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Cells Coordinate Growth and Proliferation02:36

Cells Coordinate Growth and Proliferation

Cell size is a significant factor impacting cellular design, function, and fitness. There exists some internal coordination by which cells double their masses before division, thus, achieving homeostasis. Coordination between cell growth and proliferation depends on the checkpoints in between cell cycle phases. Loss of coordination or failure in the checkpoint mechanism can drive the cell to uncontrolled growth and loss of cellular function. Like dividing cells that coordinate cellular growth,...
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Related Experiment Video

Updated: Jun 23, 2026

Manipulation and Analysis of Cell Cycle-Dependent Processes in Budding Yeast
08:13

Manipulation and Analysis of Cell Cycle-Dependent Processes in Budding Yeast

Published on: September 26, 2025

Systems level modeling of the cell cycle using budding yeast.

B P Ingalls1, B P Duncker, D R Kim

  • 1Department of Applied Mathematics, University of Waterloo.

Cancer Informatics
|May 21, 2009
PubMed
Summary

Budding yeast (Saccharomyces cerevisiae) offers valuable insights into eukaryotic cell cycle regulation and cancer. Systems-level modeling using proteomics data and mathematical frameworks can predict cell cycle responses to perturbations.

Keywords:
budding yeastcell cycledynamic modelingproteomics

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Area of Science:

  • Cell Biology
  • Systems Biology
  • Biophysics

Background:

  • Cell cycle regulation proteins are conserved across eukaryotes, making yeast a relevant model organism.
  • Budding yeast (Saccharomyces cerevisiae) is a primary source of experimental and modeling data for cell cycle studies.
  • High-throughput technologies enable simultaneous quantification of cell cycle gene transcript and protein levels.

Purpose of the Study:

  • To review existing data on the yeast cell cycle.
  • To discuss proteomics technologies for quantifying cell cycle proteins.
  • To explore mathematical frameworks for systems-level cell cycle modeling.

Main Methods:

  • Integration of high-throughput genome, transcriptome, and proteome data.
  • Application of dynamic modeling based on proteomics data.
  • Utilizing yeast as a model organism for in-depth cell cycle studies.

Main Results:

  • Yeast provides a robust platform for understanding conserved cell cycle mechanisms.
  • Proteomics technologies allow precise quantification of cell cycle components.
  • Mathematical frameworks are essential for developing predictive cell cycle models.

Conclusions:

  • Systems-level modeling of the cell cycle is achievable through integrating high-quality data with appropriate mathematical frameworks.
  • Yeast serves as a powerful model for developing predictive models of cell cycle dynamics and responses to perturbations.
  • This approach can advance our understanding of cell cycle control and its dysregulation in diseases like cancer.