ArticleInterface focus2011
An automaton model for the cell cycle.
Article in Interface focus, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Cell populations simulated in silico within SimulCell accurately reproduce the behaviour of experimental cell cultures.NPJ systems biology and applications · 2025Article
- Modeling the Circadian Control of the Cell Cycle and Its Consequences for Cancer Chronotherapy.Biology · 2023Article
- Eight Aging-Related Genes Prognostic Signature for Cervical Cancer.International journal of genomics · 2023Article
- A mathematical model for cell cycle control: graded response or quantized response.Cell cycle (Georgetown, Tex.) · 2022Article
- Modeling signaling pathways in biology with MaBoSS: From one single cell to a dynamic population of heterogeneous interacting cells.Computational and structural biotechnology journal · 2022Review
- UPMaBoSS: A Novel Framework for Dynamic Cell Population Modeling.Frontiers in molecular biosciences · 2022Review
- Data Driven Cell Cycle Model to Quantify the Efficacy of Cancer Therapeutics Targeting Specific Cell-Cycle Phases From Flow Cytometry Results.Frontiers in bioinformatics · 2021Article
- Cancer and the Circadian Clock.Cancer research · 2019Review
- ESRP1 Induces Cervical Cancer Cell G1-Phase Arrest Via Regulating Cyclin A2 mRNA Stability.International journal of molecular sciences · 2019Article
- PhysiBoSS: a multi-scale agent-based modelling framework integrating physical dimension and cell signalling.Bioinformatics (Oxford, England) · 2019Article
- The finite state projection approach to analyze dynamics of heterogeneous populations.Physical biology · 2017Article
- Systems Chronotherapeutics.Pharmacological reviews · 2017Review
- Steady-State-Preserving Simulation of Genetic Regulatory Systems.Computational and mathematical methods in medicine · 2017Article
- Bridging the gap between in vitro and in vivo: Dose and schedule predictions for the ATR inhibitor AZD6738.Scientific reports · 2015Article
- Article
- Exponentially Fitted Two-Derivative Runge-Kutta Methods for Simulation of Oscillatory Genetic Regulatory Systems.Computational and mathematical methods in medicine · 2015Article
- Phase locking and multiple oscillating attractors for the coupled mammalian clock and cell cycle.Proceedings of the National Academy of Sciences of the United States of America · 2014Article
- Quantitative analysis of the effects of iododeoxyuridine and ionising radiation treatment on the cell cycle dynamics of DNA mismatch repair deficient human colorectal cancer cells.IET systems biology · 2013Article
- MicroRNA-mediated regulation in biological systems with oscillatory behavior.BioMed research international · 2013Article
- Entrainment of the mammalian cell cycle by the circadian clock: modeling two coupled cellular rhythms.PLoS computational biology · 2012Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
we consider an automaton model that progresses spontaneously through the four successive phases of the cell cycleG1, S (DNA replication), G2 and M (mitosis). Each phase is characterized by a mean duration τ and a variability V. As soon as the prescribed duration of a given phase has passed, the transition to the next phase of the cell cycle occurs. The time at which the transition takes place varies in a random manner according to a distribution of durations of the cell cycle phases. Upon completion of the M phase, the cell divides into two cells, which immediately enter a new cycle in G1. The duration of each phase is reinitialized for the two newborn cells. At each time step in any phase of the cycle, the cell has a certain probability to be marked for exiting the cycle and dying at the nearest G1/S or G2/M transition. To allow for homeostasis, which corresponds to maintenance of the total cell number, we assume that cell death counterbalances cell replication at mitosis. In studying the dynamics of this automaton model, we examine the effect of factors such as the mean durations of the cell cycle phases and their variability, the type of distribution of the durations, the number of cells, the regulation of the cell population size and the independence of steady-state proportions of cells in each phase with respect to initial conditions. We apply the stochastic automaton model for the cell cycle to the progressive desynchronization of cell populations and to their entrainment by the circadian clock. A simple deterministic model leads to the same steady-state proportions of cells in the four phases of the cell cycle.
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