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Related Experiment Video

Updated: May 19, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Evolving Boolean networks with structural dynamism.

Larry Bull1

  • 1University of the West of England, UK. Larry.Bull@uwe.ac.uk

Artificial Life
|September 4, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a tunable model for genomic structural changes during the cell cycle, demonstrating its potential for simulated evolution and uncovering mechanisms for evolutionary innovation.

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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Following the Dynamics of Structural Variants in Experimentally Evolved Populations
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Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Area of Science:

  • Computational Biology
  • Evolutionary Genetics
  • Systems Biology

Background:

  • Genetic regulatory networks (GRNs) are fundamental to cellular function.
  • Modeling GRNs typically simplifies their dynamic structural properties.
  • Transposable elements can induce genomic structural changes.

Purpose of the Study:

  • To develop an abstract, tunable model of genomic structural change within the cell life cycle.
  • To explore the evolvability of dynamic genetic regulatory networks using simulated evolution.
  • To investigate the role of structural dynamism in evolutionary innovation.

Main Methods:

  • Extended a Boolean model of genetic regulatory networks to include state-dependent node connectivity.
  • Incorporated mechanisms to simulate transposable element effects on network structure.
  • Utilized a version of the NK model of fitness landscapes for simulated evolution.
  • Tested both synchronous and asynchronous updating schemes.

Main Results:

  • Structural dynamism was selected for in nonstationary environments under both synchronous and asynchronous updating.
  • Inherited structural reorganizations provided a mechanism for evolutionary innovation.
  • Structural dynamism also facilitated evolutionary innovation in stationary environments with asynchronous updating.

Conclusions:

  • The developed model offers a framework for studying genomic structural dynamics and evolvability.
  • Dynamic network structures can be selected for and drive evolutionary innovation.
  • Cellular state-dependent network changes are crucial for understanding evolutionary processes.