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Statistical Identification of Important Nodes in Biological Systems

Authors
  • Wang, Pei1
  • 1 Henan University,
Type
Published Article
Journal
Journal of Systems Science and Complexity
Publisher
Academy of Mathematics and Systems Science, Chinese Academy of Sciences
Publication Date
Jan 12, 2021
Pages
1–17
Identifiers
DOI: 10.1007/s11424-021-0001-2
PMID: 33456274
PMCID: PMC7801784
Source
PubMed Central
Keywords
License
Unknown

Abstract

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.

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