H. Kang and T. Kurtz
Separation of time-scales and model reduction for stochastic reaction networks
Annals of Applied ProbabilityVol. 23 No. 1
(2013) pp. 529-583
AbstractA stochastic model for a chemical reaction network is embedded in a one-parameter family of models with species numbers and rate constants scaled by powers of the parameter. A systematic approach is developed for determining appropriate choices of the exponents that can be applied to large complex networks. When the scaling implies subnetworks have different time-scales, the subnetworks can be approximated separately, providing insight into the behavior of the full network through the analysis of these lower-dimensional approximations.