Systems medicine disease maps: community-driven comprehensive representation of disease mechanisms

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.

Towards a European health research and innovation cloud (HRIC)

“…The Health Research and Innovation Cloud (HRIC), as described in this manuscript, would help to facilitate this transition, providing access to larger datasets, cutting-edge tools, and knowledge, as envisioned by Auffray et al [3]. For example, the HRIC should ease the incorporation of domain expert knowledge into systems disease maps in a format that can be both understood by all stakeholders (patients and clinicians, scientists, and drug developers) and processed by highperformance computers, thus supporting the development of innovative medicines and diagnostics [4,5]. Cloud technologies (accessed through Hadoop applications, for example) also make it possible to collaborate and to access and reuse data in situations when privacy concerns or regulation prohibits remote users from downloading data-an important benefit in Europe where national regulations can differ significantly.…”

Mechanistic modeling of the SARS-CoV-2 disease map

“…Disease maps are repositories of knowledge of disease-relevant mechanisms that provide qualitative guidance for the interpretation of experimental ndings (2). Actually, disease maps are the supporting foundation of different tools able to model the information contained in them in order to provide a detailed quantitative explanation for experimental results (5). In particular, mechanistic models of disease maps are becoming increasingly relevant for genomic data interpretation because they provide a natural link between omics data measurements and cell behavior and outcome (6), which ultimately accounts for the phenotype of the infection.…”

Challenges in the calibration of large-scale ordinary differential equation models

“…Others, such as the interplay of multiple pathways in complex diseases or the functioning of whole cells, need large models to be described (Hass et al, 2017;Karr et al, 2012;Swainston et al, 2016). Due to the advances in experimental and computational methods, many large-scale models have been developed in recent years (Froehlich et al, 2018;Khodayari and Maranas, 2017;Mazein et al, 2018). As the data acquisition speed continuously increases (Barretina et al, 2012;Li et al, 2017) and more sophisticated tools for model development appear (Gyori et al, 2017), this trend is likely to persist.…”