Quantifying Stochastic Effects in Biochemical Reaction Networks using Partitioned Leaping

TL;DR

This paper demonstrates how the partitioned leaping algorithm (PLA) can effectively quantify stochastic effects in biochemical networks, offering advantages over traditional methods and highlighting practical challenges and solutions.

Contribution

It applies the PLA to model biochemical systems, illustrating its ability to reveal subtle stochastic effects and discussing strategies to overcome implementation bottlenecks.

Findings

PLA quantifies subtle stochastic effects in biochemical networks

PLA reveals behaviors difficult for exact methods to capture

Discussion of bottlenecks and strategies for practical implementation

Abstract

"Leaping" methods show great promise for significantly accelerating stochastic simulations of complex biochemical reaction networks. However, few practical applications of leaping have appeared in the literature to date. Here, we address this issue using the "partitioned leaping algorithm" (PLA) [L.A. Harris and P. Clancy, J. Chem. Phys. 125, 144107 (2006)], a recently-introduced multiscale leaping approach. We use the PLA to investigate stochastic effects in two model biochemical reaction networks. The networks that we consider are simple enough so as to be accessible to our intuition but sufficiently complex so as to be generally representative of real biological systems. We demonstrate how the PLA allows us to quantify subtle effects of stochasticity in these systems that would be difficult to ascertain otherwise as well as not-so-subtle behaviors that would strain commonly-used "exact" stochastic methods. We also illustrate bottlenecks that can hinder the approach and exemplify and discuss possible strategies for overcoming them. Overall, our aim is to aid and motivate future applications of leaping by providing stark illustrations of the benefits of the method while at the same time elucidating obstacles that are often encountered in practice.