Complex network based computational techniques for ‘edgetic’ modelling of mutations implicated with cardiovascular disease

Abstract

Complex networks are a graph theoretic method that can model genetic mutations, in particular single nucleotide polymorphisms (snps) which are genetic variations that only occur at single position in a DNA sequence. These can potentially cause the amino acids to be changed and may affect protein function and thus structural stability which can contribute to developing diseases. We show how snps can be represented by complex graph structures, the connectivity patterns if represented by graphs can be related to human diseases, where the proteins are the nodes (vertices) and the interactions between them are represented by links (edges). Disruptions caused by mutations can be explained as loss of connectivity such as the deletion of nodes or edges in the network (hence the term edgetics). Furthermore, diseases appear to be interlinked with hub genes causing multiple problems and this has led to the concept of the human disease network or diseasome. Edgetics is a relatively newconceptwhich is proving effective for modelling the relationships between genes, diseases and drugs which were previously considered intractable problems.