进化中的偶然性与必然性--从RNA的启示
Chance and Necessity in Evolution: Lessons from RNA
摘要：
RNA中序列和二级结构或形状之间的关系表现出强大的统计特性，概括为三个概念：（1）典型形状的概念（在所有固定长度的序列中，某些形状比其他形状更频繁地实现），（2）形状空间覆盖的概念（所有典型的形状都是在任何随机序列的一个小邻域中实现的），以及（3）中性网络的概念（折叠成相同典型形状的序列形成渗透通过序列空间的网络）。神经网络放松了对变异率的要求，以保持选择的有效性。原始的（基因型）错误阈值必须根据表型错误阈值重新制定。关于适应，中性有两个看似矛盾的作用：它作为一个缓冲区，以防止突变，确保表型被保留。然而，它在很大程度上是可行的，因为它允许序列背景默默地变化，直到一个点突变可以成为表型上的后果，从而允许进化变化发生。中立性也以看似矛盾的方式影响适应性轨迹的可预测性。一方面，它增加了他们的基因型痕迹的不确定性。与此同时，中性结构从一种形状到另一种形状的访问，从而诱导RNA形状之间的拓扑结构，该拓扑结构允许连续和不连续的形状转换之间的区别。在适应性轨迹必须经历这种转变的程度上，它们的表型轨迹变得更可预测。
英文摘要：
The relationship between sequences and secondary structures or shapes in RNA exhibits robust statistical properties summarized by three notions: (1) the notion of a typical shape (that among all sequences of fixed length certain shapes are realized much more frequently than others), (2) the notion of shape space covering (that all typical shapes are realized in a small neighborhood of any random sequence), and (3) the notion of a neutral network (that sequences folding into the same typical shape form networks that percolate through sequence space). Neutral networks loosen the requirements on the mutation rate for selection to remain effective. The original (genotypic) error threshold has to be reformulated in terms of a phenotypic error threshold. With regard to adaptation, neutrality has two seemingly contradictory effects: It acts as a buffer against mutations ensuring that a phenotype is preserved. Yet it is deeply enabling, because it permits evolutionary change to occur by allowing the sequence context to vary silently until a single point mutation can become phenotypically consequential. Neutrality also influences predictability of adaptive trajectories in seemingly contradictory ways. On the one hand it increases the uncertainty of their genotypic trace. At the same time neutrality structures the access from one shape to another, thereby inducing a topology among RNA shapes which permits a distinction between continuous and discontinuous shape transformations. To the extent that adaptive trajectories must undergo such transformations, their phenotypic trace becomes more predictable.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
