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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. SYNOPSIS COVID‐19 Disease Map is a large‐scale collection of curated computational models and diagrams of molecular mechanisms involved in SARS‐CoV‐2 infection. The map supports the computational exploration of pathways affected by the virus. COVID‐19 Disease Map was built by over 20 independent biocuration teams and harmonised using systems biology standards. Biocuration efforts were assisted by the systematic use of text‐ and AI‐assisted mining of relevant bioinformatic databases and platforms. Case studies illustrate the applications of the map for visual exploration and computational analysis of SARS‐CoV‐2 pathways in combination with omic data. The map is an open‐access effort, with all content and code shared in public repositories. Graphical Abstract COVID‐19 Disease Map is a large‐scale collection of curated computational models and diagrams of molecular mechanisms involved in SARS‐CoV‐2 infection. The map supports the computational exploration of pathways affected by the virus.

Graphical Abstract

Emerging evidence suggests that the interplay between multiple signaling pathways and the immune microenvironment influences tumorigenesis in cancers such as colorectal cancer (CRC). To gain an in-depth understanding of CRC mechanisms and identify novel therapeutic targets, we constructed a molecular interaction map (MIM) integrating key signaling pathways from cancer cells and immune cells within the tumor microenvironment. This map comprises 218 molecules and 328 interactions, curated using PubMed references and official gene symbols. We dynamically simulated the MIM for individual pathways and combinations via stimulus-response and perturbation analyses, calibrating the model with two CRC datasets: GSE1323 (primary tumor-to-metastasis progression) and GSE8671 (normal mucosa-to-adenoma progression), which served as experimental conditions. Simulations revealed distinct disease signatures for GSE1323, including (1) simultaneous activation of TNF/TNFRSF1A,B and EGF/EGFR with inactivation of ERE/ESR, and (2) simultaneous activation of TNF/TNFRSF1A,B and TLR4 with inactivation of ERE/ESR. For GSE8671, the signature was simultaneous activation of TNF/TNFRSF1A,B and TLR4. In silico perturbation analysis identified potent anti-cancer effects from concurrent inhibition of MAPK3 and STAT3 (GSE1323), and ELK1/ATF2 and STAT3 or MAPK14 and STAT3 (GSE8671), significantly reducing epithelial-mesenchymal transition (EMT), proliferation, and inflammation while increasing apoptosis. Disease signatures and therapeutic targets were validated using patient data through Kaplan-Meier survival analysis and machine learning. This integrative model recapitulates cancer biology, predicts biomarkers and therapeutic targets, and is extensible to other immunogenic cancers.

Abstract 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. We discuss the tools, platforms, and guidelines necessary for the distributed development of its contents by a multi-faceted community of biocurators, domain experts, bioinformaticians, and computational biologists. We highlight the role of relevant databases and text mining approaches in enrichment and validation of the curated mechanisms. We describe the contents of the Map and their relevance to the molecular pathophysiology of COVID-19 and the analytical and computational modelling approaches that can be applied for mechanistic data interpretation and predictions. We conclude by demonstrating concrete applications of our work through several use cases and highlight new testable hypotheses. Competing Interest Statement D. Maier and A. Bauch are employed at Biomax Informatics AG and will be affected by any effect of this publication on the commercial version of the AILANI software. J.A. Bachman and B. Gyori received consulting fees from Two Six Labs, LLC. T. Helikar has served as a shareholder and/or has consulted for Discovery Collective, Inc. R. Balling and R. Schneider are founders and shareholders of MEGENO S.A. and ITTM S.A. Remaining authors have declared no competing interests. Footnotes * ↵81 FAIRDOMHub: https://fairdomhub.org/projects/190 * This version introduces small revisions of the authors' affiliations and ORCIDs * http://doi.org/10.17881/covid19-disease-map * https://fairdomhub.org/projects/190 * 4 https://fairdomhub.org/documents/661 * 5 https://reactome.org/community/training * 6 https://www.wikipathways.org/index.php/Help:Editing_Pathways * 7 http://celldesigner.org * 8 https://newteditor.org * 9 https://github.com/sbgn/ySBGN * 12 https://www.ebi.ac.uk/intact/imex/main.xhtml?query=annot:“dataset:coronavirus” * 13 https://signor.uniroma2.it/covid/