Optimization of Expanded Genetic Codes via Genetic Algorithms

  • Maísa de Carvalho Silva USP
  • Lariza Laura de Oliveira USP
  • Renato Tinós USP


In the last decades, researchers have proposed the use of genetically modified organisms that utilize unnatural amino acids, i.e., amino acids other than the 20 amino acids encoded in the standard genetic code. Unnatural amino acids have been incorporated into genetically engineered organisms for the development of new drugs, fuels and chemicals. When new amino acids are incorporated, it is necessary to modify the standard genetic code. Expanded genetic codes have been created without considering the robustness of the code. The objective of this work is the use of genetic algorithms (GAs) for the optimization of expanded genetic codes. The GA indicates which codons of the standard genetic code should be used to encode a new unnatural amino acid. The fitness function has two terms; one for robustness of the new code and another that takes into account the frequency of use of amino acids. Experiments show that, by controlling the weighting between the two terms, it is possible to obtain more or less amino acid substitutions at the same time that the robustness is minimized.


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SILVA, Maísa de Carvalho; DE OLIVEIRA, Lariza Laura; TINÓS, Renato. Optimization of Expanded Genetic Codes via Genetic Algorithms. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 15. , 2018, São Paulo. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2018 . p. 473-484. ISSN 2763-9061. DOI: https://doi.org/10.5753/eniac.2018.4440.