The field of systems biology has facilitated the modelling of large and complex biological networks. These networks, generated from prior knowledge contained in the corpus of medical and scientific literature, or from experimental data are being used to model differing macromolecule networks associated with distinct disease states. While these networks are vital in understanding disease pathology and possible treatment options, they are rife with spurious interactions. These interactions arise from the methods used to create such networks, where the ability to discriminate between direct and indirect relationships is a challenge. To combat these spurious interactions an algorithm that leverages functional enrichment in biological networks was developed. Here, functional enrichment refers to two or three node functional motifs that are ubiquitous in biological networks. The algorithm developed removes edges from an existing network based on that edge’s involvement in functional motifs relative to every other edge’s involvement. In this work, the application of this algorithm was explored using real-world clinical disease networks. Furthermore, a software package was developed to identify an edge’s membership in functional motifs with respect to the network being explored. The tools developed in this work are the first to critically analyze an edge’s relationship to functional motifs in terms of network inclusion. Therefore, the principles outlined in this work can be employed in future works aimed at removing spurious edges. These principles will also produce higher quality biological networks for the understanding of disease pathology and the development of more effective treatment options.
Library of Congress Subject Headings
Biological systems--Mathematical models; Systems biology--Mathematical models; Computational biology; Algorithms
Department, Program, or Center
Thomas H. Gosnell School of Life Sciences (COS)
Gary R. Skuse
Page, Jeffrey, "Development of a Novel Algorithm to Remove Spurious Edges from Biological Networks Through Functional Enrichment" (2021). Thesis. Rochester Institute of Technology. Accessed from
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