Model-based inference of cell cycle dynamics captures alterations of the DNA replication programme

Area of Science:

• Molecular Biology
• Cell Biology
• Biophysics

Background:

• The eukaryotic cell cycle requires precise orchestration; dysregulation is linked to diseases.
• Quantitative analysis of cell cycle progression is crucial for understanding cellular health and disease.
• Existing methods for cell cycle analysis can be complex or species-specific.

Purpose of the Study:

• To develop a model-based approach, RepliFlow, for inferring cell cycle dynamics from flow cytometry data.
• To enable quantitative analysis of DNA replication dynamics alongside cell cycle phase lengths.
• To provide a species-agnostic framework for scalable cell cycle analysis.

Main Methods:

• Development of RepliFlow, a model-based computational approach.
• Application to flow cytometry data measuring DNA content in asynchronous cell populations.
• Validation against established methods, including nucleotide incorporation assays.

Main Results:

• RepliFlow accurately infers cell cycle phase lengths and DNA replication dynamics.
• The method is species-agnostic and robust, comparable to more complex analyses.
• A minimal DNA replication model was proposed to link population-level data to microscopic observables.

Conclusions:

• RepliFlow offers a scalable and versatile framework for analyzing cell cycle dynamics.
• This approach facilitates the characterization of alterations in DNA replication programs.
• The findings advance quantitative cell biology and disease research.