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Updated: Feb 3, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Recurrence-based information processing in gene regulatory networks.

Marçal Gabalda-Sagarra1, Lucas B Carey1, Jordi Garcia-Ojalvo1

  • 1Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona Biomedical Research Park, 08003 Barcelona, Spain.

Chaos (Woodbury, N.Y.)
|November 3, 2018
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Summary
This summary is machine-generated.

Cellular information processing relies on gene regulatory networks with a core cyclic structure. This recurrent reservoir encodes environmental history through transient dynamics, enabling cells to interpret complex, time-dependent information.

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Genetics

Background:

  • Cellular information processing is crucial for adapting to environmental changes.
  • The specific network features enabling cells to encode and interpret environmental data remain largely unknown.
  • Gene regulatory networks (GRNs) control cell behavior through complex interactions.

Purpose of the Study:

  • To investigate the computational capabilities of transcriptional regulatory networks.
  • To identify key network structures responsible for cellular information processing.
  • To understand how cells encode and interpret their dynamic environments.

Main Methods:

  • Comparative analysis of transcriptional regulatory networks from five evolutionarily distant organisms.
  • Identification of recurrent cyclic structures within these networks.
  • Modeling GRNs as echo-state networks to assess computational performance.
  • Analysis of information encoding and integration within network dynamics.

Main Results:

  • A conserved cyclic recurrent structure, composed of a small gene core, was identified in all studied GRNs.
  • This recurrent core acts as a reservoir for encoding recent cellular history via nonlinear dynamics.
  • The network's remaining components function as a readout layer for interpreting the core's state.
  • GRNs exhibit characteristics of echo-state networks, excelling in memory-dependent tasks.

Conclusions:

  • Recurrent nonlinear dynamics in GRNs are fundamental for processing complex, time-dependent cellular information.
  • The identified cyclic core structure is essential for dynamical encoding and information integration.
  • Cellular information processing relies on a specialized recurrent reservoir for memory and a readout layer for interpretation.