Size control models of Saccharomyces cerevisiae cell proliferation.

Wheals, Alan E.

doi:10.1128/mcb.2.4.361

Cited by 73 publications

(58 citation statements)

References 38 publications

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“…Because these events We compute the fraction of undivided cells until size x from data ( ≈ 50 × 10 3 cells in this dataset), estimating the SE as in ref. 21. Shaded area represents the error magnified 100 times.…”

Section: Phenomenological Description Of Cell Division Control By DIVmentioning

confidence: 99%

Concerted control of Escherichia coli cell division

Osella

Nugent

Lagomarsino

2014

Proc. Natl. Acad. Sci. U.S.A.

179

260

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The coordination of cell growth and division is a long-standing problem in biology. Focusing on Escherichia coli in steady growth, we quantify cell division control using a stochastic model, by inferring the division rate as a function of the observable parameters from large empirical datasets of dividing cells. We find that (i) cells have mechanisms to control their size, (ii) size control is effected by changes in the doubling time, rather than in the single-cell elongation rate, (iii) the division rate increases steeply with cell size for small cells, and saturates for larger cells. Importantly, (iv) the current size is not the only variable controlling cell division, but the time spent in the cell cycle appears to play a role, and (v) common tests of cell size control may fail when such concerted control is in place. Our analysis illustrates the mechanisms of cell division control in E. coli. The phenomenological framework presented is sufficiently general to be widely applicable and opens the way for rigorous tests of molecular cell-cycle models.

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Section: Phenomenological Description Of Cell Division Control By DIVmentioning

confidence: 99%

Concerted control of Escherichia coli cell division

Osella

Nugent

Lagomarsino

2014

Proc. Natl. Acad. Sci. U.S.A.

179

260

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“…The lower bound serves the mathematical purpose of preventing the possibility of a new daughter cell from a mitosis immediately redividing a second time. Some biological justification for the lower bound on a can be found in the experimental data of Wheals [42], although that study concerns itself solely with asymmetric cell division in yeast.…”

Section: Sloppy Size Modelsmentioning

confidence: 99%

“…The simulations of Gusev et al [3] use two distributions for intermitotic time: a truncated normal distribution and a uniform distribution. Neither possibility has a biological justification, and both contradict studies modeling intermitotic times with real data [41][42][43][46][47][48][49]. We prefer an exponential distribution, for reasons that will become clear in Section 3.…”

Section: Continuous Time Modelingmentioning

confidence: 99%

“…In contrast to the transition probability model, which expresses the probability of mitosis as a (fixed) function of time, the sloppy size model first proposed by Wheals [42] and then formalized by Tyson and Diekmann [43], expresses the probability of mitosis as a function of cell size (where 'size' can mean mass, volume, length, or some other measure of the cell). In later papers [47,58], which benefited from increased understanding of the molecular mechanisms of mitosis, size was modeled as the number of molecules of one or more proteins in the mitosis-associated CDC protein family.…”

Section: Sloppy Size Modelsmentioning

confidence: 99%

“…Thus, the derivations in Section 2 remain applicable with the addition of a constant lag. In Section 3.2, we summarize a different model of mitosis proposed by Wheals [42] and Tyson and Diekmann [43] called the "sloppy size" model. In Section 4, we present a computer program MITOSIM that simulates mitosis using either the exponential, lagged exponential or sloppy size models, and tracks the propagation of copy number aberrations as they randomly appear in the population.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A comprehensive continuous-time model for the appearance of CGH signal due to chromosomal missegregations during mitosis

Desper

Difilippantonio

Ried

et al. 2005

Mathematical Biosciences

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Aneuploidy, the gain or loss of large regions of the genome, is a common feature in cancer cells. Irregularities in chromosomal copy number caused by missegregations of chromosomes during mitosis can be visualized by cytogenetic techniques including fluorescence in situ hybridization (FISH), spectral karyotyping (SKY) and comparative genomic hybridization (CGH). In the current work, we consider the propagation of irregular copy numbers throughout a cell population as the individual cells progress through ordinary mitotic cell cycles. We use an algebraic model to track the different copy numbers as states in a stochastic process, based on the model of chromosome instability of Gusev, Kagansky, and Dooley, and consider the average copy number of a particular chromosome within a cell population as a function of the cell division rate. We review a number of mathematical models for determining the length of the cell cycle, including the Smith-Martin transition probability model and the "sloppy size" model of Wheals, Tyson and Diekmann. The program MITOSIM simulates the growth of a population of cells using the aforementioned models of the cell cycle. MITOSIM allows the cell population to grow, with occasional resampling, until the average copy number of a given chromosome in the population reaches a preset threshold signifying a positive copy number alteration in this region. MITOSIM calculates the relationship between the missegregation rate and the growth rate of the cell population. This allows the user to test hypotheses regarding the effect chromosomal aberrations have upon the cell cycle, cell growth rates, and time to population dominance.

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A computer‐aided measuring system for the characterization of yeast populations combining 2D‐image analysis, electronic particle counter, and flow cytometry

Huls

Nanninga

Spronsen

et al. 1992

Biotech & Bioengineering

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An integrated measuring system was developed that directly compares the shape of size distributions of Saccharomyces cerevisiae populations obtained from either microscopic measurements, electronic particle counter, or flow cytometer. Because of its asymmetric mode of growth, a yeast population consists of two different subpopulations, parents and daughters. Although electronic particle counter and flow cytometer represent fast methods to assess the growth state of the population as a whole, the determination of important cell cycle parameters like the fraction of daughters or budded cells requires microscopic observation. We therefore adapted a semiautomatic and interactive 2D-image processing program for rapid and accurate determination of volume distributions of the different sub-populations. The program combines the capacity of image processing and volume calculation by contour-rotation, with the potential of visual evaluation of the cells. High-contrast images from electron micrographs are well suited for image analysis, but the necessary air drying caused the cells to shrink to 35% of their hydrated volume. As an alternative, hydrated cells overstained with the fluorochrome calcofluor and visualized by fluorescence light microscopy were used. Cell volumes calculated from length, and diameter measurements with the assumption of an ellipsoid cell shape were underestimated as compared to volumes derived from 2D-image analysis and contour rotation, because of a deviating cell shape, especially in the older parent cells with more than one bud scar. The bimodal volume distribution obtained from microscopic measurements was identical to the protein distribution measured with the flow cytometer using cells stained with dansylchloride, but differed significantly from the size distribution measured with the electronic particle counter. Compared with the flow cytometer, 2-D image analysis can thus provide accurate distributions with important additional information on, for instance, the distributions of subpopulations like parents, daughters, or budded cells.

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Size control models of Saccharomyces cerevisiae cell proliferation.

Cited by 73 publications

References 38 publications

Concerted control of Escherichia coli cell division

Concerted control of Escherichia coli cell division

A comprehensive continuous-time model for the appearance of CGH signal due to chromosomal missegregations during mitosis

A computer‐aided measuring system for the characterization of yeast populations combining 2D‐image analysis, electronic particle counter, and flow cytometry

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