Mathematical models incorporating a multi-stage cell cycle replicate normally-hidden inherent synchronization in cell proliferation
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Abstract
We present a suite of experimental data showing that cell proliferation assays, prepared using standard methods thought to produce asynchronous cell populations, persistently exhibit inherent synchronization. Our experiments use fluorescent cell cycle indicators to reveal the normally hidden cell synchronization, by highlighting oscillatory subpopulations within the total cell population. These oscillatory subpopulations would never be observed without these cell cycle indicators. On the other hand, our experimental data show that the total cell population appears to grow exponentially, as in an asynchronous population. We reconcile these seemingly inconsistent observations by employing a multi-stage mathematical model of cell proliferation that can replicate the oscillatory subpopulations. Our study has important implications for understanding and improving experimental reproducibility. In particular, inherent synchronization may affect the experimental reproducibility of studies aiming to investigate cell cycle-dependent mechanisms, including changes in migration and drug response.
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The treatment of cell migration via continuum PDEs is well established in biophysics, as is the role of cell arrangement in determining aspects of the progression of cancers.
“The treatment of cell migration via continuum PDEs is well established in biophysics36,37,38, as is the role of cell arrangement in determining aspects of the progression of cancers39.”
From Inference of weak-form partial differential equations describing migration and proliferation mechanisms in wound healing experiments on cancer cells (2025) · cites this paper as [36]
The Introduction discusses mathematical models of cell proliferation, and §2.2 uses differential equations for cell-cycle-stage populations. Our copy does not address continuum PDE models of cell migration or the effect of cell arrangement on cancer progression; searches for those terms found no supporting passage. (LLM, conf=0.75)
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References 20 · Cited by 29
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- Authors
- Vittadello, S. T., McCue, S. W., Gunasingh, G., Haass, N. K., & Simpson, M. J.
- Affiliations
- Queensland University of Technology (AU)
- Translational Research Institute (AU)
- Corresponding author
- Simpson, Matthew J.
- Venue
- Journal of The Royal Society Interface
- Publisher
- The Royal Society
- Published
- 2019-08
- Volume
- 16
- Issue
- 157
- Pages
- 20190382
- ISSN
- 1742-5662 (ISSN-L)
- 1742-5689
- PMC
- PMC6731503
- OpenAlex
- W2969950545
- Cited by
- 29
- References
- 20
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 4 · 2023: 7 · 2022: 5 · 2021: 5 · 2020: 3
- FWCI
- 1.89
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of The Royal Society Interface (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Australian Research Council (DP170100474)
- National Health and Medical Research Council (AP1084893)
- Topics
- Physical Sciences › Mathematics › Modeling and Simulation › Mathematical Biology Tumor Growth
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › Microtubule and mitosis dynamics
- Keywords
- cell cycle synchronization, cell proliferation, asynchronous cell populations, exponential growth, experimental reproducibility
- MeSH terms
- Cell Cycle, Fluorescence, Humans, Models, Biological, Cell Line, Tumor, Cell Proliferation
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