Does the genetic code have a eukaryotic origin?

A multiobjective approach to the genetic code adaptability problem

BACKGROUND

The organization of the canonical code has intrigued researches since it was first described. If we consider all codes mapping the 64 codes into 20 amino acids and one stop codon, there are more than 1.51×10(84) possible genetic codes. The main question related to the organization of the genetic code is why exactly the canonical code was selected among this huge number of possible genetic codes. Many researchers argue that the organization of the canonical code is a product of natural selection and that the code's robustness against mutations would support this hypothesis. In order to investigate the natural selection hypothesis, some researches employ optimization algorithms to identify regions of the genetic code space where best codes, according to a given evaluation function, can be found (engineering approach). The optimization process uses only one objective to evaluate the codes, generally based on the robustness for an amino acid property. Only one objective is also employed in the statistical approach for the comparison of the canonical code with random codes. We propose a multiobjective approach where two or more objectives are considered simultaneously to evaluate the genetic codes.

RESULTS

In order to test our hypothesis that the multiobjective approach is useful for the analysis of the genetic code adaptability, we implemented a multiobjective optimization algorithm where two objectives are simultaneously optimized. Using as objectives the robustness against mutation with the amino acids properties polar requirement (objective 1) and robustness with respect to hydropathy index or molecular volume (objective 2), we found solutions closer to the canonical genetic code in terms of robustness, when compared with the results using only one objective reported by other authors.

CONCLUSIONS

Using more objectives, more optimal solutions are obtained and, as a consequence, more information can be used to investigate the adaptability of the genetic code. The multiobjective approach is also more natural, because more than one objective was adapted during the evolutionary process of the canonical genetic code. Our results suggest that the evaluation function employed to compare genetic codes should consider simultaneously more than one objective, in contrast to what has been done in the literature.

Ribogenomics: the science and knowledge of RNA

Ribonucleic acid (RNA) deserves not only a dedicated field of biological research — a discipline or branch of knowledge — but also explicit definitions of its roles in cellular processes and molecular mechanisms. Ribogenomics is to study the biology of cellular RNAs, including their origin, biogenesis, structure and function. On the informational track, messenger RNAs (mRNAs) are the major component of ribogenomes, which encode proteins and serve as one of the four major components of the translation machinery and whose expression is regulated at multiple levels by other operational RNAs. On the operational track, there are several diverse types of RNAs — their length distribution is perhaps the most simplistic stratification — involving in major cellular activities, such as chromosomal structure and organization, DNA replication and repair, transcriptional/post-transcriptional regulation, RNA processing and routing, translation and cellular energy/metabolism regulation. An all-out effort exceeding the magnitude of the Human Genome Project is of essence to construct just mammalian transcriptomes in multiple contexts including embryonic development, circadian and seasonal rhythms, defined life-span stages, pathological conditions and anatomy-driven tissue/organ/cell types.