Having fun reusing computational biology models? - and why not?
Creators: name: Waltemath, Dagmar
name: Wolkenhauer, Olaf
name: Winterhalter, Christian
name: Becker, Markus M
name: König, Matthias
name: Nickerson, David
name: Balaur, Irina
Publisher: name: Zenodo
Year: 2023
Subjects: COMBINE, FID, Systems Medicine

description: Computational modeling and simulation is a standard tool in many disciplines relating to biology and medicine. Virtual experiments simulate the functioning of biomedical processes and systems on various scales and levels of detail. We believe that adherence to FAIR principles supports reproducibility, semantic descriptions of model constituents, and accessibility of all model-related data. The ability to comprehend, and potentially reuse, previous works and resources eases the burden for scientists to develop new models, methods, and tools to make impactful contributions. This desire to reuse has led to a variety of standards, guidelines, and principles to be considered when publishing virtual experiments. Among them are widely accepted requirements such as those for Good Scientific Practice or the FAIR Guiding Principles for Data Stewardship (Wilkinson et al., https://doi.org/10.1038/sdata.2016.18). The FAIR principles suggest criteria for sharing a virtual experiment with respect to findability, accessibility, interoperability and ultimately reusability of the work. The systems biology community also requests reproducibility of their simulation projects (Niarakis and Waltemath et al., https://doi.org/10.1093/bib/bbac212). For example, the EOSC-funded project (https://fair-ca-indicators.github.io/) aims to raise awareness for FAIR in the COMBINE community and specifically to promote the assessment of the quality of research outcomes using a coordinated FAIR assessment. We emphasise the benefits of following and applying FAIR principles throughout the model life-cycle, from the model development stage through to after the model is released. The main challenge in this project is achieving a community-level accepted set of FAIR indicators to capture the specifics of the COMBINE resources. For this, we rely on the engagement of community members. We continuously do this community-wide engagement with workshops and training sessions, transparent communication, online dissemination and involvement acknowledgement - all our work components are open and freely-accessible and connected via our project website to major dedicated frameworks such as zenodo for dissemination material, github for code files etc. We also target dissemination of our ongoing work, results, lessons learnt and challenges to the broader community of Computational Biology and Open Science. Following the domain-specific requirements defined in Minimum Information Guidelines for models (Le Novere et al., https://doi.org/10.1038/nbt1156) and simulations (Waltemath et al., https://doi.org/10.1371/journal.pcbi.1001122) is a first step in the direction of FAIR model sharing. Beyond the minimum requirements, using appropriate standard formats (e.g., SBML, CellML, SBGN, SED-ML, see also König et al., https://doi.org/10.1515/jib-2023-0004), together with semantic information in the form of relevant ontologies (Neal et al., https://doi.org/10.1093/bib/bby087) such as SBO (Courtot et al., https://doi.org/10.1038/msb.2011.77) and software libraries (Waltemath et al., https://doi.org/10.1515/jib-2020-0005) further enhances the FAIRness of a given study. We will present here a high-level overview of this ecosystem, which is maintained by the COMBINE Coordination Board (https://co.mbine.org/). We will show examples of community efforts for model FAIRification (e.g., https://www.ebi.ac.uk/biomodels/covid-19), and we will briefly discuss why - despite all these community efforts - model reuse is still not as enjoyable and as simple as it should be. In fact, most studies remain non-reproducible (Höpfl et al., https://doi.org/10.1038/s41598-023-29340-2; Tiwari et al., https://doi.org/10.1038/s41598-023-29340-2). Our hypothesis is that sufficient information, guidance, tool support, and incentives are missing in our community. As one consequence we wish to promote the idea of establishing an open information source for this research domain. This resource will connect disconnected resources, provide metadata about existing standards, models and guidelines and it will support scientists, for example, in discovering appropriate tools and formats, evaluating the reproducibility of their work, measuring the FAIRness of their work over time, and accessing community tools.