The system-level dynamics of  biomolecular interactions can be difficult to simulate using methods that require explicit specification of a chemical reaction network. Here, we present and evaluate a rejection-free stochastic simulation method for determining the kinetics of multivalent biomolecular interactions . The method has a computational cost independent of reaction network size, and it is based on sampling a set of chemical transformation classes defined by formal rules that characterize the interactions in a system. We apply the method to simulate interactions of an m-valent ligand with an n-valent cell-surface receptor . Simulation results show that  the rejection-free method is more efficient over wide parameter ranges than a related method that relies on rejection sampling .
The system-level dynamics of multivalent biomolecular interactions can be simulated using a rule-based kinetic Monte Carlo method in which a rejection sampling strategy is used to generate reaction events. This method becomes inefficient when simulating aggregation processes with large biomolecular complexes. Here, we present a rejection-free method for determining the kinetics of multivalent biomolecular interactions , and we apply the method to simulate simple models for ligand-receptor interactions . Simulation results show that performance of the rejection-free method is equal to or better than that of the rejection method over wide parameter ranges , and the rejection-free method is more efficient for simulating systems in which aggregation is extensive. The rejection-free method reported here should be useful for simulating a variety of systems in which multisite molecular interactions yield large molecular aggregates .
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multivalent
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difficult to simulate using methods that require explicit specification of a chemical reaction network.
simulated using a rule-based kinetic Monte Carlo method in which a rejection sampling strategy is used to generate reaction events. This method becomes inefficient when simulating aggregation processes with large biomolecular complexes.
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and evaluate
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stochastic simulation
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. The method has a computational cost independent of reaction network size, and it is based on sampling a set of chemical transformation classes defined by formal rules that characterize the interactions in a system. We
, and we
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interactions of an m-valent ligand with an n-valent cell-surface receptor
simple models for ligand-receptor interactions
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performance of
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more efficient
equal to or better than that of the rejection method
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than a related method that relies on rejection sampling
, and the rejection-free method is more efficient for simulating systems in which aggregation is extensive. The rejection-free method reported here should be useful for simulating a variety of systems in which multisite molecular interactions yield large molecular aggregates