%0 Journal Article %1 Santos2011Simulated %A Santos, Jose %A Monteagudo, Angel %D 2011 %J BMC Bioinformatics %K genetic-code in-silico-evolution optimal %N 1 %P 56+ %R 10.1186/1471-2105-12-56 %T Simulated evolution applied to study the genetic code optimality using a model of codon reassignments %U http://dx.doi.org/10.1186/1471-2105-12-56 %V 12 %X BACKGROUND:As the canonical code is not universal, different theories about its origin and organization have appeared. The optimization or level of adaptation of the canonical genetic code was measured taking into account the harmful consequences resulting from point mutations leading to the replacement of one amino acid for another. There are two basic theories to measure the level of optimization: the statistical approach, which compares the canonical genetic code with many randomly generated alternative ones, and the engineering approach, which compares the canonical code with the best possible alternative.RESULTS:Here we used a genetic algorithm to search for better adapted hypothetical codes and as a method to guess the difficulty in finding such alternative codes, allowing to clearly situate the canonical code in the fitness landscape. This novel proposal of the use of evolutionary computing provides a new perspective in the open debate between the use of the statistical approach, which postulates that the genetic code conserves amino acid properties far better than expected from a random code, and the engineering approach, which tends to indicate that the canonical genetic code is still far from optimal. We used two models of hypothetical codes: one that reflects the known examples of codon reassignment and the model most used in the two approaches which reflects the current genetic code translation table. Although the standard code is far from a possible optimum considering both models, when the more realistic model of the codon reassignments was used, the evolutionary algorithm had more difficulty to overcome the efficiency of the canonical genetic code.CONCLUSIONS:Simulated evolution clearly reveals that the canonical genetic code is far from optimal regarding its optimization. Nevertheless, the efficiency of the canonical code increases when mistranslations are taken into account with the two models, as indicated by the fact that the best possible codes show the patterns of the standard genetic code. Our results are in accordance with the postulates of the engineering approach and indicate that the main arguments of the statistical approach are not enough to its assertion of the extreme efficiency of the canonical genetic code.

@article{Santos2011Simulated, abstract = {{BACKGROUND}:As the canonical code is not universal, different theories about its origin and organization have appeared. The optimization or level of adaptation of the canonical genetic code was measured taking into account the harmful consequences resulting from point mutations leading to the replacement of one amino acid for another. There are two basic theories to measure the level of optimization: the statistical approach, which compares the canonical genetic code with many randomly generated alternative ones, and the engineering approach, which compares the canonical code with the best possible {alternative.RESULTS}:Here we used a genetic algorithm to search for better adapted hypothetical codes and as a method to guess the difficulty in finding such alternative codes, allowing to clearly situate the canonical code in the fitness landscape. This novel proposal of the use of evolutionary computing provides a new perspective in the open debate between the use of the statistical approach, which postulates that the genetic code conserves amino acid properties far better than expected from a random code, and the engineering approach, which tends to indicate that the canonical genetic code is still far from optimal. We used two models of hypothetical codes: one that reflects the known examples of codon reassignment and the model most used in the two approaches which reflects the current genetic code translation table. Although the standard code is far from a possible optimum considering both models, when the more realistic model of the codon reassignments was used, the evolutionary algorithm had more difficulty to overcome the efficiency of the canonical genetic {code.CONCLUSIONS}:Simulated evolution clearly reveals that the canonical genetic code is far from optimal regarding its optimization. Nevertheless, the efficiency of the canonical code increases when mistranslations are taken into account with the two models, as indicated by the fact that the best possible codes show the patterns of the standard genetic code. Our results are in accordance with the postulates of the engineering approach and indicate that the main arguments of the statistical approach are not enough to its assertion of the extreme efficiency of the canonical genetic code.}, added-at = {2018-12-02T16:09:07.000+0100}, author = {Santos, Jose and Monteagudo, Angel}, biburl = {https://www.bibsonomy.org/bibtex/274d433c3e1c168efb3c43c9c41caf0a6/karthikraman}, citeulike-article-id = {8865105}, citeulike-linkout-0 = {http://dx.doi.org/10.1186/1471-2105-12-56}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/21338505}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=21338505}, doi = {10.1186/1471-2105-12-56}, interhash = {87f2e00c16db30d0d77c46fc0f3bd713}, intrahash = {74d433c3e1c168efb3c43c9c41caf0a6}, issn = {1471-2105}, journal = {BMC Bioinformatics}, keywords = {genetic-code in-silico-evolution optimal}, number = 1, pages = {56+}, pmid = {21338505}, posted-at = {2011-02-22 09:45:30}, priority = {2}, timestamp = {2018-12-02T16:09:07.000+0100}, title = {Simulated evolution applied to study the genetic code optimality using a model of codon reassignments}, url = {http://dx.doi.org/10.1186/1471-2105-12-56}, volume = 12, year = 2011 }