Analysis of the optimality of the Standard Genetic Code.
Many theories have been proposed attempting to explain the origin of the genetic code. While strong reasons remain to believe that the genetic code evolved as a frozen accident, at least for the first few amino acids, other theories remain viable. In this work, we test the optimality of the standard genetic code against approximately 17 million genetic codes, and locate 18 which outperform the standard genetic code at the following three criteria: (a) robustness to point mutation; (b) robustness to frameshift mutation; and (c) ability to encode additional information in the coding region. We use a genetic algorithm to generate and score codes from different parts of the associated landscape, and are, as a result presumably more representative of the entire landscape. Our results show that while the genetic code is sub-optimal for robustness to frameshift mutation and the ability to encode additional information in the coding region, it is very strongly selected for robustness to point mutation. This coupled with the observation that the different performance indicator scores for a particular genetic code are seemingly negatively correlated, make the standard genetic code nearly optimal for the three criteria tested in this work.