Canalization 降低了生物网络中调控的非线性
Canalization reduces the nonlinearity of regulation in biological networks
AI总结：
研究生物网络进化设计原则的因果关系，发现网络可逼近性依赖于动态稳健性，增加的 canalization 几乎能完全解释生物网络的高可逼近性。
摘要：
诸如基因调控网络等生物网络具备优良特性。它们比随机网络更稳健且更可控。这促使人们寻找进化已融入生物网络的结构与动力学特征。最近一项对已发表的、专家人工校对的 Boolean 生物网络模型进行的荟萃分析揭示了若干此类特征，通常被称为设计原则。其中包括：生物网络富含某些反复出现的网络模体；其动态更新规则比预期更为冗余、偏倚且更具 canalization 特性；并且与可比的随机网络相比，生物网络的动力学能更好地被线性及低阶近似所逼近。由于这些特征大多相互关联，理清因果关系至关重要，即理解进化主动选择了哪些特征，从而真正构成进化设计原则。在此，我们表明，可逼近性强烈依赖于网络的动态稳健性，而生物网络中增加的 canalization 几乎可以完全解释其近期被提出的高可逼近性。
英文摘要：
Biological networks such as gene regulatory networks possess desirable properties. They are more robust and controllable than random networks. This motivates the search for structural and dynamical features that evolution has incorporated in biological networks. A recent meta-analysis of published, expert-curated Boolean biological network models has revealed several such features, often referred to as design principles. Among others, the biological networks are enriched for certain recurring network motifs, the dynamic update rules are more redundant, more biased and more canalizing than expected, and the dynamics of biological networks are better approximable by linear and lower-order approximations than those of comparable random networks. Since most of these features are interrelated, it is paramount to disentangle cause and effect, that is, to understand which features evolution actively selects for, and thus truly constitute evolutionary design principles. Here, we show that approximability is strongly dependent on the dynamical robustness of a network, and that increased canalization in biological networks can almost completely explain their recently postulated high approximability.
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