Splitting for rare event simulation in biochemical systems

V.H. Thanh, R. Zunino

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Estimating the probability of rare events in biochemical systems is an important task, since it can help in studying rare abnormal behavior when they do occur. A conventional Monte Carlo approach for such a task would be to simulate a system through a standard stochastic simulation algorithm (SSA), hence generating many trajectories and counting the number of the successful ones. Rare events make this approach infeasible since a prohibitively large number of trajectories would need to be generated before the estimation becomes reasonably accurate. In this paper we propose a new method, called sSSA, which estimates the probability for a rare event through a kind of biased simulation. The state space is split into interfaces defined through corresponding levels, and simulated trajectories are gradually "pushed" towards the rare event following such levels. The (unbiased) probability for the rare event is then estimated by counting the successful (biased) trajectories, and then applying a correction factor so to account for the bias. We compare both performance and accuracy for SSA and sSSA by experimenting in some concrete scenarios. Experimental results prevail that sSSA is more efficient than the naive SSA approach. Copyright © 2013 ICST.
Original languageEnglish
Title of host publicationSIMUTools 2013 - 6th International Conference on Simulation Tools and Techniques
ISBN (Electronic)978-193696876-3
Publication statusPublished - 2013
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Simulation Tools and Techniques - Cannes, France
Duration: 5 Mar 20137 Mar 2013
Conference number: 6


ConferenceInternational Conference on Simulation Tools and Techniques
Abbreviated titleSIMUTools


  • Biochemistry
  • Interface states
  • Probability
  • Stochastic models
  • Stochastic systems
  • Trajectories, Biochemical systems
  • Monte Carlo approach
  • Multilevels
  • Rare event simulation
  • Simulated trajectories
  • SSSA
  • Stochastic simulation algorithms
  • Stochastic simulations, Monte Carlo methods

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