Bilkent University
Department of Computer Engineering


Community-driven Comprehensive Representation of Disease Mechanisms


Alexander Mazein
European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, Universit de Lyon

Background: It becomes clear that there is no simple solution for understanding complex disorders such as cancer, allergic, autoimmune and neurodegenerative diseases. With intensive progress of technologies and availability of growing amount of data, approaches for data interpretation and hypothesis generation are largely underdeveloped. The direct solution is to systematically integrate information on disease mechanisms on the level of cellular and molecular processes (Fujita et al., 2013, PMID 23832570; Kuperstein et al., 2015, PMID 26192618; Mizuno et al., 2016, PMID 26849355).

Methods: Recent advances in systems biology made it possible to unambiguously represent biological pathways in a consistent way (Le Novre, 2015, PMID 25645874). Disease-specific representations are generated in CellDesigner ( following the Systems Biology Graphical Notation standard ( The involvement of domain experts from different groups ensures that different points of view are considered and all the disease hallmarks are covered and adequately represented.

Results: We present the disease maps concept as a community effort. We describe our experience in developing disease maps for Parkinson's disease, asthma and cancer, and demonstrate how these resources can be used for data visualisation and interpretation. While being complementary to generic pathway enrichment tools (g-Profiler, the Ingenuity Pathway Analysis and MetaCore), this approach focuses on integrating of information into a single hierarchically-organised network, thus enabling advanced data analysis using the full power of systems biology approaches and making possible creating dynamic predictive computational models.

Conclusion: To progress with this effort we propose building on the best practices and lessons learned from previous projects and applying shared standards, tools and protocols for generating high-quality representations and enabling the exchange of reusable pathway modules (e.g. inflammation, central metabolism, etc.). 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.

Bio: Ph.D. in Bioinformatics from the School of Informatics, University of Edinburgh, UK. Since January 2014 - a senior researcher at the European Institute for Systems Biology and Medicine in Lyon, France. Leading the Disease Maps Project: disease-specific comprehensive representation of molecular mechanisms and data interpretation in connection to EU projects funded by IMI (Innovative Medicines Initiative): U-BIOPRED and eTRIKS. Earlier positions: researcher at the Okinawa Institute of Science and Technology, post-doctoral researcher at the University of Edinburgh, scientific database curator at GeneGo (now part of Thomson Reuters - MetaCore).


DATE: 21 July 2016, Thursday @ 13:40