James R. Faeder Department of Computational Biology University of Pittsburgh School of Medicine Title: An exact hybrid particle/population approach for modeling biochemical systems Justin S. Hogg, Leonard A. Harris, Lori J. Stover, Niketh S. Nair and James R. Faeder Abstract: Practical simulation of realistic biochemical networks is often challenging due to a combination of high state-space complexity and large molecular populations that is characteristic of many biological systems. Recent particle-based simulation approaches have been developed that can efficiently handle systems with large state spaces. However, memory and computation costs, which increase linearly with the number of particles, remain an impediment to the practical application of these methods. Here, we present an exact hybrid particle/population modeling approach that can significantly reduce memory costs by treating a subset of molecular species as population variables rather than as particles. The approach is cast within the framework of rule-based modeling. We demonstrate how a model cast within a rule-based modeling language, such as the BioNetGen language (BNGL), can be transformed into a form suitable for hybrid simulation via a population-adapted particle-based method. The transformation method is implemented within the open-source rule-based modeling platform BioNetGen and the resulting hybrid model can be simulated using the particle-based simulation engine NFsim. Benchmark tests show that significant memory savings can be achieved using the new approach and a cost analysis provides a practical measure of its utility. We also discuss the intriguing possibility of applying accelerated-stochastic methods, such as $\tau$\/~leaping, to the population sub-network of the hybrid model to improve computational efficiency.