The Physiome Model Repository 2

Yu, Tingting; Lloyd, Catherine M.; Nickerson, David; Cooling, Michael T.; Miller, Andrew; Garny, Alan; Terkildsen, Jonna; Lawson, James R.; Britten, Randall; Hunter, Peter; Nielsen, Poul M. F.

doi:10.1093/bioinformatics/btq723

Cited by 171 publications

(151 citation statements)

References 9 publications

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“…automated confrontation between data and models based on (real and virtual) experiments. This would complement the existing curation of model repositories (Li et al, 2010;Yu et al, 2011) that ensures that a model contains valid mathematics, that units are consistent on both sides of an equation, and so on. With models, data, and virtual experiments available as distinct entities from shared repositories in standard formats, newly published models or datasets can be picked up by the system and analysed automatically under all suitable virtual experiments.…”

Section: Behavioural Specifications For Quality Assurance Model Reusmentioning

confidence: 99%

“…Several generations of computational models of individual cardiac cells of varying types and complexity have now been developed, and more than 100 are available within the CellML Model Repository (Yu et al, 2011). Such models take the form of (often large) systems of coupled nonlinear ordinary differential equations modelling processes such as the influx and efflux of ions across the cell membrane in response to, and generating, changes in the transmembrane voltage.…”

Section: Box 1: Terminologymentioning

confidence: 99%

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A call for virtual experiments: Accelerating the scientific process

Cooper

Vik

Waltemath

2015

Progress in Biophysics and Molecular Biology

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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. Dear Sirs,We enclose a manuscript titled "A call for virtual experiments: accelerating the scientific process" for inclusion within the Special Issue on "Multi-bio and multi-scale systems biology" in Progress in Biophysics and Molecular Biology. In it we propose the concept of "virtual experiments" as the in silico analogues of wet-lab or field experiments, and argue that this concept deserves as much attention as the computational models to which such experiments are applied. Promoting the reuse of virtual 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.Experiments are fundamental to the scientific method, whether for exploration, description or explanation. Experiments should therefore be as central in computational biology as in other sciences, for studying the implications of theories or hypotheses encoded as computational models. In current practice, however, experiments applied to models are usually embedded in opaque computer code, which is often not available for public scrutiny. We discuss how the protocol for a computational experiment should be viewed as an entity in its own right, and cleanly separated from the description of the model itself, and from surrounding code. Annotations can then facilitate the linking of models to experiment descriptions and any corresponding data available.This approach can provide many benefits for scientific progress: in specifying, assaying, and comparing the behavioural repertoires of models; as a prerequisite for reproducible research; to guide model reuse and composition; and for quality assurance in the application of computational biology models. The paper lays out these benefits to motivate wider adoption and tool support for virtual experiments. We also present a "vision of the future" in which virtual experim...

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Section: Behavioural Specifications For Quality Assurance Model Reusmentioning

confidence: 99%

Section: Box 1: Terminologymentioning

confidence: 99%

A call for virtual experiments: Accelerating the scientific process

Cooper

Vik

Waltemath

2015

Progress in Biophysics and Molecular Biology

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“…The emerging networks are furthermore enriched with semantic annotations [3] which link model parts to external knowledge in domain-specific ontologies (bioontologies) [4]. Many SBML models live in open model repositories such as BioModels Database [5], the Physiome Model Repository [6], or JWS Online [7]. These repositories distribute computational models and associated data in standard formats.…”

Section: Introductionmentioning

confidence: 99%

Annotation-Based Feature Extraction from Sets of SBML Models

Alm

Waltemath

Wolkenauer

et al. 2014

Lecture Notes in Computer Science

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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.

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“…A range of public databases exists for depositing computational models in a systematic way, including the BioModels database (Le Novère et al, 2006), the modelDB (Hines et al, 2004), the Database of Quantitative Cellular Signalling (DOQCS) (Sivakumaran et al, 2003) and Physiome (Yu et al, 2009;Yu et al, 2011). Although these databases make it much easier to retrieve the definitions and codes of published simulation models, reproducing an observation in a simulation model also requires exact knowledge of many additional factors including the initial conditions, the parameters used, the integration times, etc.…”

Section: Introductionmentioning

confidence: 99%

A Web-Based Repository of Reproducible Simulation Experiments for Systems Biology

Guravage

Merks

2011

Proceedings of 1st International Conference on Simulation and Modeling Methodologies, Technologies and Applications

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Abstract:Systems Biology requires increasingly complex simulation models. Effectively interpreting and building upon previous simulation results is both difficult and time consuming. Thus, simulation results often cannot be reproduced exactly; making it difficult for other modellers to validate results and take the next step in a simulation study. The Simulation Experiment Description Mark-up Language (SED-ML), a subset of the Minimum Information About a Simulation Experiment (MIASE) guidelines, promises to solve this problem by prescribing the form and content of the information required to reproduce simulation experiments. SED-ML is detailed enough to enable automatic rerunning of simulation experiments.Here, we present a web-based simulation-experiment repository that lets modellers develop SED-ML compliant simulation-experiment descriptions The system encourages modellers to annotate their experiments with text and images, experimental data and domain meta-information. These informal annotations aid organisation and classification of the simulations and provide rich search criteria. They complement SED-ML's formal precision to produce simulation-experiment descriptions that can be understood by both men and machines. The system combines both human-readable and formal machine-readable content, thus ensuring exact reproducibility of the simulation results of a modelling study.

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The Physiome Model Repository 2

Abstract: PMR2 is available under an open source license at http://www.cellml.org/tools/pmr/; a fully functional instance of this software can be accessed at http://models.physiomeproject.org/.

Cited by 171 publications

References 9 publications

A call for virtual experiments: Accelerating the scientific process

A call for virtual experiments: Accelerating the scientific process

Annotation-Based Feature Extraction from Sets of SBML Models

A Web-Based Repository of Reproducible Simulation Experiments for Systems Biology

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