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.


ANDERSON, J. C. et al. (2004). “An expanded genetic code with a functional quadruplet codon”, PNAS, 101(20): 7566-7571, 2004.

FREELAND, S. J. & HURST, L. D. (1998). “The genetic code is one in a million”, Journal of Molecular Evolution, 47(3): 238–248.

FREITAS A. A. (2004). “A critical review of multi-objective optimisation in data mining: a position paper”, ACM SIGKDD Explorations, 6: 77-86.

HAIG, D. & HURST, L. D. (1991). “A quantitative measure of error minimization in the genetic code”, Journal of Molecular Evolution, 33: 412–417.

LEHNINGER, A. L.; NELSON, D. L. & COX, M. M. (2005). “Lehninger Principles Of Biochemistry”, 4th ed., Freeman.

LIU, C. C., & SCHULTZ, P. G. (2010). “Adding new chemistries to the genetic code”, Annual Review of Biochemistry, 79: 413-444. MALOY, S. R.; STEWART, V. J. & TAYLOR, R. K. (1996). “Genetic analysis of pathogenic bacteria: a laboratory manual”, Cold Spring Harbor Laboratory Press.

MITCHELL, M. (1996). “An introduction to genetic algorithms”, MIT Press.

OLIVEIRA, L. L. (2015). “Algoritmos Evolutivos Aplicados na Investigação da Adaptabilidade do Código Genético”, Tese de Doutorado, Pós-Graduação em Bioinformática, Universidade de São Paulo.

OLIVEIRA, L. L. & TINÓS, R. (2014). “Entropy-based evaluation function in a multiobjective approach for the investigation of genetic code robustness”. Memetic Computing, 6: 157-170.

OLIVEIRA, L. L.; OLIVEIRA, P. S. L. & TINÓS, R. (2015). “A multiobjective approach to the genetic code adaptability problem”, BMC Bioinformatics, 16(52).

OLIVEIRA, L. L.; FREITAS, A. A. & TINÓS, R. (2017). “Multi-objective genetic algorithms in the study of the genetic code’s adaptability”, Information Sciences, 425: 48-61.

ROVNER, A. J. et al. (2015). "Recoded organisms engineered to depend on synthetic amino acids", Nature, 518: 89:93.

VOGEL, G. (1998). “Tracking the history of the genetic code”, Science, 281: 329-331.

XIAO, H., & SCHULTZ, P. G. (2016). “At the interface of chemical and biological synthesis: an expanded genetic code”, Cold Spring Harbor Perspectives in Biology, 8(9): a023945.

YOCKEY, H. P. (2005). “Information Theory, Evolution, and the Origin of Life”, Cambridge University Press, NY.

ZHANG, Y. et al. (2017). “A semi-synthetic organism that stores and retrieves increased genetic information”, Nature, 551(7682): 644.

Como Citar

Selecione um Formato
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.