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Background A useful model is one that is being (re)used. The development of a successful model does not finish with its publication. During reuse, models are being modified, i.e. expanded, corrected, and refined. Even small changes in the encoding of a model can, however, significantly affect its interpretation. Our motivation for the present study is to identify changes in models and make them transparent and traceable. Methods We analysed 13734 models from BioModels Database and the Physiome Model Repository. For each model, we studied the frequencies and types of updates between its first and latest release. To demonstrate the impact of changes, we explored the history of a Repressilator model in BioModels Database. Results We observed continuous updates in the majority of models. Surprisingly, even the early models are still being modified. We furthermore detected that many updates target annotations, which improves the information one can gain from models. To support the analysis of changes in model repositories we developed MoSt, an online tool for visualisations of changes in models. The scripts used to generate the data and figures for this study are available from GitHub github.com/binfalse/BiVeS-StatsGenerator and as a Docker image at hub.docker.com/r/binfalse/bives-statsgenerator. The website most.bio.informatik.uni-rostock.de provides interactive access to model versions and their evolutionary statistics. Conclusion The reuse of models is still impeded by a lack of trust and documentation. A detailed and transparent documentation of all aspects of the model, including its provenance, will improve this situation. Knowledge about a model’s provenance can avoid the repetition of mistakes that others already faced. More insights are gained into how the system evolves from initial findings to a profound understanding. We argue that it is the responsibility of the maintainers of model repositories to offer transparent model provenance to their users. © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/)

Computational systems biology involves integrating heterogeneous datasets in order to generate models. These models can assist with understanding and prediction of biological phenomena. Generating datasets and integrating them into models involves a wide range of scientific expertise. As a result these datasets are often collected by one set of researchers, and exchanged with others researchers for constructing the models. For this process to run smoothly the data and models must be FAIR—findable, accessible, interoperable, and reusable. In order for data and models to be FAIR they must be structured in consistent and predictable ways, and described sufficiently for other researchers to understand them. Furthermore, these data and models must be shared with other researchers, with appropriately controlled sharing permissions, before and after publication. In this chapter we explore the different data and model standards that assist with structuring, describing, and sharing. We also highlight the popular standards and sharing databases within computational systems biology.

Models are regularly updated, even after publication. The BiVeS library [3, 4, 5] offers comparison of versions of SBML [6] and CellML [7] encoded models. The result of such a comparison is a list of changes, based on the differences in both XML files. While it is now easy to see the differences in a network, it is still not possible to provide - or retrieve - information about the causes and effects of these changes. For example, together with a model update it should be possible to provide information on the characteristics of a change, e.g. stating that the changes remove an error in the kinetics.We manually analysed hundreds of model versions and their differences from the BioModels Database [9] and the CellML Model Repository [8] to study the evolution of computationalmodels. We then derived a vocabulary to describe the differences and implemented it in the COMODI ontology. The OWL encoding and a documentation are available from our website [1]. A Java library [2] allows for easy integration in software projects.We envision to use COMODI for automatic annotation of differences generated in BiVeS, and we like to provide it to modellers who wish to documentthe evolution of their models.[1] http://purl.org/net/comodi[2] https://github.com/binfalse/jCOMODI[3] https://sems.uni-rostock.de/bives/[4] Scharm et al.: An algorithm to detect and communicate the differences in computational models describing biological systems Bioinformatics, 2015[5] Waltemath et al.: Improving the reuse of computational models through version control Bioinformatics, 2013[6] Hucka et al.: The Systems Biology Markup Language(SBML): A medium for representation and exchange of biochemical network models Bioinformatics, 2003[7] Lloyd et al.: CellML: its future, presentand past Progress in Biophysicsand Molecular Biology, 2004[8] Lloyd et al.: The CellML ModelRepository Bioinformatics, 2008[9] Liet al.: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models BMC Systems Biology, 2010

Explanation for the ExampleArchive.omex. (PDF 190 kb)

Computational modeling of cardiac cellular electrophysiology has a long history, and many models are now available for different species, cell types, and experimental preparations. This success brings with it a challenge: how do we assess and compare the underlying hypotheses and emergent behaviors so that we can choose a model as a suitable basis for a new study or to characterize how a particular model behaves in different scenarios? We have created an online resource for the characterization and comparison of electrophysiological cell models in a wide range of experimental scenarios. The details of the mathematical model (quantitative assumptions and hypotheses formulated as ordinary differential equations) are separated from the experimental protocol being simulated. Each model and protocol is then encoded in computer-readable formats. A simulation tool runs virtual experiments on models encoded in CellML, and a website (https://chaste.cs.ox.ac.uk/WebLab) provides a friendly interface, allowing users to store and compare results. The system currently contains a sample of 36 models and 23 protocols, including current-voltage curve generation, action potential properties under steady pacing at different rates, restitution properties, block of particular channels, and hypo-/hyperkalemia. This resource is publicly available, open source, and free, and we invite the community to use it and become involved in future developments. Investigators interested in comparing competing hypotheses using models can make a more informed decision, and those developing new models can upload them for easy evaluation under the existing protocols, and even add their own protocols.