Gates Alexander J, Rocha Luis M
School of Informatics and Computing, Indiana University, Bloomington, IN, USA.
Program in Cognitive Science, Indiana University, Bloomington, IN, USA.
Sci Rep. 2016 Apr 18;6:24456. doi: 10.1038/srep24456.
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.
网络结构研究揭示了复杂系统的组织特征。然而，理解如何控制这些系统同样必要；例如，确定将病变细胞恢复到健康状态，或将成熟细胞恢复到多能状态的策略。最近的两种方法表明，复杂系统的可控性仅可从变量间相互作用图预测得出，而无需考虑其动态特性：结构可控性和最小支配集。我们证明，当引入动态特性时，此类仅基于结构的方法无法刻画可控性。我们研究了网络基序的布尔网络集合以及三种生化调控模型：黑腹果蝇的体节极性网络、酿酒酵母的细胞周期以及拟南芥的花器官排列。我们证明，仅基于结构的方法在关键变量的数量以及哪些关键变量集能最佳控制这些模型的动态特性方面，既存在低估也存在高估，这凸显了实际系统动态特性在确定控制方面的重要性。我们的分析进一步表明，自动机转移函数的逻辑，即其正则化程度，在结构预测预测动态特性预测动态特性的程度方面起着重要作用。
