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Fluctuating environments pose tremendous challenges to bacterial populations.It is observed in numerous bacterial species that individual cells canstochastically switch among multiple phenotypes for the population to survivein rapidly changing environments. This kind of phenotypic heterogeneity withstochastic phenotype switching is generally understood to be an adaptivebet-hedging strategy. Mathematical models are essential to gain a deeperinsight into the principle behind bet-hedging and the pattern behindexperimental data. Traditional deterministic models cannot provide a correctdescription of stochastic phenotype switching and bet-hedging, and traditionalMarkov chain models at the cellular level fail to explain their underlyingmolecular mechanisms. In this paper, we propose a nonlinear stochastic model ofmultistable bacterial systems at the molecular level. It turns out that ourmodel not only provides a clear description of stochastic phenotype switchingand bet-hedging within isogenic bacterial populations, but also provides adeeper insight into the analysis of multidimensional experimental data.Moreover, we use some deep mathematical theories to show that our stochasticmodel and traditional Markov chain models are essentially consistent andreflect the dynamic behavior of the bacterial system at two different timescales. In addition, we provide a quantitative characterization of the criticalstate of multistable bacterial systems and develop an effective data-drivenmethod to identify the critical state without resorting to specificmathematical models.

Modeling stochastic phenotype switching and bet-hedging in bacteria: stochastic nonlinear dynamics and critical state identification