Algorithm 806: SPRNG: a scalable library for pseudorandom number generation
ACM Transactions on Mathematical Software (TOMS)
The JigCell Model Builder: A Spreadsheet Interface for Creating Biochemical Reaction Network Models
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Stochastic cell cycle modeling for budding yeast
SpringSim '09 Proceedings of the 2009 Spring Simulation Multiconference
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We describe procedures for converting a macromolecular regulatory model from the most common deterministic formulation to one suitable for stochastic simulation. To avoid error, we seek to automate as much of the process as possible. However, deterministic models often omit key information necessary to a stochastic formulation. In this paper we introduce how we implement conversion in the JigCell modeling environment. Our tool makes it easier for the modeler to include complete details. Stochastic simulations are known for being computationally intensive, and thus require high performance computing facilities to be practical. We provide the first stochastic simulation results for realistic cell cycle models, using Virginia Tech's System X supercomputer.