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 not amenable to a statistical physics description. Our approach is that when a cell encounters an acute, but non-lethal, stress, its perturbed state can be modelled as random network dynamics, rather than as a regulated response. Strong perturbations may therefore reveal the dynamics of the underlying network that are amenable to a statistical physics description. Based on the striking similarity between our data on stressed bacteria and physical aging in disordered systems, our goal is to develop an experimental and theoretical framework for the statistical physics description of cells exposed to strong perturbations. We will critically probe the predictions of the statistical model using a multidisciplinary approach combining three frontline methodologies: (1) dynamics of single bacteria under acute stress in microfluidic devices and single cell transcriptomics; (2) theoretical framework and simulations for cellular networks under acute stress; and (3) new biophysical measurements of the transition from the regulated to the disrupted cellular network. This approach should provide a paradigm shift in the analysis of cells under stress, differentiating between conditions described by the regulation of gene networks from those that can be quantitatively predicted by a statistical physics framework. The new knowledge should lead to innovative ways of controlling the cellular network under strong perturbations, with implications ranging from new methodologies for synthetic biology to new avenues for treating bacterial infections and cancer.
