The ability to control growth is essential for fundamental studies of bacterial physiology and biotechnological applications. We have engineered an Escherichia coli strain in which the transcription of a key component of the gene expression machinery, RNA polymerase, is under the control of an inducible promoter. By changing the inducer concentration in the medium, we can adjust the RNA polymerase concentration and thereby switch bacterial growth between zero and the maximal growth rate supported by the medium. We show that our synthetic growth switch functions in a medium‐independent and reversible way, and we provide evidence that the switching phenotype arises from the ultrasensitive response of the growth rate to the concentration of RNA polymerase. We present an application of the growth switch in which both the wild‐type E. coli strain and our modified strain are endowed with the capacity to produce glycerol when growing on glucose. Cells in which growth has been switched off continue to be metabolically active and harness the energy gain to produce glycerol at a twofold higher yield than in cells with natural control of RNA polymerase expression. Remarkably, without any further optimization, the improved yield is close to the theoretical maximum computed from a flux balance model of E. coli metabolism. The proposed synthetic growth switch is a promising tool for gaining a better understanding of bacterial physiology and for applications in synthetic biology and biotechnology.

Background: Despite a remarkable success in the computational prediction of genes in Bacteria and Archaea, a lack of comprehensive understanding of prokaryotic gene structures prevents from further elucidation of differences among genomes. It continues to be interesting to develop new ab initio algorithms which not only accurately predict genes, but also facilitate comparative studies of prokaryotic genomes.

Natural selection is thought to shape the evolution of aging patterns, although how life-history trajectories orchestrate the inherently stochastic processes associated with aging is unclear. Tracking clonal growth-arrested Escherichia coli cohorts in an homogeneous environment at single-cell resolution, we demonstrate that the Gompertz law of exponential mortality characterizes bacterial lifespan distributions. By disentangling the rate of aging from age-independent components of longevity, we find that increasing cellular maintenance through the general stress pathway reduces the aging rate and rescales the lifespan distribution at the expense of growth. This trade-off between aging and growth underpins the evolutionary tuning of the general stress response pathway in adaptation to the organism’s feast-or-famine lifestyle. It is thus necessary to involve both natural selection and stochastic physiology to explain aging patterns.