Department of Physics, Clark University, Worcester, Massachusetts 01610, USA.
Department of Molecular Biology, Princeton University, Princeton, New Jersey 08540, USA.
Phys Rev E. 2018 Apr;97(4-1):040401. doi: 10.1103/PhysRevE.97.040401.
We introduce a minimal model for the evolution of functional protein-interaction networks using a sequence-based mutational algorithm, and apply the model to study neutral drift in networks that yield oscillatory dynamics. Starting with a functional core module, random evolutionary drift increases network complexity even in the absence of specific selective pressures. Surprisingly, we uncover a hidden order in sequence space that gives rise to long-term evolutionary memory, implying strong constraints on network evolution due to the topology of accessible sequence space.
我们使用基于序列的突变算法为功能蛋白相互作用网络的演化引入了一个最小模型，并应用该模型研究产生振荡动力学的网络中的中性漂移。从功能核心模块开始，即使在没有特定选择压力的情况下，随机进化漂移也会增加网络的复杂性。令人惊讶的是，我们在序列空间中发现了一种隐藏的秩序，这种秩序产生了长期的进化记忆，这意味着由于可访问序列空间的拓扑结构，对网络进化有很强的约束。
