Cell cycle-dependent variations in protein concentration

Computational modeling of biological systems has become an effective tool for analyzing cellular behavior and for elucidating key properties of the intricate networks that underlie experimental obser-vations. While most modeling techniques rely heavily on the concentrations of intracellular mole-cules, little attention has been paid to tracking and simulating the significant volume fluctuations that occur over each cell division cycle. Here, we use fluorescence microscopy to acquire single cell volume trajectories for a large population of Saccharomyces cerevisiae cells. Using this data, we generate a comprehensive set of statistics that govern the growth and division of these cells over many generations, and we discover several interest-ing trends in their size, growth and protein produc-tion characteristics. We use these statistics to develop an accurate model of cell cycle volume dynamics, starting at cell birth. Finally, we demon-strate the importance of tracking volume fluctua-tions by combining cell division dynamics with a minimal gene expression model for a constitutively expressed fluorescent protein. The significant oscil-lations in the cellular concentration of a stable, highly expressed protein mimic the observed exper-imental trajectories and demonstrate the fundamen-tal impact that the cell cycle has on cellular functions