Network-based approaches for modeling disease regulation and progression

Gihanna Galindez, Sepideh Sadegh, Jan Baumbach, Tim Kacprowski*, Markus List

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

19 Downloads (Pure)


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 advances 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 mechanistic insights. We provide an overview of recent network-based methods and their core ideas to facilitate the discovery of disease modules or candidate mechanisms. Knowledge generated from these computational efforts will benefit biomedical research, especially drug development and precision medicine. 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.

Original languageEnglish
JournalComputational and Structural Biotechnology Journal
Pages (from-to)780-795
Publication statusPublished - 2023


  • Disease modeling
  • Network enrichment
  • Network inference
  • Network medidince
  • Systems medicine


Dive into the research topics of 'Network-based approaches for modeling disease regulation and progression'. Together they form a unique fingerprint.

Cite this