Open Access Software

RuleMonkey: software for stochastic simulation of rule-based models

Joshua Colvin1, Michael I Monine2, Ryan N Gutenkunst2, William S Hlavacek23, Daniel D Von Hoff1 and Richard G Posner14*

Author Affiliations

1 Clinical Translational Research Division, Translational Genomics Research Institute, Phoenix, AZ 85004, USA

2 Theoretical Biology and Biophysics Group, Theoretical Division and Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM 87545, USA

3 Department of Biology, University of New Mexico, Albuquerque, NM 87131, USA

4 Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ 86011, USA

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BMC Bioinformatics 2010, 11:404  doi:10.1186/1471-2105-11-404

Published: 30 July 2010

Abstract

Background

The system-level dynamics of many molecular interactions, particularly protein-protein interactions, can be conveniently represented using reaction rules, which can be specified using model-specification languages, such as the BioNetGen language (BNGL). A set of rules implicitly defines a (bio)chemical reaction network. The reaction network implied by a set of rules is often very large, and as a result, generation of the network implied by rules tends to be computationally expensive. Moreover, the cost of many commonly used methods for simulating network dynamics is a function of network size. Together these factors have limited application of the rule-based modeling approach. Recently, several methods for simulating rule-based models have been developed that avoid the expensive step of network generation. The cost of these "network-free" simulation methods is independent of the number of reactions implied by rules. Software implementing such methods is now needed for the simulation and analysis of rule-based models of biochemical systems.

Results

Here, we present a software tool called RuleMonkey, which implements a network-free method for simulation of rule-based models that is similar to Gillespie's method. The method is suitable for rule-based models that can be encoded in BNGL, including models with rules that have global application conditions, such as rules for intramolecular association reactions. In addition, the method is rejection free, unlike other network-free methods that introduce null events, i.e., steps in the simulation procedure that do not change the state of the reaction system being simulated. We verify that RuleMonkey produces correct simulation results, and we compare its performance against DYNSTOC, another BNGL-compliant tool for network-free simulation of rule-based models. We also compare RuleMonkey against problem-specific codes implementing network-free simulation methods.

Conclusions

RuleMonkey enables the simulation of rule-based models for which the underlying reaction networks are large. It is typically faster than DYNSTOC for benchmark problems that we have examined. RuleMonkey is freely available as a stand-alone application http://public.tgen.org/rulemonkey webcite. It is also available as a simulation engine within GetBonNie, a web-based environment for building, analyzing and sharing rule-based models.