Cell cycle length and long‐time behavior of an age‐size model

Pichór, Katarzyna; Rudnicki, Ryszard

doi:10.1002/mma.8139

Cited by 4 publications

(2 citation statements)

References 72 publications

(125 reference statements)

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“…In the past decade, the use of microfluidic devices has allowed for detailed measurements of growth and division statistics of bacteria at the single-cell level [24,28]. Phenomenological models of bacterial growth, division, and cellsize regulation proposed based on these single-cell statistics [29] have sparked new interest in understanding how singlecell statistics affect population dynamics [11][12][13][14]18,[30][31][32][33]. At the core of most of these studies is the question of how different sources of variability at single-cell level affect population growth rate.…”

Section: Discussionmentioning

confidence: 99%

Evolutionary dynamics in non-Markovian models of microbial populations

Jafarpour,

Levien,

Amir

2023

Phys. Rev. E

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In the past decade, great strides have been made to quantify the dynamics of single-cell growth and division in microbes. In order to make sense of the evolutionary history of these organisms, we must understand how features of single-cell growth and division influence evolutionary dynamics. This requires us to connect processes on the single-cell scale to population dynamics. Here, we consider a model of microbial growth in finite populations which explicitly incorporates the single-cell dynamics. We study the behavior of a mutant population in such a model and ask: can the evolutionary dynamics be coarse-grained so that the forces of natural selection and genetic drift can be expressed in terms of the long-term fitness? We show that it is in fact not possible, as there is no way to define a single fitness parameter (or reproductive rate) that defines the fate of an organism even in a constant environment. This is due to fluctuations in the population averaged division rate. As a result, various details of the single-cell dynamics affect the fate of a new mutant independently from how they affect the long-term growth rate of the mutant population. In particular, we show that in the case of neutral mutations, variability in generation times increases the rate of genetic drift, and in the case of beneficial mutations, variability decreases its fixation probability. Furthermore, we explain the source of the persistent division rate fluctuations and provide analytic solutions for the fixation probability as a multispecies generalization of the Euler-Lotka equation.

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Section: Discussionmentioning

confidence: 99%

Evolutionary dynamics in non-Markovian models of microbial populations

Jafarpour,

Levien,

Amir

2023

Phys. Rev. E

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“…Early structured approaches to studying cell-cycle dynamics [32] were motivated by the observation that, on the populationscale, cell-cycles were unsynchronised and were unsuited to a mean-field approximation described by singular ODEs. Other structured models of cell-cycle progression have utilized age-size [33,34] or space-size [35,36] structured models to track the cell's progression through the cell cycle (as a single quantitative measure of age) to theoretical ends. These models still do not account for the richness and correlation of the interactions between cyclin and DNA-damage states, which would allow for an understanding of how failures in the cell-cycle may contribute to the onset and survival of cancerous cells.…”

Section: Introductionmentioning

confidence: 99%

Structured dynamics of the cell-cycle at multiple scales

Hodgkinson

Tursynkozha

Trucu

2023

Front. Appl. Math. Stat.

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The eukaryotic cell cycle comprises 4 phases (G1, S, G2, and M) and is an essential component of cellular health, allowing the cell to repair damaged DNA prior to division. Facilitating this processes, p53 is activated by DNA-damage and arrests the cell cycle to allow for the repair of this damage, while mutations in the p53 gene frequently occur in cancer. As such, this process occurs on the cell-scale but affects the organism on the cell population-scale. Here, we present two models of cell cycle progression: The first of these is concerned with the cell-scale process of cell cycle progression and the temporal biochemical processes, driven by cyclins and underlying progression from one phase to the next. The second of these models concerns the cell population-scale process of cell-cycle progression and its arrest under the influence of DNA-damage and p53-activation. Both systems take advantage of structural modeling conventions to develop novels methods for describing and exploring cell-cycle dynamics on these two divergent scales. The cell-scale model represents the accumulations of cyclins across an internal cell space and demonstrates that such a formalism gives rise to a biological clock system, with definite periodicity. The cell population-scale model allows for the exploration of interactions between various regulating proteins and the DNA-damage state of the system and quantitatively demonstrates the structural dynamics which allow p53 to regulate the G2- to M-phase transition and to prevent the mitosis of genetically damaged cells. A divergent periodicity and clear distribution of transition times is observed, as compared with the single-cell system. Comparison to a system with a reduced genetic repair rate shows a greater delay in cell cycle progression and an increased accumulation of cell in the G2-phase, as a result of the p53 biochemical feedback mechanism.

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