An Overview of Network-Based and -Free Approaches for Stochastic Simulation of Biochemical Systems.

Abhishekh Gupta,Pedro Mendes

doi:10.3390/computation6010009

Abhishekh Gupta, Pedro Mendes

Open Access

https://doi.org/10.3390/computation6010009

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Journal: Computation (Basel, Switzerland)	Publication Date: Jan 31, 2018
Citations: 18	License type: CC BY 4.0

Affiliation: University of Connecticut

Abstract

Stochastic simulation has been widely used to model the dynamics of biochemical reaction networks. Several algorithms have been proposed that are exact solutions of the chemical master equation, following the work of Gillespie. These stochastic simulation approaches can be broadly classified into two categories: network-based and -free simulation. The network-based approach requires that the full network of reactions be established at the start, while the network-free approach is based on reaction rules that encode classes of reactions, and by applying rule transformations, it generates reaction events as they are needed without ever having to derive the entire network. In this study, we compare the efficiency and limitations of several available implementations of these two approaches. The results allow for an informed selection of the implementation and methodology for specific biochemical modeling applications.

Highlights

Research in systems biology has been increasingly supported by computational models of biochemical reaction networks
These models are studied either through a deterministic approach, using differential equations to represent the temporal changes of the concentrations of the chemical species, or via a stochastic approach based on the chemical master equation (CME) and solved through
The first test was focused on exposing issues depending on the number of particles in the system; the second test was focused on exposing issues that arised closer to stable states

Summary

Introduction

Research in systems biology has been increasingly supported by computational models of biochemical reaction networks. The deterministic approach of solving a set of differential equations by numerical integration is fast and widely adopted, it is unable to estimate the variance of the species concentrations and can become inaccurate for systems with a small number of particles [1]. In these situations, the stochastic approach of using a Monte Carlo simulation algorithm for evaluation of the CME is preferred.

Methods

Results

Conclusion