RuleMonkey: software for stochastic simulation of rule-based models.

Area of Science:

• Computational Biology
• Systems Biology
• Biochemistry

Background:

• Rule-based modeling represents molecular interactions, but network generation is computationally expensive.
• Existing simulation methods are often limited by the size of the implied reaction network.
• Network-free simulation methods offer a computationally efficient alternative by avoiding explicit network generation.

Purpose of the Study:

• To introduce RuleMonkey, a software tool for network-free simulation of rule-based models.
• To provide a rejection-free simulation method suitable for models encoded in BioNetGen Language (BNGL).
• To enable analysis of large biochemical systems that were previously computationally intractable.

Main Methods:

• Implemented a network-free simulation method analogous to Gillespie's algorithm.
• Developed RuleMonkey to handle BNGL-encoded models, including those with global application conditions.
• Ensured the method is rejection-free, avoiding null events common in other network-free approaches.

Main Results:

• RuleMonkey successfully simulates rule-based models with large underlying reaction networks.
• Performance comparisons show RuleMonkey is typically faster than DYNSTOC for benchmark problems.
• Verified correct simulation results against established methods.

Conclusions:

• RuleMonkey facilitates the simulation and analysis of complex rule-based biochemical models.
• The software offers a significant speed improvement over DYNSTOC for tested models.
• RuleMonkey is available as a standalone application and integrated into the GetBonNie web environment.