Littérature scientifique sur le sujet « Cell Division - Stochastic Simulation »
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Articles de revues sur le sujet "Cell Division - Stochastic Simulation"
Van Segbroeck, Sven, Ann Nowé et Tom Lenaerts. « Stochastic Simulation of the Chemoton ». Artificial Life 15, no 2 (avril 2009) : 213–26. http://dx.doi.org/10.1162/artl.2009.15.2.15203.
Texte intégralCharlebois, Daniel A., Jukka Intosalmi, Dawn Fraser et Mads Kærn. « An Algorithm for the Stochastic Simulation of Gene Expression and Heterogeneous Population Dynamics ». Communications in Computational Physics 9, no 1 (janvier 2011) : 89–112. http://dx.doi.org/10.4208/cicp.280110.070510a.
Texte intégralThomas, Philipp, et Vahid Shahrezaei. « Coordination of gene expression noise with cell size : analytical results for agent-based models of growing cell populations ». Journal of The Royal Society Interface 18, no 178 (mai 2021) : 20210274. http://dx.doi.org/10.1098/rsif.2021.0274.
Texte intégralWen, Kunwen, Lifang Huang, Qi Wang et Jianshe Yu. « Modulation of first-passage time for gene expression via asymmetric cell division ». International Journal of Biomathematics 12, no 05 (juillet 2019) : 1950052. http://dx.doi.org/10.1142/s1793524519500529.
Texte intégralGenthon, Arthur, Reinaldo García-García et David Lacoste. « Branching processes with resetting as a model for cell division ». Journal of Physics A : Mathematical and Theoretical 55, no 7 (26 janvier 2022) : 074001. http://dx.doi.org/10.1088/1751-8121/ac491a.
Texte intégralWang, Qi, Lifang Huang, Kunwen Wen et Jianshe Yu. « The mean and noise of stochastic gene transcription with cell division ». Mathematical Biosciences & ; Engineering 15, no 5 (2018) : 1255–70. http://dx.doi.org/10.3934/mbe.2018058.
Texte intégralJi, Xiangrui, et Jie Lin. « Implications of differential size-scaling of cell-cycle regulators on cell size homeostasis ». PLOS Computational Biology 19, no 7 (28 juillet 2023) : e1011336. http://dx.doi.org/10.1371/journal.pcbi.1011336.
Texte intégralPham, Huy, Emile R. Shehada, Shawna Stahlheber, Kushagra Pandey et Wayne B. Hayes. « No Cell Left behind : Automated, Stochastic, Physics-Based Tracking of Every Cell in a Dense, Growing Colony ». Algorithms 15, no 2 (30 janvier 2022) : 51. http://dx.doi.org/10.3390/a15020051.
Texte intégralBarizien, A., M. S. Suryateja Jammalamadaka, G. Amselem et Charles N. Baroud. « Growing from a few cells : combined effects of initial stochasticity and cell-to-cell variability ». Journal of The Royal Society Interface 16, no 153 (24 avril 2019) : 20180935. http://dx.doi.org/10.1098/rsif.2018.0935.
Texte intégralBaptista, Ines S. C., et Andre S. Ribeiro. « Stochastic models coupling gene expression and partitioning in cell division in Escherichia coli ». Biosystems 193-194 (juin 2020) : 104154. http://dx.doi.org/10.1016/j.biosystems.2020.104154.
Texte intégralThèses sur le sujet "Cell Division - 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.
Biehler, Eike [Verfasser], Werner [Akademischer Betreuer] Nagel et Richard [Akademischer Betreuer] Cowan. « Cell division processes in tessellations : a stochastic geometry approach / Eike Biehler. Gutachter : Werner Nagel ; Richard Cowan ». Jena : Thüringer Universitäts- und Landesbibliothek Jena, 2012. http://d-nb.info/1029294216/34.
Texte intégralAhmadian, 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.
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.
Texte intégralWang, 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.
Joubaud, Maud. « Processus de Markov déterministes par morceaux branchants et problème d’arrêt optimal, application à la division cellulaire ». Thesis, Montpellier, 2019. http://www.theses.fr/2019MONTS031/document.
Texte intégralPiecewise deterministic Markov processes (PDMP) form a large class of stochastic processes characterized by a deterministic evolution between random jumps. They fall into the class of hybrid processes with a discrete mode and an Euclidean component (called the state variable). Between the jumps, the continuous component evolves deterministically, then a jump occurs and a Markov kernel selects the new value of the discrete and continuous components. In this thesis, we extend the construction of PDMPs to state variables taking values in some measure spaces with infinite dimension. The aim is to model cells populations keeping track of the information about each cell. We study our measured-valued PDMP and we show their Markov property. With thoses processes, we study a optimal stopping problem. The goal of an optimal stopping problem is to find the best admissible stopping time in order to optimize some function of our process. We show that the value fonction can be recursively constructed using dynamic programming equations. We construct some $epsilon$-optimal stopping times for our optimal stopping problem. Then, we study a simple finite-dimension real-valued PDMP, the TCP process. We use Euler scheme to approximate it, and we estimate some types of errors. We illustrate the results with numerical simulations
Dao, Duc Khanh. « Modeling and analysis of neuronal networks, stochastic chemical reactions in cellular micro-domains and telomere dynamics ». Paris 6, 2013. http://www.theses.fr/2013PA066513.
Texte intégralIn this PhD, we model specific stochastic events occurring in different biological contexts. In the first part, we study three different properties of neural networks. Using a mean field facilitation-depression synaptic model, we unravel the synchronous long lasting bursting observed at various scales of neural populations. Next, we study the neuronal noise induced transition between Up& Down states. To study the oscillatory peaks of the time spent in Up state, we consider the exit problem for a class of stochastic processes in a domain with an attractor located close to a limit cycle. We construct a class of systems conjugate to the Hopf bifurcation system that we study using WKB approximation and boundary layer analysis. We finally focus on neuroglial interactions and more specifically on astrocytic potassium. Using a tri-compartment model, we simulate the potassium dynamics for different stimulation protocols and we determine how astrocytic channels can influence neurotransmission. In the second part, we focus on the threshold activation for stochastic chemical reactions in cellular micro-domains. We compute the probability and the mean first time to reach a threshold for different reactions. The methods are applied to study the mitotic spindle checkpoint and the problem of gene expression and post-transcriptional regulation. The third part is finally dedicated to the stochastic dynamics of telomere length across cell divisions. We model the dynamics of telomere length as a drift and jump process, which allows predicting the distribution of telomere length and the length of the shortest telomere
Wollrab, Viktoria. « Active gels in vivo : patterns and dynamics in cytokinetic rings and their functions in cell division ». Thesis, Strasbourg, 2014. http://www.theses.fr/2014STRAF027/document.
Texte intégralActomyosin structures are involved in many cell functions. Understanding their organization and collective behavior is still challenging. We study the cytokinetic ring in mammalian cells and in fission yeasts, by orienting cells in microcavities. This allows seeing the ring in a single plane of focus. With this setup, we reveal new structures and distinct dynamics for both cellular systems. In mammalian cells we find a pattern of regular clusters of myosin and formin. The characteristics of this pattern are stable throughout closure and its formation coincides with the onset of constriction. We propose that its characteristic is an inherent property of the actomyosin network and that its formation leads to an increase in stress generation. These hypotheses are supported by our theoretical mean field model. In contrast, fission yeast rings show rotating inhomogeneities (speckles), i.e. rotations of actin, myosin, cell wall building proteins (Bgs) and other proteins. Myosin speckles dynamic is unchanged, if wall growth is inhibited. However, the inhibition of speckle motion leads to stalled closure. We propose that the ring closure is driven by the rotation of actin and myosin, which pull Bgs thereby building the septum. This model is supported by our calculations and by numerical simulations. We suggest that the transition between states of different orders and dynamics might be a way to regulate actomyosin systems in vivo
Chapitres de livres sur le sujet "Cell Division - Stochastic Simulation"
Bansaye, Vincent, et Sylvie Méléard. « Splitting Feller Diffusion for Cell Division with Parasite Infection ». Dans Stochastic Models for Structured Populations, 79–87. Cham : Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21711-6_8.
Texte intégralVitvitsky, Anton. « Cellular Automata Simulation of Bacterial Cell Growth and Division ». Dans Designing Beauty : The Art of Cellular Automata, 121–23. Cham : Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27270-2_19.
Texte intégralTranquillo, Robert T., Oana Brosteanu et Wolfgang Alt. « Dynamic Morphology of Leukocytes : Statistical Analysis and a Stochastic Model for Receptor-Mediated Cell Motion and Orientation ». Dans Biomechanics of Active Movement and Division of Cells, 437–43. Berlin, Heidelberg : Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-78975-5_15.
Texte intégralKarni, Y., M. Goldstein et E. Bar-Ziv. « Simulation of Diffusion and Chemical Reactions with a Cell-Mixing Stochastic Model ». Dans Springer Series in Chemical Physics, 346–54. Berlin, Heidelberg : Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-83224-6_28.
Texte intégralBurrage, Kevin, Pamela M. Burrage, André Leier, Tatiana Marquez-Lago et Dan V. Nicolau. « Stochastic Simulation for Spatial Modelling of Dynamic Processes in a Living Cell ». Dans Design and Analysis of Biomolecular Circuits, 43–62. New York, NY : Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-6766-4_2.
Texte intégralBurrage, Kevin, Pamela Burrage, Andre Leier et Tatiana Marquez-Lago. « A Review of Stochastic and Delay Simulation Approaches in Both Time and Space in Computational Cell Biology ». Dans Stochastic Processes, Multiscale Modeling, and Numerical Methods for Computational Cellular Biology, 241–61. Cham : Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62627-7_11.
Texte intégralBraccini, Michele, Andrea Roli, Marco Villani et Roberto Serra. « A Comparison Between Threshold Ergodic Sets and Stochastic Simulation of Boolean Networks for Modelling Cell Differentiation ». Dans Communications in Computer and Information Science, 116–28. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78658-2_9.
Texte intégral« Stochastic Simulation of Cell Signaling Pathways ». Dans Computational Modeling of Genetic and Biochemical Networks. The MIT Press, 2001. http://dx.doi.org/10.7551/mitpress/2018.003.0014.
Texte intégralZhang, Xingyi, Yunyun Niu, Linqiang Pan et Mario J. Pérez-Jiménez. « Linear Time Solution to Prime Factorization by Tissue P Systems with Cell Division ». Dans Natural Computing for Simulation and Knowledge Discovery, 207–20. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-4253-9.ch014.
Texte intégralDoumic, Marie, et Marc Hoffmann. « Individual and Population Approaches for Calibrating Division Rates in Population Dynamics : Application to the Bacterial Cell Cycle ». Dans Modeling and Simulation for Collective Dynamics, 1–81. WORLD SCIENTIFIC, 2023. http://dx.doi.org/10.1142/9789811266140_0001.
Texte intégralActes de conférences sur le sujet "Cell Division - Stochastic Simulation"
Aguinaga, Sylvain, Olivier Simonin, Jacques Bore´e et Vincent Herbert. « A Lagrangian Stochastic Model for Droplet Deposition Simulations in Connection With Wall Function Approaches ». Dans ASME 2009 Fluids Engineering Division Summer Meeting. ASMEDC, 2009. http://dx.doi.org/10.1115/fedsm2009-78126.
Texte intégralPappu, Vijay, et Prosenjit Bagchi. « 3D Computational Modeling and Simulation of Cell Motion on Adhesive Surfaces in Shear Flow ». Dans ASME 2008 Fluids Engineering Division Summer Meeting collocated with the Heat Transfer, Energy Sustainability, and 3rd Energy Nanotechnology Conferences. ASMEDC, 2008. http://dx.doi.org/10.1115/fedsm2008-55113.
Texte intégralSikarwar, Vandna, Vijayshri Chaurasia, J. S. Yadav et Yashwant Kurmi. « Stochastic model analysis for Hes1/MiR-9 brain cell division system ». Dans 2017 International Conference on Recent Innovations in Signal processing and Embedded Systems (RISE). IEEE, 2017. http://dx.doi.org/10.1109/rise.2017.8378208.
Texte intégralYi, Wenlong, Yinglong Wang, Yingzhao Jiang, Hongyu Jiang et Jun Yang. « Simulation of Plant Cell Division Based on Combinatorial Topology ». Dans 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus). IEEE, 2021. http://dx.doi.org/10.1109/elconrus51938.2021.9396716.
Texte intégralSchulz, Hans-Jörg, Adelinde M. Uhrmacher et Heidrun Schumann. « Visual analytics for stochastic simulation in cell biology ». Dans the 11th International Conference. New York, New York, USA : ACM Press, 2011. http://dx.doi.org/10.1145/2024288.2024345.
Texte intégralSoyhan, Hakan Serhad, Terese Løvås et Fabian Mauss. « A Stochastic Simulation of an HCCI Engine Using an Automatically Reduced Mechanism ». Dans ASME 2001 Internal Combustion Engine Division Fall Technical Conference. American Society of Mechanical Engineers, 2001. http://dx.doi.org/10.1115/2001-ice-416.
Texte intégralManninen, Tiina, Eeva Makiraatikka, Antti Ylipaa, Antti Pettinen, Kalle Leinonen et Marja-Leena Linne. « Discrete stochastic simulation of cell signaling : comparison of computational tools ». Dans Conference Proceedings. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2006. http://dx.doi.org/10.1109/iembs.2006.260023.
Texte intégralManninen, Tiina, Eeva Makiraatikka, Antti Ylipaa, Antti Pettinen, Kalle Leinonen et Marja-Leena Linne. « Discrete stochastic simulation of cell signaling : comparison of computational tools ». Dans Conference Proceedings. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2006. http://dx.doi.org/10.1109/iembs.2006.4397829.
Texte intégralPeng, Zhangli, Xuejin Li, George Karniadakis et Ming Dao. « Poster : Multiscale simulation of red blood cell tethering in a capillary ». Dans 67th Annual Meeting of the APS Division of Fluid Dynamics. American Physical Society, 2014. http://dx.doi.org/10.1103/aps.dfd.2014.gfm.p0039.
Texte intégralWilmer, Brady M., et William F. Northrop. « Simulation of Turbulent Combustion in Gasoline Direct Injection Spark-Ignited Engines Using a Stochastic Reactor Model ». Dans ASME 2021 Internal Combustion Engine Division Fall Technical Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/icef2021-66622.
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