The Drug-Disease-Target Network

Introduction:

The increase in availability of biological molecular data gives us a new way to study the mechanisms of biological systems. Network-based approaches as a powerful tool to study biological systems have become more and more important for the research of bioinformatics. Each biological system can be considered as a network model, and through the study of the network’s characteristics, one can try to elaborate how the molecules, the interactions and the network structures determine the functions of biological systems, which can help us understand the cellular organizations, processes and functions.

Many network-based approaches have been proposed to study biological systems in recent years. Through the analysis of characteristics of the biological networks such as small-world properties, centralities and modular structures, we can try to understand how the molecules and the network characteristics determine the functions of biological systems and uncover some universal laws that govern the biological systems, which could be potentially helpful for disease diagnosis, treatments and drug discovery.

The Drug-Disease-Target Network:

The Drugs, Disease and Genes(target) are correlated with each. This correlation can be mapped into a network. Now, this network of Drug-Disease-Target can be studied and analysed for disease diagnosis, drug discovery, treatments, identification of new targets(genes) etc.

The drug–disease–gene network consists of three sub-networks: a drug–drug network, a disease–disease network and a target–target network. The rules for constructing the three sub-networks can be described as follows:

Drug–Drug network:

1. Two drugs are connected if they are associated with a same disease.
2. Connect two drugs if they are associated with a same drug target gene. Experimental results have shown that each network is densely connected, which indicates that one disease (or one gene) is often associated with multiple drugs.

Disease-Disease network:

1. If two diseases are associated with a same drug, then connect them. From experimental results it is evident that this network is highly connected, which shows that one drug is often associated with multiple diseases.
2. Connect two diseases if they are associated with a same disease gene. This network is sparsely connected, which indicates that only a few genes are associated with multiple diseases.

Target-Target network: 

1. Two genes(target) are connected if they are associated with a same drug.
2. Connect two genes if they are associated with a same disease. The results in literature show that each network is densely connected, which indicates that one drug (or one disease) is often associated with multiple genes.

Centralities of the Drug–Disease–Target Network

The node centrality analysis for the Drug-Drug Network, the Disease-Disease Network and the Target-Target Network can be calculated as shown in this section. The role of a node is highly dependent on its topological position in the network. The measures for the node centrality are described as follows:

where dist_ij is the length of the shortest path from node i to node j; ∆_G is the diameter of the graph G; A = (a_ij) is the adjacency matrix of a network. In addition to the centrality measure other measure can also be looked into like clustering coefficient, degree distributions, shortest path length, small world effects etc to gain more insight into the biological connections. 

Conclusion:

Applying these metrics over the Drug-Disease-Target network may bring out various interesting phenomenon, depending upon how the molecules, the interactions and the network structures determine the functions of biological systems. Such analyses shall help in the discovery of new drugs, treatments, diagnosis of diseases.

References:

[1] Peng Gang Sun, The human Drug-Disease-Gene Network, Information Sciences 306 (2015) 70-80