Abstract

The internal workings of biological systems are notoriously difficult to understand. Due to the prevalence of noise and degeneracy in evolved systems, in many cases the workings of everything from gene regulatory networks to protein–protein interactome networks remain black boxes. One consequence of this black-box nature is that it is unclear at which scale to analyze biological systems to best understand their function. We analyzed the protein interactomes of over 1800 species, containing in total 8 782 166 protein–protein interactions, at different scales. We show the emergence of higher order ‘macroscales’ in these interactomes and that these biological macroscales are associated with lower noise and degeneracy and therefore lower uncertainty. Moreover, the nodes in the interactomes that make up the macroscale are more resilient compared with nodes that do not participate in the macroscale. These effects are more pronounced in interactomes of eukaryota, as compared with prokaryota; these results hold even after sensitivity tests where we recalculate the emergent macroscales under network simulations where we add different edge weights to the interactomes. This points to plausible evolutionary adaptation for macroscales: biological networks evolve informative macroscales to gain benefits of both being uncertain at lower scales to boost their resilience, and also being ‘certain’ at higher scales to increase their effectiveness at information transmission. Our work explains some of the difficulty in understanding the workings of biological networks, since they are often most informative at a hidden higher scale, and demonstrates the tools to make these informative higher scales explicit.

中文翻译：

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进化与出现：生命之树中蛋白质相互作用组中的高阶信息结构

摘要

众所周知，生物系统的内部运作难以理解。由于进化系统中噪声和退化的普遍存在，在许多情况下，从基因调控网络到蛋白质-蛋白质相互作用组网络的一切工作仍然是黑匣子。这种黑箱性质的一个后果是，尚不清楚分析生物系统以最好地了解其功能的规模。我们分析了超过 1800 个物种的蛋白质相互作用组，总共包含 8 782 166 个不同尺度的蛋白质-蛋白质相互作用。我们展示了在这些相互作用组中出现了更高阶的“宏观尺度”，并且这些生物宏观尺度与较低的噪声和退化相关，因此较低的不确定性。而且，与不参与宏观尺度的节点相比，构成宏观尺度的相互作用组中的节点更具弹性。与原核生物相比，这些效应在真核生物的相互作用组中更为明显；即使在我们在网络模拟下重新计算出现的宏观尺度的敏感性测试之后，这些结果仍然成立，我们在交互组中添加了不同的边缘权重。这表明了宏观尺度的合理进化适应：生物网络进化出信息丰富的宏观尺度，以获得在较低尺度上的不确定性以提高其弹性，以及在较高尺度上“确定”以提高其在信息传输中的有效性的好处。我们的工作解释了理解生物网络运作的一些困难，