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A hybrid smoothed dissipative particle dynamics (SDPD) spatial stochastic simulation algorithm (sSSA) for

Drawert Brian1, Jacob Bruno2, Li Zhen3

  • 1Department of Computer Science, University of North Carolina at Asheville, Asheville, North Carolina, 28804, USA.

Journal of Computational Physics
|April 30, 2019
PubMed
Summary

We developed a hybrid algorithm combining spatial stochastic simulation (sSSA) and smoothed dissipative particle dynamics (SDPD) for simulating chemically reacting systems. This method allows for mesh-free, dynamic simulation of complex fluid dynamics and chemical reactions.

Keywords:
Discrete Stochastic SimulationParticle Based Fluid DynamicsReaction-Diffusion Master Equation

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Area of Science:

  • Computational chemistry
  • Fluid dynamics
  • Biophysical modeling

Background:

  • Accurate simulation of chemically reacting systems in dynamic fluid environments is challenging.
  • Existing methods often struggle to integrate stochastic chemical kinetics with mesoscopic fluid dynamics.
  • There is a need for a unified framework to model spatially resolved, reactive systems.

Purpose of the Study:

  • To develop and validate a novel hybrid algorithm merging discrete stochastic simulation with particle-based fluid dynamics.
  • To enable simulation of chemically reacting systems in mesh-free, dynamic domains using a Lagrangian frame.
  • To provide a versatile tool for studying complex phenomena at the intersection of fluid mechanics and chemical kinetics.

Main Methods:

  • Developed a hybrid algorithm combining the spatial stochastic simulation algorithm (sSSA) with smoothed dissipative particle dynamics (SDPD).
  • Implemented discrete stochastic simulation via reaction-diffusion master equations (RDME) and deterministic reaction-diffusion equations within the SDPD framework.
  • Utilized a mesh-free, dynamic domain with a Lagrangian reference frame for simulations.

Main Results:

  • Successfully merged sSSA with SDPD to create a versatile simulation algorithm.
  • Validated the hybrid method against four canonical models, demonstrating its accuracy.
  • Demonstrated the method's applicability by simulating a yeast cell in a microfluidics chamber with a chemical gradient.

Conclusions:

  • The new hybrid algorithm effectively simulates spatially resolved, chemically reacting systems coupled with fluid dynamics.
  • This approach offers a powerful and flexible tool for mesoscopic modeling of complex biological and chemical processes.
  • The method's validation and demonstration highlight its potential for diverse applications in microfluidics and beyond.