An adaptive multi-level simulation algorithm for stochastic biological systems.

  title={An adaptive multi-level simulation algorithm for stochastic biological systems.},
  author={C. Lester and C. Yates and M. Giles and R. Baker},
  journal={The Journal of chemical physics},
  volume={142 2},
  • C. Lester, C. Yates, +1 author R. Baker
  • Published 2015
  • Computer Science, Medicine, Biology
  • The Journal of chemical physics
  • Discrete-state, continuous-time Markov models are widely used in the modeling of biochemical reaction networks. Their complexity often precludes analytic solution, and we rely on stochastic simulation algorithms (SSA) to estimate system statistics. The Gillespie algorithm is exact, but computationally costly as it simulates every single reaction. As such, approximate stochastic simulation algorithms such as the tau-leap algorithm are often used. Potentially computationally more efficient, the… CONTINUE READING
    22 Citations

    Figures, Tables, and Topics from this paper

    Explore Further: Topics Discussed in This Paper

    Multi-level methods and approximating distribution functions
    • 6
    • PDF
    Robustly simulating biochemical reaction kinetics using multi-level Monte Carlo approaches
    • 5
    • PDF
    Importance sampling for a robust and efficient multilevel Monte Carlo estimator for stochastic reaction networks
    • 2
    • PDF
    Multilevel hybrid split-step implicit tau-leap
    • 11
    • PDF
    Simulation and inference algorithms for stochastic biochemical reaction networks: from basic concepts to state-of-the-art
    • 38
    • PDF
    Estimation of Parameter Sensitivities for Stochastic Reaction Networks Using Tau-Leap Simulations
    • 6
    • PDF


    Multilevel Monte Carlo for Continuous Time Markov Chains, with Applications in Biochemical Kinetics
    • 95
    • PDF
    Hybrid Chernoff Tau-Leap
    • 24
    • PDF
    Solving the chemical master equation for monomolecular reaction systems analytically
    • 253
    • PDF
    Efficient formulation of the stochastic simulation algorithm for chemically reacting systems.
    • 428
    • PDF