In this talk we investigate stochastic chemical reaction networks with scaling methods. This approach is used to study the stability properties of the associated Markov processes, but also to investigate the transient behavior of these networks. It may give insight on the impact of complex features of these networks such as their polynomial rates, leading to the coexistence of multiple timescales. Several examples are discussed.

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