Approximation of Event Probabilities in Noisy Cellular Processes

  • Frédéric Didier
  • Thomas A. Henzinger
  • Maria Mateescu
  • Verena Wolf
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5688)


Molecular noise, which arises from the randomness of the discrete events in the cell, significantly influences fundamental biological processes. Discrete -state continuous-time stochastic models (CTMC) can be used to describe such effects, but the calculation of the probabilities of certain events is computationally expensive.

We present a comparison of two analysis approaches for CTMC. On one hand, we estimate the probabilities of interest using repeated Gillespie simulation and determine the statistical accuracy that we obtain. On the other hand, we apply a numerical reachability analysis that approximates the probability distributions of the system at several time instances. We use examples of cellular processes to demonstrate the superiority of the reachability analysis if accurate results are required.


Markov Chain State Space Event Probability Transition Class Precision Level 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Frédéric Didier
    • 1
  • Thomas A. Henzinger
    • 1
  • Maria Mateescu
    • 1
  • Verena Wolf
    • 1
    • 2
  1. 1.EPFLSwitzerland
  2. 2.Saarland UniversityGermany

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