Applications of Network Analysis

Complex networks are ubiquitous in the real world, and have statistical characteristics such as small-world and scale-free properties. The network cluster structure is one of the important topological properties of complex networks. Using clustering algorithms in complex biological networks to reveal the cluster structure in biological networks is important for analyzing the topology and prediction of biological networks. It is of great significance to measure its functions.

Network AnalysisFigure 1. Virtual Screening.


Complex biological systems can be represented as computable networks and analyzed. For example, an ecosystem can be modeled as a network of interacting species, or proteins can be modeled as a network of amino acids. To further break down proteins, amino acids can be expressed as a network of connecting atoms, such as carbon, nitrogen and oxygen.

Nodes and edges are the basic components of the network.

The nodes represent the units in the network, and the edges represent the interactions between the units. Nodes can represent various biological units from individual organisms to individual neurons in the brain. Two important attributes of the network are degree and intermediateness.

The degree is the number of edges connecting nodes, and intermediateness is a measure of the centrality of nodes in the network. A node with high intermediateness essentially acts as a bridge between different parts of the network (that is, interaction must pass through this node to reach other parts of the network).


 Applications of Network Analysis

  • Protein–protein interaction networks
  • Gene regulatory networks (DNA–protein interaction networks)
  • Gene co-expression networks
  • Metabolic networks
  • Signaling networks
  • Neuronal networks


  • Complex network clustering algorithm is applied in biological network
  • Application in protein interaction network

The PPI network is a typical complex biological network. Studies have shown that in the PPI network, closely connected clusters are often composed of proteins with similar functions.

  • Complex network application in metabolic network.
  • Finding clusters or modules of highly relevant genes.
  • Summarizing such modules using an intramodular hub gene or the module eigengene.
  • Relating modules to one another and to external sample traits with eigengene network methodology.
  • Calculating the measures of module membership.
  • Gene Network Reverse Engineering.
  • Plant Biology - Co-expression analyses have been extensively used to search for novel genes involved in specific plant pathways.


Complex network clustering methods have been widely used in the research of complex biological networks such as protein networks and metabolic networks. Studies have shown that protein networks and metabolic networks are mostly scale-free networks with the characteristics of small-world and cluster structure. The in-depth study of these networks' topological properties will help to understand the functions of these networks further. Using the method of cluster analysis to decompose the protein network and the metabolic network into several relatively independent sub-networks, on the one hand, can help predict the biological functions of the network and help biologists discover the biological significance of the sub-networks (or network clusters) , On the other hand can reduce the complexity of the problem.

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