Abstract

   The development of computational approaches in systems biology has
   reached a state of maturity that allows their transition to systems
   medicine. Despite this progress, intuitive visualisation and
   context-dependent knowledge representation still present a major
   bottleneck. In this paper, we describe the Disease Maps Project, an
   effort towards a community-driven computationally readable
   comprehensive representation of disease mechanisms. We outline the key
   principles and the framework required for the success of this
   initiative, including use of best practices, standards and protocols.
   We apply a modular approach to ensure efficient sharing and reuse of
   resources for projects dedicated to specific diseases. Community-wide
   use of disease maps will accelerate the conduct of biomedical research
   and lead to new disease ontologies defined from mechanism-based disease
   endotypes rather than phenotypes.

   Subject terms: Diseases, Systems biology, Scientific community

The concept

Disease mechanisms in the context of translational medicine projects

   Large amounts of high-throughput data are routinely generated in an
   effort to better understand diseases, adding to our extensive and
   diverse biomedical knowledge. Common objectives include the
   identification of disease biomarkers, molecular mechanisms, potential
   drug targets and disease subtypes for better diagnostics and
   stratification of patients.^[71]1

   Using such diverse and complex high-throughput datasets to meet the
   current and future demands of research in basic and translational
   medicine is challenging. Our experience in large-scale translational
   medicine projects (Supplementary material S[72]1) is that the
   difficulties associated with such tasks are often vastly
   underestimated. When it comes to disease-specific functional analysis
   and systematic data interpretation, computational and mathematical
   tools have not developed at the same pace as laboratory technologies.
   Interpreting data in a given context still mainly relies on statistical
   approaches, e.g., pathway enrichment analysis. To advance beyond
   context-independent use of canonical pathways, dedicated knowledge maps
   are needed, which would provide the molecular mechanisms involved in
   given diseases.

   Charting maps, from geography to anatomy, is an essential scientific
   activity in many fields. Maps do not only chart a territory but also
   facilitate our understanding.^[73]2 A mechanistic representation was
   first applied on a large scale to metabolic pathways in the form of the
   wall charts created by Nicholson^[74]3 and Michal.^[75]4 Mechanistic
   representation of extensive signalling pathways was pioneered by Kurt
   Kohn^[76]5 and Hiroaki Kitano^[77]6 and developed into the Systems
   Biology Graphical Notation (SBGN) standard.^[78]7

   In order to bridge knowledge maps and the big data of health-care
   research, we have engaged in the development of highly detailed and
   specific representations of known disease mechanisms (Table
   [79]1).^[80]8–[81]10 Having these resources, we employed complementary
   techniques that use prior knowledge for data and network analysis and
   hypothesis generation^[82]11 in systems medicine projects (Fig. [83]1).

Table 1.

   Comparison of published disease maps
   Feature AlzPathway Parkinson’s disease map ACSN
   Webpage [84]http://alzpathway.org [85]http://pdmap.uni.lu
   [86]https://acsn.curie.fr
   Online exploration Payao (Apache Flex) MINERVA (Google Maps API)
   NaviCell (Google Maps API)
   Content development CellDesigner^a CellDesigner^a CellDesigner^a
   Standard formats SBML, BioPAX SBML SBML, BioPAX
   Number of nodes 1538 5073 5975
   Number of processes 1127 2108 4826
   Number of proteins 721 2973 2371
   Number of metabolites 300 703 595
   Number of genes 33 202 159
   Number of references >100 Review articles 1307 2919