Proceedings Article10.1063/1.3497968
Fast Exact Stochastic Simulation Algorithms Using Partial Propensities
Rajesh Ramaswamy,Ivo F. Sbalzarini +1 more
- 17 Sep 2010
- Vol. 1281, Iss: 1, pp 1338-1341
TL;DR: The framework presented here defines the design space of adaptations of partial‐propensity exact stochastic simulation algorithms for chemical reaction networks and shows which modules partial-propensity SSAs are composed of and how partial‐ Propensity variants of known SSAs can be constructed by adjusting the sampling strategy used.
read more
Abstract: We review the class of partial‐propensity exact stochastic simulation algorithms (SSA) for chemical reaction networks. We show which modules partial‐propensity SSAs are composed of and how partial‐propensity variants of known SSAs can be constructed by adjusting the sampling strategy used. We demonstrate this on the example of two instances, namely the partial‐propensity variant of Gillespie’s original direct method and that of the SSA with composition‐rejection sampling (SSA‐CR). Partial‐propensity methods may outperform the corresponding classical SSA, particularly on strongly coupled reaction networks. Changing the different modules of partial‐propensity SSAs provides flexibility in tuning them to perform particularly well on certain classes of reaction networks. The framework presented here defines the design space of such adaptations.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Noise-induced modulation of the relaxation kinetics around a non-equilibrium steady state of non-linear chemical reaction networks.
Rajesh Ramaswamy,Rajesh Ramaswamy,Ivo F. Sbalzarini,Ivo F. Sbalzarini,Nélido González-Segredo,Nélido González-Segredo +5 more
TL;DR: It is found that the lifetimes of species change with burst input and confinement, and lifetime is quantified as the integral of the time autocorrelation function (ACF) of concentration fluctuations around a non-equilibrium steady state of the reaction network.
12
pSSAlib: The partial-propensity stochastic chemical network simulator.
Oleksandr Ostrenko,Pietro Incardona,Pietro Incardona,Rajesh Ramaswamy,Lutz Brusch,Ivo F. Sbalzarini +5 more
TL;DR: The software library pSSAlib is described in detail and applied to a new model of the endocytic pathway in eukaryotic cells, leading to the discovery of a stochastic counterpart of the cut-out switch motif underlying early-to-late endosome conversion.
Lazy Updating of hubs can enable more realistic models by speeding up stochastic simulations.
Kurt W. Ehlert,Laurence Loewe +1 more
TL;DR: This work implemented Lazy Updating for the Sorting Direct Method and it is easily integrated into other SSAs such as Gillespie's Direct Method or the Next Reaction Method, an add-on for SSAs designed to reduce the cost of simulating hubs.
9
•Posted Content
Global parameter identification of stochastic reaction networks from single trajectories
TL;DR: A novel combination of an adaptive Monte Carlo sampler, called Gaussian Adaptation (GaA), and efficient exact stochastic simulation algorithms (SSA) that allows parameter identification from single Stochastic trajectories are proposed.
6
Global Parameter Identification of Stochastic Reaction Networks from Single Trajectories
TL;DR: In this article, a Gaussian Adaptation (GaA) sampler is used to estimate the parameters of a stochastic biochemical network model from a single measured time-course of the concentration of some of the involved species.
References
•Book
Non-uniform random variate generation
Luc Devroye
- 16 Apr 1986
TL;DR: A survey of the main methods in non-uniform random variate generation can be found in this article, where the authors provide information on the expected time complexity of various algorithms, before addressing modern topics such as indirectly specified distributions, random processes and Markov chain methods.
4K
Non-Uniform Random Variate Generation.
B. J. T. Morgan,Luc Devroye +1 more
TL;DR: This chapter reviews the main methods for generating random variables, vectors and processes in non-uniform random variate generation, and provides information on the expected time complexity of various algorithms before addressing modern topics such as indirectly specified distributions, random processes, and Markov chain methods.
3.7K