ArticlePLoS computational biology2025
Model-based inference of cell cycle dynamics captures alterations of the DNA replication programme.
Article in PLoS computational biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Experimental evolution of cellular miniaturization reveals a putative mechanism for cell size evolution.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Optimizing scheduling in dual-pulse nucleoside labeling experiments for cell-cycle analysis.Biophysical journal · 2026Article
- Compensatory evolution to DNA replication stress is robust to nutrient availability.Molecular systems biology · 2025Article
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3 authors.
Funding
Abstract
The eukaryotic cell cycle comprises several processes that must be carefully orchestrated and completed in a timely manner. Alterations in cell cycle dynamics have been linked to the onset of various diseases, underscoring the need for quantitative methods to analyze cell cycle progression. Here we develop RepliFlow, a model-based approach to infer cell cycle dynamics from flow cytometry data of DNA content in asynchronous cell populations. We show that RepliFlow captures not only changes in the length of each cell cycle phase but also alterations in the underlying DNA replication dynamics. RepliFlow is species-agnostic and recapitulates results from more sophisticated analyses based on nucleotide incorporation. Finally, we propose a minimal DNA replication model that enables the derivation of microscopic observables from population-wide DNA content measurements. Our work presents a scalable framework for inferring cell cycle dynamics from flow cytometry data, enabling the characterization of replication programme alterations.
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