Cell size regulation and proliferation fluctuations in single-cell derived colonies (original) (raw)
Related papers
Sloppy size control of the cell division cycle
Journal of Theoretical Biology, 1986
In an asynchronous, exponentially proliferating cell culture there is a great deal of variability among individual cells in size at birth, size at division and generation time (=age at division). To account for this variability we assume that individual cells grow according to some given growth law and that, after reaching a minimum size, they divide with a certain probability (per unit time) which increases with increasing cell size. This model is called sloppy size control because cell division is assumed to be a random process with size~dependent probability. We derive general equations for the distribution of cell size at division, the distribution of generation time, and the correlations between generation times of closely related cells. Our theoretical re~ults are compared in detail with experimental results (obtained by Miyata and coworkers) for cell division in fission yeast, Schizosaccharomyces pombe. The agreement between theory and experiment is superior to that found for any other simple models of the coordination of cell growth and division.
Stationary Size Distributions of Growing Cells with Binary and Multiple Cell Division
Journal of Statistical Physics, 2011
Populations of unicellular organisms that grow under constant environmental conditions are considered theoretically. The size distribution of these cells is calculated analytically, both for the usual process of binary division, in which one mother cell produces always two daughter cells, and for the more complex process of multiple division, in which one mother cell can produce 2 n daughter cells with n = 1, 2, 3,. .. . The latter mode of division is inspired by the unicellular algae Chlamydomonas reinhardtii. The uniform response of the whole population to different environmental conditions is encoded in the individual rates of growth and division of the cells. The analytical treatment of the problem is based on size-dependent rules for cell growth and stochastic transition processes for cell division. The comparison between binary and multiple division shows that these different division processes lead to qualitatively different results for the size distribution and the population growth rates.
Modeling cell size regulation under complex and dynamic environments
In nature, cells face changes in environmental conditions that can modify their growth rate. In these dynamic environments, recent experiments found changes in cell size regulation. Currently, there are few clues about the origin of these cell size changes. In this work, we model cell division as a stochastic process that occurs at a rate proportional to the size. We propose that this rate is zero if the cell is smaller than a minimum size. We show how this model predicts some of the properties found in cell size regulation. For example, among our predictions, we found that the mean cell size is an exponential function of the growth rate under steady conditions. We predict that cells become smaller and the way the division strategy changes during dynamic nutrient depletion. Finally, we use the model to predict cell regulation in an arbitrary complex dynamic environment.
Dynamics extracted from fixed cells reveal feedback linking cell growth to cell cycle
Biologists have long been concerned about what constrains variation in cell size, but progress in this field has been slow and stymied by experimental limitations1. Here we describe a new method, ergodic rate analysis (ERA), that uses single-cell measurements of fixed steady-state populations to accurately infer the rates of molecular events, including rates of cell growth. ERA exploits the fact that the number of cells in a particular state is related to the average transit time through that state2. With this method, it is possible to calculate full time trajectories of any feature that can be labelled in fixed cells, for example levels of phosphoproteins or total cellular mass. Using ERA we find evidence for a sizediscriminatory process at the G1/S transition that acts to decrease cell-to-cell size variation.
Cell volume distributions reveal cell growth rates and division times
Journal of Theoretical Biology, 2009
A population of cells in culture displays a range of phenotypic responses even when those cells are derived from a single cell and are exposed to a homogeneous environment. Phenotypic variability can have a number of sources including the variable rates at which individual cells within the population grow and divide. We have examined how such variations contribute to population responses by measuring cell volumes within genetically identical populations of cells where individual members of the population are continuously growing and dividing, and we have derived a function describing the stationary distribution of cell volumes that arises from these dynamics. The model includes stochastic parameters for the variability in cell cycle times and growth rates for individual cells in a proliferating cell line. We used the model to analyze the volume distributions obtained for two different cell lines and one cell line in the absence and presence of aphidicolin, a DNA polymerase inhibitor. The derivation and application of the model allows one to relate the stationary population distribution of cell volumes to extrinsic biological noise present in growing and dividing cell cultures.
Current Biology, 2016
To maintain a constant cell size, dividing cells have to coordinate cell cycle events with cell growth. This coordination has for long been supposed to rely on the existence of size thresholds determining cell cycle progression [1]. In budding yeast, size is controlled at the G1/S transition [11]. In agreement with this hypothesis, the size at birth influences the time spent in G1: smaller cells have a longer G1 period [3]. Nevertheless, even though cells born smaller have a longer G1, the compensation is imperfect and they still bud at smaller cell sizes. In bacteria, several recent studies have shown that the incremental model of size control, in which size is controlled by addition of a constant volume (in contrast to a size threshold), is able to quantitatively explain the experimental data on 4 different bacterial species [6, 5, 6, 7].
A stochastic model of cell division (with application to fission yeast)
Mathematical Biosciences, 1987
During steady-state asynchronous growth and division of cells in culture, there is considerable variation in the size and age of cells at division. We attribute this variability to stochastic fluctuations in the rates of synthesis and degradation of a division-activator protein. We show that the probability density functions for size and age at division are approximately gaussian with means and variances specified in terms of N (the total number of activator molecules in a dividing cell), X (the first-order rate constant for activator degradation), and r (the specific growth rate of the cells). By comparing theoretical distributions with experimental histograms for dividing yeast cells, we conclude that, in fission yeast N a 220 and X/r = 8.
Cell population heterogeneity driven by stochastic partition and growth optimality
Scientific Reports
for their simplest variants 20 and abundant experimental evidence supporting the theoretical results 23-25. The main ingredients common to these approaches are: a properly tuned gene regulatory circuit, and gene expression noise. However, in many contexts partition errors during cell division are more relevant than gene expression noise 26-32. Up to now partition noise, which is in average symmetric, has not been studied as a potential source of bimodality in a cell population. The novelty of this contribution is to propose such a model and justify its relevance in actual biological scenarios. In particular, we study the division of a cell that carries a certain number of components that influence its growth rate. Stochastic models of partitioning errors have until now assumed that the growth rate of cells is homogeneous 26-32. As we show here, relaxing this assumption is key to obtain a bimodal distribution.