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Revealing gene regulation-based neural network computing in bacteria.

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Bacteria possess gene regulatory networks (GRNs) that function like artificial neural networks. This study models these networks as gene regulatory neural networks (GRNNs) to understand bacterial computing.

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Bioinformatics

Background:

  • Bacteria sense environmental signals using complex signaling transduction networks, including gene regulatory networks (GRNs).
  • GRNs exhibit structural and functional similarities to artificial neural networks, suggesting inherent computational properties.
  • Bacterial gene expression dynamics offer a unique perspective on their non-neuronal computational capabilities.

Purpose of the Study:

  • To develop a model quantifying gene-to-gene interaction dynamics within GRNs, termed gene regulatory neural networks (GRNNs).
  • To extract and validate a GRNN for pyocyanin production in *Pseudomonas aeruginosa* using transcriptomic and experimental data.
  • To analyze the impact of genetic and environmental factors on GRNN computing behavior and reliability.

Main Methods:

  • Developed a weight extraction technique to convert GRNs into GRNNs based on transcriptomic data.
  • Validated GRNN computational accuracy using wet-lab experimental data for pyocyanin production in *Pseudomonas aeruginosa*.
  • Modeled ecosystem-wide cell-cell communication and analyzed GRNN structural changes via mutagenesis.

Main Results:

  • Successfully extracted and validated a GRNN for pyocyanin production, demonstrating its computational accuracy.
  • Mutagenesis studies revealed how structural GRNN changes affect bacterial computing behavior.
  • Ecosystem modeling showed that cell-cell communication impacts GRNN computing reliability, with GRNNs clustering into perceptron-like units.

Conclusions:

  • Bacterial GRNs can be modeled as GRNNs, revealing their natural computing capabilities.
  • This framework provides insights into bacterial adaptation, virulence, and ecosystem dynamics.
  • The study lays groundwork for molecular machine learning and living artificial intelligence systems.