Biological systems can be modeled and described by biological networks. Biological networks are typical complex networks with widely real-world applications. Many problems arising in biological systems can be boiled down to the identification of important nodes. For example, biomedical researchers frequently need to identify important genes that potentially leaded to disease phenotypes in animal and explore crucial genes that were responsible for stress responsiveness in plants. To facilitate the identification of important nodes in biological systems, one needs to know network structures or behavioral data of nodes (such as gene expression data). If network topology was known, various centrality measures can be developed to solve the problem; while if only behavioral data of nodes were given, some sophisticated statistical methods can be employed. This paper reviewed some of the recent works on statistical identification of important nodes in biological systems from three aspects, that is, 1) in general complex networks based on complex networks theory and epidemic dynamic models; 2) in biological networks based on network motifs; and 3) in plants based on RNA-seq data. The identification of important nodes in a complex system can be seen as a mapping from the system to the ranking score vector of nodes, such mapping is not necessarily with explicit form. The three aspects reflected three typical approaches on ranking nodes in biological systems and can be integrated into one general framework. This paper also proposed some challenges and future works on the related topics. The associated investigations have potential real-world applications in the control of biological systems, network medicine and new variety cultivation of crops.

生物系统可以通过生物网络进行建模和描述。生物网络是典型的复杂网络，在现实世界中具有广泛的应用。生物系统中出现的许多问题可以归结为重要节点的识别。例如，生物医学研究人员经常需要识别可能导致动物疾病表型的重要基因，并探索负责植物应激反应的关键基因。为了便于识别生物系统中的重要节点，需要了解节点的网络结构或行为数据（例如基因表达数据）。如果网络拓扑已知，就可以制定各种中心性措施来解决问题；而如果只给出节点的行为数据，则可以采用一些复杂的统计方法。本文从三个方面回顾了近年来生物系统中重要节点统计识别的一些工作，即：1）基于复杂网络理论和流行动力学模型的一般复杂网络； 2）基于网络基序的生物网络； 3) 基于 RNA-seq 数据的植物。复杂系统中重要节点的识别可以看作是系统到节点排序得分向量的映射，这种映射不一定具有显式形式。这三个方面反映了生物系统中节点排序的三种典型方法，可以整合到一个通用框架中。本文还提出了相关主题的一些挑战和未来的工作。相关研究在生物系统控制、网络医学和农作物新品种培育方面具有潜在的现实应用。