Network-based approaches for modeling disease regulation and progression

Molecular interaction networks lay the foundation for studying how biological functions are controlled by the complex interplay of genes and proteins. Investigating perturbed processes using biological networks has been instrumental in uncovering mechanisms that underlie complex disease phenotypes. Rapid ad-vances in omics technologies have prompted the generation of high-throughput datasets, enabling large-scale, network-based analyses. Consequently, various modeling techniques, including network enrichment, differential network extraction, and network inference, have proven to be useful for gaining new me-chanistic insights. We provide an overview of recent network-based methods and their core ideas to fa-cilitate the discovery of disease modules or candidate mechanisms. Knowledge generated from these computational efforts will benefit biomedical research, especially drug development and precision medi-cine. We further discuss current challenges and provide perspectives in the field, highlighting the need for more integrative and dynamic network approaches to model disease development and progression. (c) 2022 Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).

Automated summary

Molecular interaction networks are the basis for studying the control of biological functions by genes and proteins. Utilizing these networks, researchers have uncovered mechanisms underlying complex diseases. Advancements in omics technologies have enabled large-scale network analysis, and various modeling techniques have proven useful for gaining new insights. This article provides an overview of recent network-based methods and discusses the relevance to biomedical research, highlighting the need for integrative and dynamic network approaches.