The theory of complex network plays an important role in a wide variety of disciplines, ranging from communications to molecular and population biology. In this thesis, at first the general concepts in complex networks, especially in biological networks are expressed. Actually the focus of this thesis is on graph theory methods for studying biological networks. There are several biological domains where graph theory techniques are applied for knowledge extraction from data. We have 0cm 0cm 0pt" 1. Modeling of bio-molecular networks. The approaches of graph modelling for biological systems and molecules and some properties of network that will be useful for network analyzes such as degree distribution, average diameter, density, clustering coefficient and betweenness centrality are introduced. Also, after reviewing the models that have been proposed for studying these networks, the best known model, named by “Stickiness Networks” that has provided by Przulj and Higham in 2006 based on network’s graphlets and stickness indexes of proteins is explained and these models are compared with each other. The modeling of bio-molecular networks present modeling methods of bio-molecular networks such as protein-protein intraction networks, metabolic networks and transcriptional regularity networks. 2. Measurement of centrality and importance in bio-molecular networks. To identify the most important nodes in a large complex network is of fundamental importance in computational biology. We will introduce several researches that applied centrality measures to identify structurally important genes or proteins in interaction networks and investigated the biological significance of the genes or proteins identified in this way. 3. Identifying motifs or functional modules in biological networks. Most important biological processes such as signal transduction, transcription and translation involve many proteins or genes. Most relevant processes in biological networks correspond to the motifs or functional modules. This suggests that certain functional modules occure with very high frequency in biological networks and be used to categories them. At the end, some algorithms and softwares that have been designed in this field, modility of motif and graphlet discovery that are some local properties of a network are expressed.