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Relating SARS-CoV-2 variants using cellular automata imaging.

Luryane F Souza1,2, Tarcísio M Rocha Filho3, Marcelo A Moret4,5

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This study uses cellular automata to model SARS-CoV-2 variants, visualizing spike protein evolution. The method effectively classifies virus variants based on common ancestors and mutations, offering an alternative to phylogenetic trees.

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Area of Science:

  • Computational Biology
  • Virology
  • Information Theory

Background:

  • The SARS-CoV-2 virus has evolved into numerous variants.
  • Understanding the evolutionary relationships and mutations of these variants is crucial for public health.
  • Existing methods for variant classification, like phylogenetic trees, can be complex.

Purpose of the Study:

  • To develop a novel computational method for classifying SARS-CoV-2 variants.
  • To visualize and analyze the evolution of the SARS-CoV-2 spike protein using cellular automata.
  • To assess the utility of this method as an alternative to traditional phylogenetic analysis.

Main Methods:

  • Representing SARS-CoV-2 biological sequences as digital sequences.
  • Employing cellular automata with a specific rule to simulate sequence evolution.
  • Analyzing the spike protein's visual representation derived from cellular automaton evolution.
  • Utilizing information theory and Hamming distance to compare variant evolution stages.

Main Results:

  • The cellular automaton evolution provides a visible representation of key spike protein features.
  • Hamming distance analysis successfully grouped variants with shared ancestry and mutations.
  • The proposed method demonstrated the ability to classify and cluster SARS-CoV-2 variants effectively.

Conclusions:

  • Cellular automata offer a simplified yet effective approach for SARS-CoV-2 variant classification.
  • This computational method can serve as a viable alternative for constructing phylogenetic trees.
  • The study highlights the potential of information theory and computational modeling in virology.