Characterization of dependencies between growth and division in budding yeast

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 yeastSaccharomyces cerevisiae, this coordination or ‘size control’ appears as an inverse correlation between cell size and the rate of cell-cycle progression, routinely observed in G1prior 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, in unicellular organisms, coordination between growth and division has commonly been analysedwithinthe cycle of a single cell without accounting for correlations in growth and division characteristicsbetweencycles 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/G2/M durations are systematically longer in daughters than in mothers, (ii) of dependencies between S/G2/M and size at budding that echo the classical G1dependencies, 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.

Document Details

Document Type
Pub Defense Publication
Publication Date
Feb 01, 2017
Source ID
10.1098/rsif.2016.0993

Entities

People

  • Alexander J. Hartemink
  • Edwin S. Iversen
  • Michael B Mayhew

Organizations

  • Defense Advanced Research Projects Agency
  • Duke University
  • Lawrence Livermore National Laboratory
  • National Institutes of Health

Tags

Fields of Study

  • Biology

Readers

  • Computational Modeling and Simulation
  • Materials Science (Mechanical Engineering).
  • Theoretical Analysis.