A continuous-time stochastic Boolean model provides a quantitative description of the budding yeast cell cycle
Teeraphan Laomettachit1,2, Pavel Kraikivski3, John J Tyson4
1Bioinformatics and Systems Biology Program, School of Bioresources and Technology, King Mongkut's University of Technology Thonburi, Bangkok, 10150, Thailand. teeraphan.lao@kmutt.ac.th.
Scientific Reports
|November 26, 2022
Summary
This study presents a hybrid Boolean model for budding yeast cell cycle regulation, integrating discrete gene activity with continuous cell growth. The model accurately simulates cell cycle progression and robustness, offering a simpler alternative to complex kinetic models.
Area of Science:
- Cell Biology
- Systems Biology
- Computational Biology
Background:
- Cell division cycle regulation involves complex gene and protein interactions.
- Existing models range from qualitative Boolean networks to quantitative differential equations and stochastic simulations.
Purpose of the Study:
- To develop a continuous-time stochastic model for budding yeast cell cycle regulation.
- To integrate Boolean variables for gene activity with a continuous variable for cell growth.
- To simulate cell populations and compare statistical properties with experimental data.
Main Methods:
- Developed a hybrid model using seven Boolean variables for cell cycle regulators and one continuous variable for cell growth.
- Implemented asynchronous updates of Boolean variables based on biochemical logic using Gillespie's stochastic simulation algorithm.
- Continuously updated time and cell size during simulations.
Main Results:
- The model accurately simulates statistical properties of cell cycle progression in budding yeast.
- The cell cycle demonstrated 91% robustness against random perturbations of Boolean variables.
- 9% of perturbations resulted in lethal errors in cell cycle progression.
Conclusions:
- The hybrid Boolean model provides a good account of yeast cell growth and division.
- This modeling approach is a viable and less demanding alternative to detailed reaction-kinetic modeling.
- The model's accuracy suggests its utility for understanding cell cycle dynamics.
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