Abstract
BackgroundThe 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.ResultsHere, 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.ConclusionsRuleMonkey 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. It is also available as a simulation engine within GetBonNie, a web-based environment for building, analyzing and sharing rule-based models.
Highlights
We present a software tool, RuleMonkey, which implements a network-free stochastic simulation method for determining the system-level dynamics of molecular interactions represented by rules
The following models were considered (Table 1; Figs. 2, 3, 4): 1) the multisite phosphorylation model introduced by Colvin et al [14], testcase1.bngl; 2) the TLBR model introduced by Yang et al [19] and considered by Colvin et al [14] and here in Fig. 2B, testcase2a.bngl and testcase2b.bngl; 3) a model introduced here with reaction events occurring on disparate time scales, stiff.bngl
RuleMonkey is a general-purpose simulator for rulebased models encoded in BioNetGen language (BNGL)
Summary
Validation We validated RuleMonkey by comparing simulation results against those obtained using BioNetGen [25,29], DYNSTOC [14], and problem-specific codes [19,28,44]. The performance of RuleMonkey for seven benchmark problems is compared against that of DYNSTOC [14], problem-specific codes implementing network-free procedures [20,44], and the simulate-ssa procedure of BioNetGen [21,25,29]. Of the available general-purpose simulation codes compliant with the conventions of BNGL, RuleMonkey and DYNSTOC are the most widely applicable (because these tools implement general-purpose network-free simulation methods), and RuleMonkey is more efficient than DYNSTOC (Table 1). We compared the performances of RuleMonkey and DYNSTOC for problems with reactions occurring on different time scales (Fig. 4). As the rate of the fastest reaction in a system increases, the efficiency of DYNSTOC degrades relative to that of RuleMonkey
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