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Related Experiment Video

Updated: Jul 7, 2026

Studying Cell Cycle-regulated Gene Expression by Two Complementary Cell Synchronization Protocols
12:02

Studying Cell Cycle-regulated Gene Expression by Two Complementary Cell Synchronization Protocols

Published on: June 6, 2017

Self-Organizing Maps with Statistical Phase Synchronization (SOMPS) for analyzing cell cycle-specific gene expression

Chang Sik Kim1

  • 1Institute of Animal Resources Research, Kangwon National University. cskim@kangwon.ac.kr

Statistical Applications in Genetics and Molecular Biology
|February 5, 2008
PubMed
Summary
This summary is machine-generated.

This study proposes viewing yeast cell cycle gene expression as collective synchronization. A new algorithm, SOMPS, identifies interacting gene groups and their interaction strengths, revealing synchronized biological processes.

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Last Updated: Jul 7, 2026

Studying Cell Cycle-regulated Gene Expression by Two Complementary Cell Synchronization Protocols
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Published on: June 6, 2017

Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
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08:33

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:

  • Systems Biology
  • Computational Biology
  • Genomics

Background:

  • Yeast cell cycle involves complex gene interactions with periodic expression patterns.
  • Understanding these interactions is crucial for deciphering cellular regulation.

Purpose of the Study:

  • To apply statistical multivariate phase synchronization theory to yeast cell cycle gene expression.
  • To develop and evaluate a novel algorithm for analyzing collective synchronization in transcriptomics.

Main Methods:

  • Development of the Self-Organizing Maps with statistical Phase Synchronization (SOMPS) algorithm.
  • Application of SOMPS to yeast cell cycle-specific gene expression data.
  • Analysis of gene groups, periodicity, and interaction strengths within the cell cycle transcriptome.

Main Results:

  • Identified gene groups with shared biological processes and significant interactions using phase synchronization.
  • Characterized prominent gene clusters exhibiting high periodicity within the SOMPS output.
  • Quantified the strength of biological interactions between genes based on synchronization coupling.

Conclusions:

  • Cell cycle-specific gene expression can be modeled as a collective synchronization phenomenon.
  • The SOMPS algorithm effectively identifies biologically relevant gene interactions and patterns.
  • Multivariate phase synchronization provides a framework for understanding complex gene regulatory networks.