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Stochastic self-tuning hybrid algorithm for reaction-diffusion systems
Á Ruiz-Martínez1, T M Bartol2, T J Sejnowski2
1Department of Mechanical and Aerospace Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093, USA.
The Journal of Chemical Physics
|January 3, 2020
Summary
This study introduces a hybrid algorithm combining Brownian and Gillespie methods to efficiently model biochemical systems with varying reactant concentrations. The novel approach accurately simulates complex reactions regardless of particle count, enhancing computational efficiency.
Area of Science:
- Biochemistry
- Computational Biology
- Chemical Kinetics
Background:
- Biochemical systems often feature reactants with disparate concentrations.
- Modeling these systems requires methods that can handle both continuum and discrete descriptions.
Purpose of the Study:
- To develop a hybrid self-tuning algorithm for modeling biochemical systems with wide variations in reactant concentrations.
- To enhance the efficiency and accuracy of simulations for complex multireaction systems.
Main Methods:
- Combines microscopic Brownian dynamics for diffusion with mesoscopic Gillespie-type methods for reactions.
- Employs a self-tuning approach with redefined propensities and optimized mesh size and time step.
Main Results:
- The hybrid algorithm demonstrates efficiency across diverse scenarios, from reaction-dominated to sparse reaction systems.
- Simulation accuracy is maintained irrespective of the number of particles in the system.
- The method proves robust and versatile for modeling complex multireaction dynamics.
Conclusions:
- The developed hybrid algorithm provides an accurate and computationally efficient tool for simulating biochemical phenomena with large concentration variations.
- This approach is suitable for a broad spectrum of complex multireaction systems, improving modeling capabilities.
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