A hybrid tau-leap for simulating chemical kinetics with applications to parameter estimation

Area of Science:

• Computational chemistry
• Biophysics
• Chemical kinetics

Background:

• Stochastic models are crucial for chemical kinetics but computationally expensive.
• The Gillespie stochastic simulation algorithm (SSA) faces limitations with large models.
• Bayesian inference for chemical models is hindered by the computational burden of SSA.

Purpose of the Study:

• To develop a computationally efficient algorithm for simulating stochastic chemical kinetics.
• To address the limitations of existing methods in Bayesian inference contexts.
• To improve the speed of parameter estimation for complex chemical systems.

Main Methods:

• Introduction of a novel hybrid τ-leap algorithm.
• Dynamic switching between τ-leap (high density) and SSA (low density).
• Utilizing Poisson formulation properties for intermediate regimes.

Main Results:

• Significant computational savings demonstrated compared to standard SSA.
• Maintained accuracy comparable to SSA across various scenarios.
• Enabled faster parameter estimation in Bayesian inference.

Conclusions:

• The hybrid τ-leap algorithm offers a substantial performance improvement for stochastic chemical kinetics.
• This method effectively reduces computational cost in Bayesian inference.
• Provides a viable solution for simulating complex chemical systems more efficiently.