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