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将生物序列组织成受限部分和非受限部分决定了基因型-表型图谱的基本特性。

The organization of biological sequences into constrained and unconstrained parts determines fundamental properties of genotype-phenotype maps.

作者信息

Greenbury S F, Ahnert S E

机构信息

Theory of Condensed Matter, Cavendish Laboratory, University of Cambridge, Cambridge CB3 0HE, UK.

Theory of Condensed Matter, Cavendish Laboratory, University of Cambridge, Cambridge CB3 0HE, UK

出版信息

J R Soc Interface. 2015 Dec 6;12(113):20150724. doi: 10.1098/rsif.2015.0724.

Abstract

Biological information is stored in DNA, RNA and protein sequences, which can be understood as genotypes that are translated into phenotypes. The properties of genotype-phenotype (GP) maps have been studied in great detail for RNA secondary structure. These include a highly biased distribution of genotypes per phenotype, negative correlation of genotypic robustness and evolvability, positive correlation of phenotypic robustness and evolvability, shape-space covering, and a roughly logarithmic scaling of phenotypic robustness with phenotypic frequency. More recently similar properties have been discovered in other GP maps, suggesting that they may be fundamental to biological GP maps, in general, rather than specific to the RNA secondary structure map. Here we propose that the above properties arise from the fundamental organization of biological information into 'constrained' and 'unconstrained' sequences, in the broadest possible sense. As 'constrained' we describe sequences that affect the phenotype more immediately, and are therefore more sensitive to mutations, such as, e.g. protein-coding DNA or the stems in RNA secondary structure. 'Unconstrained' sequences, on the other hand, can mutate more freely without affecting the phenotype, such as, e.g. intronic or intergenic DNA or the loops in RNA secondary structure. To test our hypothesis we consider a highly simplified GP map that has genotypes with 'coding' and 'non-coding' parts. We term this the Fibonacci GP map, as it is equivalent to the Fibonacci code in information theory. Despite its simplicity the Fibonacci GP map exhibits all the above properties of much more complex and biologically realistic GP maps. These properties are therefore likely to be fundamental to many biological GP maps.

摘要

生物信息存储在DNA、RNA和蛋白质序列中,这些序列可被理解为转化为表型的基因型。基因型-表型(GP)图谱的特性在RNA二级结构方面已得到详细研究。这些特性包括每个表型的基因型高度偏向分布、基因型稳健性与可进化性的负相关、表型稳健性与可进化性的正相关、形状空间覆盖,以及表型稳健性与表型频率大致呈对数比例关系。最近,在其他GP图谱中也发现了类似特性,这表明这些特性可能一般是生物GP图谱的基本特性,而非RNA二级结构图谱所特有。在此,我们提出上述特性源于生物信息在最广泛意义上的基本组织方式,即分为“受限”和“不受限”序列。我们将那些对表型影响更直接、因此对突变更敏感的序列描述为“受限”序列,例如蛋白质编码DNA或RNA二级结构中的茎。另一方面,“不受限”序列可以更自由地突变而不影响表型,例如内含子或基因间DNA或RNA二级结构中的环。为了检验我们的假设,我们考虑一种高度简化的GP图谱,其基因型具有“编码”和“非编码”部分。我们将其称为斐波那契GP图谱,因为它在信息论中相当于斐波那契编码。尽管其简单,但斐波那契GP图谱展现出了更为复杂且生物学上更现实的GP图谱的所有上述特性。因此,这些特性很可能是许多生物GP图谱的基本特性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8787/4707848/9ca65e417618/rsif20150724-g1.jpg

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