Data-driven method for efficient characterization of rare event probabilities in biochemical systems

DOI10.1007/s11538-018-0509-0zbMath1422.92073OpenAlexW2890086335WikidataQ91500211 ScholiaQ91500211MaRDI QIDQ2325577

Min K. Roh

Publication date: 26 September 2019

Published in: Bulletin of Mathematical Biology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11538-018-0509-0

zbMATH Keywords

importance samplingstochastic simulationSSAGillespie algorithmdwSSArare event probability estimation

Mathematics Subject Classification ID

Kinetics in biochemical problems (pharmacokinetics, enzyme kinetics, etc.) (92C45) Computational methods for stochastic equations (aspects of stochastic analysis) (60H35) Computational methods for problems pertaining to biology (92-08) Systems biology, networks (92C42)

Related Items (2)

Learning-based importance sampling via stochastic optimal control for stochastic reaction networks ⋮ Stochastic simulation of biochemical systems: in memory of Dan T. Gillespie's contributions

Cites Work

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• Multilevel hybrid split-step implicit tau-leap
• Multiscale stochastic simulation algorithm with stochastic partial equilibrium assumption for chemically reacting systems
• Asymptotic analysis of multiscale approximations to reaction networks
• Optimization of computer simulation models with rare events
• Separation of time-scales and model reduction for stochastic reaction networks
• A multiple time interval finite state projection algorithm for the solution to the chemical master equation

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