A Statistical Physics Approach to Bacteria under Strong Perturbations

Statistical physics successfully accounts for phenomena involving a large number of components using a probabilistic approach with predictions for collective properties of the system. While biological cells contain a very large number of interacting components, (proteins, RNA molecules, metabolites, etc.), the cellular network is understood as a particular, highly specific, choice of interactions shaped by evolution, and therefore difficultly amenable to a statistical physics description. Here we show that when a cell encounters an acute but non-lethal stress, its perturbed state can be modelled as random network dynamics. Strong perturbations may therefore reveal the dynamics of the underlying network that are amenable to a statistical physics description. We show that our experimental measurements of the recovery dynamics of bacteria from a strong perturbation can be described in the framework of physical aging in disordered systems (Kaplan Y. et al, Nature 2021). Further experiments on gene expression confirm predictions of the model. The predictive description of cells under and after strong perturbations should lead to new ways to fight bacterial infections, as well as the relapse of cancer after treatment.