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

IF 3.6 2区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

PLoS Computational Biology Pub Date : 2025-10-14 DOI:10.1371/journal.pcbi.1013570

Adolfo Alsina, Marco Fumasoni, Pablo Sartori

引用次数: 0

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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基于模型的细胞周期动力学推断捕获DNA复制程序的改变。

真核细胞周期包括几个过程，这些过程必须精心安排并及时完成。细胞周期动力学的改变与各种疾病的发病有关，强调需要定量方法来分析细胞周期进展。在这里，我们开发了RepliFlow，这是一种基于模型的方法，可以从异步细胞群体中DNA含量的流式细胞术数据推断细胞周期动力学。我们发现，RepliFlow不仅捕获了每个细胞周期阶段长度的变化，还捕获了潜在DNA复制动力学的变化。RepliFlow是物种不可知的，并概括了基于核苷酸结合的更复杂分析的结果。最后，我们提出了一个最小的DNA复制模型，使微观可观察的推导从人口范围内的DNA含量测量。我们的工作提出了一个可扩展的框架，用于从流式细胞术数据推断细胞周期动力学，从而表征复制程序的改变。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY

CiteScore

7.10

自引率

4.70%

发文量

820

审稿时长

2.5 months

期刊介绍： PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.