear luminal and microvascular flow that simulates the hydrodynamics in a living human gut. The multiaxial deformations in a convoluted microchannel not only induced dynamic cell strains but also enhanced particle mixing in the lumen microchannel. Under this physiodynamic condition, an organoid-derived epithelium obtained from the patients diagnosed with Crohn’s disease, ulcerative colitis, or colorectal cancer independently formed 3D epithelial layers with disease-specific differentiations. Moreover, co-culture with the human fecal microbiome in an anoxic–oxic interface resulted in the formation of stochastic microcolonies without a loss of epithelial barrier function. We envision that the patient-specific PMI Chip that conveys genetic, epigenetic, and environmental factors of individual patients will potentially demonstrate the pathophysiological dynamics and complex host–microbiome crosstalk to target a patient-specific disease modeling.

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24

Luvsantseren, Purevdolgor, Enkhbayar Purevjav, and Khenmedeh Lochin. "Stochastic simulation of cell cycle." Advanced Studies in Biology 5 (2013): 1–9. http://dx.doi.org/10.12988/asb.2013.13001.

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25

Dershowitz, Bill, Paul LaPointe, Thorsten Eiben, and Lingli Wei. "Integration of Discrete Feature Network Methods With Conventional Simulator Approaches." SPE Reservoir Evaluation & Engineering 3, no. 02 (April 1, 2000): 165–70. http://dx.doi.org/10.2118/62498-pa.

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Summary The discrete feature network (DFN) approach offers many key advantages over conventional dual porosity (DP) approaches, particularly when issues of connectivity dominate recovery and reservoir stimulation in fractured and heterogeneous reservoirs. DP models have been developed for complex multiphase and thermal effects, and have been implemented for basin scale modeling. However, DP models address only the dual porosity nature of fractured reservoirs, generally simplifying connectivity and scale-dependent heterogeneity issues which are modeled efficiently and more accurately by the DFN approach. This paper describes the development of techniques to integrate DFN and DP approaches. These techniques allow the analyst to maintain many of the advantages of the DP simulator approach without losing the realism of complex fracture system geometry and connectivity, as captured by DFN models. The techniques described are currently used within a DOE funded research project for linking a DFN and a DP thermal simulation model for the Yates field, Texas. The paper describes some of the geological and engineering aspects of the Yates field and gives two examples of how DP parameters for the thermal simulation can be derived using DFN modeling techniques. Introduction Reservoir simulation can be significantly more challenging for fractured reservoirs than it is for conventional clastic reservoirs. The dual porosity (DP) approach for the simulation of fractured reservoirs adds a second interacting continuum to reflect storage and permeability characteristics but does not adequately address connectivity issues. These effects, which play a key role in fractured reservoirs, are generally better addressed by discrete feature network (DFN) models.1 Another advantage of DFN models is that they are generally implemented as stochastic models, in which multiple realizations provide a quantitative measure for uncertainty and variability. Despite the significant simplifications made regarding the geometry of the fracture network in equivalent porous medium DP models (Fig. 1) and the recent progress made in developing powerful DFN modeling software, DP models still offer advantages regarding the level of sophistication of available multiphase flow solvers. In many cases, DP models also offer advantages regarding model size and speed. As a result, there is a need to link DP and DFN models to be able to take maximum advantage of each approach. This paper presents a series of techniques, which can be used to develop DP models that more accurately reflect the anisotropy, heterogeneity, and most important, the scale-dependent connectivity structure of fractured reservoirs. These techniques will allow the DP approach to take advantage of some of the features of the DFN approach. The approach adopted is to derive grid cell and well parameters through DFN models. The first section of this paper discusses which fracture porosity parameters can be derived for DP models from DFN models and how they are derived. The second section describes different techniques that can be used to link DP and DFN models. At the end of the paper two examples are given based on data from the Yates field, Texas. DP Input Parameter from DFN Modeling Fracture System Porosity. The fracture system porosity fF can be directly calculated as the product of the fracture intensity expressed as fracture area per unit volume (P32) and the storage aperture of the fractures (e):… Because the fracture system porosity depends on the number of fractures per unit volume, the fracture size distribution and the fracture aperture distribution, a different porosity needs to be calculated for every portion of the continuum model where these parameters vary. Using a full field DFN model, the fracture system porosity can be calculated separately for each grid cell. The primary issue in definition of fracture porosity from fracture intensity P32 is the selection of an appropriate measure for storage aperture e. Possible measures include:aperture derived from transient hydraulic response,mechanical aperture,aperture derived from fracture permeability or transmissivity ("cubic law"),aperture derived from geophysical measurements (gamma density, matrix porosity), andcorrelations to fracture size and orientation. Directional Fracture System Permeability. The permeability of the fracture system depends on the fracture intensity, the connectivity of the fracture network, and the distribution of fracture transmissivities. Approaches for calculation of approximate measures of grid cell effective directional permeability include the tensor approach of Oda,2 and the use of DFN simulations with a range of orientations for a unit gradient. Oda's2 method begins by considering the orientation of fractures in a grid cell, expressed as a unit normal vector n. Integrating the fractures over all of the unit normals N, Oda obtained the mass moment of inertia of fracture normals distributed over a unit sphere: ….For a specific grid cell with known fracture areas Ak and transmissivities Tk obtained from the DFN model, an empirical fracture tensor can be calculated by adding the individual fractures weighted by their area and transmissivity:…. Oda's permeability tensor is derived from Fij by assuming that Fij expresses fracture flow as a vector along the fracture's unit normal. Assuming that fractures are impermeable in a direction parallel to their unit normal, Fij must be rotated into the planes of permeability ….

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26

Marov, M. Ya, A. E. Korolev, V. P. Osipov, and A. A. Samylkin. "Numerical stochastic simulation of cluster formation." Doklady Physics 55, no. 6 (June 2010): 283–86. http://dx.doi.org/10.1134/s1028335810060091.

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27

Nakaoka, Shinji, and Kazuyuki Aihara. "Stochastic simulation of structured skin cell population dynamics." Journal of Mathematical Biology 66, no. 4-5 (December 20, 2012): 807–35. http://dx.doi.org/10.1007/s00285-012-0618-6.

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28

Deodatis, George. "Simulation of Ergodic Multivariate Stochastic Processes." Journal of Engineering Mechanics 122, no. 8 (August 1996): 778–87. http://dx.doi.org/10.1061/(asce)0733-9399(1996)122:8(778).

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29

Zakian, Pooya. "Stochastic finite cell method for structural mechanics." Computational Mechanics 68, no. 1 (May 19, 2021): 185–210. http://dx.doi.org/10.1007/s00466-021-02026-0.

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30

Daigle, Bernie J., Mohammad Soltani, Linda R. Petzold, and Abhyudai Singh. "Inferring single-cell gene expression mechanisms using stochastic simulation." Bioinformatics 31, no. 9 (January 7, 2015): 1428–35. http://dx.doi.org/10.1093/bioinformatics/btv007.

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31

Van Segbroeck, Sven, Ann Nowé, and Tom Lenaerts. "Stochastic Simulation of the Chemoton." Artificial Life 15, no. 2 (April 2009): 213–26. http://dx.doi.org/10.1162/artl.2009.15.2.15203.

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Gánti's chemoton model is an illustrious example of a minimal cell model. It is composed of three stoichiometrically coupled autocatalytic subsystems: a metabolism, a template replication process, and a membrane enclosing the other two. Earlier studies on chemoton dynamics yield inconsistent results. Furthermore, they all appealed to deterministic simulations, which do not take into account the stochastic effects induced by small population sizes. We present, for the first time, results of a chemoton simulation in which these stochastic effects have been taken into account. We investigate the dynamics of the system and analyze in depth the mechanisms responsible for the observed behavior. Our results suggest that, in contrast to the most recent study by Munteanu and Solé, the stochastic chemoton reaches a unique stable division time after a short transient phase. We confirm the existence of an optimal template length and show that this is a consequence of the monomer concentration, which depends on the template length and the initiation threshold. Since longer templates imply shorter division times, these results motivate the selective pressure toward longer templates observed in nature.

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32

Garner, Andrew J. P., Qing Liu, Jayne Thompson, Vlatko Vedral, and mile Gu. "Provably unbounded memory advantage in stochastic simulation using quantum mechanics." New Journal of Physics 19, no. 10 (October 12, 2017): 103009. http://dx.doi.org/10.1088/1367-2630/aa82df.

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33

Taflanidis, Alexandros A., and James L. Beck. "An efficient framework for optimal robust stochastic system design using stochastic simulation." Computer Methods in Applied Mechanics and Engineering 198, no. 1 (November 2008): 88–101. http://dx.doi.org/10.1016/j.cma.2008.03.029.

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34

GONZÁLEZ–VÉLEZ, VIRGINIA, and HORACIO GONZÁLEZ–VÉLEZ. "PARALLEL STOCHASTIC SIMULATION OF MACROSCOPIC CALCIUM CURRENTS." Journal of Bioinformatics and Computational Biology 05, no. 03 (June 2007): 755–72. http://dx.doi.org/10.1142/s0219720007002679.

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This work introduces MACACO, a macroscopic calcium currents simulator. It provides a parameter-sweep framework which computes macroscopic Ca 2+ currents from the individual aggregation of unitary currents, using a stochastic model for L-type Ca 2+ channels. MACACO uses a simplified 3-state Markov model to simulate the response of each Ca 2+ channel to different voltage inputs to the cell. In order to provide an accurate systematic view for the stochastic nature of the calcium channels, MACACO is composed of an experiment generator, a central simulation engine and a post-processing script component. Due to the computational complexity of the problem and the dimensions of the parameter space, the MACACO simulation engine employs a grid-enabled task farm. Having been designed as a computational biology tool, MACACO heavily borrows from the way cell physiologists conduct and report their experimental work.

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35

Sørensen, J. D., and R. Brincker. "Simulation of stochastic loads for fatigue experiments." Experimental Mechanics 29, no. 2 (June 1989): 174–82. http://dx.doi.org/10.1007/bf02321372.

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36

Liu, Di. "Stochastic Simulation of the Cell Cycle Model for Budding Yeast." Communications in Computational Physics 9, no. 2 (February 2011): 390–405. http://dx.doi.org/10.4208/cicp.311009.100310a.

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AbstractWe use the recently proposed Nested Stochastic Simulation Algorithm (Nested SSA) to simulate the cell cycle model for budding yeast. The results show that Nested SSA is able to significantly reduce the computational cost while capturing the essential dynamical features of the system.

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37

Hadfi, Rafik, Sho Tokuda, and Takayuki Ito. "Traffic Simulation in Urban Networks Using Stochastic Cell Transmission Model." Computational Intelligence 33, no. 4 (April 3, 2017): 826–42. http://dx.doi.org/10.1111/coin.12115.

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38

Pate, Edward, and Roger Cooke. "Simulation of stochastic processes in motile crossbridge systems." Journal of Muscle Research and Cell Motility 12, no. 4 (August 1991): 376–93. http://dx.doi.org/10.1007/bf01738593.

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39

Deodatis, George, and Raymond C. Micaletti. "Simulation of Highly Skewed Non-Gaussian Stochastic Processes." Journal of Engineering Mechanics 127, no. 12 (December 2001): 1284–95. http://dx.doi.org/10.1061/(asce)0733-9399(2001)127:12(1284).

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40

Liang, Jianwen, Samit Ray Chaudhuri, and Masanobu Shinozuka. "Simulation of Nonstationary Stochastic Processes by Spectral Representation." Journal of Engineering Mechanics 133, no. 6 (June 2007): 616–27. http://dx.doi.org/10.1061/(asce)0733-9399(2007)133:6(616).

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41

Denk, G., C. Penski, and S. Schäffler. "Noise analysis in circuit simulation with stochastic differential equations." ZAMM - Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik 78, S3 (1998): 887–90. http://dx.doi.org/10.1002/zamm.19980781517.

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42

Foudil-Bey, Nacim, Jean-Jacques Royer, Li Cheng, Fouad Erchiqui, and Jean-Claude Mareschal. "Neural network stochastic simulation applied for quantifying uncertainties." International Journal of Multiphysics 7, no. 1 (March 2013): 31–40. http://dx.doi.org/10.1260/1750-9548.7.1.31.

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43

Schuëller, G. I. "Computational stochastic mechanics – recent advances." Computers & Structures 79, no. 22-25 (September 2001): 2225–34. http://dx.doi.org/10.1016/s0045-7949(01)00078-5.

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44

Malyarenko, Anatoliy, and Martin Ostoja-Starzewski. "Towards stochastic continuum damage mechanics." International Journal of Solids and Structures 184 (February 2020): 202–10. http://dx.doi.org/10.1016/j.ijsolstr.2019.02.023.

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45

Thornburg, Zane R., Benjamin R. Gilbert, Julio Maia, John E. Stone, Vinson Lam, Elizabeth Villa, and Zaida Luthey-Schulten. "Stochastic Spatial Simulation of Genetic Information Processes in the Minimal Cell." Biophysical Journal 120, no. 3 (February 2021): 109a. http://dx.doi.org/10.1016/j.bpj.2020.11.881.

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46

Smolle, Josef, and Haro Stettner. "Computer Simulation of Tumour Cell Invasion by a Stochastic Growth Model." Journal of Theoretical Biology 160, no. 1 (January 1993): 63–72. http://dx.doi.org/10.1006/jtbi.1993.1004.

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47

Deodatis, George, Masanobu Shinozuka, and Apostolos Papageorgiou. "Stochastic Wave Representation of Seismic Ground Motion. II: Simulation." Journal of Engineering Mechanics 116, no. 11 (November 1990): 2381–99. http://dx.doi.org/10.1061/(asce)0733-9399(1990)116:11(2381).

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48

Shimoni, Yishai, German Nudelman, Fernand Hayot, and Stuart C. Sealfon. "Multi-Scale Stochastic Simulation of Diffusion-Coupled Agents and Its Application to Cell Culture Simulation." PLoS ONE 6, no. 12 (December 21, 2011): e29298. http://dx.doi.org/10.1371/journal.pone.0029298.

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49

Kumaraian, Mohitrajhu Lingan, Jayamanideep Rebbagondla, Tittu Varghese Mathew, and Sundararajan Natarajan. "Stochastic Vibration Analysis of Functionally Graded Plates with Material Randomness Using Cell-Based Smoothed Discrete Shear Gap Method." International Journal of Structural Stability and Dynamics 19, no. 04 (April 2019): 1950037. http://dx.doi.org/10.1142/s0219455419500378.

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A cell-based smoothed finite element method with discrete shear gap technique is used to study the stochastic free vibration behavior of functionally graded plates with material uncertainty. The plate kinematics is based on the first-order shear deformation theory and the effective material properties are estimated by simple rule of mixtures. The input random field is represented by the Karhunen–Loéve expansion and the polynomial chaos expansion is used to represent the stochastic output response. The accuracy of the proposed approach in terms of the first- and the second-order statistical moments are demonstrated by comparing the results with the Monte Carlo Simulations. A systematic parametric study is carried out to bring out the influence of the material gradient index, the plate aspect ratio and the skewness of the plate on the stochastic global response of functionally graded plates. It is inferred that all the considered parameters significantly influence the statistical moments of the first fundamental mode.

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50

Shinozuka, Masanobu, and George Deodatis. "Simulation of Stochastic Processes by Spectral Representation." Applied Mechanics Reviews 44, no. 4 (April 1, 1991): 191–204. http://dx.doi.org/10.1115/1.3119501.

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The subject of this paper is the simulation of one-dimensional, uni-variate, stationary, Gaussian stochastic processes using the spectral representation method. Following this methodology, sample functions of the stochastic process can be generated with great computational efficiency using a cosine series formula. These sample functions accurately reflect the prescribed probabilistic characteristics of the stochastic process when the number N of the terms in the cosine series is large. The ensemble-averaged power spectral density or autocorrelation function approaches the corresponding target function as the sample size increases. In addition, the generated sample functions possess ergodic characteristics in the sense that the temporally-averaged mean value and the autocorrelation function are identical with the corresponding targets, when the averaging takes place over the fundamental period of the cosine series. The most important property of the simulated stochastic process is that it is asymptotically Gaussian as N → ∞. Another attractive feature of the method is that the cosine series formula can be numerically computed efficiently using the Fast Fourier Transform technique. The main area of application of this method is the Monte Carlo solution of stochastic problems in engineering mechanics and structural engineering. Specifically, the method has been applied to problems involving random loading (random vibration theory) and random material and geometric properties (response variability due to system stochasticity).

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