1,948 research outputs found
The auxiliary region method: A hybrid method for coupling PDE- and Brownian-based dynamics for reaction-diffusion systems
Reaction-diffusion systems are used to represent many biological and physical
phenomena. They model the random motion of particles (diffusion) and
interactions between them (reactions). Such systems can be modelled at multiple
scales with varying degrees of accuracy and computational efficiency. When
representing genuinely multiscale phenomena, fine-scale models can be
prohibitively expensive, whereas coarser models, although cheaper, often lack
sufficient detail to accurately represent the phenomenon at hand. Spatial
hybrid methods couple two or more of these representations in order to improve
efficiency without compromising accuracy.
In this paper, we present a novel spatial hybrid method, which we call the
auxiliary region method (ARM), which couples PDE and Brownian-based
representations of reaction-diffusion systems. Numerical PDE solutions on one
side of an interface are coupled to Brownian-based dynamics on the other side
using compartment-based "auxiliary regions". We demonstrate that the hybrid
method is able to simulate reaction-diffusion dynamics for a number of
different test problems with high accuracy. Further, we undertake error
analysis on the ARM which demonstrates that it is robust to changes in the free
parameters in the model, where previous coupling algorithms are not. In
particular, we envisage that the method will be applicable for a wide range of
spatial multi-scales problems including, filopodial dynamics, intracellular
signalling, embryogenesis and travelling wave phenomena.Comment: 29 pages, 14 figures, 2 table
Hybrid approaches for multiple-species stochastic reaction-diffusion models
Reaction-diffusion models are used to describe systems in fields as diverse
as physics, chemistry, ecology and biology. The fundamental quantities in such
models are individual entities such as atoms and molecules, bacteria, cells or
animals, which move and/or react in a stochastic manner. If the number of
entities is large, accounting for each individual is inefficient, and often
partial differential equation (PDE) models are used in which the stochastic
behaviour of individuals is replaced by a description of the averaged, or mean
behaviour of the system. In some situations the number of individuals is large
in certain regions and small in others. In such cases, a stochastic model may
be inefficient in one region, and a PDE model inaccurate in another. To
overcome this problem, we develop a scheme which couples a stochastic
reaction-diffusion system in one part of the domain with its mean field
analogue, i.e. a discretised PDE model, in the other part of the domain. The
interface in between the two domains occupies exactly one lattice site and is
chosen such that the mean field description is still accurate there. This way
errors due to the flux between the domains are small. Our scheme can account
for multiple dynamic interfaces separating multiple stochastic and
deterministic domains, and the coupling between the domains conserves the total
number of particles. The method preserves stochastic features such as
extinction not observable in the mean field description, and is significantly
faster to simulate on a computer than the pure stochastic model.Comment: 38 pages, 8 figure
Coupling particle-based reaction-diffusion simulations with reservoirs mediated by reaction-diffusion PDEs
Open biochemical systems of interacting molecules are ubiquitous in
life-related processes. However, established computational methodologies, like
molecular dynamics, are still mostly constrained to closed systems and
timescales too small to be relevant for life processes. Alternatively,
particle-based reaction-diffusion models are currently the most accurate and
computationally feasible approach at these scales. Their efficiency lies in
modeling entire molecules as particles that can diffuse and interact with each
other. In this work, we develop modeling and numerical schemes for
particle-based reaction-diffusion in an open setting, where the reservoirs are
mediated by reaction-diffusion PDEs. We derive two important theoretical
results. The first one is the mean-field for open systems of diffusing
particles; the second one is the mean-field for a particle-based
reaction-diffusion system with second-order reactions. We employ these two
results to develop a numerical scheme that consistently couples particle-based
reaction-diffusion processes with reaction-diffusion PDEs. This allows modeling
open biochemical systems in contact with reservoirs that are time-dependent and
spatially inhomogeneous, as in many relevant real-world applications
A Lifting Relation from Macroscopic Variables to Mesoscopic Variables in Lattice Boltzmann Method: Derivation, Numerical Assessments and Coupling Computations Validation
In this paper, analytic relations between the macroscopic variables and the
mesoscopic variables are derived for lattice Boltzmann methods (LBM). The
analytic relations are achieved by two different methods for the exchange from
velocity fields of finite-type methods to the single particle distribution
functions of LBM. The numerical errors of reconstructing the single particle
distribution functions and the non-equilibrium distribution function by
macroscopic fields are investigated. Results show that their accuracy is better
than the existing ones. The proposed reconstruction operator has been used to
implement the coupling computations of LBM and macro-numerical methods of FVM.
The lid-driven cavity flow is chosen to carry out the coupling computations
based on the numerical strategies of domain decomposition methods (DDM). The
numerical results show that the proposed lifting relations are accurate and
robust
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