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Related Experiment Video

Updated: Jul 16, 2025

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Identifying a developmental transition in honey bees using gene expression data.

Bryan C Daniels1, Ying Wang2, Robert E Page3,4

  • 1School of Complex Adaptive Systems, Arizona State University, Tempe, Arizona, United States of America.

Plos Computational Biology
|September 21, 2023
PubMed
Summary
This summary is machine-generated.

Scientists developed a new statistical method to identify gene expression changes during developmental transitions. This approach helps understand collective state changes in systems biology, using honey bee data as a model.

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Area of Science:

  • Systems biology
  • Dynamical systems theory
  • Genetics

Background:

  • Gene interactions drive multiple functional states in organisms.
  • Transitions between states can be linked to critical points in dynamical systems.
  • Characterizing these collective transitions is a key challenge in systems biology.

Purpose of the Study:

  • To develop a statistical method for identifying bistability near continuous transitions using high-dimensional gene expression data.
  • To apply this method to understand developmental transitions in honey bees.
  • To demonstrate a novel approach for inferring collective transitions from gene expression patterns.

Main Methods:

  • Developed a statistical method analyzing gene expression data distributions near transitions.
  • Applied the method to high-dimensional gene expression data from honey bees.
  • Utilized the expected shape of gene expression distributions to identify bistability.

Main Results:

  • Successfully identified the emergence of bistability in honey bee developmental transition data.
  • Linked the identified bistability to genes known to be involved in the nest-to-forage behavioral transition.
  • Validated the method's ability to detect collective transitions.

Conclusions:

  • The developed statistical method can identify bistability and collective transitions from gene expression data.
  • Inferring gene expression distribution shapes offers a powerful tool beyond correlative analysis.
  • This approach has broad potential for analyzing collective transitions in various biological systems.