The JWS online simulation database

Peters, Martin; Eicher, Johann J.; Niekerk, David D. van; Waltemath, Dagmar; Snoep, Jacky L.

doi:10.1093/bioinformatics/btw831

Cited by 35 publications

(26 citation statements)

References 6 publications

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“…Similarly, deposition of models to open repositories including, but not limited to BioModels, Physiome 50 or JWSOnline 51 will get an additional score as they promote FAIR sharing. The advantage of submitting models to open model repositories include provision of (1) a sophisticated search engine to make models findable, (2) a version-controlled storage system to make the models readily accessible, (3) support for interoperable standard formats and (4) curation and annotation services to promote reusability.…”

Section: Discussionmentioning

confidence: 99%

Reproducibility in systems biology modelling

Tiwari

Kananathan

Roberts

et al. 2020

Preprint

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The reproducibility crisis has emerged as an important concern across many fields of science including life science, since many published results failed to reproduce. Systems biology modelling, which involves mathematical representation of biological processes to study complex system behaviour, was expected to be least affected by this crisis. While lack of reproducibility of experimental results and computational analysis could be a repercussion of several compounded factors, it was not fully understood why systems biology models with well defined mathematical expressions fail to reproduce and how prevalent it is. Hence, we systematically attempted to reproduce 455 kinetic models of biological processes published in peer-reviewed research articles from 152 journals; which is collectively a work of about 1400 scientists from 49 countries. Our investigation revealed that about half (49%) of the models could not be reproduced using the information provided in the published manuscripts. With further effort, an additional 12% of the models could be reproduced either by empirical correction or support from authors. The other 37% remained non-reproducible models due to missing parameter values, missing initial concentration, inconsistent model structure, or a combination of these factors. Among the corresponding authors of the non-reproducible model we contacted, less than 30% responded. Our analysis revealed that models published in journals across several fields of life science failed to reproduce, revealing a common problem in the peer-review process. Hence, we propose an 8-point reproducibility scorecard that can be used by authors, reviewers and journal editors to assess each model and address the reproducibility crisis.

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Section: Discussionmentioning

confidence: 99%

Reproducibility in systems biology modelling

Tiwari

Kananathan

Roberts

et al. 2020

Preprint

View full text Add to dashboard Cite

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“…It also helps ensure that the tools themselves are accurate and free of idiosyncrasies that could affect the analysis results. Model repositories such as BioModels [ 58 , 59 ], JWS Online [ 60 , 61 ], and the CellML model repository [ 62 ] have enabled widespread support for the SBML and CellML standards. We believe that better tool support for SED–ML and COMBINE archives will help create a trend toward better adoption of these formats by repositories.…”

Section: Discussionmentioning

confidence: 99%

“…The availability of authoring tools such as Tellurium will make it possible for model repositories to begin implementing support for SED–ML and COMBINE archives. Indeed, the JWS Online repository [ 60 , 61 ] already has support for exporting COMBINE archives of models and simulations, which can be read by Tellurium. We hope that other databases will follow suit so that it will be possible to automatically extract dynamical information from these repositories.…”

Section: Discussionmentioning

confidence: 99%

Tellurium notebooks—An environment for reproducible dynamical modeling in systems biology

et al. 2018

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The considerable difficulty encountered in reproducing the results of published dynamical models limits validation, exploration and reuse of this increasingly large biomedical research resource. To address this problem, we have developed Tellurium Notebook, a software system for model authoring, simulation, and teaching that facilitates building reproducible dynamical models and reusing models by 1) providing a notebook environment which allows models, Python code, and narrative to be intermixed, 2) supporting the COMBINE archive format during model development for capturing model information in an exchangeable format and 3) enabling users to easily simulate and edit public COMBINE-compliant models from public repositories to facilitate studying model dynamics, variants and test cases. Tellurium Notebook, a Python–based Jupyter–like environment, is designed to seamlessly inter-operate with these community standards by automating conversion between COMBINE standards formulations and corresponding in–line, human–readable representations. Thus, Tellurium brings to systems biology the strategy used by other literate notebook systems such as Mathematica. These capabilities allow users to edit every aspect of the standards–compliant models and simulations, run the simulations in–line, and re–export to standard formats. We provide several use cases illustrating the advantages of our approach and how it allows development and reuse of models without requiring technical knowledge of standards. Adoption of Tellurium should accelerate model development, reproducibility and reuse.

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“…Complementary efforts that link models with associated datasets also contribute to this goal. For example, the Simulation Database 62…”

Section: Challenges In Model Reuse and Integrationmentioning

confidence: 99%

Harmonizing semantic annotations for computational models in biology

Neal

König

Nickerson

et al. 2018

Preprint

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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 semantic annotation practices among the COmputational Modeling in BIology NEtwork (COMBINE) community and provide a set of recommendations for building a consensus approach to semantic annotation.All rights reserved. No reuse allowed without permission.(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

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The JWS online simulation database

Cited by 35 publications

References 6 publications

Reproducibility in systems biology modelling

Reproducibility in systems biology modelling

Tellurium notebooks—An environment for reproducible dynamical modeling in systems biology

Harmonizing semantic annotations for computational models in biology

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