Patterns of interdivision time correlations reveal hidden cell cycle factors
Fern A Hughes1,2, Alexis R Barr2,3, Philipp Thomas1
1Department of Mathematics, Imperial College London, London, United Kingdom.
Elife
|November 15, 2022
Summary
Cell division timing is influenced by inherited factors, creating distinct correlation patterns. These patterns reveal hidden rhythms, often linked to circadian cycles, which can be altered in diseases like cancer.
Area of Science:
- Cell biology
- Quantitative biology
- Systems biology
Background:
- Cell division timing is a stochastic process influenced by intracellular factors.
- Inherited factors can lead to non-intuitive correlation patterns in cell cycle timing across generations.
- Existing models of cell cycle control do not fully capture these inheritance dynamics.
Purpose of the Study:
- To formulate a unified framework for hidden inherited factors affecting the cell cycle.
- To identify distinct interdivision time correlation patterns.
- To analyze inheritance motifs in diverse cell types using Bayesian inference.
Main Methods:
- Developed a theoretical framework for hidden inherited factors in cell cycle control.
- Utilized Bayesian inference to analyze single-cell datasets.
- Examined cell division data from bacteria, mammalian, and cancer cells.
Main Results:
- Identified three distinct interdivision time correlation patterns: aperiodic, alternator, and oscillator.
- Inferred patterns revealed interpretable inheritance dynamics and hidden rhythmicity.
- Found that cell cycle factors are often driven by circadian rhythms, with potential period differences in cancer.
Conclusions:
- Interdivision time correlation patterns do not pinpoint a single cell cycle model but suggest a distribution of mechanisms.
- Inheritance dynamics and hidden rhythmicity are crucial emergent phenomena impacting cell proliferation.
- Altered correlation patterns may serve as indicators of disease, particularly in cancer.
Keywords:
cell biologycell cyclecellular noisecircadian rhythmcomputational biologyhumanlineage correlationmousepatternsstochastic modellingsystems biologyMore Related Videos
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