WikiPathways (https://www.wikipathways.org) is a biological pathway database known for its collaborative nature and open science approaches. With the core idea of the scientific community developing and curating biological knowledge in pathway models, WikiPathways lowers all barriers for accessing and using its content. Increasingly more content creators, initiatives, projects and tools have started using WikiPathways. Central in this growth and increased use of WikiPathways are the various communities that focus on particular subsets of molecular pathways such as for rare diseases and lipid metabolism. Knowledge from published pathway figures helps prioritize pathway development, using optical character and named entity recognition. We show the growth of WikiPathways over the last three years, highlight the new communities and collaborations of pathway authors and curators, and describe various technologies to connect to external resources and initiatives. The road toward a sustainable, community-driven pathway database goes through integration with other resources such as Wikidata and allowing more use, curation and redistribution of WikiPathways content.

Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction‐based models and packages that extend the core with features suited to other model types including constraint‐based models, reaction‐diffusion models, logical network models, and rule‐based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single‐cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution.

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...

The FAIR principles have been widely cited, endorsed and adopted by a broad range of stakeholders since their publication in 2016. By intention, the 15 FAIR guiding principles do not dictate specific technological implementations, but provide guidance for improving Findability, Accessibility, Interoperability and Reusability of digital resources. This has likely contributed to the broad adoption of the FAIR principles, because individual stakeholder communities can implement their own FAIR solutions. However, it has also resulted in inconsistent interpretations that carry the risk of leading to incompatible implementations. Thus, while the FAIR principles are formulated on a high level and may be interpreted and implemented in different ways, for true interoperability we need to support convergence in implementation choices that are widely accessible and (re)-usable. We introduce the concept of FAIR implementation considerations to assist accelerated global participation and convergence towards accessible, robust, widespread and consistent FAIR implementations. Any self-identified stakeholder community may either choose to reuse solutions from existing implementations, or when they spot a gap, accept the challenge to create the needed solution, which, ideally, can be used again by other communities in the future. Here, we provide interpretations and implementation considerations (choices and challenges) for each FAIR principle.

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