COAST: Controllable approximative stochastic reaction algorithm

Area of Science:

• Computational Chemistry
• Biophysics
• Systems Biology

Background:

• Stochastic simulations are crucial for modeling chemical reactions, especially at low particle numbers.
• Existing methods face computational challenges with large systems or require manual parameter tuning.
• Accurate and efficient simulation algorithms are needed for complex chemical systems.

Purpose of the Study:

• To introduce COAST, an adaptive approximative algorithm for stochastic chemical reaction simulations.
• To enable efficient simulations across various particle number regimes (small, intermediate, and large).
• To provide a single error parameter for user-controlled approximation accuracy.

Main Methods:

• COAST employs a hybrid approach, integrating exact stochastic models, discrete Gaussian distribution models, and deterministic kinetics.
• The algorithm automatically selects the appropriate modeling level for each reaction channel per time step.
• Approximation levels are managed by a single, easily adjustable error parameter.

Main Results:

• COAST simulations demonstrate strong agreement with results from exact stochastic algorithms.
• The algorithm achieves asymptotically faster run times compared to exact methods, especially for larger particle numbers.
• Adaptive modeling significantly improves computational efficiency without sacrificing accuracy.

Conclusions:

• COAST offers a computationally efficient and accurate solution for stochastic simulations of chemical reaction systems.
• The adaptive, multi-level modeling approach effectively bridges different scales of particle numbers.
• This algorithm provides a flexible and user-friendly tool for researchers in computational chemistry and related fields.