Graph Theory in Computational Biology: Modeling and Analysis

Abstract

Abstract: This paper focuses on the use of graph theory to model protein-protein interaction and gene regulatory networks with the view of elucidating aspects of such systems. Based on the STRING and KEGG databases, 5000 proteins and 25,000 interactions were included in the PPI network analysis. Significant results are described as follows: The degree centrality of Protein A is the highest in the network equals to 150; the betweenness is also high for Protein C equals to 0.072. Several Objectives of the Analysis: There were five
functional communities identified and Community C1 associated with cell cycle and DNA repair. In the GRN consisting of 500 genes and 2000 interactions, Gene Y dwarfed the other genes in the network and was ranked with the highest degree of centrality (55) and
betweenness centrality (0.085). The evaluation of shortest path estimated the binary distance where significant cores were recognized for formulating the network; for instance, Path P1 = 3 and contains Genes G001, G002, and G005. These results thus substantiate
the use of graph-based methods towards clarifying significant proteins and control processes. As a result of this study, it is shown how graph theory has the promise in explaining the structural and functional properties of biological networks and as such, provides directions that can inform future research and development of computational biology.