Inclusion of the fitness sharing technique in an evolutionary algorithm to analyze the fitness landscape of the genetic code adaptability

Abstract

BackgroundThe canonical code, although prevailing in complex genomes, is not universal. It was shown the canonical genetic code superior robustness compared to random codes, but it is not clearly determined how it evolved towards its current form.The error minimization theory considers the minimization of point mutation adverse effect as the main selection factor in the evolution of the code. We have used simulated evolution in a computer to search for optimized codes, which helps to obtain information about the optimization level of the canonical code in its evolution.A genetic algorithm searches for efficient codes in a fitness landscape that corresponds with the adaptability of possible hypothetical genetic codes. The lower the effects of errors or mutations in the codon bases of a hypothetical code, the more efficient or optimal is that code.The inclusion of the fitness sharing technique in the evolutionary algorithm allows the extent to which the canonical genetic code is in an area corresponding to a deep local minimum to be easily determined, even in the high dimensional spaces considered.ResultsThe analyses show that the canonical code is not in a deep local minimum and that the fitness landscape is not a multimodal fitness landscape with deep and separated peaks. Moreover, the canonical code is clearly far away from the areas of higher fitness in the landscape.ConclusionsGiven the non-presence of deep local minima in the landscape, although the code could evolve and different forces could shape its structure, the fitness landscape nature considered in the error minimization theory does not explain why the canonical code ended its evolution in a location which is not an area of a localized deep minimum of the huge fitness landscape.

The influence of different types of translational inaccuracies on the genetic code structure

“…In terms of minimization of amino acid replacements resulting from point mutations in codons, measured here by the average conductance, the SGC is placed between the M 1 codes, characterized by the lowest conductance, and the codes from the M 2 and M 3 models. In agreement with our simulation study, other analyses also showed that the SGC is not perfectly optimized in this respect and better codes can be found [11,44,50,57,58,[87][88][89]. Therefore, it is possible that the assignments of amino acids to codons occurred in accordance with other mechanisms, while the minimization of mutation errors was adjusted by the direct optimization of the mutational pressure around the established genetic code [90][91][92][93][94].…”

Optimization of the standard genetic code according to three codon positions using an evolutionary algorithm

“…The random codes represent only a very tiny fraction of all possibilities and are not necessarily representative of the whole space of the theoretical codes. The EA technique was already successfully applied in various studies on the optimality of the genetic code [ 23 , 24 , 59 – 62 ].…”

Some theoretical aspects of reprogramming the standard genetic code

“…This assumption follows the adaptation hypothesis, which claims that the SGC evolved to minimize harmful consequences of mutations or mistranslations of coded proteins [Woese, 1965, Sonneborn, 1965, Epstein, 1966, Goldberg and Wittes, 1966, Haig and Hurst, 1991, Freeland and Hurst, 1998, Freeland et al, 2000, Gilis et al, 2001. Although this code did not turn out perfectly optimized in this respect [B lażej et al, 2018a, B lażej et al, 2016, Massey, 2008, Novozhilov et al, 2007, Santos et al, 2011, Santos and Monteagudo, 2017, B lażej et al, 2018b, B lażej et al, 2019b, Wnetrzak et al, 2019, it shows a general tendency to error minimization in the global scale. This property is better exhibited by its alternative versions [B lażej et al, 2018c, B lażej et al, 2019a], which occurred later in the evolution.…”