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Related Experiment Videos

Emergent criticality from coevolution in random Boolean networks.

Min Liu1, Kevin E Bassler

  • 1Department of Physics, University of Houston, Houston, Texas 77204-5005, USA. mliu3@uh.edu

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|December 13, 2006
PubMed
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This study models gene regulatory networks, finding that critical states emerge from evolving network structures and dynamics. Evolved networks show properties relevant to biological gene regulation.

Area of Science:

  • Computational Biology
  • Systems Biology
  • Network Science

Background:

  • Gene regulatory networks (GRNs) control cellular functions through complex interactions.
  • Understanding the interplay between network structure (topology) and activity (dynamics) is crucial for GRNs.
  • Boolean network models offer a simplified yet powerful framework for studying GRN properties.

Purpose of the Study:

  • To investigate the coevolution of network topology and dynamics in a model GRN.
  • To determine if critical states emerge spontaneously during network evolution.
  • To analyze the properties of evolved networks, particularly in- and out-degree distributions.

Main Methods:

  • Utilized an evolutionary Boolean network model to simulate GRN development.

Related Experiment Videos

  • Simulated the interplay between network structure and dynamic rules over evolutionary time.
  • Analyzed network properties, including in-degree distribution and critical state emergence.

    • Main Results:

    • A critical state spontaneously emerges due to the coevolution of topology and dynamics.
    • The final evolved network state is independent of initial conditions.
    • For large networks, the model approaches a random Boolean network with uniform in-degree 2.
    • Biologically relevant network sizes exhibit finite-size effects, including broad in-degree distributions and average in-degrees between 2 and 3.

    Conclusions:

    • The evolutionary process drives GRN models towards a critical state.
    • Finite-size effects are significant in biologically realistic GRN models.
    • These findings provide insights into the structural and dynamic properties of actual gene regulatory networks.