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A general class of stochastic gene expression models with self regulation isconsidered. One or more genes randomly switch between regulatory states, eachhaving a different mRNA transcription rate. The gene or genes are selfregulating when the proteins they produce affect the rate of switching betweenregulatory states. Under weak noise conditions, the deterministic forces aremuch stronger than fluctuations from gene switching and protein synthesis.Metastable transitions, such as bistable switching, can occur under weak noiseconditions, causing dramatic shifts in the expression of a gene. A general toolused to describe metastability is the quasi stationary analysis (QSA). A largedeviation principle is derived so that the QSA can explicitly account forrandom gene switching without using an adiabatic limit or diffusionapproximation, which are unreliable and inaccurate for metastable events.Thisallows the existing asymptotic and numerical methods that have been developedfor continuous Markov processes to be used to analyze the full model.

Asymptotic and numerical methods for metastable events in stochastic gene networks