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Background: Bacterial persisters are a tiny fraction of preexisting dormant cells inside bacterial populations.Although isogenic with the rest of the population, persister cells exhibit a distinct phenotype, and are slow growing and generally antibiotics tolerant.Upon antibiotics treatment, persister cells survive while the normal growing cells die.However, after treatment, persister cells can spontaneously switch to normal growing state and regenerate the whole bacterial population.Therefore, persisters represent a challenge for antibiotics treatment, and are responsible for several kinds of persistent infections such as tuberculosis.We set out to build a model to explain the nature of stochastic bacterial persister formation.Methods: The HipA-induced persister formation model was built based on previous models and recent experimental findings.A chemical reaction network composed of 7 species and 14 reactions with manually tuned parameters was then simulated using Gillespie algorithm to capture the stochastic nature of persister formation.Statistics was then done on the stochastic time course from the simulation.Results: We have built a model for the expression dynamics of hipAB operon, which included the expression of hipAB operon, the oligomerization of the toxin HipA and antitoxin HipB, as well as the autoregulations in the system.Stochastic simulation could correctly reproduce the bimodal distribution of HipA protein level, which corresponds to the two phenotypic states of bacteria.The bimodal distribution could be the result of cooperative positive feedback without bistability.Conclusions: We have constructed and simulated the HipA-induced E.Coli persister formation process using stochastic simulations.A biomodal distribution representing normal state and persistence state could be reproduced from our simulation.The model is still being improved to better describe experimental evidences.Fully elucidation of persister formation mechanism could provide a starting point for new therapeutic approach against severe infections .