A robustness analysis of eukaryotic cell cycle concerning Cdc25 and wee1 proteins

2006 ◽  
Author(s):  
Takehito Azuma ◽  
Hisao Moriya ◽  
Hayato Matsumuro ◽  
Hiroaki Kitano
Keyword(s):  
Cell Cycle ◽  
Robustness Analysis ◽  
Eukaryotic Cell

Cyclin specificity: how many wheels do you need on a unicycle?

2001 ◽  
Vol 114 (10) ◽  
pp. 1811-1820 ◽  
Author(s):  
M.E. Miller ◽  
F.R. Cross
Keyword(s):  
Cell Cycle ◽  
Subcellular Localization ◽  
Cell Cycle Progression ◽  
Eukaryotic Cell ◽  
Cyclin Dependent Kinase ◽  
Temporal Expression ◽  
Cycle Progression

Cyclin-dependent kinase (CDK) activity is essential for eukaryotic cell cycle events. Multiple cyclins activate CDKs in all eukaryotes, but it is unclear whether multiple cyclins are really required for cell cycle progression. It has been argued that cyclins may predominantly act as simple enzymatic activators of CDKs; in opposition to this idea, it has been argued that cyclins might target the activated CDK to particular substrates or inhibitors. Such targeting might occur through a combination of factors, including temporal expression, protein associations, and subcellular localization.

Cyclomodulins: bacterial effectors that modulate the eukaryotic cell cycle

2005 ◽  
Vol 13 (3) ◽  
pp. 103-110 ◽  
Author(s):  
Jean-Philippe Nougayrède ◽  
Frédéric Taieb ◽  
Jean De Rycke ◽  
Eric Oswald
Keyword(s):  
Cell Cycle ◽  
Eukaryotic Cell ◽  
Bacterial Effectors

Eukaryotic Cell-Cycle Control

1992 ◽  
Vol 20 (2) ◽  
pp. 239-242 ◽  
Author(s):  
Paul Nurse
Keyword(s):  
Cell Cycle ◽  
Cell Cycle Control ◽  
Eukaryotic Cell

scholarly journals Nuclear incorporation of iron during the eukaryotic cell cycle

2016 ◽  
Vol 23 (6) ◽  
pp. 1490-1497 ◽  
Author(s):  
Ian Robinson ◽  
Yang Yang ◽  
Fucai Zhang ◽  
Christophe Lynch ◽  
Mohammed Yusuf ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Fluorescence Microscopy ◽  
Phase Contrast ◽  
Eukaryotic Cell ◽  
Inhomogeneous Distribution ◽  
Cell Nuclei ◽  
X Ray ◽  
Microscopy Images ◽  
Different Levels

Scanning X-ray fluorescence microscopy has been used to probe the distribution of S, P and Fe within cell nuclei. Nuclei, which may have originated at different phases of the cell cycle, are found to show very different levels of Fe present with a strongly inhomogeneous distribution. P and S signals, presumably from DNA and associated nucleosomes, are high and relatively uniform across all the nuclei; these agree with X-ray phase contrast projection microscopy images of the same samples. Possible reasons for the Fe incorporation are discussed.

scholarly journals Effects of cell cycle variability on lineage and population measurements of mRNA abundance

2020 ◽  
Author(s):  
Ruben Perez-Carrasco ◽  
Casper Beentjes ◽  
Ramon Grima
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Negative Binomial ◽  
Eukaryotic Cell ◽  
Cycle Duration ◽  
Cell Cycle Duration ◽  
Monotonically Decreasing Function ◽  
Binomial Mixture ◽  
A Cell ◽  
The Mean

AbstractMany models of gene expression do not explicitly incorporate a cell cycle description. Here we derive a theory describing how mRNA fluctuations for constitutive and bursty gene expression are influenced by stochasticity in the duration of the cell cycle and the timing of DNA replication. Analytical expressions for the moments show that omitting cell cycle duration introduces an error in the predicted mean number of mRNAs that is a monotonically decreasing function of η, which is proportional to the ratio of the mean cell cycle duration and the mRNA lifetime. By contrast, the error in the variance of the mRNA distribution is highest for intermediate values of η consistent with genome-wide measurements in many organisms. Using eukaryotic cell data, we estimate the errors in the mean and variance to be at most 3% and 25%, respectively. Furthermore, we derive an accurate negative binomial mixture approximation to the mRNA distribution. This indicates that stochasticity in the cell cycle can introduce fluctuations in mRNA numbers that are similar to the effect of bursty transcription. Finally, we show that for real experimental data, disregarding cell cycle stochasticity can introduce errors in the inference of transcription rates larger than 10%.

scholarly journals Successive Paradigm Shifts in the Bacterial Cell Cycle and Related Subjects

Life ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 27
Author(s):  
Vic Norris
Keyword(s):  
Cell Cycle ◽  
Steady State ◽  
Growth Rates ◽  
Bacterial Cell ◽  
Paradigm Shift ◽  
Eukaryotic Cell ◽  
Origins Of Life ◽  
Paradigm Shifts ◽  
Bacterial Physiology ◽  
Bacterial Cell Cycle

A paradigm shift in one field can trigger paradigm shifts in other fields. This is illustrated by the paradigm shifts that have occurred in bacterial physiology following the discoveries that bacteria are not unstructured, that the bacterial cell cycle is not controlled by the dynamics of peptidoglycan, and that the growth rates of bacteria in the same steady-state population are not at all the same. These paradigm shifts are having an effect on longstanding hypotheses about the regulation of the bacterial cell cycle, which appear increasingly to be inadequate. I argue that, just as one earthquake can trigger others, an imminent paradigm shift in the regulation of the bacterial cell cycle will have repercussions or “paradigm quakes” on hypotheses about the origins of life and about the regulation of the eukaryotic cell cycle.

scholarly journals Quantitative Characterization of a Mitotic Cyclin Threshold Regulating Exit from Mitosis

2005 ◽  
Vol 16 (5) ◽  
pp. 2129-2138 ◽  
Author(s):  
Frederick R. Cross ◽  
Lea Schroeder ◽  
Martin Kruse ◽  
Katherine C. Chen
Keyword(s):  
Cell Cycle ◽  
Budding Yeast ◽  
Cell Cycle Control ◽  
Predictive Ability ◽  
Eukaryotic Cell ◽  
Model Predictions ◽  
Mitotic Cyclin ◽  
Constitutive Overexpression

Regulation of cyclin abundance is central to eukaryotic cell cycle control. Strong overexpression of mitotic cyclins is known to lock the system in mitosis, but the quantitative behavior of the control system as this threshold is approached has only been characterized in the in vitro Xenopus extract system. Here, we quantitate the threshold for mitotic block in budding yeast caused by constitutive overexpression of the mitotic cyclin Clb2. Near this threshold, the system displays marked loss of robustness, in that loss or even heterozygosity for some regulators becomes deleterious or lethal, even though complete loss of these regulators is tolerated at normal cyclin expression levels. Recently, we presented a quantitative kinetic model of the budding yeast cell cycle. Here, we use this model to generate biochemical predictions for Clb2 levels, asynchronous as well as through the cell cycle, as the Clb2 overexpression threshold is approached. The model predictions compare well with biochemical data, even though no data of this type were available during model generation. The loss of robustness of the Clb2 overexpressing system is also predicted by the model. These results provide strong confirmation of the model's predictive ability.

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