SBI – Department of Systems Biology and Bioinformatics
Faculty of Computer Science and Electrical Engineering
University of Rostock
Ulmenstrasse 69 | 18057 Rostock
Germany
+49 381 498-7571
olaf.wolkenhauer@uni-rostock.de
AKOP: Atlas of Knee Osteoarthritis and Pain Signaling
AKOP (Atlas of Knee Osteoarthritis & Pain Signaling) is an interactive systems biology resource that links molecular mechanisms of knee osteoarthritis with pain-related signaling pathways in a unified disease map. By integrating curated knowledge on inflammation, tissue remodeling, cellular communication, and pain processes, AKOP enables researchers to explore KOA as a complex, multi-tissue disease rather than as an isolated cartilage disorder. Hosted on the MINERVA platform, the atlas supports data overlay, visual analytics, and hypothesis generation, thereby providing a foundation for mechanistic interpretation of omics and imaging data, biomarker discovery, and future patient stratification approaches.
Knee osteoarthritis (KOA) is a complex and heterogeneous disease involving structural joint degeneration, inflammation, altered tissue remodeling, and pain signaling across multiple cell types and tissues. To support a more mechanistic understanding of this complexity, we developed AKOP, the Atlas of Knee Osteoarthritis & Pain Signaling, as an interactive, web-based disease map hosted on the MINERVA platform.
AKOP integrates curated molecular interactions, signaling pathways, phenotypes, and disease-relevant processes linked to osteoarthritis progression and pain mechanisms. The atlas is designed to connect heterogeneous biomedical knowledge with omics and imaging-derived data, enabling interactive exploration, hypothesis generation, and map-based interpretation of molecular changes in KOA. As a structured network resource, AKOP supports the contextualization of transcriptomic and other experimental datasets within disease mechanisms that are relevant to joint pathology and symptom development.
With AKOP, we aim to provide a systems medicine resource for researchers investigating the molecular basis of knee osteoarthritis, pain signaling, and treatment response. The platform facilitates knowledge integration, visual analytics, and in silico exploration of KOA-associated mechanisms, and thereby contributes to data-driven approaches for biomarker discovery, patient stratification, and mechanism-informed therapeutic research.
