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Updated: Dec 14, 2025

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

Published on: September 26, 2025

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Extended Robust Boolean Network of Budding Yeast Cell Cycle.

Sajad Shafiekhani1,2,3, Mojtaba Shafiekhani4, Sara Rahbar1,2

  • 1Department of Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.

Journal of Medical Signals and Sensors
|July 18, 2020
PubMed
Summary

This study enhances the budding yeast cell cycle network model using a Markov chain and genetic algorithm, increasing system robustness and maximizing cell cycle phase transition probabilities for better stability.

Keywords:
Boolean networkMarkov chain modelbudding yeast cell cyclegenetic algorithm

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Biophysics

Background:

  • Investigating the dynamics of transition probabilities in the budding yeast cell cycle (BYCC) network.
  • Identifying the robust protein interaction structure within the BYCC Boolean network (BN).
  • Utilizing budding yeast as a model for studying intracellular cell cycle regulation via mathematical modeling.

Purpose of the Study:

  • To explore cell cycle network dynamics and protein interaction robustness.
  • To enhance the BYCC Boolean network model with apoptosis and extended cell cycle phases.
  • To optimize kinetic parameters for stable cell cycle transitions.

Main Methods:

  • Extended a deterministic Boolean network (BN) of cell cycle proteins to a Markov chain model.
  • Incorporated apoptosis and additional cell cycle phases (G1, S, G2, M, stationary G1).
  • Employed a genetic algorithm (GA) to estimate kinetic parameters, maximizing transition probabilities and stabilizing network structure.

Main Results:

  • Optimized kinetic parameters using GA maximized cell cycle phase transition probabilities.
  • Increased the relative basin size of stationary G1 from 86% to 96.48%.
  • Reduced the number of attractors from 7 to 5, enhancing system robustness.

Conclusions:

  • Protein interaction structure significantly impacts cell cycle network robustness and phase transition probabilities.
  • Markov chain and Boolean network models are effective for studying cell cycle network stability and dynamics.