RT Journal Article
SR Electronic
T1 Characterization of Dependencies Between Growth and Division in Budding Yeast
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 082735
DO 10.1101/082735
A1 Michael B. Mayhew
A1 Edwin S. Iversen
A1 Alexander J. Hartemink
YR 2016
UL http://biorxiv.org/content/early/2016/12/08/082735.abstract
AB Cell growth and division are processes vital to the proliferation and development of life. Coordination between these two processes has been recognized for decades in a variety of organisms. In the budding yeast Saccharomyces cerevisiae, this coordination or ‘size control’ appears as an inverse correlation between cell size and the rate of cell-cycle progression, routinely observed in G1 prior to cell division commitment. Beyond this point, cells are presumed to complete S/G2/M at similar rates and in a size-independent manner. As such, studies of dependence between growth and division have focused on G1. Moreover, coordination between growth and division has commonly been analyzed within the cycle of a single cell without accounting for correlations in growth and division characteristics between cycles of related cells. In a comprehensive analysis of three published time-lapse microscopy datasets, we analyze both intra-and inter-cycle dependencies between growth and division, revisiting assumptions about the coordination between these two processes. Interestingly, we find evidence (1) that S/G2/M durations are systematically longer in daughters than in mothers, (2) of dependencies between S/G2/M and size at budding that echo the classical G1 dependencies, and, (3) in contrast with recent bacterial studies, of negative dependencies between size at birth and size accumulated during the cell cycle. In addition, we develop a novel hierarchical model to uncover inter-cycle dependencies, and we find evidence for such dependencies in cells growing in sugar-poor environments. Our analysis highlights the need for experimentalists and modelers to account for new sources of cell-to-cell variation in growth and division, and our model provides a formal statistical framework for the continued study of dependencies between biological processes.