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Evolution of communication protocols using an artificial regulatory network.

W Garrett Mitchener1

  • 1College of Charleston.

Artificial Life
|August 23, 2014
PubMed
Summary
This summary is machine-generated.

Evolutionary computation using the Utrecht Machine (UM) demonstrates how artificial regulatory networks develop communication mechanisms. Gene duplication and recombination accelerate this process, revealing novel evolutionary dynamics.

Keywords:
Virtual machinecommunicationevolutiongene duplicationrecombinationregulatory network

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Area of Science:

  • Computational Biology
  • Evolutionary Computation
  • Artificial Life

Background:

  • Studying the evolution of complex biological systems requires robust computational models.
  • Artificial regulatory networks offer a platform for simulating evolutionary processes.
  • Understanding the emergence of biochemical computation is a key challenge.

Purpose of the Study:

  • To investigate how evolution discovers biochemical computation mechanisms using a discrete artificial regulatory network.
  • To analyze the role of genetic operations like deletion, duplication, and recombination in evolving complex functions.
  • To explore the dynamics of evolving communication systems with a chicken-and-egg problem structure.

Main Methods:

  • Development of the Utrecht Machine (UM), a discrete artificial regulatory network with a binary genome.
  • Simulation of a sender-receiver agent using two UMs to encode, transmit, and decode binary words.
  • In-depth case study and analysis of 60,000 simulation runs with varied parameter settings.

Main Results:

  • Evolutionary processes, including gene duplication and recombination, were observed to develop communication mechanisms.
  • Stronger selection pressures resulted in slower evolution and less efficient solutions.
  • Implicit selection for robust mechanisms and genomes at the codon level was identified.
  • Populations were observed to cross fitness valleys through outlying members, indicating complex evolutionary landscapes.
  • Emergent phenomena like back mutations and population-level memory effects were noted.

Conclusions:

  • The Utrecht Machine effectively models the evolution of biochemical computation and communication.
  • Gene duplication and recombination are crucial for enlarging regulatory networks and accelerating evolution.
  • Existing population genetics models may need refinement to incorporate regulatory network structure and observed evolutionary phenomena.