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

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Published on: September 26, 2025

Modular logical modelling of the budding yeast cell cycle.

Adrien Fauré1, Aurélien Naldi, Fabrice Lopez

  • 1Université de la Méditerranée & INSERM U928 - TAGC, Marseille, France. faure@tagc.univ-mrs.fr

Molecular Biosystems
|September 19, 2009
PubMed
Summary

This study presents a new logical model for yeast cell cycle regulation. It integrates three key modules into a comprehensive system, enabling more complex simulations and predictive analysis.

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Area of Science:

  • Systems biology
  • Computational biology
  • Molecular systems biology

Background:

  • Modeling complex biological systems like regulatory networks is challenging due to numerous components and qualitative data.
  • Developing incremental strategies is crucial for building comprehensive and predictive quantitative models.

Purpose of the Study:

  • To define a logical model for three regulatory modules controlling the mitotic cell cycle in budding yeast.
  • To combine these modules into a single, comprehensive model for simulating sophisticated experiments.

Main Methods:

  • Defined a logical model for the core cell cycle, morphogenetic checkpoint, and exit from mitosis modules.
  • Assessed model consistency through systematic analysis of behaviors under various genetic backgrounds and perturbations.
  • Utilized compositional facilities of the logical formalism to integrate the three models.

Main Results:

  • Developed a comprehensive logical model of the budding yeast mitotic cell cycle with over thirty regulatory components.
  • The integrated model retains the characteristics of individual modules.
  • The model enables the simulation of more sophisticated biological experiments.

Conclusions:

  • The developed logical model provides a framework for understanding and simulating complex cell cycle regulation in budding yeast.
  • This incremental and compositional approach facilitates the creation of larger-scale, predictive biological models.
  • The model supports advanced analysis of cellular processes and perturbations.