The interconnectivity of advanced biological systems is essential for their proper functioning. In modern connectomics, biological entities such as proteins, genes, RNA, DNA, and metabolites are often represented as nodes, while the physical, biochemical, or functional interactions between them are represented as edges. Among these entities, metabolites are particularly significant as they exhibit a closer relationship to an organism’s phenotype compared to genes or proteins. Moreover, the metabolome has the ability to amplify small proteomic and transcriptomic changes, even those from minor genomic changes. Metabolic networks, which consist of complex systems comprising hundreds of metabolites and their interactions, play a critical role in biological research by mediating energy conversion and chemical reactions within cells.
1. Introduction
Biological networks are widely used as graphical representations to describe and analyze biological systems. In these networks, graphs are used to represent biological entities, such as proteins, genes, RNA, DNA, and metabolites, as nodes. The edges of the network correspond to the physical, biochemical, or functional interactions between these entities
[1]. Through analysis of these biological networks, the interrelationships between different biological entities can be revealed, including protein–protein, protein–DNA, protein–metabolite, and other associations. This allows the networks to capture the basic characteristics of biological systems and reveal the information patterns within them
[2].
In order to deeply understand and quantify the characteristics and behaviors of biological networks, researchers utilize a series of evaluation indicators (
Figure 1). Indicators such as node degree, clustering coefficient, average shortest path length, and centrality are widely used to measure the degree of node connection, community structure, global connectivity, and node importance in networks
[3][4]. Small-world properties describe the global structure of networks
[5]. Additionally, modularity identifies functional modules and subnetworks, providing comprehensive evaluation for deeper understanding of biological system structure and function
[6][7][8].
Currently, biological networks are classified based on different features and purposes. For example, protein–protein interaction networks describe protein interactions
[9], gene regulatory networks reveal complex gene expression regulation mechanisms
[10], and metabolic networks graphically represent metabolic processes
[11]. Brain networks describe neuron and synapse interactions
[12], while social networks represent social relationships between individuals
[13]. Among these, metabolic networks have high plasticity and complexity as the basis of life activities and information transmission within organisms. They are complex network structures composed of interactions among multiple biological entities
[11][14]. Metabolic networks are crucial in biological research to understand the complexity of biological systems and reveal interactions and regulatory relationships among different entities.
2. Construction Methods of Metabolic Networks
Metabolic networks can be represented by various types of relationships, including statistical correlations, causal relationships, biochemical reactions, and chemical structural similarities
[14][15]. Statistical correlations and causal relationships are used to describe the relationships between molecules
[16][17], while biochemical reactions and chemical structural similarities describe the interactions between molecules
[18][19]. By constructing networks using these different relationship types, algorithms from network theory can be applied to metabolic networks to gain a more comprehensive understanding of metabolic processes
[2]. The codes for constructing metabolic networks are provided in
Table 1.
Table 1. Codes for metabolic networks.
2.1. Correlation-Based Metabolic Network
Correlation-based metabolic networks are widely used in metabolic research. These networks use the correlations among metabolites to establish connectivity relationships, simplifying multidimensional data while preserving most interpretive information (
Figure 2)
[22]. This method reveals coordinated behaviors between biological components and allows an analysis of network properties to better understand metabolite interactions and identify key metabolites in pathways
[23][24]. Furthermore, correlation-based networks can also be applied to study metabolic disease pathogenesis and discover new treatments
[22][25].