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检索条件"主题词=stochastic simulation algorithm"

共 106 条记录，以下是71-80 订阅

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simulation algorithms for Continuous Time Markov Chain Models

Simulation Algorithms for Continuous Time Markov Chain Model...

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International Workshop on simulation and Modeling related to Computational Science and Robotics Technology (SiMCRT)

作者： Banks, H. T. Broido, Anna Canter, Brandi Gayvert, Kaitlyn Hu, Shuhua Joyner, Michele Link, Kathryn North Carolina State Univ Ctr Res Sci Computat Raleigh NC 27695 USA Boston Coll Dept Math Chestnut Hill MA 02167 USA East Tennessee State Univ Dept Math & Stat Johnson City TN 37614 USA SUNY Coll Geneseo Dept Math Geneseo NY USA Bryn Mawr Coll Dept Math Bryn Mawr PA 19010 USA

ISBN: (纸本)9781614990925;9781614990918

Continuous time Markov chains are often used in the literature to model the dynamics of a system with low species count and uncertainty in transitions. In this paper, we investigate three particular algorithms that can be used to numerically simulate continuous time Markov chain models (a stochastic simulation algorithm, explicit and implicit tau-leaping algorithms). To compare these methods, we used them to analyze two stochastic infection models with different level of complexity. One of these models describes the dynamics of Vancomycin-Resistant Enterococcus (VRE) infection in a hospital, and the other is for the early infection of Human Immunodeficiency Virus (HIV) within a host. The relative efficiency of each algorithm is determined based on computational time and degree of precision required. The numerical results suggest that all three algorithms have similar computational efficiency for the VRE model due to the low number of species and small number of transitions. However, we found that with the larger and more complex HIV model, implementation and modification of tau-Leaping methods are preferred.

关键词： stochastic simulation algorithm explicit tau-leaping method implicit tau-leaping method VRE HIV

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Abstraction-Based Segmental simulation of Chemical Reaction Networks 20th

Abstraction-Based Segmental Simulation of Chemical Reaction ...

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20th International Conference on Computational Methods in Systems Biology (CMSB)

作者： Helfrich, Martin Ceska, Milan Kretinsky, Jan Marticek, Stefan Tech Univ Munich Munich Germany Brno Univ Technol Brno Czech Republic

ISBN: (纸本)9783031150333;9783031150340

Simulating chemical reaction networks is often computationally demanding, in particular due to stiffness. We propose a novel simulation scheme where long runs are not simulated as a whole but assembled from shorter precomputed segments of simulation runs. On the one hand, this speeds up the simulation process to obtain multiple runs since we can reuse the segments. On the other hand, questions on diversity and genuineness of our runs arise. However, we ensure that we generate runs close to their true distribution by generating an appropriate abstraction of the original system and utilizing it in the simulation process. Interestingly, as a by-product, we also obtain a yet more efficient simulation scheme, yielding runs over the system's abstraction. These provide a very faithful approximation of concrete runs on the desired level of granularity, at a low cost. Our experiments demonstrate the speedups in the simulations while preserving key dynamical as well as quantitative properties.

关键词： Chemical reaction networks Population models stochastic simulation algorithm Model abstraction

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Agreement on stochastic Multi-Robot Systems with Communication Failures

Agreement on Stochastic Multi-Robot Systems with Communicati...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Shaw, Fayette W. Chiu, Albert McLurkin, James D.

ISBN: (纸本)9781424466757

Agreement algorithms allow individual agents in a population to estimate a global quantity by sharing information. A common example is computing the global mean of a sensor measurement from each agent. We present a practical agreement algorithm, input-based consensus (IBC), that produces bounded error and recovery in the face of significant communications failures in a stochastic distributed system. We compare our algorithm to linear average consensus (LAC), which produces an exact result under ideal conditions, but is not robust to message loss. For both algorithms, we measure performance with respect to a varying percentage of dropped messages. The algorithms are examined analytically, simulated using the stochastic simulation algorithm, and demonstrated experimentally on a testbed of 20 robots. In all cases, the IBC algorithm produced reasonable values, even when tested with up to 90% message loss.

关键词： agreement algorithm communication failure input-based consensus linear average consensus multi-robot systems stochastic distributed system stochastic multirobot system stochastic simulation algorithm stochastic systems

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Implementing Scientific simulations on GPU-accelerated Edge Devices 34

Implementing Scientific Simulations on GPU-accelerated Edge ...

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34th International Conference on Information Networking (ICOIN)

作者： Lim, Sungmin Kang, Pilsung Sun Moon Univ Div Comp Sci & Engn Asan South Korea

ISBN: (纸本)9781728141985

In this paper, we investigate the potentials of the state-of-the-art edge devices in the context of scientific computing. We implement one of the major scientific applications - stochastic simulation in biochemistry - on a small cluster of modern, GPU-accelerated edge systems. By comparing the performance of the edge implementation with that of a real-world stochastic simulation software package on a multi-core desktop system, we evaluate the computational capability and estimate the usefulness of the modern hardware-accelerated edge devices.

关键词： edge computing GPU (graphics processing unit) scientific computing stochastic simulation algorithm

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simulation Methods in Systems Biology

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8th International School on Formal Methods for the Design of Computer, Communication and Software Systems

作者： Gillespie, Daniel T. Dan T Gillespie Consulting Castaic CA 91384 USA

ISBN: (纸本)9783540688921

This chapter reviews the theory of stochastic chemical kinetics and several simulation methods that are based on that theory. An effort is made to delineate the logical connections among the major elements of the theory, such as the chemical master equation, the stochastic simulation algorithm, tau-leaping, the chemical Langevin equation, the chemical Fokker-Planck equation, and the deterministic reaction rate equation. Focused presentations are given of two approximate simulation strategies that aim to improve simulation efficiency for systems with "multiscale" complications of the kind that are often encountered in cellular systems: The first, explicit tau-leaping, deals with systems that have a wide range of molecular populations. The second, the slow-scale stochastic simulation algorithm, is designed for systems that have a wide range of reaction rates. The latter procedure is shown to provide a stochastic generalization of the Michaelis-Menten analysis of the enzyme-substrate reaction set.

关键词： stochastic chemical kinetics master equation stochastic simulation algorithm tau-leaping Langevin equation Fokker-Planck equation multiscale stiff systems slow-scale stochastic simulation algorithm Michaelis-Menten enzyme-substrate reaction

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A Study of a Metapopulation Model Using the stochastic Reaction Diffusion Master Equation 2nd

A Study of a Metapopulation Model Using the Stochastic React...

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2nd Southwest Data Science Conference (SDSC)

作者： Rahman, Md Mustafijur Sidje, Roger B. Univ Alabama Dept Math Tuscaloosa AL 35487 USA

ISBN: (纸本)9783031618154;9783031618161

Many biological systems involve both the noise in chemical reaction process and diffusion in space between the molecules involved in the reactions. Since the copy numbers of participating species often are small, their stochastic behavior comes into play. The reaction-diffusion master equation (RDME) is a stochastic modeling of the reaction-diffusion process. In RDME, the space is partitioned into compartments and a well mixing of species is considered inside each compartment. Contrary to the chemical master equation (CME) that does not analyze the diffusion of the molecules between the components, the RDME is more complex and has a substantially larger state space than the CME. In this study we tackle a metapopulation model using the RDME formulation. Numerical methods are used to analyze and predict the behavior of the model.

关键词： metapopulation reaction diffusion master equation chemical master equation stochastic simulation algorithm

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Simulating vacuum residue hydroconversion by means of Monte-Carlo techniques

Simulating vacuum residue hydroconversion by means of Monte-...

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International Symposium on Advances in Hydroprocessing of Oil Fractions (ISAHOF)

作者： de Oliveira, Luis Pereira Verstraete, Jan J. Kolb, Max IFP Energies Nouvelles Rond Point Echangeur Solaize F-69360 Solaize France Ecole Normale Super Lyon Chim Lab F-69364 Lyon 07 France

The present work focuses on the development of a novel methodology for the kinetic modeling of heavy oil conversion processes. The methodology models both the feedstock composition and the process reactions at a molecular level. The composition modeling consists of generating a set of molecules whose properties are close to those of the process feedstock analyses. This synthetic mixture of molecules is generated by a two-step molecular reconstruction algorithm. In its first step, an equimolar set of molecules is built by assembling structural blocks in a stochastic manner. In the second step, the mole fractions of the molecules are adjusted by maximizing an information entropy criterion. Once the composition of the feedstock is represented, the conversion process is simulated by applying, event by event, its main reactions to the set of molecules by means of a kinetic Monte Carlo (kMC) method. The methodology has been applied to hydroconversion of Ural vacuum residue and both the feed and the predicted effluents were favorably compared to the experimental yield pattern. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Composition modeling Kinetic modeling Information entropy maximization Vacuum residue hydrocracking stochastic simulation algorithm Kinetic Monte-Carlo

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Towards Learning stochastic Population Models by Gradient Descent 38

Towards Learning Stochastic Population Models by Gradient De...

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38th ACM SIGSIM International Conference on Principles of Advanced Discrete simulation (ACM SIGSIM PADS)

作者： Kreikemeyer, Justin N. Andelfinger, Philipp Uhrmacher, Adelinde M. Univ Rostock Inst Visual & Analyt Comp Rostock Germany

ISBN: (纸本)9798400703638

Increasing effort is put into the development of methods for learning mechanistic models from data. This task entails not only the accurate estimation of parameters but also a suitable model structure. Recent work on the discovery of dynamical systems formulates this problem as a linear equation system. Here, we explore several simulation-based optimization approaches, which allow much greater freedom in the objective formulation and weaker conditions on the available data. We show that even for relatively small stochastic population models, simultaneous estimation of parameters and structure poses major challenges for optimization procedures. Particularly, we investigate the application of the local stochastic gradient descent method, commonly used for training machine learning models. We demonstrate accurate estimation of models but find that enforcing the inference of parsimonious, interpretable models drastically increases the difficulty. We give an outlook on how this challenge can be overcome.

关键词： automatic model generation gradient descent stochastic simulation algorithm discrete-event simulation differentiable simulation

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Systems biology of microbiota metabolites and adipocyte transcription factor network

Systems biology of microbiota metabolites and adipocyte tran...

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作者： Choi, Kyungoh Texas A&M University

学位级别：Ph.D.

The overall goal of this research is to understand roles of gut microbiota metabolites and adipocyte transcription factor (TF) network in health and disease by developing systematic analysis methods. As microbiota can perform diverse biotransformation reactions, the spectrum of metabolites present in the gastrointestinal (GI) tract is extremely complex but only a handful of bioactive microbiota metabolites have been identified. We developed a metabolomics workflow that integrates in silico discovery with targeted mass spectrometry. A computational pathway analysis where microbiota metabolisms are modeled as a single metabolic network is utilized to predict a focused set of targets for multiple reaction monitoring (MRM) analysis. We validated our methodology by predicting, quantifying in murine cecum and feces and characterizing tryptophan (TRP)-derived metabolites as ligands for the aryl hydrocarbon receptor. The adipocyte process of lipid droplet accumulation and differentiation is regulated by multiple TFs that function together in a network. Although individual TF activation is previously reported, construction of an integrated network has been limited due to different measurement conditions. We developed an integrated network model of key TFs—PPARγ, C/EBPβ, CREB, NFAT, FoxO1, and SREBP-1c—underlying adipocyte differentiation. A hypothetic model was determined based on literature, and stochastic simulation algorithm (SSA) was applied to simulate TF dynamics. TF activation profiles at different stages of differentiation were measured using 3T3-L1 reporter cell lines where binding of a TF to its DNA binding element drives expression of the Gaussia luciferase gene. Reaction trajectories calculated by SSA showed good agreement with experimental measurement. The TF model was further validated by perturbing dynamics of CREB using forskolin, and comparing the predicted response with experimental data. We studied the molecular recognition mechanism underlying anti-inflam

关键词： microbiota metabolites metabolomics adipocyte transcription factor network stochastic simulation algorithm NR4A2 indole Thesis

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Regularized maximum likelihood estimation of sparse stochastic monomolecular biochemical reaction networks 19

Regularized maximum likelihood estimation of sparse stochast...

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19th IFAC World Congress on International Federation of Automatic Control, IFAC 2014

作者： Jang, Hong Kim, Kwang-Ki K. Lee, Jay H. Braatz, Richard D. Department of Biomolecular and Chemical Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States Massachusetts Institute of Technology CambridgeMA United States

ISBN: (纸本)9783902823625

A sparse parameter estimation method is proposed for identifying a stochastic monomolecular biochemical reaction network system. Identification of a reaction network can be achieved by estimating a sparse parameter matrix containing the reaction network structure and kinetics information. stochastic dynamics of a biochemical reaction network system is usually modeled by a chemical master equation, which is composed of several ordinary differential equations describing the time evolution of probability distributions for all possible states. This paper considers closed monomolecular reaction systems for which an exact analytical solution of the corresponding chemical master equation is available. The estimation method presented in this paper incorporates the closed-form solution into a regularized maximum likelihood estimation (MLE) for which model complexity is penalized, whereas most of existing studies on sparse reaction network identification use deterministic models for regularized least-square estimation. A simulation result is provided to verify performance improvement of the presented regularized MLE over the least squares (LSE) based on a deterministic mass-average model in the case of a small population size. Improved reaction structure detection is achieved by adding a penalty term for t1 regularization to the exact maximum likelihood function. © IFAC.

关键词： Chemical master equation Exact maximum likelihood estimation Monomolecular biochemical reaction network Regularized maximum likelihood estimation Sparse parameter estimation stochastic simulation algorithm

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