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Manipulation and Analysis of Cell Cycle-Dependent Processes in Budding Yeast

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Published on: September 26, 2025

An automaton model for the cell cycle.

Atilla Altinok¹, Didier Gonze, Francis Lévi

¹Unité de Chronobiologie théorique, Faculté des Sciences , Université Libre de Bruxelles , Campus Plaine, C.P. 231, B-1050 Brussels , Belgium.

Interface Focus

|March 16, 2012

Summary

This summary is machine-generated.

This study introduces a cell cycle automaton model to simulate cell division and death. The model demonstrates that cell population homeostasis is maintained, and steady-state proportions are independent of initial conditions.

Keywords:

cell cycle cellular automaton desynchronization model synchronization

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Combining Mitotic Cell Synchronization and High Resolution Confocal Microscopy to Study the Role of Multifunctional Cell Cycle Proteins During Mitosis

Combining Mitotic Cell Synchronization and High Resolution Confocal Microscopy to Study the Role of Multifunctional Cell Cycle Proteins During Mitosis

Published on: December 5, 2017

Area of Science:

Mathematical Biology
Cell Biology
Systems Biology

Background:

The cell cycle comprises four successive phases: G1, S (DNA replication), G2, and M (mitosis).
Cell cycle progression involves phase durations with mean values and variability, influencing cell cycle dynamics.
Cellular homeostasis, or stable cell number, is crucial for tissue function and is maintained by balancing cell division and death.

Purpose of the Study:

To develop and analyze a stochastic automaton model simulating cell cycle progression, division, and death.
To investigate the impact of cell cycle phase durations, variability, and population size on cellular homeostasis.
To explore the model's application in understanding cell population desynchronization and circadian entrainment.

Main Methods:

A stochastic automaton model was developed, where cells progress through G1, S, G2, and M phases with defined durations and random variations.
Cell death probability was incorporated at G1/S and G2/M transitions to regulate population size.
The model's dynamics were analyzed concerning phase durations, variability, distribution types, cell number, and population regulation.

Main Results:

The automaton model successfully simulates cell cycle progression and division, maintaining homeostasis through regulated cell death.
Steady-state proportions of cells in each phase were found to be independent of initial conditions.
The model's dynamics were explored in scenarios of cell population desynchronization and entrainment by circadian rhythms.

Conclusions:

The stochastic automaton model provides a framework for understanding cell cycle dynamics and population homeostasis.
Factors like mean phase durations and variability significantly influence cell cycle progression and population stability.
The model's findings align with deterministic approaches, suggesting robust principles governing cell cycle regulation.