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One of the most challenging problems in microbiology is to understand how asmall fraction of microbes that resists killing by antibiotics can emerge in apopulation of genetically identical cells, the phenomenon known as persistenceor drug tolerance. Its characteristic signature is the biphasic kill curve,whereby microbes exposed to a bactericidal agent are initially killed veryrapidly but then much more slowly. Here we relate this problem to the moregeneral problem of understanding the emergence of distinct growth phenotypes inclonal populations. We address the problem mathematically by adopting theframework of the phenomenon of so-called weak ergodicity breaking, well knownin dynamical physical systems, which we extend to the biological context. Weshow analytically and by direct stochastic simulations that distinct growthphenotypes can emerge as a consequence of slow-down of stochastic fluctuationsin the expression of a gene controlling growth rate. In the regime of fast genetranscription, the system is ergodic, the growth rate distribution is unimodal,and accounts for one phenotype only. In contrast, at slow transcription andfast translation, weakly non-ergodic components emerge, the populationdistribution of growth rates becomes bimodal, and two distinct growthphenotypes are identified. When coupled to the well-established growth ratedependence of antibiotic killing, this model describes the observed fast andslow killing phases, and reproduces much of the phenomenology of bacterialpersistence. The model has major implications for efforts to develop controlstrategies for persistent infections.

Slow protein fluctuations explain the emergence of growth phenotypes and persistence in clonal bacterial populations