The system-level dynamics of biomolecular interactions can be difficult to specify and simulate using methods that involve explicit specification of a chemical reaction network. Here, we present a stochastic simulation method for determining the kinetics of multivalent biomolecular interactions , which has a computational cost independent of reaction network size . The method is based on sampling a set of chemical transformation classes that characterize the interactions in a system. We apply the method to simulate multivalent ligand-receptor interaction systems . Simulation results reveal insights into ligand-receptor binding kinetics that are not available from previously developed equilibrium models .
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 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 .
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multivalent ligand-receptor interaction systems
interactions of an m-valent ligand with an n-valent cell-surface receptor
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reveal insights into ligand-receptor binding kinetics that are not available from previously developed equilibrium models
show that the rejection-free method is more efficient over wide parameter ranges than a related method that relies on rejection sampling