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

## TL;DR

RepliFlow is a new method that uses flow cytometry data to accurately infer cell cycle dynamics and DNA replication changes in different cell types.

## Contribution

RepliFlow introduces a model-based, species-agnostic framework for analyzing cell cycle dynamics from DNA content data.

## Key findings

- RepliFlow captures changes in the length of each cell cycle phase.
- The method also identifies alterations in DNA replication dynamics.
- RepliFlow is validated across different datasets and cell types.

## 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.

The cell cycle is the coordinated sequence of events between two consecutive cell divisions. In eukaryotes, the cell cycle consists of four main phases: G1, S, G2, and mitosis. Understanding the constrains regulating cell cycle progression requires quantifying the amount of time that cells spend in each cell cycle phase. While several methods have been developed to measure cell cycle progression, a typical approach is to use flow cytometry, a technology that allows to rapidly measure the DNA content of thousands of cells. Whereas conventional analysis of flow cytometry data frequently depends on heuristic approximations, we have developed RepliFlow, a model-based computational method to infer the amount of time allocated to each cell cycle phase from DNA content distributions. Applicable to different cell types, RepliFlow additionally captures alterations to the DNA replication dynamics without requiring specialized experimental techniques. We demonstrate the applicability of Repliflow across different datasets, establishing it as a robust framework for quantitative analysis of cell cycle dynamics.