Genetic codes optimized as a traveling salesman problem
| Autor: | Wei-Gang Qiu, Brian Sulkow, Oliver Attie, Chong Di |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Evolutionary Genetics
0301 basic medicine Chemical Phenomena Computer science Gene Identification and Analysis Social Sciences Genetic Networks medicine.disease_cause Biochemistry Travelling salesman problem Hopfield network Learning and Memory 0302 clinical medicine Animal Cells Natural Selection Psychology Amino Acids Phylogeny Neurons chemistry.chemical_classification Mutation Multidisciplinary Artificial neural network Genetic code Amino acid Nucleic acids Genetic Code Mutation (genetic algorithm) Medicine Transfer RNA Cellular Types Hydrophobic and Hydrophilic Interactions Network Analysis Research Article Computer and Information Sciences Evolutionary Processes Neural Networks Science Computational biology Evolution Molecular 03 medical and health sciences Phylogenetics Genetics medicine Learning Selection Genetic Non-coding RNA Selection (genetic algorithm) Evolutionary Biology Models Genetic Cognitive Psychology Biology and Life Sciences Cell Biology 030104 developmental biology chemistry Cellular Neuroscience RNA Cognitive Science 030217 neurology & neurosurgery Neuroscience |
| Zdroj: | PLoS ONE PLoS ONE, Vol 14, Iss 10, p e0224552 (2019) |
| ISSN: | 1932-6203 |
| DOI: | 10.1371/journal.pone.0224552 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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