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Multiobjective Evolutionary Algorithms Applied to the Optimization of Expanded Genetic Codes

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Intelligent Systems (BRACIS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14197))

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Abstract

There is great interest in the creation of genetically modified organisms that use amino acids different from the naturally encoded amino acids. Unnatural amino acids have been incorporated into genetically modified organisms to develop new drugs, fuels and chemicals. When incorporating new amino acids, it is necessary to change the standard genetic code. Expanded genetic codes have been created without considering the robustness of the code. In this work, multi-objective genetic algorithms are proposed for the optimization of expanded genetic codes. Two different approaches are compared: weighted and Pareto. The expanded codes are optimized in relation to the frequency of replaced codons and two measures based on robustness (for polar requirement and molecular volume). The experiments indicate that multi-objective approaches allow to obtain a list of expanded genetic codes optimized according to combinations of the three objectives. Thus, specialists can choose an optimized solution according to their needs.

This work was partially supported by São Paulo Research Foundation - FAPESP (under grants #2021/09720-2 and #2013/07375-0), National Council for Scientific and Technological Development - CNPq (under grant #306689/2021-9), and Center for Artificial Intelligence - C4AI (supported by FAPESP, under grant #2019/07665-4, and IBM Corporation).

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Notes

  1. 1.

    The binary encoding of the chromosome is used here because we consider only one unnatural amino acid. If more than one new amino acid is considered, the integer encoding must be used, where integer \(i>0\) represents the i-th new amino acid. The only modification needed in this case is in the way the new chromosomes are generated and mutated.

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Authors and Affiliations

  1. Department of Computing and Mathematics, FFCLRP, University of São Paulo, Ribeirão Preto, Brazil

    Maísa de Carvalho Silva, Paulo Guilherme Pinheiro Pereira, Lariza Laura de Oliveira & Renato Tinós

Authors
  1. Maísa de Carvalho Silva
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  2. Paulo Guilherme Pinheiro Pereira
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  3. Lariza Laura de Oliveira
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  4. Renato Tinós
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Corresponding author

Correspondence to Renato Tinós .

Editor information

Editors and Affiliations

  1. Federal University of São Carlos, São Carlos, Brazil

    Murilo C. Naldi

  2. Centro Universitario da FEI, São Bernardo do Campo, Brazil

    Reinaldo A. C. Bianchi

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de Carvalho Silva, M., Pereira, P.G.P., de Oliveira, L.L., Tinós, R. (2023). Multiobjective Evolutionary Algorithms Applied to the Optimization of Expanded Genetic Codes. In: Naldi, M.C., Bianchi, R.A.C. (eds) Intelligent Systems. BRACIS 2023. Lecture Notes in Computer Science(), vol 14197. Springer, Cham. https://doi.org/10.1007/978-3-031-45392-2_1

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  • DOI: https://doi.org/10.1007/978-3-031-45392-2_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-45391-5

  • Online ISBN: 978-3-031-45392-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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