Inference of cell division parameters in bacteria under transient growth conditions

Most quantitative models of bacterial cell division assume steady-state growth, yet natural environments are highly dynamic. Here, we infer division control parameters of E. coli from single-cell size trajectories under nonsteady growth. We describe growth as a continuous process interrupted by stochastic divisions, whose propensity depends on cell size and on the size added since birth. Combining an estimation of time-dependent single-cell growth rates with a maximum-likelihood estimator that we previously validated on synthetic data, we obtain time-resolved estimates of the mean added size at division, its noise, and a strategy parameter that measures how added size depends on birth size. This parameter vanishes for the adder, in which cells add, on average, a fixed size regardless of their size at birth, and is negative for sizer-adder control, in which larger-born cells add less size. We apply this approach to published single-cell data covering steady exponential growth, gradual nutrient depletion, and exit from starvation. We implement a time-resolved model selection based on the Akaike information criterion, concluding that the adder is sufficient during steady growth, whereas size dynamics during entry into and exit from stationary phase are better explained by transient deviations toward sizer-adder control. The framework provides a general method to quantify time-resolved changes in the scale, stochasticity, and strategy of division in nonsteady environments.