Graphs and Graph Theory in Computational Biology

Dear Colleagues,

The field of computational biology boasts a rich historical connection with graph theory, where graphs serve as a sophisticated and versatile data structure for modeling a wide range of complex problems. For example, de Bruijn graphs find utility in solving sequence assembly problems, while protein–protein interaction networks, expressed as undirected or directed graphs, illuminate the physical bindings between proteins and their associated regulatory mechanisms. Furthermore, the features of real-world data, such as small-world and scale-free network structures, presented by power-law degree distributions and hub nodes, are identified in biological networks. Recent advancements, like graph neural networks, have revolutionized the analysis of non-Euclidean distance data, providing a mathematical framework for machine learning applications within biological datasets. In light of these developments, this Special Issue embarks on a compelling exploration of how graphs and graph theory are employed in computational biology. We extend a warm invitation to all contributions that delve into the utilization of graphs in addressing the challenges and opportunities presented by computational biology. Topics of interest include, but are not limited to, network motifs, graph isomorphism, graph neural networks, gene regulatory networks, and protein–protein interactions.

Dr. Wen-Yu Chung
Guest Editor

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• systems biology
• network biology
• graph theory
• graph representation learning
• topology
• genetic interactions
• interactome