Symmetry and simplicity spontaneously emerge from the algorithmic nature of evolution.
Symmetry and simplicity spontaneously emerge from the algorithmic nature of evolution.
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DOI：
10.1073/pnas.2113883119
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发表时间：
2022-03-15
影响因子：
11.1
通讯作者：
Louis AA
中科院分区：
文献类型：
--
作者：
Johnston IG;Dingle K;Greenbury SF;Camargo CQ;Doye JPK;Ahnert SE;Louis AA
为什么进化倾向于对称结构，而它们只代表了所有可能形式的一小部分？就像猴子随机输入计算机语言会优先产生可以由较短算法生成的输出一样，算法信息论中的编码定理预测，当被发育过程解码时，随机突变会优先产生具有较短算法描述的表型。由于对称结构需要较少的信息进行编码，因此它们更有可能作为潜在的变体出现。结合频率到达机制，这种算法偏差预测了低复杂性（高对称性）表型比自然选择更普遍，也解释了在蛋白质复合物、RNA二级结构和基因调控网络中观察到的模式。工程师通常将系统设计成模块化和对称的，以增加对扰动的鲁棒性，并便于以后的更改。生物结构也经常表现出模块化和对称性，但这种趋势的起源却鲜为人知。通过类比工程设计，我们很容易认为对称和模块化是自然选择的结果。然而，与工程师不同，进化不能提前计划，因此这些特征也必须提供一些直接的选择优势，这与观察到对称性的系统的广度很难调和。在这里，我们介绍了一种基于进化算法的非适应性假设。这表明对称结构的优先出现不仅是由于自然选择，而且还因为它们需要较少的特定信息来编码，因此更有可能通过随机突变出现表型变异。算法信息论的论点可以形式化这种直觉，导致预测许多基因型-表现型图谱呈指数级偏向于描述复杂性较低的表现型。对对称的偏爱是这种对可压缩描述的偏爱的一种特殊情况。我们用大量的生物学数据验证了这些预测，表明蛋白质复合物、RNA二级结构和模型基因调控网络都表现出预期的指数倾向于更简单（和更对称）的表型。较低的描述复杂度也与较高的突变鲁棒性相关，这可能有助于多组件的复杂模块化装配的进化。
Why does evolution favor symmetric structures when they only represent a minute subset of all possible forms? Just as monkeys randomly typing into a computer language will preferentially produce outputs that can be generated by shorter algorithms, so the coding theorem from algorithmic information theory predicts that random mutations, when decoded by the process of development, preferentially produce phenotypes with shorter algorithmic descriptions. Since symmetric structures need less information to encode, they are much more likely to appear as potential variation. Combined with an arrival-of-the-frequent mechanism, this algorithmic bias predicts a much higher prevalence of low-complexity (high-symmetry) phenotypes than follows from natural selection alone and also explains patterns observed in protein complexes, RNA secondary structures, and a gene regulatory network. Engineers routinely design systems to be modular and symmetric in order to increase robustness to perturbations and to facilitate alterations at a later date. Biological structures also frequently exhibit modularity and symmetry, but the origin of such trends is much less well understood. It can be tempting to assume—by analogy to engineering design—that symmetry and modularity arise from natural selection. However, evolution, unlike engineers, cannot plan ahead, and so these traits must also afford some immediate selective advantage which is hard to reconcile with the breadth of systems where symmetry is observed. Here we introduce an alternative nonadaptive hypothesis based on an algorithmic picture of evolution. It suggests that symmetric structures preferentially arise not just due to natural selection but also because they require less specific information to encode and are therefore much more likely to appear as phenotypic variation through random mutations. Arguments from algorithmic information theory can formalize this intuition, leading to the prediction that many genotype–phenotype maps are exponentially biased toward phenotypes with low descriptional complexity. A preference for symmetry is a special case of this bias toward compressible descriptions. We test these predictions with extensive biological data, showing that protein complexes, RNA secondary structures, and a model gene regulatory network all exhibit the expected exponential bias toward simpler (and more symmetric) phenotypes. Lower descriptional complexity also correlates with higher mutational robustness, which may aid the evolution of complex modular assemblies of multiple components.
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影响因子：
14.9
作者：
Kin T;Yamada K;Terai G;Okida H;Yoshinari Y;Ono Y;Kojima A;Kimura Y;Komori T;Asai K
通讯作者：
Asai K
影响因子：
1.8
作者：
HOFACKER, IL;FONTANA, W;SCHUSTER, P
通讯作者：
SCHUSTER, P
影响因子：
4.3
作者：
Levy ED;Pereira-Leal JB;Chothia C;Teichmann SA
通讯作者：
Teichmann SA
影响因子：
14.9
作者：
Giegerich, R;Voss, B;Rehmsmeier, M
通讯作者：
Rehmsmeier, M
影响因子：
64.8
作者：
Azevedo, RBR;Lohaus, R;Leroi, AM
通讯作者：
Leroi, AM