Large amount of high-throughput data become available in the effort to better understand complex diseases. Despite the availability of various approaches, tools for disease-specific functional analysis are greatly underdeveloped. The direct approach to solve this problem is developing highly accurate comprehensive computerized representations of disease mechanisms on the level of cellular and molecular interactions.

Results

We present the concept of disease maps as a large-scale community effort. We describe the AsthmaMap example, a conceptual model of asthma mechanisms that includes 3 interconnected layers of granularity with 22 cell types and more than 2000 of proteins involved. The intermediate and detailed layers are developed correspondingly in SBGN Activity Flow and Process Description languages. This approach is designed to be used for data visualisation and interpretation on the level of signalling pathways and cell-tocell communication. While being complementary to generic pathway enrichment tools (g-Profiler, Ingenuity Pathway Analysis and MetaCore), a disease map focuses on integrating information into a single flexible hierarchically-organised network, thus enabling advanced data analysis (e.g. via NaviCell, MINERVA) and making possible creating dynamic predictive computational models.

Conclusion

To progress with this approach we propose building on the best practices and lessons learned from the previous projects and applying shared standards, tools and protocols to generating high-quality representations and enabling the exchange of reusable pathway modules (e.g. inflammation). We envision this strategy will facilitate powerful advances in systems medicine for understanding disease mechanisms, cross-disease comparison, finding disease comorbidities, suggesting drug repositioning, generating new hypotheses, and after careful validation, redefining disease ontologies based on their endotypes - confirmed molecular mechanisms.

Contact : karin.sadoul@univ-grenoble-alpes.fr