Littérature scientifique sur le sujet « Cell Mechanics -Stochastic Simulation »
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Articles de revues sur le sujet "Cell Mechanics -Stochastic Simulation"
Hanjalić, K., et S. Kenjereš. « RANS-Based Very Large Eddy Simulation of Thermal and Magnetic Convection at Extreme Conditions ». Journal of Applied Mechanics 73, no 3 (2 octobre 2005) : 430–40. http://dx.doi.org/10.1115/1.2150499.
Texte intégralGao, Huajian, Jin Qian et 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 (juin 2011) : 1217–32. http://dx.doi.org/10.1098/rsif.2011.0157.
Texte intégralLi, Long, Wei Kang et Jizeng Wang. « Mechanical Model for Catch-Bond-Mediated Cell Adhesion in Shear Flow ». International Journal of Molecular Sciences 21, no 2 (16 janvier 2020) : 584. http://dx.doi.org/10.3390/ijms21020584.
Texte intégralSadikin, Indera, Djoko Suharto, Bangkit Meliana, Kemal Supelli et Abdul Arya. « Probabilistic Fracture Mechanics Analysis for Optimization of High-Pressure Vessel Inspection ». Advanced Materials Research 33-37 (mars 2008) : 79–84. http://dx.doi.org/10.4028/www.scientific.net/amr.33-37.79.
Texte intégralSun, J. Q., et 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 (1 décembre 1990) : 1018–25. http://dx.doi.org/10.1115/1.2897620.
Texte intégralFritzsche, Marco, Christoph Erlenkämper, Emad Moeendarbary, Guillaume Charras et Karsten Kruse. « Actin kinetics shapes cortical network structure and mechanics ». Science Advances 2, no 4 (avril 2016) : e1501337. http://dx.doi.org/10.1126/sciadv.1501337.
Texte intégralBurini, D., et N. Chouhad. « A multiscale view of nonlinear diffusion in biology : From cells to tissues ». Mathematical Models and Methods in Applied Sciences 29, no 04 (avril 2019) : 791–823. http://dx.doi.org/10.1142/s0218202519400062.
Texte intégralCanela-Xandri, Oriol, Samira Anbari et Javier Buceta. « TiFoSi : an efficient tool for mechanobiology simulations of epithelia ». Bioinformatics 36, no 16 (26 juin 2020) : 4525–26. http://dx.doi.org/10.1093/bioinformatics/btaa592.
Texte intégralVermolen, F. J., et 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 (26 mars 2011) : 183–95. http://dx.doi.org/10.1007/s10237-011-0302-6.
Texte intégralChen, Jian, Xiongfei Li, Wei Li, Cong Li, Baoshan Xie, Shuowei Dai, Jian-Jun He et Yanjie Ren. « Research on energy absorption properties of open-cell copper foam for current collector of Li-ions ». Materials Science-Poland 37, no 1 (1 mars 2019) : 8–15. http://dx.doi.org/10.2478/msp-2019-0011.
Texte intégralThèses sur le sujet "Cell Mechanics -Stochastic Simulation"
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.
Texte intégralSzekely, 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.
Texte intégralChen, Minghan. « Stochastic Modeling and Simulation of Multiscale Biochemical Systems ». Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/90898.
Texte intégralDoctor 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.
Staber, Brian. « Stochastic analysis, simulation and identification of hyperelastic constitutive equations ». Thesis, Paris Est, 2018. http://www.theses.fr/2018PESC1042/document.
Texte intégralThis 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
Ahmadian, Mansooreh. « Hybrid Modeling and Simulation of Stochastic Effects on Biochemical Regulatory Networks ». Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/99481.
Texte intégralDoctor 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.
Hohenegger, Christel. « Small Scale Stochastic Dynamics For Particle Image Velocimetry Applications ». Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/10464.
Texte intégralCharlebois, 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.
Texte intégralLiu, Haipei, et 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.
Texte intégralpublished_or_final_version
Mechanical Engineering
Doctoral
Doctor of Philosophy
Wang, Shuo. « Analysis and Application of Haseltine and Rawlings's Hybrid Stochastic Simulation Algorithm ». Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/82717.
Texte intégralPh. D.
Wijanto, Florent. « Multiscale mechanics of soft tissues ». Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLX093.
Texte intégralFibre 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
Livres sur le sujet "Cell Mechanics -Stochastic Simulation"
Advances in cell mechanics. Heidelberg : Springer, 2011.
Trouver le texte intégralArnaud, Chauvière, Preziosi Luigi et Verdier Claude 1962-, dir. Cell mechanics : From single scale-based models to multiscale modeling. Boca Raton : Chapman & Hall/CRC, 2009.
Trouver le texte intégralArnaud, Chauvière, Preziosi Luigi et Verdier Claude, dir. Cell mechanics : From single scale-based models to multiscale modeling. Boca Raton : Chapman & Hall/CRC, 2009.
Trouver le texte intégralLuigi, Preziosi, et Verdier Claude, dir. Cell mechanics : From single scale-based models to multiscale modeling. Boca Raton : Chapman & Hall/CRC, 2009.
Trouver le texte intégralChauvière, Arnaud. Cell mechanics : From single scale-based models to multiscale modeling. Boca Raton : Chapman & Hall/CRC, 2009.
Trouver le texte intégralChauvière, Arnaud. Cell mechanics : From single scale-based models to multiscale modeling. Boca Raton : Chapman & Hall/CRC, 2009.
Trouver le texte intégralJakubowski, Jacek. Stochastyczna symulacja stateczności wyrobisk w nieciągłym masywie skalnym. Kraków : Wydawnictwa AGH, 2010.
Trouver le texte intégralBris, Claude. Systèmes multi-échelles : Modélisation et simulation. Berlin, Heidelberg : Springer Berlin Heidelberg, 2005.
Trouver le texte intégralVerdier, Claude, Luigi Preziosi et Arnaud Chauvière. Cell Mechanics. Taylor & Francis Group, 2019.
Trouver le texte intégralBris, Claude Le. Systèmes multi-èchelles : Modélisation et simulation (Mathématiques et Applications). Springer, 2005.
Trouver le texte intégralChapitres de livres sur le sujet "Cell Mechanics -Stochastic Simulation"
de Simone, P., A. Ghersi et R. Mauro. « Monte Carlo Simulation of Beams on Winkler Foundation ». Dans Computational Stochastic Mechanics, 523–32. Dordrecht : Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3692-1_44.
Texte intégralWedig, Walter V. « Simulation and Analysis of Mechanical Systems with Parameter Fluctuation ». Dans Nonlinear Stochastic Mechanics, 523–29. Berlin, Heidelberg : Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-84789-9_45.
Texte intégralBielewicz, E., J. Górski et H. Walukiewicz. « Random Fields. Digital Simulation and Applications in Structural Mechanics ». Dans Computational Stochastic Mechanics, 557–68. Dordrecht : Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3692-1_47.
Texte intégralKareem, A., et Y. Li. « Simulation of Multi-Variate Stationary and Nonstationary Random Processes : A Recent Development ». Dans Computational Stochastic Mechanics, 533–44. Dordrecht : Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3692-1_45.
Texte intégralCheng, A. H.-D., K. Hackl et C. Y. Yang. « Chaos, Stochasticity, and Stability of a Nonlinear Oscillator with Control Part II : Simulation ». Dans Computational Stochastic Mechanics, 239–52. Dordrecht : Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3692-1_21.
Texte intégralSeya, H., H. H. M. Hwang et M. Shinozuka. « Probabilistic Seismic Response Analysis of a Steel Frame Structure Using Monte Carlo Simulation ». Dans Computational Stochastic Mechanics, 499–510. Dordrecht : Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3692-1_42.
Texte intégralDe López La Cruz, J., M. A. Gutiérrez et L. Koene. « Stochastic simulation of pitting corrosion ». Dans III European Conference on Computational Mechanics, 665. Dordrecht : Springer Netherlands, 2006. http://dx.doi.org/10.1007/1-4020-5370-3_665.
Texte intégralGiesa, Tristan, Graham Bratzel et Markus J. Buehler. « Modeling and Simulation of Hierarchical Protein Materials ». Dans Nano and Cell Mechanics, 389–409. Chichester, UK : John Wiley & Sons, Ltd, 2012. http://dx.doi.org/10.1002/9781118482568.ch15.
Texte intégralJacobs, Christopher R., et Daniel J. Kelly. « Cell mechanics : The role of simulation ». Dans Computational Methods in Applied Sciences, 1–14. Dordrecht : Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-1254-6_1.
Texte intégralZhu, Dong. « Numerical Simulation of Surface Contact and Mixed Lubrication — Deterministic Approach vs. Stochastic Approach ». Dans Computational Mechanics, 394. Berlin, Heidelberg : Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-75999-7_194.
Texte intégralActes de conférences sur le sujet "Cell Mechanics -Stochastic Simulation"
Lin, Chan-Chiao, Huei Peng, Min Joong Kim et Jessy W. Grizzle. « Integrated Dynamic Simulation Model With Supervisory Control Strategy for a PEM Fuel Cell Hybrid Vehicle ». Dans ASME 2004 International Mechanical Engineering Congress and Exposition. ASMEDC, 2004. http://dx.doi.org/10.1115/imece2004-61775.
Texte intégralPappu, Vijay, et Prosenjit Bagchi. « Capture, Deformation, Rolling and Detachment of a Cell on an Adhesive Surface in a Shear Flow ». Dans ASME 2008 International Mechanical Engineering Congress and Exposition. ASMEDC, 2008. http://dx.doi.org/10.1115/imece2008-67742.
Texte intégralJohnston, Joel, et Aditi Chattopadhyay. « Stochastic Multiscale Modeling and Damage Progression for Composite Materials ». Dans ASME 2013 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/imece2013-66566.
Texte intégralAmirpourabasi, Arezoo, Mohammad Pourgol-Mohammad et Hanieh Niroomand-Oscuii. « Reliability Evaluation for Biomechanics Transient Stresses : Case Study of Biological Cell Vitality in Freezing Process ». Dans ASME 2014 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/imece2014-39468.
Texte intégralde Carvalho, Thiago P., Hervé P. Morvan, David Hargreaves, Hatem Oun et Andrew Kennedy. « Experimental and Tomography-Based CFD Investigations of the Flow in Open Cell Metal Foams With Application to Aero Engine Separators ». Dans ASME Turbo Expo 2015 : Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/gt2015-43509.
Texte intégralYan, Karen Chang, Aren Moy et Michael Sebok. « Modeling of Diffusive Behavior of Macromolecules Encapsulated in Electrospun Fibers ». Dans ASME 2016 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/imece2016-67770.
Texte intégral« Numerical simulation of stochastic process as a model of technical object state changes ». Dans 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.
Texte intégralWielgos, Piotr, Tomasz Lipecki et Andrzej Flaga. « Simulation of stochastic wind action on transmission power lines ». Dans 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.
Texte intégralNaik, Pranjal, et Sayan Gupta. « Parallel Computing in Stochastic Finite Element Analysis ». Dans 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.
Texte intégralBocchini, Paolo, Dan M. Frangopol et George Deodatis. « Computationally Efficient Simulation Techniques for Bridge Network Maintenance Optimization under Uncertainty ». Dans 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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