TY - GEN
T1 - The impact of the transversion/transition ratio on the optimal genetic code graph partition
AU - Aloqalaa, Daniyah A.
AU - Kowalski, Dariusz R.
AU - Błazej, Paweł
AU - Wnetrzak, Małgorzata
AU - Mackiewicz, Dorota
AU - Mackiewicz, Paweł
N1 - Publisher Copyright:
© 2019 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.
PY - 2019
Y1 - 2019
N2 - The standard genetic code (SGC) is a system of rules ascribing 20 amino acids and stop translation signal to 64 codons, i.e triplets of nucleotides. It was proposed that the structure of the SGC evolved to minimize harmful consequences of mutations and translational errors. To study this problem, we described the SGC structure by a graph, in which codons are vertices and edges correspond to single nucleotide mutations occurring between the codons. We also introduced weights (W) for mutation types to distinguish transversions from transitions. Using this representation, the SGC is a partition of the set of vertices into 21 disjoint subsets. In this case, the question about the potential robustness of the genetic code to the mutations can be reformulated into the optimal graph clustering task. To investigate this problem, we applied an appropriate clustering algorithm, which searched for the codes characterized by the minimum average calculated from the set W-conductance of codon groups. Our algorithm found three best codes for various ranges of the applied weights. The average W-conductance of the SGC was the most similar to that of the best codes in the range of weights corresponding to the observed transversion/transition ratio in natural mutational pressures. However, it should be noted that the optimization of the SGC was not as perfect as the best codes. It implies that the evolution of the SGC was driven not only by the selection for the robustness against mutations or mistranslations but also other factors, e.g. subsequent addition of amino acids to the code according to the expansion of amino acid metabolic pathways.
AB - The standard genetic code (SGC) is a system of rules ascribing 20 amino acids and stop translation signal to 64 codons, i.e triplets of nucleotides. It was proposed that the structure of the SGC evolved to minimize harmful consequences of mutations and translational errors. To study this problem, we described the SGC structure by a graph, in which codons are vertices and edges correspond to single nucleotide mutations occurring between the codons. We also introduced weights (W) for mutation types to distinguish transversions from transitions. Using this representation, the SGC is a partition of the set of vertices into 21 disjoint subsets. In this case, the question about the potential robustness of the genetic code to the mutations can be reformulated into the optimal graph clustering task. To investigate this problem, we applied an appropriate clustering algorithm, which searched for the codes characterized by the minimum average calculated from the set W-conductance of codon groups. Our algorithm found three best codes for various ranges of the applied weights. The average W-conductance of the SGC was the most similar to that of the best codes in the range of weights corresponding to the observed transversion/transition ratio in natural mutational pressures. However, it should be noted that the optimization of the SGC was not as perfect as the best codes. It implies that the evolution of the SGC was driven not only by the selection for the robustness against mutations or mistranslations but also other factors, e.g. subsequent addition of amino acids to the code according to the expansion of amino acid metabolic pathways.
KW - Code Degeneracy
KW - Graph Theory
KW - Mutation
KW - Set Conductance
KW - Standard Genetic Code
KW - Transition
KW - Transversion
UR - http://www.scopus.com/inward/record.url?scp=85064714130&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85064714130&partnerID=8YFLogxK
U2 - 10.5220/0007381000550065
DO - 10.5220/0007381000550065
M3 - Conference contribution
AN - SCOPUS:85064714130
T3 - BIOINFORMATICS 2019 - 10th International Conference on Bioinformatics Models, Methods and Algorithms, Proceedings; Part of 12th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2019
SP - 55
EP - 65
BT - BIOINFORMATICS 2019 - 10th International Conference on Bioinformatics Models, Methods and Algorithms, Proceedings; Part of 12th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2019
A2 - De Maria, Elisabetta
A2 - Fred, Ana
A2 - Gamboa, Hugo
PB - SciTePress
T2 - 10th International Conference on Bioinformatics Models, Methods and Algorithms, BIOINFORMATICS 2019 - Part of 12th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2019
Y2 - 22 February 2019 through 24 February 2019
ER -