The Experts below are selected from a list of 22782 Experts worldwide ranked by ideXlab platform
Linda R. Petzold - One of the best experts on this subject based on the ideXlab platform.
-
Refining the weighted stochastic Simulation Algorithm
Journal of Chemical Physics, 2009Co-Authors: Daniel T. Gillespie, Linda R. PetzoldAbstract: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.
-
The multinomial Simulation Algorithm for discrete stochastic Simulation of reaction-diffusion systems.
Journal of Chemical Physics, 2009Co-Authors: Sotiria Lampoudi, Daniel T. Gillespie, Linda R. PetzoldAbstract:The Inhomogeneous Stochastic Simulation Algorithm (ISSA) is a variant of the stochastic Simulation Algorithm in which the spatially inhomogeneous volume of the system is divided into homogeneous subvolumes, and the chemical reactions in those subvolumes are augmented by diffusive transfers of molecules between adjacent subvolumes. The ISSA can be prohibitively slow when the system is such that diffusive transfers occur much more frequently than chemical reactions. In this paper we present the Multinomial Simulation Algorithm (MSA), which is designed to, on the one hand, outperform the ISSA when diffusive transfer events outnumber reaction events, and on the other, to handle small reactant populations with greater accuracy than deterministic-stochastic hybrid Algorithms. The MSA treats reactions in the usual ISSA fashion, but uses appropriately conditioned binomial random variables for representing the net numbers of molecules diffusing from any given subvolume to a neighbor within a prescribed distance. Simulation results illustrate the benefits of the Algorithm.
-
multiscale stochastic Simulation Algorithm with stochastic partial equilibrium assumption for chemically reacting systems
Journal of Computational Physics, 2005Co-Authors: Daniel T. Gillespie, Linda R. PetzoldAbstract:In this paper, we introduce a multiscale stochastic Simulation Algorithm (MSSA) which makes use of Gillespies sto- chastic Simulation Algorithm (SSA) together with a new stochastic formulation of the partial equilibrium assumption (PEA). This method is much more efficient than SSA alone. It works even with a very small population of fast species. Implementation details are discussed, and an application to the modeling of the heat shock response of E. Coli is presented which demonstrates the excellent efficiency and accuracy obtained with the new method. � 2005 Elsevier Inc. All rights reserved.
-
efficient formulation of the stochastic Simulation Algorithm for chemically reacting systems
Journal of Chemical Physics, 2004Co-Authors: Hong Li, Linda R. PetzoldAbstract:In this paper we examine the different formulations of Gillespie’s stochastic Simulation Algorithm (SSA) [D. Gillespie, J. Phys. Chem. 81, 2340 (1977)] with respect to computational efficiency, and propose an optimization to improve the efficiency of the direct method. Based on careful timing studies and an analysis of the time-consuming operations, we conclude that for most practical problems the optimized direct method is the most efficient formulation of SSA. This is in contrast to the widely held belief that Gibson and Bruck’s next reaction method [M. Gibson and J. Bruck, J. Phys. Chem. A 104, 1876 (2000)] is the best way to implement the SSA for large systems. Our analysis explains the source of the discrepancy.
Daniel T. Gillespie - One of the best experts on this subject based on the ideXlab platform.
-
Refining the weighted stochastic Simulation Algorithm
Journal of Chemical Physics, 2009Co-Authors: Daniel T. Gillespie, Linda R. PetzoldAbstract: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.
-
The multinomial Simulation Algorithm for discrete stochastic Simulation of reaction-diffusion systems.
Journal of Chemical Physics, 2009Co-Authors: Sotiria Lampoudi, Daniel T. Gillespie, Linda R. PetzoldAbstract:The Inhomogeneous Stochastic Simulation Algorithm (ISSA) is a variant of the stochastic Simulation Algorithm in which the spatially inhomogeneous volume of the system is divided into homogeneous subvolumes, and the chemical reactions in those subvolumes are augmented by diffusive transfers of molecules between adjacent subvolumes. The ISSA can be prohibitively slow when the system is such that diffusive transfers occur much more frequently than chemical reactions. In this paper we present the Multinomial Simulation Algorithm (MSA), which is designed to, on the one hand, outperform the ISSA when diffusive transfer events outnumber reaction events, and on the other, to handle small reactant populations with greater accuracy than deterministic-stochastic hybrid Algorithms. The MSA treats reactions in the usual ISSA fashion, but uses appropriately conditioned binomial random variables for representing the net numbers of molecules diffusing from any given subvolume to a neighbor within a prescribed distance. Simulation results illustrate the benefits of the Algorithm.
-
multiscale stochastic Simulation Algorithm with stochastic partial equilibrium assumption for chemically reacting systems
Journal of Computational Physics, 2005Co-Authors: Daniel T. Gillespie, Linda R. PetzoldAbstract:In this paper, we introduce a multiscale stochastic Simulation Algorithm (MSSA) which makes use of Gillespies sto- chastic Simulation Algorithm (SSA) together with a new stochastic formulation of the partial equilibrium assumption (PEA). This method is much more efficient than SSA alone. It works even with a very small population of fast species. Implementation details are discussed, and an application to the modeling of the heat shock response of E. Coli is presented which demonstrates the excellent efficiency and accuracy obtained with the new method. � 2005 Elsevier Inc. All rights reserved.
Francesco Tapparo - One of the best experts on this subject based on the ideXlab platform.
-
Saving Space in a Time Efficient Simulation Algorithm
Fundamenta Informaticae, 2020Co-Authors: Silvia Crafa, Francesco Ranzato, Francesco TapparoAbstract:A number of Algorithms for computing the Simulation preorder on Kripke structures and on labelled transition systems are available. Among them, the Algorithm by Ranzato and Tapparo [2007] has the best time complexity,while the Algorithm by Gentilini et al. [2003] - successively corrected by van Glabbeek and Ploeger [2008] - has the best space complexity. Both space and time complexities are critical issues in a Simulation Algorithm, in particular memory requirements are crucial in the context of model checking when dealing with large state spaces. Here, we propose a new Simulation Algorithm that is obtained as a space saving modification of the time efficient Algorithm by Ranzato and Tapparo: a symbolic representation of sets is embedded in this Algorithm so that any set of states manipulated by the Algorithm can be efficiently stored as a set of blocks of a suitable state partition. It turns out that this novel Simulation Algorithm has a space complexity comparable with Gentilini et al.'s Algorithm while improving on Gentilini et al.'s time bound.
-
Saving Space in a Time Efficient Simulation Algorithm
2009 Ninth International Conference on Application of Concurrency to System Design, 2009Co-Authors: Silvia Crafa, Francesco Ranzato, Francesco TapparoAbstract:A number of Algorithms are available for computing the Simulation relation on Kripke structures and on labelled transition systems representing concurrent systems. Among them, the Algorithm by Ranzato and Tapparo [2007] has the best time complexity, while the Algorithm by Gentilini et al. [2003] - successively corrected by van Glabbeek and Ploeger [2008] - has the best space complexity. Both space and time complexities are critical issues in a Simulation Algorithm, in particular memory requirements are crucial in the context of model checking when dealing with large state spaces.We propose here a new Simulation Algorithm thatis obtained as a space saving modification of the time efficient Algorithm by Ranzato and Tapparo: a symbolic representation of sets is embedded in thisAlgorithm so that any set of states manipulated by the Algorithm can be efficiently stored as a set of blocks of a suitable state partition. It turns out that this new Simulation Algorithm retains a space complexity comparable with Gentilini et al.'s Algorithm while improving on Gentilini et al.'s time bound.
-
ACSD - Saving Space in a Time Efficient Simulation Algorithm
2009 Ninth International Conference on Application of Concurrency to System Design, 2009Co-Authors: Silvia Crafa, Francesco Ranzato, Francesco TapparoAbstract:A number of Algorithms are available for computing the Simulation relation on Kripke structures and on labelled transition systems representing concurrentsystems. Among them, the Algorithm by Ranzato and Tapparo~[2007] has the best time complexity, while the Algorithm by Gentilini et al.~[2003]~--~successivelycorrected by van Glabbeek and Ploeger~[2008]~--~has thebest space complexity. Both space and time complexities are critical issues in a Simulation Algorithm, in particular memory requirements are crucial in the context of model checking when dealing with large state spaces.We propose here a new Simulation Algorithm thatis obtained as a space saving modification of the time efficient Algorithm by Ranzato and Tapparo: a symbolic representation of sets is embedded in thisAlgorithm so that any set of states manipulated by the Algorithm can be efficiently stored as a set of blocks of a suitable state partition. It turns out that this new Simulation Algorithm retains a space complexity comparable with Gentilini et al.'s Algorithm while improving on Gentilini et al.'s time bound.
Francesco Ranzato - One of the best experts on this subject based on the ideXlab platform.
-
Saving Space in a Time Efficient Simulation Algorithm
Fundamenta Informaticae, 2020Co-Authors: Silvia Crafa, Francesco Ranzato, Francesco TapparoAbstract:A number of Algorithms for computing the Simulation preorder on Kripke structures and on labelled transition systems are available. Among them, the Algorithm by Ranzato and Tapparo [2007] has the best time complexity,while the Algorithm by Gentilini et al. [2003] - successively corrected by van Glabbeek and Ploeger [2008] - has the best space complexity. Both space and time complexities are critical issues in a Simulation Algorithm, in particular memory requirements are crucial in the context of model checking when dealing with large state spaces. Here, we propose a new Simulation Algorithm that is obtained as a space saving modification of the time efficient Algorithm by Ranzato and Tapparo: a symbolic representation of sets is embedded in this Algorithm so that any set of states manipulated by the Algorithm can be efficiently stored as a set of blocks of a suitable state partition. It turns out that this novel Simulation Algorithm has a space complexity comparable with Gentilini et al.'s Algorithm while improving on Gentilini et al.'s time bound.
-
An efficient Simulation Algorithm on Kripke structures
Acta Informatica, 2014Co-Authors: Francesco RanzatoAbstract:A number of Algorithms for computing the Simulation preorder (and equivalence) on Kripke structures are available. Let $$\varSigma $$ Σ denote the state space, $${\rightarrow }$$ ? the transition relation and $$P_{\mathrm {sim}}$$ P sim the partition of $$\varSigma $$ Σ induced by Simulation equivalence. While some Algorithms are designed to reach the best space bounds, whose dominating additive term is $$|P_{\mathrm {sim}}|^2$$ | P sim | 2 , other Algorithms are devised to attain the best time complexity $$O(|P_{\mathrm {sim}}||{\rightarrow }|)$$ O ( | P sim | | ? | ) . We present a novel Simulation Algorithm which is both space and time efficient: it runs in $$O(|P_ {\mathrm {sim}}|^2 \log |P_{\mathrm {sim}}| + |\varSigma |\log |\varSigma |)$$ O ( | P sim | 2 log | P sim | + | Σ | log | Σ | ) space and $$O(|P_{\mathrm {sim}}||{\rightarrow }|\log |\varSigma |)$$ O ( | P sim | | ? | log | Σ | ) time. Our Simulation Algorithm thus reaches the best space bounds while closely approaching the best time complexity.
-
MFCS - A More Efficient Simulation Algorithm on Kripke Structures
Mathematical Foundations of Computer Science 2013, 2013Co-Authors: Francesco RanzatoAbstract:A number of Algorithms for computing the Simulation preorder (and equivalence) on Kripke structures are available. Let Σ denote the state space, → the transition relation and P sim the partition of Σ induced by Simulation equivalence. While some Algorithms are designed to reach the best space bounds, whose dominating additive term is |P sim|2, other Algorithms are devised to attain the best time complexity O(|P sim|| → |). We present a novel Simulation Algorithm which is both space and time efficient: it runs in O(|P sim|2 log|P sim| + |Σ|log|Σ|) space and O(|P sim|| → |log|Σ|) time. Our Simulation Algorithm thus reaches the best space bounds while closely approaching the best time complexity.
-
Saving Space in a Time Efficient Simulation Algorithm
2009 Ninth International Conference on Application of Concurrency to System Design, 2009Co-Authors: Silvia Crafa, Francesco Ranzato, Francesco TapparoAbstract:A number of Algorithms are available for computing the Simulation relation on Kripke structures and on labelled transition systems representing concurrent systems. Among them, the Algorithm by Ranzato and Tapparo [2007] has the best time complexity, while the Algorithm by Gentilini et al. [2003] - successively corrected by van Glabbeek and Ploeger [2008] - has the best space complexity. Both space and time complexities are critical issues in a Simulation Algorithm, in particular memory requirements are crucial in the context of model checking when dealing with large state spaces.We propose here a new Simulation Algorithm thatis obtained as a space saving modification of the time efficient Algorithm by Ranzato and Tapparo: a symbolic representation of sets is embedded in thisAlgorithm so that any set of states manipulated by the Algorithm can be efficiently stored as a set of blocks of a suitable state partition. It turns out that this new Simulation Algorithm retains a space complexity comparable with Gentilini et al.'s Algorithm while improving on Gentilini et al.'s time bound.
-
ACSD - Saving Space in a Time Efficient Simulation Algorithm
2009 Ninth International Conference on Application of Concurrency to System Design, 2009Co-Authors: Silvia Crafa, Francesco Ranzato, Francesco TapparoAbstract:A number of Algorithms are available for computing the Simulation relation on Kripke structures and on labelled transition systems representing concurrentsystems. Among them, the Algorithm by Ranzato and Tapparo~[2007] has the best time complexity, while the Algorithm by Gentilini et al.~[2003]~--~successivelycorrected by van Glabbeek and Ploeger~[2008]~--~has thebest space complexity. Both space and time complexities are critical issues in a Simulation Algorithm, in particular memory requirements are crucial in the context of model checking when dealing with large state spaces.We propose here a new Simulation Algorithm thatis obtained as a space saving modification of the time efficient Algorithm by Ranzato and Tapparo: a symbolic representation of sets is embedded in thisAlgorithm so that any set of states manipulated by the Algorithm can be efficiently stored as a set of blocks of a suitable state partition. It turns out that this new Simulation Algorithm retains a space complexity comparable with Gentilini et al.'s Algorithm while improving on Gentilini et al.'s time bound.
Christoph Zechner - One of the best experts on this subject based on the ideXlab platform.
-
selected node stochastic Simulation Algorithm
Journal of Chemical Physics, 2018Co-Authors: Lorenzo Duso, Christoph ZechnerAbstract:Stochastic Simulations of biochemical networks are of vital importance for understanding complex dynamics in cells and tissues. However, existing methods to perform such Simulations are associated with computational difficulties and addressing those remains a daunting challenge to the present. Here we introduce the selected-node stochastic Simulation Algorithm (snSSA), which allows us to exclusively simulate an arbitrary, selected subset of molecular species of a possibly large and complex reaction network. The Algorithm is based on an analytical elimination of chemical species, thereby avoiding explicit Simulation of the associated chemical events. These species are instead described continuously in terms of statistical moments derived from a stochastic filtering equation, resulting in a substantial speedup when compared to Gillespie’s stochastic Simulation Algorithm (SSA). Moreover, we show that statistics obtained via snSSA profit from a variance reduction, which can significantly lower the number of M...
