COMMEnTOPEn 2 Scientific Data | (2020) 7:136 | https://doi.), an effort to build a comprehensive, standardized knowledge repository of SARS-CoV-2 virus-host interaction mechanisms, guided by input from domain experts and based on published work. This knowledge, available in the vast body of existing literature 1,2 and the fast-growing number of new SARS-CoV-2 publications, needs rigorous and efficient organization in both human and machine-readable formats.This endeavour is an open collaboration between clinical researchers, life scientists, pathway curators, computational biologists and data scientists. Currently, 162 contributors from 25 countries around the world are participating in the project, including partners from Reactome 3 , WikiPathways 4 , IMEx Consortium 5 , Pathway Commons 6 , DisGeNET 7 , ELIXIR 8 , and the Disease Maps Community 9 . With this effort, we aim for long-term community-based development of high-quality models and knowledge bases, linked to data repositories.The COVID-19 Disease Map will be a platform for visual exploration and computational analyses of molecular processes involved in SARS-CoV-2 entry, replication, and host-pathogen interactions, as well as immune response, host cell recovery and repair mechanisms. The map will support the research community and improve our understanding of this disease to facilitate the development of efficient diagnostics and therapies. Figure 1 illustrates the initial scope and layout of the map and its life cycle.At the time this Comment went to press, the COVID-19 Disease Map contains pathways of (i) the virus replication cycle and its transcription mechanisms; (ii) SARS-CoV-2 impact on ACE2-regulated pulmonary blood pressure, apoptosis, Cul2-mediated ubiquitination, heme catabolism, Interferon 2 and PAMP signalling, and endoplasmic reticulum stress; (iii) SARS-CoV-2 proteins Nsp4, Nsp6, Nsp14 and Orf3a. Moreover, the map incorporates the COVID-19 collection of WikiPathway diagrams 10 and a pre-published genome-scale metabolic model of human alveolar macrophages with SARS-CoV-2 11 . All these contributed open-access resources are referenced at https://fairdomhub.org/projects/190#models.By combining diagrammatic representation of COVID-19 mechanisms with underlying models, the map fulfils a dual role. First, it is a graphical, interactive representation of disease-relevant molecular mechanisms linking different knowledge bases. Second, it is a computational resource of reviewed content for graph-based analyses 12 and disease modelling 13 . Thus, it provides a platform for domain experts, such as clinicians, virologists, and immunologists, to collaborate with data scientists and computational biologists for a rigorous model building, accurate data interpretation and drug repositioning. It offers a shared mental map to understand gender, age, and other susceptibility features of the host, disease progression, defence mechanisms, and response to treatment. Finally, it can be used together with the maps of other human diseases to study comorbidities.In...

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Motivation Molecular interaction maps have emerged as a meaningful way of representing biological mechanisms in a comprehensive and systematic manner. However, their static nature provides limited insights to the emerging behavior of the described biological system under different conditions. Computational modelling provides the means to study dynamic properties through in silico simulations and perturbations. We aim to bridge the gap between static and dynamic representations of biological systems with CaSQ, a software tool that infers Boolean rules based on the topology and semantics of molecular interaction maps built with CellDesigner. Results We developed CaSQ by defining conversion rules and logical formulas for inferred Boolean models according to the topology and the annotations of the starting molecular interaction maps. We used CaSQ to produce executable files of existing molecular maps that differ in size, complexity and the use of SBGN standards. We also compared, where possible, the manually built logical models corresponding to a molecular map to the ones inferred by CaSQ. The tool is able to process large and complex maps built with CellDesigner (either following SBGN standards or not) and produce Boolean models in a standard output format, SBML-qual, that can be further analyzed using popular modelling tools. References, annotations and layout of the CellDesigner molecular map are retained in the obtained model, facilitating interoperability and model reusability. Availability The present tool is available online: https://lifeware.inria.fr/∼soliman/post/casq/ and distributed as a Python package under the GNU GPLv3 license. The code can be accessed here: https://gitlab.inria.fr/soliman/casq Supplementary information Supplementary data are available at Bioinformatics online.

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We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.

Rheumatoid arthritis (RA) is a progressive, inflammatory autoimmune disease of unknown aetiology. The complex mechanism of aetiopathogenesis, progress and chronicity of the disease involves genetic, epigenetic and environmental factors. To understand the molecular mechanisms underlying disease phenotypes, one has to place implicated factors in their functional context. However, integration and organization of such data in a systematic manner remains a challenging task. Molecular maps are widely used in biology to provide a useful and intuitive way of depicting a variety of biological processes and disease mechanisms. Recent large-scale collaborative efforts such as the Disease Maps Project demonstrate the utility of such maps as versatile tools to organize and formalize disease-specific knowledge in a comprehensive way, both human and machine-readable. We present a systematic effort to construct a fully annotated, expert validated, state-of-the-art knowledge base for RA in the form of a molecular map. The RA map illustrates molecular and signalling pathways implicated in the disease. Signal transduction is depicted from receptors to the nucleus using the Systems Biology Graphical Notation (SBGN) standard representation. High-quality manual curation, use of only human-specific studies and focus on small-scale experiments aim to limit false positives in the map. The state-of-the-art molecular map for RA, using information from 353 peer-reviewed scientific publications, comprises 506 species, 446 reactions and 8 phenotypes. The species in the map are classified to 303 proteins, 61 complexes, 106 genes, 106 RNA entities, 2 ions and 7 simple molecules. The RA map is available online at ramap.elixir-luxembourg.org as an open-access knowledge base allowing for easy navigation and search of molecular pathways implicated in the disease. Furthermore, the RA map can serve as a template for omics data visualization.

The Disease Maps Project builds upon a network of scientific and clinical groups that exchange best practices, share information, and develop systems biomedicine tools. The project aims for an integrated, highly curated and user-friendly platform for disease-related knowledge. The primary focus of disease maps is on interconnected signaling, metabolic and gene regulatory network pathways represented in standard formats. The involvement of domain experts ensures that the key disease hallmarks are covered and relevant, up-to-date knowledge is adequately represented. Expert-curated and computer readable, disease maps may serve as a compendium of knowledge, allow for data-supported hypothesis generation, or serve as a scaffold for the generation of predictive mathematical models.This article summarizes the 2nd Disease Maps Community meeting, highlighting its important topics and outcomes. We outline milestones on the roadmap for the future development of disease maps, including creating and maintaining standardized disease maps; sharing parts of maps that encode common human disease mechanisms; providing technical solutions for complexity management of maps; and web-tools for in-depth exploration of such maps. A dedicated discussion was focused on mathematical modeling approaches, as one of the main goals of disease map development is the generation of mathematically interpretable representations in order to predict disease comorbidity or drug response and to suggest drug repositioning, altogether supporting clinical decisions.