BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models

Li, Chen; Donizelli, Marco; Rodríguez, Nicolás; Dharuri, Harish; Endler, Lukas; Chelliah, Vijayalakshmi; Li, Lu; He, Enuo; Henry, Arnaud; Stefan, Melanie I.; Snoep, Jacky L.; Hucka, Michael; Novère, Nicolas Le; Laibe, Camille

doi:10.1186/1752-0509-4-92

Cited by 503 publications

(421 citation statements)

References 41 publications

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“…A steadily increasing number of computational models are available from open repositories such as the BioModels Database [1] or the Physiome Model Repository (PMR2, [2]). These repositories provide the infrastructures necessary to collect and maintain model code and associated meta data.…”

Section: Introductionmentioning

confidence: 99%

An algorithm to detect and communicate the differences in computational models describing biological systems

Scharm

Waltemath

2014

Preprint

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Repositories, such as the BioModels Database and the Physiome Model Repository support the reuse of models and ensure transparency about results in publications linked to those models. With thousands of models available, a framework to track the differences between models and their versions is essential to compare and combine models. Difference detection allows users to study the history of models but also helps in the detection of errors and inconsistencies. However, current repositories lack suitable methods to track a model's development over time. Consequently, researchers have problems to grasp the differences between models and their versions.Focusing on SBML and CellML, we developed an algorithm to accurately detect and describe differences between versions of a model with respect to (i) the models' encoding, (ii) the structure of biological networks, and (iii) mathematical expressions. Our method is implemented in a comprehensive and open library called BiVeS. Our work facilitates the reuse and extension of existing models. It also supports collaborative modelling. Finally, it contributes to better reproducibility of modelling results and to the challenge of model provenance.Our algorithm is the first tailor-made detector of differences between versions of computational models in standard formats.

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Section: Introductionmentioning

confidence: 99%

An algorithm to detect and communicate the differences in computational models describing biological systems

Scharm

Waltemath

2014

Preprint

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“…This ensures ground truth in model annotation as annotations in the curated branch are manually reviewed [5]. In addition to the cell cycle set, the two random sets (RS1 and RS2), the thematic test sets for apoptosis (APOP), calcium oscillation (CA) and NF-κB (NFKB), and the set containing all 490 curated models (BMDB) were assembled from the curated branch.…”

Section: Test Setsmentioning

confidence: 99%

“…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%

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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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“…This is routinely done in large biochemical model bases (see e.g. [4]), but these checks cannot detect every deficiency that may arise from the many different uses (simulation, parameter estimation, experiment design, etc.) of these models.…”

Section: Introductionmentioning

confidence: 99%

On the Verification and Correction of Large-Scale Kinetic Models in Systems Biology

Gábor

Hangos

Szederkényi

et al. 2013

Computational Methods in Systems Biology

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Abstract. In this paper we consider the problem of verification of large dynamic models of biological systems. We present syntactical criteria based on biochemical kinetics to ensure the plausibility of a model and the positivity of its solution. These criteria include the positivity of the rate functions, their kinetic type dependence on the reactant species concentrations, and the absence of the negative cross-effects that together guarantee the nonnegativity of the dynamics. Further, the stoichiometric matrix of the truncated reaction system is checked against conservation using its algebraic properties. Algorithmic procedures are then proposed for checking these criteria with emphasis on good scaling up properties. In addition to these verification procedures, we also provide, for certain typical errors, model correcting methods. The capabilities and usefulness of these procedures are illustrated on biochemical models taken from the Biomodels database. In particular, a set of 11 kinetic models related with E. coli are checked, finding two with deficiencies. Correcting actions for these models are proposed.

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BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models

Cited by 503 publications

References 41 publications

An algorithm to detect and communicate the differences in computational models describing biological systems

An algorithm to detect and communicate the differences in computational models describing biological systems

Annotation-Based Feature Extraction from Sets of SBML Models

On the Verification and Correction of Large-Scale Kinetic Models in Systems Biology

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