Asymmetric cell division with stochastic growth rate. Dedicated to the memory of the late Spartak Agamirzayev

2018 ◽  
Vol 41 (17) ◽  
pp. 8059-8069
Author(s):  
Messoud Efendiev ◽  
Bruce Brunt ◽  
Ali A. Zaidi ◽  
Touqeer H. Shah
Keyword(s):  
Growth Rate ◽  
Cell Division ◽  
Asymmetric Cell Division ◽  
Stochastic Growth ◽  
Stochastic Growth Rate

Environment-specific elasticity and sensitivity analysis of the stochastic growth rate

2009 ◽  
Vol 220 (5) ◽  
pp. 605-610 ◽  
Author(s):  
Per Åberg ◽  
Carl Johan Svensson ◽  
Hal Caswell ◽  
Henrik Pavia
Keyword(s):  
Growth Rate ◽  
Sensitivity Analysis ◽  
Stochastic Growth ◽  
Stochastic Growth Rate

scholarly journals Derivatives of the stochastic growth rate

2011 ◽  
Vol 80 (1) ◽  
pp. 1-15 ◽  
Author(s):  
David Steinsaltz ◽  
Shripad Tuljapurkar ◽  
Carol Horvitz
Keyword(s):  
Growth Rate ◽  
Stochastic Growth ◽  
Stochastic Growth Rate ◽  
Derivatives Of

Modulation of first-passage time for gene expression via asymmetric cell division

2019 ◽  
Vol 12 (05) ◽  
pp. 1950052
Author(s):  
Kunwen Wen ◽  
Lifang Huang ◽  
Qi Wang ◽  
Jianshe Yu
Keyword(s):  
Gene Expression ◽  
Growth Rate ◽  
Cell Division ◽  
Asymmetric Cell Division ◽  
First Passage Time ◽  
Passage Time ◽  
Exponential Growth Rate ◽  
First Passage ◽  
Division Rate ◽  
The Mean

How to balance the size of exponentially growing cells has always been a focus of biologists. Recent experiments have uncovered that the cell is divided into two daughter cells only when the level of time-keeper protein reaches a fixed threshold and cell division in prokaryote is not completely symmetric. The timing of cell division is essentially random because gene expression is stochastic, but cells seen to manage to have precise timing of cell division events. Although the inter-cellular variability of gene expression has attracted much attention, the randomness of event timing has been rarely studied. In our analysis, the timing of cell division is formulated as the first-passage time (denoted by FPT) for time-keeper protein’s level to cross a critical threshold firstly, we derive exact analytical formulae for the mean and noise of FPT based on stochastic gene expression model with asymmetric cell division. The results of numerical simulation show that the regulatory factors (division rate, newborn cell size, exponential growth rate and threshold) have significant influence on the mean and noise of FPT. We also show that both the increase of division rate and newborn cell size could reduce the mean of FPT and increase the noise of FPT, the larger the exponential growth rate is, the smaller the mean and noise of FPT will be; and the larger the threshold value is, the higher the mean of FPT is and the lower the noise is. In addition, compared with symmetric division, asymmetric division can reduce the mean of FPT and improve the noise of FPT. In summary, our results provide insight into the relationship between regulatory factors and FPT and reveal that asymmetric division is an effective mechanism to shorten the mean of FPT.

The accuracy of alternative stochastic growth rate estimates for salmon populations

2002 ◽  
Vol 59 (6) ◽  
pp. 1014-1023 ◽  
Author(s):  
Richard A Hinrichsen
Keyword(s):  
Growth Rate ◽  
The Other ◽  
Leslie Matrix ◽  
Stochastic Growth ◽  
Structure Information ◽  
Model Method ◽  
Leslie Matrix Model ◽  
Stochastic Growth Rate ◽  
Biased Estimates ◽  
Least Squares Approach

The accuracies of four alternative estimators of stochastic growth rate for salmon populations are examined using bootstrapping. The first estimator is based on a stochastic Leslie matrix model that uses age-specific spawner counts. The other three estimators use spawner counts with limited age-structure information: a Botsford–Brittnacher model method and two diffusion approximation methods, namely, the least squares approach of Dennis and the robust approach of Holmes. Accuracy of the estimators was quantified using median bias and interquartile ranges of the stochastic growth rate estimates. The Botsford–Brittnacher estimator was found to be unreliable due to large bias. Of the remaining estimators, the stochastic Leslie approach tended to produce the most reliable estimates but had the greatest data demands. With severe lognormal measurement error, the Dennis estimators produced less biased estimates than the other methods, but precision of the stochastic growth rate was generally highest using the stochastic Leslie estimator.

Centromere assembly and non-random sister chromatid segregation in stem cells

2020 ◽  
Vol 64 (2) ◽  
pp. 223-232 ◽  
Author(s):  
Ben L. Carty ◽  
Elaine M. Dunleavy
Keyword(s):  
Stem Cells ◽  
Cell Division ◽  
Cell Fate ◽  
Sister Chromatid ◽  
Asymmetric Cell Division ◽  
Protein A ◽  
Germline Stem Cells ◽  
Sister Chromatids ◽  
Centromere Protein A ◽  
Oocyte Meiosis

Abstract Asymmetric cell division (ACD) produces daughter cells with separate distinct cell fates and is critical for the development and regulation of multicellular organisms. Epigenetic mechanisms are key players in cell fate determination. Centromeres, epigenetically specified loci defined by the presence of the histone H3-variant, centromere protein A (CENP-A), are essential for chromosome segregation at cell division. ACDs in stem cells and in oocyte meiosis have been proposed to be reliant on centromere integrity for the regulation of the non-random segregation of chromosomes. It has recently been shown that CENP-A is asymmetrically distributed between the centromeres of sister chromatids in male and female Drosophila germline stem cells (GSCs), with more CENP-A on sister chromatids to be segregated to the GSC. This imbalance in centromere strength correlates with the temporal and asymmetric assembly of the mitotic spindle and potentially orientates the cell to allow for biased sister chromatid retention in stem cells. In this essay, we discuss the recent evidence for asymmetric sister centromeres in stem cells. Thereafter, we discuss mechanistic avenues to establish this sister centromere asymmetry and how it ultimately might influence cell fate.

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