Volume growth of daughter and parent cells during the cell cycle of Saccharomyces cerevisiae a/alpha as determined by image cytometry

Woldringh, Conrad L.; Huls, P. G.; Vischer, Norbert O. E.

doi:10.1128/jb.175.10.3174-3181.1993

Cited by 92 publications

(118 citation statements)

References 21 publications

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“…Cell size increases with age Previous investigations into the ageing morphology of yeast have demonstrated that there is an increase in cell size with age (Bartholomew & Mittwer, 1953;Mortimer & Johnston, 1959;Lorincz & Carter, 1979;Woldringh et al, 1993;Barker & Smart, 1996). BB11 aged cell fractions were prepared using sucrose gradients to separate cells on the basis of size.…”

Section: Resultsmentioning

confidence: 99%

Chitin scar breaks in aged Saccharomyces cerevisiae

Powell

Quain²,

Smart

2003

Microbiology

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Ageing in budding yeast is not determined by chronological lifespan, but by the number of times an individual cell is capable of dividing, termed its replicative capacity. As cells age they are subject to characteristic cell surface changes. Saccharomyces cerevisiae reproduces asexually by budding and as a consequence of this process both mother and daughter cell retain chitinous scar tissue at the point of cytokinesis. Daughter cells exhibit a frail structure known as the birth scar, while mother cells display a more persistent bud scar. The number of bud scars present on the cell surface is directly related to the number of times a cell has divided and thus constitutes a biomarker for replicative cell age. It has been proposed that the birth scar may be subject to stretching caused by expansion of the daughter cell; however, no previous analysis of the effect of cell age on birth or bud scar size has been reported. This paper provides evidence that scar tissue expands with the cell during growth. It is postulated that symmetrically arranged breaks in the bud scar allow these rigid chitinous structures to expand without compromising cellular integrity.

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Section: Resultsmentioning

confidence: 99%

Chitin scar breaks in aged Saccharomyces cerevisiae

Powell

Quain²,

Smart

2003

Microbiology

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“…Cells exhibit variability even at the time of release, and synchrony deteriorates further over time because individual cells progress through the cell cycle at different rates. Moreover, asymmetric cell division is a major source of synchrony loss in many kinds of cells and especially in budding yeast (6)(7)(8)(9)(10). After yeast cell division, newborn daughter cells are smaller than their mothers, and the cycle period of daughters is significantly longer than that of mothers.…”

mentioning

confidence: 99%

Branching process deconvolution algorithm reveals a detailed cell-cycle transcription program

Guo¹,

Bernard²,

Orlando

et al. 2013

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

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Due to cell-to-cell variability and asymmetric cell division, cells in a synchronized population lose synchrony over time. As a result, time-series measurements from synchronized cell populations do not reflect the underlying dynamics of cell-cycle processes. Here, we present a branching process deconvolution algorithm that learns a more accurate view of dynamic cell-cycle processes, free from the convolution effects associated with imperfect cell synchronization. Through wavelet-basis regularization, our method sharpens signal without sharpening noise and can remarkably increase both the dynamic range and the temporal resolution of time-series data. Although applicable to any such data, we demonstrate the utility of our method by applying it to a recent cell-cycle transcription time course in the eukaryote Saccharomyces cerevisiae. Our method more sensitively detects cellcycle-regulated transcription and reveals subtle timing differences that are masked in the original population measurements. Our algorithm also explicitly learns distinct transcription programs for mother and daughter cells, enabling us to identify 82 genes transcribed almost entirely in early G1 in a daughter-specific manner.O ne of the most fundamental processes in biology is the cell cycle, the intricate progression of events necessary for a cell's division. To better understand how cell-cycle events are regulated, studies in many organisms have monitored the dynamics of various molecular species (e.g., transcript levels, protein levels, nucleosome positions) throughout the cell cycle. Ideally, the dynamics of these species would be studied within individual cells traversing the cell cycle.Unfortunately, current technology enables accurate, genomewide quantification of many molecular species only in populations of cells. To provide insight into the dynamics of cell-cycle processes, the cells in such a population should be as synchronized as possible as they progress through the cell division cycle. To effect this synchrony, cells are arrested or selected at one stage of the cell cycle and then released to progress through subsequent division cycles. Molecular species can then be measured in the population at various time points after release (1-5).Measurements of cell populations would not be substantially different from average measurements of individual cells if the cells in the population were always perfectly synchronized. However, perfect cell synchrony is neither attainable at synchronization nor maintainable after release. Cells exhibit variability even at the time of release, and synchrony deteriorates further over time because individual cells progress through the cell cycle at different rates. Moreover, asymmetric cell division is a major source of synchrony loss in many kinds of cells and especially in budding yeast (6-10). After yeast cell division, newborn daughter cells are smaller than their mothers, and the cycle period of daughters is significantly longer than that of mothers. This is most likely due to mechanisms-not yet we...

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“…Volumetric growth around a cell cycle has been measured (25,26) in bacteria and budding yeast and is found to be exponential. The observed exponential growth rate means that the rate at which macromolecules are removed by dilution is accurately modeled by a linear decay term.…”

Section: Ncr and The Ure2-gln3 Subcircuit Modelmentioning

confidence: 99%

Structure theorems and the dynamics of nitrogen catabolite repression in yeast

Boczko

Cooper²,

Gedeon

et al. 2005

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

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By using current biological understanding, a conceptually simple, but mathematically complex, model is proposed for the dynamics of the gene circuit responsible for regulating nitrogen catabolite repression (NCR) in yeast. A variety of mathematical ''structure'' theorems are described that allow one to determine the asymptotic dynamics of complicated systems under very weak hypotheses. It is shown that these theorems apply to several subcircuits of the full NCR circuit, most importantly to the URE2-GLN3 subcircuit that is independent of the other constituents but governs the switching behavior of the full NCR circuit under changes in nitrogen source. Under hypotheses that are fully consistent with biological data, it is proven that the dynamics of this subcircuit is simple periodic behavior in synchrony with the cell cycle. Although the current mathematical structure theorems do not apply to the full NCR circuit, extensive simulations suggest that the dynamics is constrained in much the same way as that of the URE2-GLN3 subcircuit. This finding leads to the proposal that mathematicians study genetic circuits to find new geometries for which structure theorems may exist.A s microarray technology has brought systems biology to the theater, the desire to understand gene regulatory networks has brought mathematical modeling to center stage. Recent work has focused on two goals: the determination of recurring network motifs (1) followed by an understanding of their design significance through an analysis of their dynamics (2, 3). Thus, it is now a central objective in mathematics, neuroscience, molecular biology, and medicine to understand how and to what extent the structure of the connections between the components of a system determine its dynamic behavior. We refer to mathematical results that relate these two properties as structure theorems. Our impression is that this body of work has received insufficient attention from biologists, bioinformaticians, engineers, and physicists pursuing gene regulatory networks. The aim here is to demonstrate through an important example, the process of nitrogen catabolite repression (NCR) in yeast, the power of the theory, its current limitations, and some promising new directions. Fig. 1 shows a circuit of five genes whose dynamics ultimately control NCR in yeast. Although much is known about the architecture of the NCR circuit and the interactions among its components, quantitative models do not exist, and neither a molecular-level nor a systems-level understanding is at hand.The power and promise of the structure theorem approach is to be able to infer dynamics from a (possibly annotated) graph of biological interactions, like that shown in Fig. 1. One of the earliest structure theories, called the chemical reaction network theory, was developed by M. Feinberg (4,5). This theory provides a classification of the dynamics of chemical reaction networks based on the associated circuit diagram. This method is completely scalable because the results depend only on the possible re...

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Volume growth of daughter and parent cells during the cell cycle of Saccharomyces cerevisiae a/alpha as determined by image cytometry

Cited by 92 publications

References 21 publications

Chitin scar breaks in aged Saccharomyces cerevisiae

Chitin scar breaks in aged Saccharomyces cerevisiae

Branching process deconvolution algorithm reveals a detailed cell-cycle transcription program

Structure theorems and the dynamics of nitrogen catabolite repression in yeast

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