Genetic codes optimized as a traveling salesman problem


Autoři: Oliver Attie aff001;  Brian Sulkow aff001;  Chong Di aff001;  Weigang Qiu aff001
Působiště autorů: Department of Biological Sciences, Hunter College, City University of New York, New York, United States of America aff001;  Graduate Center, City University of New York, New York, United States of America aff002;  Department of Physiology and Biophysics & Institute for Computational Biomedicine, Weil Cornell Medical College, New York, New York, United States of America aff003
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0224552

Souhrn

The Standard Genetic Code (SGC) is robust to mutational errors such that frequently occurring mutations minimally alter the physio-chemistry of amino acids. The apparent correlation between the evolutionary distances among codons and the physio-chemical distances among their cognate amino acids suggests an early co-diversification between the codons and amino acids. Here we formulated the co-minimization of evolutionary distances between codons and physio-chemical distances between amino acids as a Traveling Salesman Problem (TSP) and solved it with a Hopfield neural network. In this unsupervised learning algorithm, macromolecules (e.g., tRNAs and aminoacyl-tRNA synthetases) associating codons with amino acids were considered biological analogs of Hopfield neurons associating “tour cities” with “tour positions”. The Hopfield network efficiently yielded an abundance of genetic codes that were more error-minimizing than SGC and could thus be used to design artificial genetic codes. We further argue that as a self-optimization algorithm, the Hopfield neural network provides a model of origin of SGC and other adaptive molecular systems through evolutionary learning.

Klíčová slova:

Evolutionary genetics – Genetic networks – Learning – Natural selection – Neural networks – Neurons – Transfer RNA – Genetic code


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