Stochastic Simulation

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Linda R. Petzold - One of the best experts on this subject based on the ideXlab platform.

  • a hybrid smoothed dissipative particle dynamics sdpd spatial Stochastic Simulation algorithm sssa for advection diffusion reaction problems
    Journal of Computational Physics, 2019
    Co-Authors: Brian Drawert, Bruno Jacob, Linda R. Petzold
    Abstract:

    We have developed a new algorithm which merges discrete Stochastic Simulation, using the spatial Stochastic Simulation algorithm (sSSA), with the particle based fluid dynamics Simulation framework of smoothed dissipative particle dynamics (SDPD). This hybrid algorithm enables discrete Stochastic Simulation of spatially resolved chemically reacting systems on a mesh-free dynamic domain with a Lagrangian frame of reference. SDPD combines two popular mesoscopic techniques: smoothed particle hydrodynamics and dissipative particle dynamics (DPD), linking the macroscopic and mesoscopic hydrodynamics effects of these two methods. We have implemented discrete Stochastic Simulation using the reaction-diffusion master equations (RDME) formalism, and deterministic reaction-diffusion equations based on the SDPD method. We validate the new method by comparing our results to four canonical models, and demonstrate the versatility of our method by simulating a flow containing a chemical gradient past a yeast cell in a microfluidics chamber.

  • a hybrid smoothed dissipative particle dynamics sdpd spatial Stochastic Simulation algorithm sssa for advection diffusion reaction problems
    Journal of Computational Physics, 2019
    Co-Authors: Brian Drawert, Bruno Jacob, Zhen Li, Taumu Yi, Linda R. Petzold
    Abstract:

    Author(s): Brian, Drawert; Bruno, Jacob; Zhen, Li; Tau-Mu, Yi; Linda, Petzold | Abstract: We have developed a new algorithm which merges discrete Stochastic Simulation, using the spatial Stochastic Simulation algorithm (sSSA), with the particle based fluid dynamics Simulation framework of smoothed dissipative particle dynamics (SDPD). This hybrid algorithm enables discrete Stochastic Simulation of spatially resolved chemically reacting systems on a mesh-free dynamic domain with a Lagrangian frame of reference. SDPD combines two popular mesoscopic techniques: smoothed particle hydrodynamics and dissipative particle dynamics (DPD), linking the macroscopic and mesoscopic hydrodynamics effects of these two methods. We have implemented discrete Stochastic Simulation using the reaction-diffusion master equations (RDME) formalism, and deterministic reaction-diffusion equations based on the SDPD method. We validate the new method by comparing our results to four canonical models, and demonstrate the versatility of our method by simulating a flow containing a chemical gradient past a yeast cell in a microfluidics chamber.

  • Automatic identification of model reductions for discrete Stochastic Simulation
    The Journal of chemical physics, 2012
    Co-Authors: Linda R. Petzold
    Abstract:

    Multiple time scales in cellular chemical reaction systems present a challenge for the efficiency of Stochastic Simulation. Numerous model reductions have been proposed to accelerate the Simulation of chemically reacting systems by exploiting time scale separation. However, these are often identified and deployed manually, requiring expert knowledge. This is time-consuming, prone to error, and opportunities for model reduction may be missed, particularly for large models. We propose an automatic model analysis algorithm using an adaptively weighted Petri net to dynamically identify opportunities for model reductions for both the Stochastic Simulation algorithm and tau-leaping Simulation, with no requirement of expert knowledge input. Results are presented to demonstrate the utility and effectiveness of this approach.

  • stochkit2 software for discrete Stochastic Simulation of biochemical systems with events
    Bioinformatics, 2011
    Co-Authors: Kevin R Sanft, Min K Roh, Rone Kwei Lim, Linda R. Petzold
    Abstract:

    Summary: StochKit2 is the first major upgrade of the popular StochKit Stochastic Simulation software package. StochKit2 provides highly efficient implementations of several variants of Gillespie's Stochastic Simulation algorithm (SSA), and tau-leaping with automatic step size selection. StochKit2 features include automatic selection of the optimal SSA method based on model properties, event handling, and automatic parallelism on multicore architectures. The underlying structure of the code has been completely updated to provide a flexible framework for extending its functionality. Availability: StochKit2 runs on Linux/Unix, Mac OS X and Windows. It is freely available under GPL version 3 and can be downloaded from http://sourceforge.net/projects/stochkit/. Contact: ude.bscu.gnireenigne@dloztep

  • Refining the weighted Stochastic Simulation algorithm
    Journal of Chemical Physics, 2009
    Co-Authors: Daniel T. Gillespie, Linda R. Petzold
    Abstract:

    The weighted Stochastic Simulation algorithm (wSSA) recently introduced by Kuwahara and Mura [J. Chem. Phys. 129, 165101 (2008)] is an innovative variation on the Stochastic Simulation algorithm (SSA). It enables one to estimate, with much less computational effort than was previously thought possible using a Monte Carlo Simulation procedure, the probability that a specified event will occur in a chemically reacting system within a specified time when that probability is very small. This paper presents some procedural extensions to the wSSA that enhance its effectiveness in practical applications. The paper also attempts to clarify some theoretical issues connected with the wSSA, including its connection to first passage time theory and its relation to the SSA.

Brian Drawert - One of the best experts on this subject based on the ideXlab platform.

  • a hybrid smoothed dissipative particle dynamics sdpd spatial Stochastic Simulation algorithm sssa for advection diffusion reaction problems
    Journal of Computational Physics, 2019
    Co-Authors: Brian Drawert, Bruno Jacob, Linda R. Petzold
    Abstract:

    We have developed a new algorithm which merges discrete Stochastic Simulation, using the spatial Stochastic Simulation algorithm (sSSA), with the particle based fluid dynamics Simulation framework of smoothed dissipative particle dynamics (SDPD). This hybrid algorithm enables discrete Stochastic Simulation of spatially resolved chemically reacting systems on a mesh-free dynamic domain with a Lagrangian frame of reference. SDPD combines two popular mesoscopic techniques: smoothed particle hydrodynamics and dissipative particle dynamics (DPD), linking the macroscopic and mesoscopic hydrodynamics effects of these two methods. We have implemented discrete Stochastic Simulation using the reaction-diffusion master equations (RDME) formalism, and deterministic reaction-diffusion equations based on the SDPD method. We validate the new method by comparing our results to four canonical models, and demonstrate the versatility of our method by simulating a flow containing a chemical gradient past a yeast cell in a microfluidics chamber.

  • a hybrid smoothed dissipative particle dynamics sdpd spatial Stochastic Simulation algorithm sssa for advection diffusion reaction problems
    Journal of Computational Physics, 2019
    Co-Authors: Brian Drawert, Bruno Jacob, Zhen Li, Taumu Yi, Linda R. Petzold
    Abstract:

    Author(s): Brian, Drawert; Bruno, Jacob; Zhen, Li; Tau-Mu, Yi; Linda, Petzold | Abstract: We have developed a new algorithm which merges discrete Stochastic Simulation, using the spatial Stochastic Simulation algorithm (sSSA), with the particle based fluid dynamics Simulation framework of smoothed dissipative particle dynamics (SDPD). This hybrid algorithm enables discrete Stochastic Simulation of spatially resolved chemically reacting systems on a mesh-free dynamic domain with a Lagrangian frame of reference. SDPD combines two popular mesoscopic techniques: smoothed particle hydrodynamics and dissipative particle dynamics (DPD), linking the macroscopic and mesoscopic hydrodynamics effects of these two methods. We have implemented discrete Stochastic Simulation using the reaction-diffusion master equations (RDME) formalism, and deterministic reaction-diffusion equations based on the SDPD method. We validate the new method by comparing our results to four canonical models, and demonstrate the versatility of our method by simulating a flow containing a chemical gradient past a yeast cell in a microfluidics chamber.

Bruno Jacob - One of the best experts on this subject based on the ideXlab platform.

  • a hybrid smoothed dissipative particle dynamics sdpd spatial Stochastic Simulation algorithm sssa for advection diffusion reaction problems
    Journal of Computational Physics, 2019
    Co-Authors: Brian Drawert, Bruno Jacob, Linda R. Petzold
    Abstract:

    We have developed a new algorithm which merges discrete Stochastic Simulation, using the spatial Stochastic Simulation algorithm (sSSA), with the particle based fluid dynamics Simulation framework of smoothed dissipative particle dynamics (SDPD). This hybrid algorithm enables discrete Stochastic Simulation of spatially resolved chemically reacting systems on a mesh-free dynamic domain with a Lagrangian frame of reference. SDPD combines two popular mesoscopic techniques: smoothed particle hydrodynamics and dissipative particle dynamics (DPD), linking the macroscopic and mesoscopic hydrodynamics effects of these two methods. We have implemented discrete Stochastic Simulation using the reaction-diffusion master equations (RDME) formalism, and deterministic reaction-diffusion equations based on the SDPD method. We validate the new method by comparing our results to four canonical models, and demonstrate the versatility of our method by simulating a flow containing a chemical gradient past a yeast cell in a microfluidics chamber.

  • a hybrid smoothed dissipative particle dynamics sdpd spatial Stochastic Simulation algorithm sssa for advection diffusion reaction problems
    Journal of Computational Physics, 2019
    Co-Authors: Brian Drawert, Bruno Jacob, Zhen Li, Taumu Yi, Linda R. Petzold
    Abstract:

    Author(s): Brian, Drawert; Bruno, Jacob; Zhen, Li; Tau-Mu, Yi; Linda, Petzold | Abstract: We have developed a new algorithm which merges discrete Stochastic Simulation, using the spatial Stochastic Simulation algorithm (sSSA), with the particle based fluid dynamics Simulation framework of smoothed dissipative particle dynamics (SDPD). This hybrid algorithm enables discrete Stochastic Simulation of spatially resolved chemically reacting systems on a mesh-free dynamic domain with a Lagrangian frame of reference. SDPD combines two popular mesoscopic techniques: smoothed particle hydrodynamics and dissipative particle dynamics (DPD), linking the macroscopic and mesoscopic hydrodynamics effects of these two methods. We have implemented discrete Stochastic Simulation using the reaction-diffusion master equations (RDME) formalism, and deterministic reaction-diffusion equations based on the SDPD method. We validate the new method by comparing our results to four canonical models, and demonstrate the versatility of our method by simulating a flow containing a chemical gradient past a yeast cell in a microfluidics chamber.

R Petzoldlinda - One of the best experts on this subject based on the ideXlab platform.

Steve Mclaughlin - One of the best experts on this subject based on the ideXlab platform.

  • A Survey of Stochastic Simulation and Optimization Methods in Signal Processing
    IEEE Journal of Selected Topics in Signal Processing, 2016
    Co-Authors: Marcelo Pereyra, Philip Schniter, Émilie Chouzenoux, Jean-christophe Pesquet, Jean-yves Tourneret, Alfred O. Hero, Steve Mclaughlin
    Abstract:

    Modern signal processing (SP) methods rely very heavily on probability and statistics to solve challenging SP problems. SP methods are now expected to deal with ever more complex models, requiring ever more sophisticated computational inference techniques. This has driven the development of statistical SP methods based on Stochastic Simulation and optimization. Stochastic Simulation and optimization algorithms are computationally intensive tools for performing statistical inference in models that are analytically intractable and beyond the scope of deterministic inference methods. They have been recently successfully applied to many difficult problems involving complex statistical models and sophisticated (often Bayesian) statistical inference techniques. This survey paper offers an introduction to Stochastic Simulation and optimization methods in signal and image processing. The paper addresses a variety of high-dimensional Markov chain Monte Carlo (MCMC) methods as well as deterministic surrogate methods, such as variational Bayes, the Bethe approach, belief and expectation propagation and approximate message passing algorithms. It also discusses a range of optimization methods that have been adopted to solve Stochastic problems, as well as Stochastic methods for deterministic optimization. Subsequently, areas of overlap between Simulation and optimization, in particular optimization-within-MCMC and MCMC-driven optimization are discussed.