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The Cell Cycle Control System01:28

The Cell Cycle Control System

The cell cycle regulation directs how a cell proceeds from one phase to the next and begins mitosis. The cell cycle control system includes intracellular regulatory molecules and external triggers. They provide "stop" or "advance" signals and operate at specific cell cycle stages termed checkpoints to ensure that a particular process is completed before the cell advances to the next phase.
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Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
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Published on: June 9, 2023

Modeling the cell cycle: from deterministic models to hybrid systems.

R Alfieri1, E Bartocci, E Merelli

  • 1Institute for Biomedical Technologies - CNR, Segrate, Italy. roberta.alfieri@itb.cnr.it

Bio Systems
|April 2, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a novel hybrid systems approach for modeling the cell cycle, combining continuous and discrete dynamics. This method effectively models the R-point transition, offering advantages over traditional deterministic models.

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Combining Mitotic Cell Synchronization and High Resolution Confocal Microscopy to Study the Role of Multifunctional Cell Cycle Proteins During Mitosis

Published on: December 5, 2017

Area of Science:

  • Mathematical Biology
  • Systems Biology
  • Computational Biology

Background:

  • The cell cycle is a complex biological process often studied using mathematical models.
  • Existing deterministic models face challenges in integrating continuous and discrete dynamics for biological realism.
  • A need exists for advanced modeling techniques that can efficiently handle parameter variations and variable introductions.

Purpose of the Study:

  • To develop a novel hybrid systems approach for modeling biological processes, specifically the cell cycle.
  • To implement a hybrid model that combines continuous dynamics with discrete events using hybrid automata.
  • To investigate the R-point transition in the mammalian cell cycle and the role of E2F transcription factors.

Main Methods:

  • Utilized a model reduction technique (modified Prony's method) to identify key dynamical system features.
  • Employed hybrid automata technology to construct a hybrid system model.
  • Applied the methodology to a simplified model of a mammalian cell cycle control point, focusing on the R-point transition.

Main Results:

  • Developed a hybrid system model that preserves the properties of the original deterministic model.
  • Successfully modeled the R-point transition, a critical control point in the cell cycle.
  • Identified the key parameter governing the transition from quiescence to the active state after mitogenic stimulation.

Conclusions:

  • The proposed hybrid systems approach offers an effective way to model complex biological systems like the cell cycle.
  • This methodology overcomes limitations of purely deterministic models, allowing for more efficient parameter and variable handling.
  • This work presents the first known hybrid model for the R-point transition, providing a new tool for cell cycle research.