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The need to harness large amounts of data, possibly within a short period of time, became apparent during the Covid-19 pandemic outbreak. In 2022, the Corona Data Exchange Platform (CODEX), which had been developed within the German Network University Medicine (NUM), was extended by a number of common components, including a section on FAIR science. The FAIR principles enable research networks to evaluate how well they comply with current standards in open and reproducible science. To be more transparent, but also to guide scientists on how to improve data and software reusability, we disseminated an online survey within the NUM. Here we present the outcomes and lessons learnt.

Extensive workflows have been designed to FAIRify data from various domains. These tend to be cumbersome and overwhelming. This work summarises our own experiences with FAIRification in health data management and provides simple steps that can be implemented to achieve a relatively low but improved level of FAIRness. The steps lead the data steward to register the data in a repository and then annotate it with the metadata recommended by that repository. It further leads the data steward to provide the data in a machine-readable format using an established and accessible language, establish a well-defined framework to describe and structure the (meta)data as well as publish the (meta)data. We hope that following the simple roadmap described in this work helps to demystify the FAIR data principles in the health domain.

The interest in the application of AI in medicine has intensely increased over the past decade with most of the changes in the past five years. Most recently, the application of deep learning algorithms in prediction and classification of cardiovascular diseases (CVD) using computed tomography (CT) images showed promising results. The notable and exciting advancement in this area of study is, however, associated with different challenges related to the findability (F), accessibility(A), interoperability(I), reusability(R) of both data and source code. The aim of this work is to identify reoccurring missing FAIR-related features and to assess the level of FAIRness of data and models used to predict/diagnose cardiovascular diseases from CT images. We evaluated the FAIRness of data and models in published studies using the RDA (Research Data Alliance) FAIR Data maturity model and FAIRshake toolkit. The finding showed that although AI is anticipated to bring ground breaking solutions for complex medical problems, the findability, accessibility, interoperability and reusability of data/metadata/code is still a prominent challenge.

HealthECCO is the driving force behind the COVID-19 knowledge graph spanning multiple biomedical data domains. One way to access CovidGraph is SemSpect, an interface designed for data exploration in graphs. To showcase the possibilities that arise from integrating a variety of COVID-19 related data sources over the last three years, we present three use cases from the (bio-)medical domain. Availability: The project is open source and freely available from: https://healthecco.org/covidgraph/. The source code and documentation are available on GitHub: https://github.com/covidgraph.

In medicine and biomedical research, sex- and gender-related aspects are ubiquitous. If not considered adequately, a lower quality of research data can be expected together with a lower generalizability of study results with real-world settings. From a translational perspective, a lack of sex- and gender-sensitivity in acquired data can have negative implications for diagnosis, treatment (outcome and side effects), and risk prediction. To establish improved recognition and reward settings we set out to develop a pilot of systemic sex and gender awareness in a German medical faculty, with actions such as implementing equality in routine clinical practice and research, as well as in scientific practice (incl. science education). We believe that the change of culture will have a positive effect on research outcomes, lead to a rethinking in the scientific domain, foster sex- and gender-related clinical studies, and influence the design of good scientific practices.

Summary: Reliable and integrated data are prerequisites for effective research on the recent coronavirus disease 2019 (COVID-19) pandemic. The CovidGraph project integrates and connects heterogeneous COVID-19 data in a knowledge graph, referred to as 'CovidGraph'. It provides easy access to multiple data sources through a single point of entry and enables flexible data exploration., Availability and Implementation: More information on CovidGraph is available from the project website: https://healthecco.org/covidgraph/. Source code and documentation are provided on GitHub: https://github.com/covidgraph., Supplementary Information: Supplementary data is available at Bioinformatics online., (© The Author(s) 2022. Published by Oxford University Press.)

Computational models are often employed in systems biology to study the dynamic behaviours of complex systems. With the rise in the number of computational models, finding ways to improve the reusability of these models and their ability to reproduce virtual experiments becomes critical. Correct and effective model annotation in community-supported and standardised formats is necessary for this improvement. Here, we present recent efforts toward a common framework for annotated, accessible, reproducible and interoperable computational models in biology, and discuss key challenges of the field., (© The Author(s) 2022. Published by Oxford University Press.)

Bibliometric analysis is a scientific method that allows researchers to explore the current trend in a certain research area using citation information. This study aims to provide a meta-view of artificial intelligence studies focused on biomedicine in the last five years, which will provide an insight into current trends and future research directions. Besides the observation of increased publication rates in the area of AI in biomedicine, the results indicate a lower contribution from and a sparser network connectivity of countries with limited resources. Thus, working toward collaboration in terms of infrastructure and implementing alternative solutions such as FAIR (Findable, Accessible, Interoperable and Reproducible) and open access platforms could improve the collaborative nature of international health projects.

CovidGraph, developed by the HealthECCO community, is a platform designed to foster research and data exploration to fight COVID-19. It is built on a graph database and encompasses data sources from different biomedical data domains including publications, clinical trials, patents, case statistics, molecular data and systems biology models. The tool provides multiple interfaces for data exploration and thus serves as a single point of entry for data driven COVID-19 research. Availability and Implementation: CovidGraph is available from the project website: https://healthecco.org/covidgraph/. The source code and documentation are provided on GitHub: https://github.com/covidgraph.

Background: Retrospective research on real-world data provides the ability to gain evidence on specific topics especially when running across different sites in research networks. Those research networks have become increasingly relevant in recent years; not least due to the special situation caused by the COVID-19 pandemic. An important requirement for those networks is the data harmonization by ensuring the semantic interoperability. Aims: In this paper we demonstrate (1) how to facilitate digital infrastructures to run a retrospective study in a research network spread across university and non-university hospital sites; and (2) to answer a medical question on COVID-19 related change in diagnostic counts for diabetes-related eye diseases. Materials and methods: The study is retrospective and non-interventional and runs on medical case data documented in routine care at the participating sites. The technical infrastructure consists of the OMOP CDM and other OHDSI tools that is provided in a transferable format. An ETL process to transfer and harmonize the data to the OMOP CDM has been utilized. Cohort definitions for each year in observation have been created centrally and applied locally against medical case data of all participating sites and analyzed with descriptive statistics. Results: The analyses showed an expectable drop of the total number of diagnoses and the diagnoses for diabetes in general; whereas the number of diagnoses for diabetes-related eye diseases surprisingly decreased stronger compared to non-eye diseases. Differences in relative changes of diagnoses counts between sites show an urgent need to process multi-centric studies rather than single-site studies to reduce bias in the data. Conclusions: This study has demonstrated the ability to utilize an existing portable and standardized infrastructure and ETL process from a university hospital setting and transfer it to non-university sites. From a medical perspective further activity is needed to evaluate data quality of the utilized real-world data documented in routine care and to investigate its eligibility of this data for research.

The evolution of biochemical models is difficult to track. At present, it is not possible to inspect the differences between model versions at the network level. Biochemical models are often constructed in a distributed, non-linear process: collaborators create model versions on different branches from novel information, model extensions, during curation and adaption. To discuss and align the versions, it is helpful to abstract the changes to the network level. The differences between two model versions can be detected by the software tool BiVeS. However, it cannot show the structural changes resulting from the differences. Here, we present a method to visualise the differences between model versions effectively. We developed a JSON schema to communicate the differences at the network level and extended BiVeS accordingly. Additionally, we developed DiVil, a web-based tool to represent the model and the differences as a standardised network using D3. It combines an automatic layout with an interactive user interface to improve the visualisation and to inspect the model. The network can be exported in standardised formats as images or markup language. Our method communicates the structural differences between model versions. It facilitates the discussion of changes and thus supports the collaborative and non-linear nature of model development. Availability and implementation: DiVil prototype: https://divil.bio.informatik.uni-rostock.de, Code on GitHub: https://github.com/Gebbi8/DiVil, licensed under Apache License 2.0. Contact: url="tom.gebhardt@uni-rostock.de., Competing Interests: The authors have declared that no competing interests exist.

Background: Provenance supports the understanding of data genesis, and it is a key factor to ensure the trustworthiness of digital objects containing (sensitive) scientific data. Provenance information contributes to a better understanding of scientific results and fosters collaboration on existing data as well as data sharing. This encompasses defining comprehensive concepts and standards for transparency and traceability, reproducibility, validity, and quality assurance during clinical and scientific data workflows and research., Objective: The aim of this scoping review is to investigate existing evidence regarding approaches and criteria for provenance tracking as well as disclosing current knowledge gaps in the biomedical domain. This review covers modeling aspects as well as metadata frameworks for meaningful and usable provenance information during creation, collection, and processing of (sensitive) scientific biomedical data. This review also covers the examination of quality aspects of provenance criteria., Methods: This scoping review will follow the methodological framework by Arksey and O'Malley. Relevant publications will be obtained by querying PubMed and Web of Science. All papers in English language will be included, published between January 1, 2006 and March 23, 2021. Data retrieval will be accompanied by manual search for grey literature. Potential publications will then be exported into a reference management software, and duplicates will be removed. Afterwards, the obtained set of papers will be transferred into a systematic review management tool. All publications will be screened, extracted, and analyzed: title and abstract screening will be carried out by 4 independent reviewers. Majority vote is required for consent to eligibility of papers based on the defined inclusion and exclusion criteria. Full-text reading will be performed independently by 2 reviewers and in the last step, key information will be extracted on a pretested template. If agreement cannot be reached, the conflict will be resolved by a domain expert. Charted data will be analyzed by categorizing and summarizing the individual data items based on the research questions. Tabular or graphical overviews will be given, if applicable., Results: The reporting follows the extension of the Preferred Reporting Items for Systematic reviews and Meta-Analyses statements for Scoping Reviews. Electronic database searches in PubMed and Web of Science resulted in 469 matches after deduplication. As of September 2021, the scoping review is in the full-text screening stage. The data extraction using the pretested charting template will follow the full-text screening stage. We expect the scoping review report to be completed by February 2022., Conclusions: Information about the origin of healthcare data has a major impact on the quality and the reusability of scientific results as well as follow-up activities. This protocol outlines plans for a scoping review that will provide information about current approaches, challenges, or knowledge gaps with provenance tracking in biomedical sciences., International Registered Report Identifier (irrid): DERR1-10.2196/31750., (©Kerstin Gierend, Frank Krüger, Dagmar Waltemath, Maximilian Fünfgeld, Thomas Ganslandt, Atinkut Alamirrew Zeleke. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 22.11.2021.)

A comprehensible representation of a molecular network is key to communicating and understanding scientific results in systems biology. The Systems Biology Graphical Notation (SBGN) has emerged as the main standard to represent such networks graphically. It has been implemented by different software tools, and is now largely used to communicate maps in scientific publications. However, learning the standard, and using it to build large maps, can be tedious. Moreover, SBGN maps are not grounded on a formal semantic layer and therefore do not enable formal analysis. Here, we introduce a new set of patterns representing recurring concepts encountered in molecular networks, called SBGN bricks. The bricks are structured in a new ontology, the Bricks Ontology (BKO), to define clear semantics for each of the biological concepts they represent. We show the usefulness of the bricks and BKO for both the template-based construction and the semantic annotation of molecular networks. The SBGN bricks and BKO can be freely explored and downloaded at sbgnbricks.org., (© The Author(s) 2021. Published by Oxford University Press.)

COVID-19 poses a major challenge to individuals and societies around the world. Yet, it is difficult to obtain a good overview of studies across different medical fields of research such as clinical trials, epidemiology, and public health. Here, we describe a consensus metadata model to facilitate structured searches of COVID-19 studies and resources along with its implementation in three linked complementary web-based platforms. A relational database serves as central study metadata hub that secures compatibilities with common trials registries (e.g. ICTRP and standards like HL7 FHIR, CDISC ODM, and DataCite). The Central Search Hub was developed as a single-page application, the other two components with additional frontends are based on the SEEK platform and MICA, respectively. These platforms have different features concerning cohort browsing, item browsing, and access to documents and other study resources to meet divergent user needs. By this we want to promote transparent and harmonized COVID-19 research.

Background: Data stewardship is an essential driver of research and clinical practice. Data collection, storage, access, sharing, and analytics are dependent on the proper and consistent use of data management principles among the investigators. Since 2016, the FAIR (findable, accessible, interoperable, and reusable) guiding principles for research data management have been resonating in scientific communities. Enabling data to be findable, accessible, interoperable, and reusable is currently believed to strengthen data sharing, reduce duplicated efforts, and move toward harmonization of data from heterogeneous unconnected data silos. FAIR initiatives and implementation trends are rising in different facets of scientific domains. It is important to understand the concepts and implementation practices of the FAIR data principles as applied to human health data by studying the flourishing initiatives and implementation lessons relevant to improved health research, particularly for data sharing during the coronavirus pandemic., Objective: This paper aims to conduct a scoping review to identify concepts, approaches, implementation experiences, and lessons learned in FAIR initiatives in the health data domain., Methods: The Arksey and O'Malley stage-based methodological framework for scoping reviews will be used for this review. PubMed, Web of Science, and Google Scholar will be searched to access relevant primary and grey publications. Articles written in English and published from 2014 onwards with FAIR principle concepts or practices in the health domain will be included. Duplication among the 3 data sources will be removed using a reference management software. The articles will then be exported to a systematic review management software. At least two independent authors will review the eligibility of each article based on defined inclusion and exclusion criteria. A pretested charting tool will be used to extract relevant information from the full-text papers. Qualitative thematic synthesis analysis methods will be employed by coding and developing themes. Themes will be derived from the research questions and contents in the included papers., Results: The results will be reported using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-analyses Extension for Scoping Reviews) reporting guidelines. We anticipate finalizing the manuscript for this work in 2021., Conclusions: We believe comprehensive information about the FAIR data principles, initiatives, implementation practices, and lessons learned in the FAIRification process in the health domain is paramount to supporting both evidence-based clinical practice and research transparency in the era of big data and open research publishing., International Registered Report Identifier (irrid): PRR1-10.2196/22505., (©Esther Thea Inau, Jean Sack, Dagmar Waltemath, Atinkut Alamirrew Zeleke. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 02.02.2021.)

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., (© 2020 California Institute of Technology Published under the terms of the CC BY 4.0 license.)

Life science researchers use computational models to articulate and test hypotheses about the behavior of biological systems. Semantic annotation is a critical component for enhancing the interoperability and reusability of such models as well as for the integration of the data needed for model parameterization and validation. Encoded as machine-readable links to knowledge resource terms, semantic annotations describe the computational or biological meaning of what models and data represent. These annotations help researchers find and repurpose models, accelerate model composition and enable knowledge integration across model repositories and experimental data stores. However, realizing the potential benefits of semantic annotation requires the development of model annotation standards that adhere to a community-based annotation protocol. Without such standards, tool developers must account for a variety of annotation formats and approaches, a situation that can become prohibitively cumbersome and which can defeat the purpose of linking model elements to controlled knowledge resource terms. Currently, no consensus protocol for semantic annotation exists among the larger biological modeling community. Here, we report on the landscape of current annotation practices among the COmputational Modeling in BIology NEtwork community and provide a set of recommendations for building a consensus approach to semantic annotation., (© The Author(s) 2018. Published by Oxford University Press.)

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.

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 https://github.com/binfalse/BiVeS-StatsGenerator and as a Docker image at https://hub.docker.com/r/binfalse/bives-statsgenerator/ . The website https://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.

Systems biology models are rapidly increasing in complexity, size and numbers. When building large models, researchers rely on software tools for the retrieval, comparison, combination and merging of models, as well as for version control. These tools need to be able to quantify the differences and similarities between computational models. However, depending on the specific application, the notion of 'similarity' may greatly vary. A general notion of model similarity, applicable to various types of models, is still missing. Here we survey existing methods for the comparison of models, introduce quantitative measures for model similarity, and discuss potential applications of combined similarity measures. To frame model comparison as a general problem, we describe a theoretical approach to defining and computing similarities based on a combination of different model aspects. The six aspects that we define as potentially relevant for similarity are underlying encoding, references to biological entities, quantitative behaviour, qualitative behaviour, mathematical equations and parameters and network structure. We argue that future similarity measures will benefit from combining these model aspects in flexible, problem-specific ways to mimic users' intuition about model similarity, and to support complex model searches in databases., (© The Author 2016. Published by Oxford University Press.)

Computational models in biology encode molecular and cell biological processes. Many of these models can be represented as biochemical reaction networks. Studying such networks, one is mostly interested in systems that share similar reactions and mechanisms. Typical goals of an investigation thus include understanding of model parts, identification of reoccurring patterns and recognition of biologically relevant motifs. The large number and size of available models, however, require automated methods to support researchers in achieving their goals. Specifically for the problem of finding patterns in large networks only partial solutions exist. We propose a workflow that identifies frequent structural patterns in biochemical reaction networks encoded in the Systems Biology Markup Language. The workflow utilizes a subgraph mining algorithm to detect the network patterns. Once patterns are identified, the textual pattern description can automatically be converted into a graphical representation. Furthermore, information about the distribution of patterns among a selected set of models can be retrieved. The workflow was validated with 575 models from the curated branch of BioModels. In this paper, we highlight interesting and frequent structural patterns. Furthermore, we provide exemplary patterns that incorporate terms from the Systems Biology Ontology. Our workflow can be applied to a custom set of models or to models already existing in our graph database MaSyMoS. The occurrences of frequent patterns may give insight into the encoding of central biological processes, evaluate postulated biological motifs or serve as a similarity measure for models that share common structures.Database URL: https://github.com/FabienneL/BioNet-Mining.

Summary: JWS Online is a web-based platform for construction, simulation and exchange of models in standard formats. We have extended the platform with a database for curated simulation experiments that can be accessed directly via a URL, allowing one-click reproduction of published results. Users can modify the simulation experiments and export them in standard formats. The Simulation database thus lowers the bar on exploring computational models, helps users create valid simulation descriptions and improves the reproducibility of published simulation experiments., Availability and Implementation: The Simulation Database is available on line at https://jjj.bio.vu.nl/models/experiments/ ., Contact: jls@sun.ac.za ., (© The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com)

Summary: The Simulation Experiment Description Markup Language (SED-ML) is a standardized format for exchanging simulation studies independently of software tools. We present the SED-ML Web Tools, an online application for creating, editing, simulating and validating SED-ML documents. The Web Tools implement all current SED-ML specifications and, thus, support complex modifications and co-simulation of models in SBML and CellML formats. Ultimately, the Web Tools lower the bar on working with SED-ML documents and help users create valid simulation descriptions., Availability and Implementation: http://sysbioapps.dyndns.org/SED-ML_Web_Tools/ ., Contact: fbergman@caltech.edu ., (© The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com)

Background: When modeling in Systems Biology and Systems Medicine, the data is often extensive, complex and heterogeneous. Graphs are a natural way of representing biological networks. Graph databases enable efficient storage and processing of the encoded biological relationships. They furthermore support queries on the structure of biological networks., Results: We present the Java-based framework STON (SBGN TO Neo4j). STON imports and translates metabolic, signalling and gene regulatory pathways represented in the Systems Biology Graphical Notation into a graph-oriented format compatible with the Neo4j graph database., Conclusion: STON exploits the power of graph databases to store and query complex biological pathways. This advances the possibility of: i) identifying subnetworks in a given pathway; ii) linking networks across different levels of granularity to address difficulties related to incomplete knowledge representation at single level; and iii) identifying common patterns between pathways in the database.

Objective: Only reproducible results are of significance to science. The lack of suitable standards and appropriate support of standards in software tools has led to numerous publications with irreproducible results. Our objectives are to identify the key challenges of reproducible research and to highlight existing solutions., Results: In this paper, we summarize problems concerning reproducibility in systems biology and systems medicine. We focus on initiatives, standards, and software tools that aim to improve the reproducibility of simulation studies., Conclusions: The long-term success of systems biology and systems medicine depends on trustworthy models and simulations. This requires openness to ensure reusability and transparency to enable reproducibility of results in these fields.

Objective: Whole-cell (WC) modeling is a promising tool for biological research, bioengineering, and medicine. However, substantial work remains to create accurate comprehensive models of complex cells., Methods: We organized the 2015 Whole-Cell Modeling Summer School to teach WC modeling and evaluate the need for new WC modeling standards and software by recoding a recently published WC model in the Systems Biology Markup Language., Results: Our analysis revealed several challenges to representing WC models using the current standards., Conclusion: We, therefore, propose several new WC modeling standards, software, and databases., Significance: We anticipate that these new standards and software will enable more comprehensive models.

COMBINE archives are standardised containers for data files related to a simulation study in computational biology. This manuscript describes a fully featured archive of a previously published simulation study, including (i) the original publication, (ii) the model, (iii) the analyses, and (iv) metadata describing the files and their origin. With the archived data at hand, it is possible to reproduce the results of the original work. The archive can be used for both, educational and research purposes. Anyone may reuse, extend and update the archive to make it a valuable resource for the scientific community., Competing Interests: No competing interests were disclosed.

Background: Open model repositories provide ready-to-reuse computational models of biological systems. Models within those repositories evolve over time, leading to different model versions. Taken together, the underlying changes reflect a model's provenance and thus can give valuable insights into the studied biology. Currently, however, changes cannot be semantically interpreted. To improve this situation, we developed an ontology of terms describing changes in models. The ontology can be used by scientists and within software to characterise model updates at the level of single changes. When studying or reusing a model, these annotations help with determining the relevance of a change in a given context., Methods: We manually studied changes in selected models from BioModels and the Physiome Model Repository. Using the BiVeS tool for difference detection, we then performed an automatic analysis of changes in all models published in these repositories. The resulting set of concepts led us to define candidate terms for the ontology. In a final step, we aggregated and classified these terms and built the first version of the ontology., Results: We present COMODI, an ontology needed because COmputational MOdels DIffer. It empowers users and software to describe changes in a model on the semantic level. COMODI also enables software to implement user-specific filter options for the display of model changes. Finally, COMODI is a step towards predicting how a change in a model influences the simulation results., Conclusion: COMODI, coupled with our algorithm for difference detection, ensures the transparency of a model's evolution, and it enhances the traceability of updates and error corrections. COMODI is encoded in OWL. It is openly available at http://comodi.sems.uni-rostock.de/ .

Motivation: Repositories support the reuse of models and ensure transparency about results in publications linked to those models. With thousands of models available in repositories, such as the BioModels database or the Physiome Model Repository, a framework to track the differences between models and their versions is essential to compare and combine models. Difference detection not only allows users to study the history of models but also helps in the detection of errors and inconsistencies. Existing repositories lack algorithms to track a model's development over time., Results: Focusing on SBML and CellML, we present an algorithm to accurately detect and describe differences between coexisting versions of a model with respect to (i) the models' encoding, (ii) the structure of biological networks and (iii) mathematical expressions. This algorithm is implemented in a comprehensive and open source library called BiVeS. BiVeS helps to identify and characterize changes in computational models and thereby contributes to the documentation of a model's history. Our work facilitates the reuse and extension of existing models and supports collaborative modelling. Finally, it contributes to better reproducibility of modelling results and to the challenge of model provenance., Availability and Implementation: The workflow described in this article is implemented in BiVeS. BiVeS is freely available as source code and binary from sems.uni-rostock.de. The web interface BudHat demonstrates the capabilities of BiVeS at budhat.sems.uni-rostock.de., (© The Author 2015. Published by Oxford University Press.)

Background: Model repositories such as BioModels Database provide computational models of biological systems for the scientific community. These models contain rich semantic annotations that link model entities to concepts in well-established bio-ontologies such as Gene Ontology. Consequently, thematically similar models are likely to share similar annotations. Based on this assumption, we argue that semantic annotations are a suitable tool to characterize sets of models. These characteristics improve model classification, allow to identify additional features for model retrieval tasks, and enable the comparison of sets of models., Results: In this paper we discuss four methods for annotation-based feature extraction from model sets. We tested all methods on sets of models in SBML format which were composed from BioModels Database. To characterize each of these sets, we analyzed and extracted concepts from three frequently used ontologies, namely Gene Ontology, ChEBI and SBO. We find that three out of the methods are suitable to determine characteristic features for arbitrary sets of models: The selected features vary depending on the underlying model set, and they are also specific to the chosen model set. We show that the identified features map on concepts that are higher up in the hierarchy of the ontologies than the concepts used for model annotations. Our analysis also reveals that the information content of concepts in ontologies and their usage for model annotation do not correlate., Conclusions: Annotation-based feature extraction enables the comparison of model sets, as opposed to existing methods for model-to-keyword comparison, or model-to-model comparison.

Model repositories such as the BioModels Database, the CellML Model Repository or JWS Online are frequently accessed to retrieve computational models of biological systems. However, their storage concepts support only restricted types of queries and not all data inside the repositories can be retrieved. In this article we present a storage concept that meets this challenge. It grounds on a graph database, reflects the models' structure, incorporates semantic annotations and simulation descriptions and ultimately connects different types of model-related data. The connections between heterogeneous model-related data and bio-ontologies enable efficient search via biological facts and grant access to new model features. The introduced concept notably improves the access of computational models and associated simulations in a model repository. This has positive effects on tasks such as model search, retrieval, ranking, matching and filtering. Furthermore, our work for the first time enables CellML- and Systems Biology Markup Language-encoded models to be effectively maintained in one database. We show how these models can be linked via annotations and queried. Database URL: https://sems.uni-rostock.de/projects/masymos/, (© The Author(s) 2015. Published by Oxford University Press.)

The Computational Modeling in Biology Network (COMBINE) is a consortium of groups involved in the development of open community standards and formats used in computational modeling in biology. COMBINE's aim is to act as a coordinator, facilitator, and resource for different standardization efforts whose domains of use cover related areas of the computational biology space. In this perspective article, we summarize COMBINE, its general organization, and the community standards and other efforts involved in it. Our goals are to help guide readers toward standards that may be suitable for their research activities, as well as to direct interested readers to relevant communities where they can best expect to receive assistance in how to develop interoperable computational models.

Experimentation is fundamental to the scientific method, whether for exploration, description or explanation. We argue that promoting the reuse of virtual experiments (the in silico analogues of wet-lab or field experiments) would vastly improve the usefulness and relevance of computational models, encouraging critical scrutiny of models and serving as a common language between modellers and experimentalists. We review the benefits of reusable virtual experiments: in specifying, assaying, and comparing the behavioural repertoires of models; as prerequisites for reproducible research; to guide model reuse and composition; and for quality assurance in the translational application of models. A key step towards achieving this is that models and experimental protocols should be represented separately, but annotated so as to facilitate the linking of models to experiments and data. Lastly, we outline how the rigorous, streamlined confrontation between experimental datasets and candidate models would enable a "continuous integration" of biological knowledge, transforming our approach to systems biology., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

Background: With the ever increasing use of computational models in the biosciences, the need to share models and reproduce the results of published studies efficiently and easily is becoming more important. To this end, various standards have been proposed that can be used to describe models, simulations, data or other essential information in a consistent fashion. These constitute various separate components required to reproduce a given published scientific result., Results: We describe the Open Modeling EXchange format (OMEX). Together with the use of other standard formats from the Computational Modeling in Biology Network (COMBINE), OMEX is the basis of the COMBINE Archive, a single file that supports the exchange of all the information necessary for a modeling and simulation experiment in biology. An OMEX file is a ZIP container that includes a manifest file, listing the content of the archive, an optional metadata file adding information about the archive and its content, and the files describing the model. The content of a COMBINE Archive consists of files encoded in COMBINE standards whenever possible, but may include additional files defined by an Internet Media Type. Several tools that support the COMBINE Archive are available, either as independent libraries or embedded in modeling software., Conclusions: The COMBINE Archive facilitates the reproduction of modeling and simulation experiments in biology by embedding all the relevant information in one file. Having all the information stored and exchanged at once also helps in building activity logs and audit trails. We anticipate that the COMBINE Archive will become a significant help for modellers, as the domain moves to larger, more complex experiments such as multi-scale models of organs, digital organisms, and bioengineering.

Motivation: Only models that are accessible to researchers can be reused. As computational models evolve over time, a number of different but related versions of a model exist. Consequently, tools are required to manage not only well-curated models but also their associated versions., Results: In this work, we discuss conceptual requirements for model version control. Focusing on XML formats such as Systems Biology Markup Language and CellML, we present methods for the identification and explanation of differences and for the justification of changes between model versions. In consequence, researchers can reflect on these changes, which in turn have considerable value for the development of new models. The implementation of model version control will therefore foster the exploration of published models and increase their reusability.

Background: The increasing use of computational simulation experiments to inform modern biological research creates new challenges to annotate, archive, share and reproduce such experiments. The recently published Minimum Information About a Simulation Experiment (MIASE) proposes a minimal set of information that should be provided to allow the reproduction of simulation experiments among users and software tools., Results: In this article, we present the Simulation Experiment Description Markup Language (SED-ML). SED-ML encodes in a computer-readable exchange format the information required by MIASE to enable reproduction of simulation experiments. It has been developed as a community project and it is defined in a detailed technical specification and additionally provides an XML schema. The version of SED-ML described in this publication is Level 1 Version 1. It covers the description of the most frequent type of simulation experiments in the area, namely time course simulations. SED-ML documents specify which models to use in an experiment, modifications to apply on the models before using them, which simulation procedures to run on each model, what analysis results to output, and how the results should be presented. These descriptions are independent of the underlying model implementation. SED-ML is a software-independent format for encoding the description of simulation experiments; it is not specific to particular simulation tools. Here, we demonstrate that with the growing software support for SED-ML we can effectively exchange executable simulation descriptions., Conclusions: With SED-ML, software can exchange simulation experiment descriptions, enabling the validation and reuse of simulation experiments in different tools. Authors of papers reporting simulation experiments can make their simulation protocols available for other scientists to reproduce the results. Because SED-ML is agnostic about exact modeling language(s) used, experiments covering models from different fields of research can be accurately described and combined.

The use of computational modeling to describe and analyze biological systems is at the heart of systems biology. Model structures, simulation descriptions and numerical results can be encoded in structured formats, but there is an increasing need to provide an additional semantic layer. Semantic information adds meaning to components of structured descriptions to help identify and interpret them unambiguously. Ontologies are one of the tools frequently used for this purpose. We describe here three ontologies created specifically to address the needs of the systems biology community. The Systems Biology Ontology (SBO) provides semantic information about the model components. The Kinetic Simulation Algorithm Ontology (KiSAO) supplies information about existing algorithms available for the simulation of systems biology models, their characterization and interrelationships. The Terminology for the Description of Dynamics (TEDDY) categorizes dynamical features of the simulation results and general systems behavior. The provision of semantic information extends a model's longevity and facilitates its reuse. It provides useful insight into the biology of modeled processes, and may be used to make informed decisions on subsequent simulation experiments.