Improving the reuse of computational models through version control

Waltemath, Dagmar; Henkel, Ron; Hälke, Robert; Scharm, Martin

doi:10.1093/bioinformatics/btt018

Cited by 28 publications

(32 citation statements)

References 27 publications

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“…From the software tools surveyed, currently only Tools-4-Metatool (Xavier et al, 2011), Compare Subsystems (Oberhardt et al, 2011), SemanticSBML (Krause et al, 2010), COBRA (Becker et al, 2007), The FAME (Boele et al, 2012), MetRxn (Kumar et al, 2012), BudHat (Waltemath et al, 2013), PINT (Wang et al, 2010) and MEMOSys (Pabinger et al, 2011) provide functionality that is related to the comparison of models. Software tools mostly rely on internal or external identifiers, like KEGG ID and ChEBI ID and do not tolerate even small differences in metabolite names like brackets, quotes, apostrophes, spaces, upper/lower case letters and some more symbols which may be caused by the modelers style of defining metabolites.…”

Section: Current Approach Of Metabolic Model Comparisonmentioning

confidence: 99%

Comparative Analysis of Biochemical Network Reconstructions

Mednis¹

2016

BJMC

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Abstract. Reconstruction of genome-scale metabolic network is a result of assembling various information sources about all biochemical reactions expected in the metabolic network of interest. Despite the efforts of leading bio-models databases to make comparison of biochemical networks by elements names obsolete, interest from researchers in using string similarity metrics in comparison of metabolites names has been growing. Multiple challenges in comparison of reconstructions are discussed in this article and an insight into current approach of metabolic model comparison has been given. The discussion of challenges and attempts to solve them are followed by the author's proposed algorithm for models comparison that can be particularly useful in case of reconstructions. Special attention is given to the use of metabolites names and chemical formulas. The author's proposed algorithm has been implemented in a software tool ModeRator. The article is concluded with use cases of the comparison algorithm and the software tool.

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Section: Current Approach Of Metabolic Model Comparisonmentioning

confidence: 99%

Comparative Analysis of Biochemical Network Reconstructions

Mednis¹

2016

BJMC

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“…In concert with systems for difference detection between models (e.g. (Waltemath et al, 2013)), one could identify the specific model change(s) leading to differences in behaviour. By integrating functional curation as a public service, both for model repositories and model developers, researchers may easily characterise or benchmark their models under a wide range of scenarios (including easily varying published experiments to address their own specific setup), while checking whether standard behaviours are retained.…”

Section: Challenges and Visions Of A Futurementioning

confidence: 99%

“…Papers in computational biology focus on describing and analyzing the effects of such changes, and on confronting models with experimental data (Hilborn, 1997). This confrontation often generates new hypotheses, and many if not most new models arise by modification of existing ones (Smith et al, 2007;Waltemath et al, 2013). However, most virtual experiments are not built to be reproducible (Waltemath et al, 2011b), and thus die with the paper they are published in.…”

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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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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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“…Examples for neglected meta-data include the structure of the model (Henkel 65 et al, 2012), model versions (Waltemath et al, 2013a), and associated simulation setups (Waltemath et al, 66 2013b). Given the eorts made to encode the biological knowledge in bio-ontologies, and then to link model 67 entities with semantic information, the current situation is indeed unsatisfying.…”

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confidence: 99%

Combining computational models, semantic annotations, and associated simulation experiments in a graph database

Henkel

Walthemath

2014

Preprint

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13Model repositories such as the BioModels Database or the CellML Model Repository are 14 frequently accessed to retrieve computational models describing biological systems. However, 15 the current designs of these databases limit the types of supported queries, and many data 16 in these repositories cannot easily be accessed. Computational methods for model retrieval 17 cannot be applied. In this paper we present a storage concept that meets this challenge. It 18 grounds on a graph database, reects the models' structure, incorporates semantic annotations 19 and experiment descriptions, and ultimately connects dierent types of model-related data. 20The connections between heterogeneous model-related data and bio-ontologies enable ecient 21 search via biological facts and grant access to new model features such as network structure. 22The introduced concept notably improves the access of computational models and associated

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Improving the reuse of computational models through version control

Cited by 28 publications

References 27 publications

Comparative Analysis of Biochemical Network Reconstructions

Comparative Analysis of Biochemical Network Reconstructions

A call for virtual experiments: Accelerating the scientific process

Combining computational models, semantic annotations, and associated simulation experiments in a graph database

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