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In probability theory, tau-leaping, or τ-leaping, is an approximate method for the simulation of a stochastic system.[1] It is based on the Gillespie algorithm, performing all reactions for an interval of length tau before updating the propensity functions.[2] By updating the rates less often this sometimes allows for more efficient simulation and thus the consideration of larger systems.

Many variants of the basic algorithm have been considered.[3][4][5][6][7]


The algorithm is analogous to the Euler method for deterministic systems, but instead of making a fixed change

the change is

where is a Poisson distributed random variable with mean .

Given a state with events occurring at rate and with state change vectors (where indexes the state variables, and indexes the events), the method is as follows:

  1. Initialise the model with initial conditions .
  2. Calculate the event rates .
  3. Choose a time step . This may be fixed, or by some algorithm dependent on the various event rates.
  4. For each event generate , which is the number of times each event occurs during the time interval .
  5. Update the state by
    where is the change on state variable due to event . At this point it may be necessary to check that no populations have reached unrealistic values (such as a population becoming negative due to the unbounded nature of the Poisson variable ).
  6. Repeat from Step 2 onwards until some desired condition is met (e.g. a particular state variable reaches 0, or time is reached).

Algorithm for efficient step size selection[edit]

This algorithm is described by Cao et al.[4] The idea is to bound the relative change in each event rate by a specified tolerance (Cao et al. recommend , although it may depend on model specifics). This is achieved by bounding the relative change in each state variable by , where depends on the rate that changes the most for a given change in .Typically is equal the highest order event rate, but this may be more complex in different situations (especially epidemiological models with non-linear event rates).

This algorithm typically requires computing auxiliary values (where is the number of state variables ), and should only require reusing previously calculated values . An important factor in this since is an integer value, then there is a minimum value by which it can change, preventing the relative change in being bounded by 0, which would result in also tending to 0.

  1. For each state variable , calculate the auxiliary values
  2. For each state variable , determine the highest order event in which it is involved, and obtain
  3. Calculate time step as

This computed is then used in Step 3 of the leaping algorithm.


  1. ^ Gillespie, D. T. (2001). "Approximate accelerated stochastic simulation of chemically reacting systems" (PDF). The Journal of Chemical Physics. 115 (4): 1716–1733. Bibcode:2001JChPh.115.1716G. doi:10.1063/1.1378322.
  2. ^ Erhard, F.; Friedel, C. C.; Zimmer, R. (2010). "FERN – Stochastic Simulation and Evaluation of Reaction Networks". Systems Biology for Signaling Networks. p. 751. doi:10.1007/978-1-4419-5797-9_30. ISBN 978-1-4419-5796-2.
  3. ^ Cao, Y.; Gillespie, D. T.; Petzold, L. R. (2005). "Avoiding negative populations in explicit Poisson tau-leaping". The Journal of Chemical Physics. 123 (5): 054104. Bibcode:2005JChPh.123e4104C. CiteSeerX doi:10.1063/1.1992473. PMID 16108628.
  4. ^ a b Cao, Y.; Gillespie, D. T.; Petzold, L. R. (2006). "Efficient step size selection for the tau-leaping simulation method" (PDF). The Journal of Chemical Physics. 124 (4): 044109. Bibcode:2006JChPh.124d4109C. doi:10.1063/1.2159468. PMID 16460151.
  5. ^ Anderson, David F. (2008-02-07). "Incorporating postleap checks in tau-leaping". The Journal of Chemical Physics. 128 (5): 054103. arXiv:0708.0377. Bibcode:2008JChPh.128e4103A. doi:10.1063/1.2819665. ISSN 0021-9606. PMID 18266441.
  6. ^ Chatterjee, Abhijit; Vlachos, Dionisios G.; Katsoulakis, Markos A. (2005-01-08). "Binomial distribution based τ-leap accelerated stochastic simulation". The Journal of Chemical Physics. 122 (2): 024112. Bibcode:2005JChPh.122b4112C. doi:10.1063/1.1833357. ISSN 0021-9606. PMID 15638577.
  7. ^ Moraes, Alvaro; Tempone, Raul; Vilanova, Pedro (2014-04-24). "Hybrid Chernoff Tau-Leap". Multiscale Modeling & Simulation. 12 (2): 581–615. CiteSeerX doi:10.1137/130925657. ISSN 1540-3467.