Reusability and composability in process description maps: RAS–RAF–MEK–ERK signalling

Briefings in Bioinformatics, vol. 22

Abstract

Detailed maps of the molecular basis of the disease are powerful tools for interpreting data and building predictive models. Modularity and composability are considered necessary network features for large-scale collaborative efforts to build comprehensive molecular descriptions of disease mechanisms. An effective way to create and manage large systems is to compose multiple subsystems. Composable network components could effectively harness the contributions of many individuals and enable teams to seamlessly assemble many individual components into comprehensive maps. We examine manually built versions of the RAS-RAF-MEK-ERK cascade from the Atlas of Cancer Signalling Network, PANTHER and Reactome databases and review them in terms of their reusability and composability for assembling new disease models. We identify design principles for managing complex systems that could make it easier for investigators to share and reuse network components. We demonstrate the main challenges including incompatible levels of detail and ambiguous representation of complexes and highlight the need to address these challenges.

Authors 5

1. Alexander Mazein corresponding

   Lyon 1 Université · Centre National de la Recherche Scientifique · Inserm · University of Luxembourg · Université de Lyon · Centre International de Recherche en Infectiologie · Luxembourg Centre for Systems Biomedicine

   Affiliation as printed

   European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France

   Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg

2. Adrien Rougny

   National Institute of Advanced Industrial Science and Technology

   Affiliation as printed

   Biotechnology Research Institute for Drug Discovery, National Institute of Advanced Industrial Science and Technology (AIST), Aomi, Tokyo, Japan

   Com. Bio Big Data Open Innovation Lab. (CBBD-OIL), AIST, Aomi, Tokyo, Japan

3. Julio Sáez-Rodríguez Aachen Joint Research Centre for Computational Biomedicine (JRC-COMBINE)

   University Hospital Heidelberg · RWTH Aachen University

   Affiliation as printed

   Institute for Computational Biomedicine, Heidelberg University Hospital and Heidelberg University, Faculty of Medicine, Bioquant Heidelberg, Heidelberg 69120, Germany

   Joint Research Centre for Computational Biomedicine (JRC-COMBINE), RWTH Aachen University, Faculty of Medicine, 52074 Aachen, Germany

4. Marek Ostaszewski

   University of Luxembourg · Luxembourg Centre for Systems Biomedicine

   Affiliation as printed

   Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg

5. Reinhard Schneider

   University of Luxembourg · Luxembourg Centre for Systems Biomedicine

   Affiliation as printed

   Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg