Evolutionary algorithms applied to the investigation of genetic code adaptability

• Grant number: 12/24559-4
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate
• Start date: July 01, 2013
• End date: June 30, 2014
• Field of knowledge: Physical Sciences and Mathematics - Computer Science
• Principal Investigator: Renato Tinós
• Grantee: Lariza Laura de Oliveira
• Supervisor: Alex Freitas
• Host Institution: Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto (FFCLRP). Universidade de São Paulo (USP). Ribeirão Preto , SP, Brazil
• Institution abroad: University of Kent, England
• Associated to the scholarship: 11/00561-7 - Use of Self Organizing Evolutionary Algorithms in the Investigation of the Adaptability of the Genetic Code, BP.DR

Abstract

The canonical genetic code is almost universal and is present in most complex organisms. A question that has intrigued researchers is why exactly this code was chosen instead of another one. Many researchers believe that the genetic code is a product of natural selection, what is supported when the canonical genetic code is compared with other hypothetical codes considering some evaluation functions like the robustness against mutations. In order to validate this hypothesis, optimization algorithms have been used to identify regions of the genetic code space where best codes, according to a given evaluation function, can be found. However, these optimization algorithms have been used considering only one objective function, which is based on the robustness against mutations when amino acids properties like polar requirement are considered. The main objective of this project is use multi-objective approaches to consider more than one objective simultaneously in the optimization process. Hence, new amino acids properties will be studied. This project will be developed in the School of Computing, in the Kent University, located in Canterbury, Kent, England. The project will be supervised by the professor Alex Freitas, who is a Reader in Computational Intelligence and have experience in Evolutionary computation and Bioinformatics. (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

DE OLIVEIRA, LARIZA LAURA; FREITAS, ALEX A.; TINOS, RENATO. Multi-objective genetic algorithms in the study of the genetic code's adaptability. INFORMATION SCIENCES, v. 425, p. 48-61, JAN 2018. (12/24559-4)

DE OLIVEIRA, LARIZA LAURA; DE OLIVEIRA, PAULO S. L.; TINOS, RENATO. A multiobjective approach to the genetic code adaptability problem. BMC Bioinformatics, v. 16, FEB 19 2015. (12/24559-4, 11/00561-7)