复杂网络的控制需要结构和动态
Control of complex networks requires both structure and dynamics
AI总结：
研究复杂系统组织的结构特征，但需进一步理解其控制方法，如逆转病变细胞至健康状态。结构方法无法准确预测动态控制，需考虑实际系统动态。
摘要：
网络结构研究揭示了复杂系统组织的特征。然而，也需要理解如何控制它们，例如逆转病变细胞至健康状态或成熟细胞至多能状态。两种近期方法表明，复杂系统的可控性可通过变量间相互作用的图结构预测，而无需考虑其动态：结构可控性和最小支配集。我们证明，当引入动态时，仅结构的方法无法表征可控性。我们研究了布尔网络集合中的网络动机以及三个生物化学调节模型：果蝇的段极性网络、酿酒酵母的细胞周期和拟南芥的花器官排列。我们证明，仅结构的方法在数量和关键变量集上均低估和高估了这些模型的动力学控制，突显了实际系统动态在确定控制中的重要性。我们的分析进一步表明，自动机转换函数的逻辑，即它们的通道化程度，在结构预测动态的广度方面起重要作用。
英文摘要：
The study of network structure has uncovered signatures of the organization of complex systems. However, there is also a need to understand how to control them; for example, identifying strategies to revert a diseased cell to a healthy state, or a mature cell to a pluripotent state. Two recent methodologies suggest that the controllability of complex systems can be predicted solely from the graph of interactions between variables, without considering their dynamics: structural controllability and minimum dominating sets. We demonstrate that such structure-only methods fail to characterize controllability when dynamics are introduced. We study Boolean network ensembles of network motifs as well as three models of biochemical regulation: the segment polarity network in Drosophila melanogaster, the cell cycle of budding yeast Saccharomyces cerevisiae, and the floral organ arrangement in Arabidopsis thaliana. We demonstrate that structure-only methods both undershoot and overshoot the number and which sets of critical variables best control the dynamics of these models, highlighting the importance of the actual system dynamics in determining control. Our analysis further shows that the logic of automata transition functions, namely how canalizing they are, plays an important role in the extent to which structure predicts dynamics.
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