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A Statistical Analysis of the Robustness of Alternate Genetic Coding Tables

机译:备用遗传编码表的鲁棒性的统计分析

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The rules that specify how the information contained in DNA is translated into amino acid “language” during protein synthesis are called “the genetic code”, commonly called the “Standard” or “Universal” Genetic Code Table. As a matter of fact, this coding table is not at all “universal”: in addition to different genetic code tables used by different organisms, even within the same organism the nuclear and mitochondrial genes may be subject to two different coding tables. Results In an attempt to understand the advantages and disadvantages these coding tables may bring to an organism, we have decided to analyze various coding tables on genes subject to mutations, and have estimated how these genes “survive” over generations. We have used this as indicative of the “evolutionary” success of that particular coding table. We find that the “standard” genetic code is not actually the most robust of all coding tables, and interestingly, Flatworm Mitochondrial Code (FMC) appears to be the highest ranking coding table given our assumptions. Conclusions It is commonly hypothesized that the more robust a genetic code, the better suited it is for maintenance of the genome. Our study shows that, given the assumptions in our model, Standard Genetic Code is quite poor when compared to other alternate code tables in terms of robustness. This brings about the question of why Standard Code has been so widely accepted by a wider variety of organisms instead of FMC, which needs to be addressed for a thorough understanding of genetic code evolution.
机译:指定在蛋白质合成过程中将DNA中包含的信息如何转换为氨基酸“语言”的规则称为“遗传密码”,通常称为“标准”或“通用”遗传密码表。实际上,该编码表根本不是“通用的”:除了不同生物使用的不同遗传密码表外,即使在同一生物内,核和线粒体基因也可能要使用两个不同的编码表。结果为了试图理解这些编码表可能给有机体带来的利弊,我们决定分析关于突变基因的各种编码表,并估计这些基因如何在世代中“生存”。我们以此来表示该特定编码表的“进化”成功。我们发现“标准”遗传密码实际上并不是所有编码表中最强大的,有趣的是,根据我们的假设,扁虫线粒体密码(FMC)似乎是排名最高的编码表。结论通常认为,遗传密码越健壮,就越适合维护基因组。我们的研究表明,考虑到我们模型中的假设,与健壮性相比,其他遗传密码表与标准遗传密码相比非常差。这就提出了一个问题,为什么标准密码而不是FMC为何已被更广泛的生物体如此广泛地接受,而对于彻底理解遗传密码的进化则需要解决这一问题。

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