Genetic algorithms applied to the investigation of genetic code adaptability

Author(s)

Lariza Laura de Oliveira

• Document type: Doctoral Thesis
• Press: São Paulo.
• Institution: Universidade de São Paulo (USP). Instituto de Matemática e Estatística (IME/SBI)
• Defense date: 2015-11-30

Examining board members

Renato Tinós; Marcelo Mendes Brandão; Odemir Martinez Bruno; André Carlos Ponce de Leon Ferreira de Carvalho; Sergio Russo Matioli

• Advisor: Renato Tinós

Abstract

The genetic code is highly preserved and it is present in most living organisms. If we consider all codes mapping the 64 codes into 20 amino acids and one stop codon, there are more than 1.51 × 1084 possible genetic codes. The main question related to the organization of the genetic code is why exactly the standard code was selected among this huge number of possible genetic codes.The hypothesis that the genetic code has evolved is supported by its robustness against mutations. Many researchers argue that the organization of the standard code is a product of natural selection and that the codes robustness against mutations would support this hypothesis. Two methodologies have been used to investigate this hypothesis: the first one is the statistical approach which estimates the number of random codes which are better than the standard genetic code. The second is the engineering approach, which compare the standard code with the best hypothetical codes obtained by an optimization algorithm. Both approaches have been used considering only one objective function, which is usually based on the robustness against changes using the polar requirement. In this research, we propose to consider more than one objective simultaneously for the evaluation of genetic codes. For this purpose, three approaches using multi-objective genetic algorithms were employed, are they: lexicographic, weighted, and Pareto-based. The results indicate that considering more than one objective function is promising: the hypothetical codes generated are more similar to the standard genetic code, when compared with the results obtained by the monoobjective approach. (AU)

• FAPESP's process: 11/00561-7 - Use of Self Organizing Evolutionary Algorithms in the Investigation of the Adaptability of the Genetic Code
• Grantee: Lariza Laura de Oliveira
• Support Opportunities: Scholarships in Brazil - Doctorate