Characterization of dependencies between growth and division in budding yeast.

Mayhew, Michael B; Iversen, Edwin S; Hartemink, Alexander J · J R Soc Interface · 2017

basic_science · Level V

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Abstract

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 <i>Saccharomyces cerevisiae</i>, this coordination or 'size control' appears as an inverse correlation between cell size and the rate of cell-cycle progression, routinely observed in G<sub>1</sub> prior to cell division commitment. Beyond this point, cells are presumed to complete S/G<sub>2</sub>/M at similar rates and in a size-independent manner. As such, studies of dependence between growth and division have focused on G<sub>1</sub> Moreover, in unicellular organisms, coordination between growth and division has commonly been analysed <i>within</i> the cycle of a single cell without accounting for correlations in growth and division characteristics <i>between</i> cycles of related cells. In a comprehensive analysis of three published time-lapse microscopy datasets, we analyse both intra- and inter-cycle dependencies between growth and division, revisiting assumptions about the coordination between these two processes. Interestingly, we find evidence (i) that S/G<sub>2</sub>/M durations are systematically longer in daughters than in mothers, (ii) of dependencies between S/G<sub>2</sub>/M and size at budding that echo the classical G<sub>1</sub> dependencies, and (iii) 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 modellers 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.

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