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Related Concept Videos

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.
Cyclins and cyclin-dependent kinases (Cdks) are the primary cell cycle regulators and function at the cell...
The Cell Cycle Control System02:11

The Cell Cycle Control System

The cell cycle is an organized set of events that leads the cell to divide into two daughter cells, each containing chromosomes identical to the parent cell. It is the cell cycle that leads to the formation of an entire organism from a single-cell zygote. Besides, cell division also functions in the renewal or repair of tissues in adult multicellular eukaryotes. For example, in the bone marrow, the stem cells divide to form new blood cells. Although essential for several functions, cell...
The Cell Cycle Control System02:11

The Cell Cycle Control System

The cell cycle is an organized set of events that leads the cell to divide into two daughter cells, each containing chromosomes identical to the parent cell. It is the cell cycle that leads to the formation of an entire organism from a single-cell zygote. Besides, cell division also functions in the renewal or repair of tissues in adult multicellular eukaryotes. For example, in the bone marrow, the stem cells divide to form new blood cells. Although essential for several functions, cell...
What is the Cell Cycle?01:04

What is the Cell Cycle?

The cell cycle refers to the sequence of events occurring throughout a typical cell’s life. In eukaryotic cells, the somatic cell cycle has two stages: interphase and the mitotic phase. During interphase, the cell grows, performs its basic metabolic functions, copies its DNA, and prepares for mitotic cell division. Then, during mitosis and cytokinesis, the cell divides its nuclear and cytoplasmic materials, respectively. This generates two daughter cells that are identical to the original...
What is the Cell Cycle?00:56

What is the Cell Cycle?

The cell cycle refers to the sequence of events occurring throughout a typical cell’s life. In eukaryotic cells, the somatic cell cycle has two stages: the interphase and the mitotic phase. During interphase, the cell grows, performs its basic metabolic functions, copies its DNA, and prepares for mitotic cell division. Then, during mitosis and cytokinesis, the cell divides its nuclear and cytoplasmic materials, respectively. This generates two daughter cells that are identical to the original...
What is the Cell Cycle?00:56

What is the Cell Cycle?

The cell cycle refers to the sequence of events occurring throughout a typical cell’s life. In eukaryotic cells, the somatic cell cycle has two stages: the interphase and the mitotic phase. During interphase, the cell grows, performs its basic metabolic functions, copies its DNA, and prepares for mitotic cell division. Then, during mitosis and cytokinesis, the cell divides its nuclear and cytoplasmic materials, respectively. This generates two daughter cells that are identical to the original...

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Related Experiment Video

Updated: Jun 21, 2026

Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

What cycles the cell? -Robust autonomous cell cycle models.

Orit Lavi1, Yoram Louzoun

  • 1Department of Mathematics, Bar-Ilan University, Ramat Gan 52900, Israel.

Mathematical Medicine and Biology : a Journal of the IMA
|July 8, 2009
PubMed
Summary

This study introduces a new way to model how cells cycle through growth and division. While scientists know many of the parts involved in the cell cycle, they still don't fully understand how these parts work together to create the cycle's rhythmic behavior. The researchers developed a step-by-step method to build mathematical models of the cell cycle. They start with simple systems and gradually add complexity while ensuring the models remain stable and accurate. This approach helps create models that are less sensitive to changes in parameters, making them more reliable for studying both normal and cancerous cell cycles. The method could be useful for other biological processes as well.

Keywords:
cell cycle dynamicscomputational modelingbiological system behaviorrobust mathematical models

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Manipulation and Analysis of Cell Cycle-Dependent Processes in Budding Yeast
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Combining Mitotic Cell Synchronization and High Resolution Confocal Microscopy to Study the Role of Multifunctional Cell Cycle Proteins During Mitosis
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Combining Mitotic Cell Synchronization and High Resolution Confocal Microscopy to Study the Role of Multifunctional Cell Cycle Proteins During Mitosis

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Last Updated: Jun 21, 2026

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: December 5, 2017

Area of Science:

  • Cell cycle regulation in systems biology
  • Computational modeling in biological dynamics
  • Cancer cell proliferation mechanisms

Background:

Despite extensive research, the exact mechanisms driving cell cycle dynamics remain unclear. While many biochemical components are known, their interactions do not yet yield a complete picture of the system's behavior. Prior research has shown that molecular interactions alone cannot fully explain the rhythmic progression of the cell cycle. This gap motivated the development of new modeling approaches. No prior work had resolved how to translate known components into robust dynamic models. The challenge lies in capturing cyclic behavior under variable conditions. This uncertainty drives the need for alternative methodologies. The search continues for a framework that can integrate known biology into predictive models.

Purpose Of The Study:

The study aimed to develop a modeling framework for cell cycle dynamics. It focused on embryonic and cancerous cycles, where robustness is essential. The goal was to define mathematical constraints for cyclic behavior. The approach builds from simplified systems to more complex ones. This method allows for the inclusion of known biological constraints. The study sought to improve model robustness to parameter changes. It proposed a stepwise expansion of variable systems. The methodology aims to bridge gaps between known biology and dynamic behavior.

Main Methods:

The approach begins with defining a key system property: cyclic behavior. Mathematical constraints are set for two-variable systems to reproduce this behavior. These systems are expanded to three variables iteratively. At each step, known biological constraints are applied. The process continues until a detailed model is achieved. The method ensures robustness to function and rate constant changes. It uses a bottom-up approach, starting from minimal systems. The framework is designed to be adaptable to various subcellular mechanisms.

Main Results:

The methodology successfully produced robust models of cell cycle dynamics. Two-variable systems were constrained to exhibit cyclic behavior. These systems were expanded to three variables while maintaining robustness. The process was repeated until detailed biological descriptions emerged. The resulting models are resilient to parameter variations. The approach was validated for embryonic and cancerous cycles. Mathematical constraints were critical in maintaining system behavior. The models align with known microbiological principles.

Conclusions:

The study demonstrated a methodology for building robust cell cycle models. The approach uses mathematical constraints to ensure cyclic behavior. It iteratively expands systems while applying biological knowledge. The resulting models are resilient to changes in parameters. This framework can be adapted to other subcellular mechanisms. The authors propose that this method improves model accuracy. It provides a way to translate known biology into dynamic models. The approach may help clarify cell cycle regulation in different contexts.

The core mechanism involves defining cyclic behavior and setting mathematical constraints on simplified systems. These systems are iteratively expanded while maintaining robustness to parameter changes.

The methodology ensures robustness by applying mathematical constraints and iteratively expanding systems. This process limits variability in function and rate constants, aligning with known biological principles.

A two-variable system is used to capture the essential cyclic behavior of the cell cycle. It provides a minimal framework that can be expanded while maintaining the core dynamics.

Known biological constraints are applied at each expansion step to ensure the models remain biologically relevant. These constraints guide the transition from simplified to detailed systems.

This methodology differs by iteratively expanding systems while applying constraints. Traditional approaches often rely on fixed parameters, which may not capture dynamic behavior as effectively.

The authors propose that this approach improves model robustness and accuracy. It may help clarify cell cycle regulation in various biological contexts.