Efficient stochastic sampling of first-passage times with applications to self-assembly simulations

TL;DR

This paper introduces two novel methods to accelerate stochastic simulation algorithms for stiff reaction systems, enabling efficient sampling of first-passage times crucial for modeling complex biological and self-assembly processes.

Contribution

The authors develop exact and approximate eigenvalue-based methods to significantly reduce sampling times in SSA models, especially for stiff systems, expanding their practical applicability.

Findings

Eigenvalue methods substantially reduce sampling times.

Methods are effective for complex self-assembly networks.

Approaches are applicable to a wide range of stiff reaction systems.

Abstract

Models of reaction chemistry based on the stochastic simulation algorithm (SSA) have become a crucial tool for simulating complicated biological reaction networks due to their ability to handle extremely complicated reaction networks and to represent noise in small-scale chemistry. These methods can, however, become highly inefficient for stiff reaction systems, those in which different reaction channels operate on widely varying time scales. In this paper, we develop two methods for accelerating sampling in SSA models: an exact method and a scheme allowing for sampling accuracy up to any arbitrary error bound. Both methods depend on analysis of the eigenvalues of continuous time Markov model graphs that define the behavior of the SSA. We demonstrate these methods for the specific application of sampling breakage times for multiply-connected bond networks, a class of stiff system important to models of self-assembly processes. We show theoretically and empirically that our eigenvalue methods provide substantially reduced sampling times for a wide range of network breakage models. These techniques are also likely to have broad use in accelerating SSA models so as to apply them to systems and parameter ranges that are currently computationally intractable.