Spatial-Stochastic Simulation of Reaction-Diffusion Systems

TL;DR

This paper reviews and discusses various spatial-stochastic simulation techniques for reaction-diffusion systems in biochemistry, emphasizing accuracy, efficiency, and recent advancements in modeling spatial effects at the single-molecule level.

Contribution

It introduces and compares different spatial-stochastic simulation methods, including adaptations of Brownian Dynamics and event-driven algorithms like eGFRD, highlighting their advantages and challenges.

Findings

Brownian Dynamics can be adapted for chemical reactions with specific schemes.

Event-driven methods like eGFRD improve efficiency and accuracy.

Spatial effects significantly influence biochemical system behavior.

Abstract

Biochemical networks play a crucial role in biological systems, implementing a broad range of vital functions. They normally operate at low copy numbers and in spatial settings, but this is often ignored and well-stirred conditions are assumed. Yet, it is increasingly becoming clear that even microscopic spatial inhomogeneities oftentimes can induce significant differences on the macroscopic level. Since experimental observation of single-molecule behavior is extremely challenging, theoretical modeling of biochemical reactions on the single-particle level is an important tool for understanding spatial effects in biochemical systems. While purely analytical models quickly become intractable here, spatial-stochastic simulations can capture a wide range of biochemical processes with the necessary levels of detail. Here we discuss different techniques for spatial-stochastic simulation of reaction-diffusion systems, and explain important precautions required to make them biochemically accurate and efficient. We illustrate non-negligible accuracy issues arising even in the most simple approaches to biochemical simulation, and present methods to deal with them. We first explain how Brownian Dynamics, a widely used particle-based diffusion simulation technique with fixed propagation time, can be adapted to incorporate chemical reactions, and portray a range of schemes that elaborate on this idea. We then introduce event-driven spatial-stochastic simulation methods, in which system updates are performed asynchronously with situation-dependent, varying time steps; here we particularly focus on eGFRD, a computationally efficient particle-based algorithm that makes use of analytical functions to accurately sample interparticle reactions and diffusive motion with large jumps in time and space. We end by briefly presenting recent developments in the field of spatial-stochastic biochemical simulation.