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Considerable evidence suggests that anticipating sudden shifts from one stateto another in bistable dynamical systems is a challenging task, examplesinclude ecosystems, financial markets, complex diseases, etc. In this paper, weinvestigate the effects of additive, multiplicative and cross correlatedstochastic perturbations on determining regime shifts in a bistable generegulatory sys- tem, which gives rise to two distinct states of low and highconcentrations of protein. We obtain the stationary probability density andmean first passage time of the system. We show that increasingadditive(multiplicative) noise intensity induces regime shift from a low(high)to a high(low) pro- tein concentration state. However, an increase in crosscorrelation intensity always induces regime shifts from high to low proteinconcentration state. For both bifurcation (often called tipping point) andnoise induced (called stochastic switching) regime shifts, we further explorethe robustness of recently developed critical slowing down based early warningsignal (EWS) indicators (e.g., rising variance and lag-1 autocorrelation) onour simulated time series data. We identify that using EWS indicators,prediction of an impending bifurcation induced regime shift is relativelyeasier than that of a noise induced regime shift in the considered system.Moreover, the success of EWS indicators also strongly depends upon the natureof noise. Our results establish the key fact that finding more robust indicatorto forewarn regime shifts for a broader class complex natural systems is stillin its infancy and demands extensive research.

Anticipating regime shifts in gene expression: The case of an autoactivating positive feedback loop