Significance Individual bacteria that share identical genomes and growth environments can display substantial cell-to-cell differences in expression of stress-response genes and single-cell growth rates. This phenotypic heterogeneity can impact the survival of single cells facing sudden stress. However, the windows of time that cells spend in vulnerable or tolerant states are often unknown. We quantify the temporal expression of a suite of stress-response reporters, while simultaneously monitoring growth. We observe pulsatile expression across genes with a range of stress-response functions, finding that single-cell growth rates are often anticorrelated with reporter levels. These dynamic phenotypic differences have a concrete link to function, in which individual cells undergoing a pulse of elevated expression and slow growth are predisposed to survive antibiotic exposure.

Applying an ecological framework to bacterial antibiotic responses improves understanding of population recovery.

The production of recombinant proteins is a problem of significant interest in bioengineering. Because of the existing trade-off between cellular growth and protein production, these two processes are separated in time in most commonly-employed strategies: a growth phase is followed by a production phase. Here, we investigate the potential of an alternative strategy using artificial cell specialization and differentiation systems in which cells either grow ("growers") or produce proteins ("producers") and growers can irreversibly "differentiate" into producers. Inspired by an existing twopopulation system implemented in yeast, we propose a model of a "yeast synthetic stem cell system" and define an optimal control problem to maximize bioproduction. Analytically, we first establish the well-posedness of the problem. Then, we prove the existence of an optimal control and derive non trivial optimality conditions. We finally use these results to find numerical optimal solutions. We conclude by a discussion of extensions of this work to models that capture the heterogeneity of the cell response to differentiation signals.