Adaptive Hybrid Simulations for Multiscale Stochastic Reaction Networks

TL;DR

This paper introduces an adaptive hybrid simulation method for stochastic reaction networks that automatically partitions reactions into discrete or continuous parts, adapting to changing dynamics for efficient and accurate probability distribution estimation.

Contribution

The authors develop an automatic, adaptive hybrid simulation approach that dynamically adjusts to changing timescales in stochastic reaction networks, improving efficiency without user intervention.

Findings

Achieves accurate probability distributions with less computational time.

Effectively adapts to changing reaction dynamics and species copy numbers.

Demonstrated success on systems biology examples.

Abstract

The probability distribution describing the state of a Stochastic Reaction Network evolves according to the Chemical Master Equation (CME). It is common to estimated its solution using Monte Carlo methods such as the Stochastic Simulation Algorithm (SSA). In many cases these simulations can take an impractical amount of computational time. Therefore many methods have been developed that approximate the Stochastic Process underlying the Chemical Master Equation. Prominent strategies are Hybrid Models that regard the firing of some reaction channels as being continuous and applying the quasi-stationary assumption to approximate the dynamics of fast subnetworks. However as the dynamics of a Stochastic Reaction Network changes with time these approximations might have to be adapted during the simulation. We develop a method that approximates the solution of a CME by automatically partitioning the reaction dynamics into discrete/continuous components and applying the quasi-stationary assumption on identifiable fast subnetworks. Our method does not require user intervention and it adapts to exploit the changing timescale separation between reactions and/or changing magnitudes of copy numbers of constituent species. We demonstrate the efficiency of the proposed method by considering examples from Systems Biology and showing that very good approximations to the exact probability distributions can be achieved in significantly less computational time.