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

Colvin J, Monine MI, Gutenkunst RN, Hlavacek WS, Von Hoff DD, Posner RG - BMC Bioinformatics (2010)

Bottom Line: 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.In addition, the method is rejection free, unlike other network-free methods that introduce 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Clinical Translational Research Division, Translational Genomics Research Institute, Phoenix, AZ 85004, USA.

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 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. It is also available as a simulation engine within GetBonNie, a web-based environment for building, analyzing and sharing rule-based models.

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RuleMonkey is efficient for simulation of rule-based models characterized by large-scale networks. We compare RuleMonkey (solid lines marked by triangles), DYNSTOC (solid lines marked by open dots) and a problem-specific implementation of the method of Yang et al. [19] (dotted lines); these methods are used to simulate the TLBR model. a) Scaling of computational cost with system size, where size is measured by NR, the number of cell-surface receptors. b) Scaling of computational cost with dimensionless parameter β = NRk+2/koff, which controls the (equilibrium) extent of ligand-induced receptor crosslinking. The rate constant k+2 characterizes receptor crosslinking, and the rate constant koff characterizes dissociation of ligand-receptor bonds. The value of β was adjusted by varying k+2 while holding NR = 300 and koff = 0.01 s-1 fixed. In each panel, the y-axis indicates the normalized total CPU time per reaction event required to simulate the kinetics of the TLBR model from time t = 0 to 1000 s with all ligand initially free. Parameters used are the same as those reported for Fig. 3 of Colvin et al. [14]. See the BioNetGen input file testcase2a.bngl (Additional file 2).
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Figure 3: RuleMonkey is efficient for simulation of rule-based models characterized by large-scale networks. We compare RuleMonkey (solid lines marked by triangles), DYNSTOC (solid lines marked by open dots) and a problem-specific implementation of the method of Yang et al. [19] (dotted lines); these methods are used to simulate the TLBR model. a) Scaling of computational cost with system size, where size is measured by NR, the number of cell-surface receptors. b) Scaling of computational cost with dimensionless parameter β = NRk+2/koff, which controls the (equilibrium) extent of ligand-induced receptor crosslinking. The rate constant k+2 characterizes receptor crosslinking, and the rate constant koff characterizes dissociation of ligand-receptor bonds. The value of β was adjusted by varying k+2 while holding NR = 300 and koff = 0.01 s-1 fixed. In each panel, the y-axis indicates the normalized total CPU time per reaction event required to simulate the kinetics of the TLBR model from time t = 0 to 1000 s with all ligand initially free. Parameters used are the same as those reported for Fig. 3 of Colvin et al. [14]. See the BioNetGen input file testcase2a.bngl (Additional file 2).

Mentions: We validated RuleMonkey by comparing simulation results against those obtained using BioNetGen [25,29], DYNSTOC [14], and problem-specific codes [19,28,44]. The following models were considered (Table 1; Figs. 2, 3, 4): 1) the multisite phosphorylation model introduced by Colvin et al. [14], testcase1.bngl (see Additional file 1); 2) the TLBR (trivalent ligand-bivalent receptor) model introduced by Yang et al. [19] and considered by Colvin et al. [14] and here in Fig. 2B, testcase2a.bngl (see Additional file 2) and testcase2b.bngl (see Additional file 3); 3) a model introduced here with reaction events occurring on disparate time scales, stiff.bngl (see Additional file 4); 4) a model introduced here for interaction of a pentavalent ligand with a trivalent cell-surface receptor, pltr.bngl (see Additional file 5); 5) the model of Blinov et al. [45] for early events in epidermal growth factor receptor (EGFR) signaling, egfr net.bngl (see Additional file 6); 6) the model of Goldstein et al. [46] and Faeder et al. [47] for early events in IgE receptor (FcϵRI) signaling, fceri.bngl (see Additional file 7); and 7) the model of Nag et al. [44] for crosslinking of phosphorylated LAT molecules by intracellular Grb2-Sos1 complexes, lat.bngl (see Additional file 8). The BioNetGen input files that specify these models and RuleMonkey simulations of them are available as Additional files 12345678. They are also available at the RuleMonkey web site [40].


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

Colvin J, Monine MI, Gutenkunst RN, Hlavacek WS, Von Hoff DD, Posner RG - BMC Bioinformatics (2010)

RuleMonkey is efficient for simulation of rule-based models characterized by large-scale networks. We compare RuleMonkey (solid lines marked by triangles), DYNSTOC (solid lines marked by open dots) and a problem-specific implementation of the method of Yang et al. [19] (dotted lines); these methods are used to simulate the TLBR model. a) Scaling of computational cost with system size, where size is measured by NR, the number of cell-surface receptors. b) Scaling of computational cost with dimensionless parameter β = NRk+2/koff, which controls the (equilibrium) extent of ligand-induced receptor crosslinking. The rate constant k+2 characterizes receptor crosslinking, and the rate constant koff characterizes dissociation of ligand-receptor bonds. The value of β was adjusted by varying k+2 while holding NR = 300 and koff = 0.01 s-1 fixed. In each panel, the y-axis indicates the normalized total CPU time per reaction event required to simulate the kinetics of the TLBR model from time t = 0 to 1000 s with all ligand initially free. Parameters used are the same as those reported for Fig. 3 of Colvin et al. [14]. See the BioNetGen input file testcase2a.bngl (Additional file 2).
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Figure 3: RuleMonkey is efficient for simulation of rule-based models characterized by large-scale networks. We compare RuleMonkey (solid lines marked by triangles), DYNSTOC (solid lines marked by open dots) and a problem-specific implementation of the method of Yang et al. [19] (dotted lines); these methods are used to simulate the TLBR model. a) Scaling of computational cost with system size, where size is measured by NR, the number of cell-surface receptors. b) Scaling of computational cost with dimensionless parameter β = NRk+2/koff, which controls the (equilibrium) extent of ligand-induced receptor crosslinking. The rate constant k+2 characterizes receptor crosslinking, and the rate constant koff characterizes dissociation of ligand-receptor bonds. The value of β was adjusted by varying k+2 while holding NR = 300 and koff = 0.01 s-1 fixed. In each panel, the y-axis indicates the normalized total CPU time per reaction event required to simulate the kinetics of the TLBR model from time t = 0 to 1000 s with all ligand initially free. Parameters used are the same as those reported for Fig. 3 of Colvin et al. [14]. See the BioNetGen input file testcase2a.bngl (Additional file 2).
Mentions: We validated RuleMonkey by comparing simulation results against those obtained using BioNetGen [25,29], DYNSTOC [14], and problem-specific codes [19,28,44]. The following models were considered (Table 1; Figs. 2, 3, 4): 1) the multisite phosphorylation model introduced by Colvin et al. [14], testcase1.bngl (see Additional file 1); 2) the TLBR (trivalent ligand-bivalent receptor) model introduced by Yang et al. [19] and considered by Colvin et al. [14] and here in Fig. 2B, testcase2a.bngl (see Additional file 2) and testcase2b.bngl (see Additional file 3); 3) a model introduced here with reaction events occurring on disparate time scales, stiff.bngl (see Additional file 4); 4) a model introduced here for interaction of a pentavalent ligand with a trivalent cell-surface receptor, pltr.bngl (see Additional file 5); 5) the model of Blinov et al. [45] for early events in epidermal growth factor receptor (EGFR) signaling, egfr net.bngl (see Additional file 6); 6) the model of Goldstein et al. [46] and Faeder et al. [47] for early events in IgE receptor (FcϵRI) signaling, fceri.bngl (see Additional file 7); and 7) the model of Nag et al. [44] for crosslinking of phosphorylated LAT molecules by intracellular Grb2-Sos1 complexes, lat.bngl (see Additional file 8). The BioNetGen input files that specify these models and RuleMonkey simulations of them are available as Additional files 12345678. They are also available at the RuleMonkey web site [40].

Bottom Line: 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.In addition, the method is rejection free, unlike other network-free methods that introduce 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Clinical Translational Research Division, Translational Genomics Research Institute, Phoenix, AZ 85004, USA.

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 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. It is also available as a simulation engine within GetBonNie, a web-based environment for building, analyzing and sharing rule-based models.

Show MeSH
Related in: MedlinePlus