Cell size distribution of lineage data: analytic results and parameter inference

Abstract

Recent advances in single-cell technologies have enabled time-resolved measurements of the cell size over several cell cycles. This data encodes information on how cells correct size aberrations so that they do not grow abnormally large or small. Here we formulate a piecewise deterministic Markov model describing the evolution of the cell size over many generations, for all three cell size homeostasis strategies (timer, sizer, and adder). The model is solved to obtain an analytical expression for the non-Gaussian cell size distribution in a cell lineage; the theory is used to understand how the shape of the distribution is influenced by the parameters controlling the dynamics of the cell cycle and by the choice of cell tracking protocol. The theoretical cell size distribution is found to provide an excellent match to the experimental cell size distribution of E. coli lineage data collected under various growth conditions.

Representation and inference of size control laws by neural network aided point processes

“…To elucidate laws of cell growth and division, a large body of research has been conducted by employing techniques from physical and mathematical sciences [1,[9][10][11][12][13][14]. Especially, simple physical and mathematical models have been pursued, which can explain cell growth and division with a small number of relevant variables.…”