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The dynamics of molecular interactions in cellular regulatory systems are sometimes challenging to model using traditional approaches, such as that of ordinary differential equations (ODEs). The molecules in these systems (viz., proteins) are composed of multiple functional components, which can each interact with multiple binding partners. The functional components can also undergo modifications that affect their behavior. The consequence is a large reachable but sparsely populated state space, which can be impractical to capture in a traditional model for biochemical kinetics. To overcome this challenge, an agentbased approach to modeling biochemical kinetics has been developed, called rulebased modeling. In this approach, rules are used to characterize the interactions of molecules in terms of local properties of molecular components, which entails coarsegraining of the kinetics of interactions, as rules tend to implicitly define and parameterize multiple reactions through inheritance. This simplification allows concise specification of models, more aligned with mechanistic understanding than, say, ODE models. The conciseness of a rulebased model derives from assumptions of component modularity, or independence. In this talk, I will describe how the modular representation of molecular components in a rulebased model allows one to apply concepts of equationfree computation to perform approximate simulations that characterize collective variables corresponding to observable quantities of interest. The approach has some similarities with renormalization in physics. Equationfree computation has the potential to make available to those who wish to analyze rulebased models the entire toolbox of numerical methods available for ODE models. In this talk, I will illustrate the potential of equationfree computation through a discussion of an accelerated simulation algorithm based on projective integration. Host: Kipton Barros, T4 and CNLS 