Abstract
In the pre-genome era, traditional molecular biology provides very informative knowledge on how individual bio-molecule, i.e. DNA, RNA and protein, perform biological functions. Networks of interactions among bio-molecules are fundamental to all biological processes; for example, the Gene Regulatory Network (GRN) can be described as a complex network of genes regulated by protein binding. Cellular processes are controlled by various types of biochemical networks; such as (i) metabolic networks, (ii) Protein-Protein Interaction (PPI) networks, (iii) GRN, and (iv) signal transduction networks. Biochemical networks are complex in nature; they consist of a large number of bio-molecules, interacting with each other give rise to biological responses and stabilities. In the post-genome era, it is more productive to investigate how bio-molecules regulate or cooperate on a system level. The graph theory approach is a powerful tool for investigating the underlying topological structures of different molecular networks. A great diversity of graph theoretical notions is discussed to characterize biological networks. T he theory of complex networks plays an important role, ranging from computer science, sociology, engineering and physics, to bioinformatics etc. Within the fields of bioinformatics, potential applications of network analysis include drug target identification, determining bio molecules’ pathways and function, and designing effective strategies for treating various diseases. Molecular networks are the basis of biological processes. Such networks can be decomposed into smaller modules, also known as network motifs. These motifs show interesting dynamical behaviors, in which co-operatively effects between the motif components play a critical role in human diseases. Some of the network motifs are interconnected which can be merged together and form more complex structures, the so-called Coupled Motif Structures (CMS). These structures exhibit mixed dynamical behavior, which may lead biological organisms to perform specific functions.
Citation
Huang CH. Graph Theory: A Powerful Research Tool for Biological Network Analysis. SM J Biol. 2015; 1(1): 1001.