=Paper=
{{Paper
|id=Vol-3415/paper-20
|storemode=property
|title=Contributions to the reuse and reproducibility of computational biology models
|pdfUrl=https://ceur-ws.org/Vol-3415/paper-20.pdf
|volume=Vol-3415
|dblpUrl=https://dblp.org/rec/conf/swat4ls/HenkelW23
}}
==Contributions to the reuse and reproducibility of computational biology models==
https://ceur-ws.org/Vol-3415/paper-20.pdf
Contributions to the reuse and reproducibility of
computational biology models
Ron Henkel1,∗ , Dagmar Waltemath1
1
University Medicine Greifswald, Departement Medical Informatics, Institute for Community Medicine,
Walther-Rathenau-Straße 48, 17475 Greifswald, Germany
Abstract
Research that can not be reproduced is of no scientific value - this also holds true for computational
models in systems biology. This poster elucidates the identification of challenges and suggested solutions
for model retrieval and ranking, model version control and model storage. Furthermore, it describes
how models should be managed, stored and retrieved within a knowledge graph in order to foster model
reproducibility and reuse.
Keywords
Graph Database, Systems Biology, Computational Models, Knowledge Graph
The ability to reproduce research is at the heart of science. Results can only be scientifically
relevant if it can be reproduced [1], providing that experimental settings are coherently reported.
This also holds true for computational research, such as models developed to gain deeper insights
into biological systems as it is done in the life sciences and systems biology in particular [2].
With the rapidly increasing number of available models and new ones being developed [3],
crucial tasks for any researcher in this field are to find models of relevance, to keep track of
changes in a model of interest, and to ultimately run the model in order to reproduce scientific
findings from in a publication [4]. However, without a sophisticated storage and organisation of
models, those tasks are in the best case tedious, thus rendering model reuse and reproducibility
of modeling results nearly impossible.
This poster describes how to improve the reuse and reproducibility of computational biology
models by suggesting and implementing innovative and efficient means for model storage [5],
model retrieval [6] and comparison [7, 8], and model provenance [9]. The model management
concepts developed and implemented, provide a graph-based model storage and a sophisticated
model retrieval framework, two crucial steps towards a FAIR model management [10]. In
addition, as the storage and retrieval concept also include model meta-information from a
variety of domains, the thereby created knowledge graph is able to provide results for domain-
spanning queries. Having such a knowledge graph at hand offers a variety of new research
SWAT4HCLS 2023: The 14th International Conference on Semantic Web Applications and Tools for Health Care and Life
Sciences
∗
Corresponding author.
Envelope-Open ron.henkel@uni-greifswald.de (R. Henkel); dagmar.waltemath@uni-greifswald.de (D. Waltemath)
GLOBE https://www.medizin.uni-greifswald.de/medizininformatik/ (R. Henkel);
https://www.medizin.uni-greifswald.de/medizininformatik/ (D. Waltemath)
Orcid 0000-0001-6211-2719 (R. Henkel); 0000-0002-5886-5563 (D. Waltemath)
© 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
CEUR
http://ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
possibilities, e.g., for model analysis [11] and data exploration [12].
With a scientifically broader view, reusing concepts and implementations developed in context
of systems biology in medical sciences seems to be the next logical step. Such a transition will
connect different domains such as systems biology, systems medicine and medical informatics
and might serve as a way to bridge the gaps between those scientific disciplines [13].
References
[1] O. Wolkenhauer, Why model?, Frontiers in physiology 5 (2014) 21.
[2] R. A. McDougal, A. S. Bulanova, W. W. Lytton, Reproducibility in computational neuro-
science models and simulations, IEEE Transactions on Biomedical Engineering 63 (2016)
2021–2035.
[3] R. S. Malik-Sheriff, M. Glont, T. V. Nguyen, K. Tiwari, M. G. Roberts, A. Xavier, M. T. Vu,
J. Men, M. Maire, S. Kananathan, et al., Biomodels—15 years of sharing computational
models in life science, Nucleic acids research 48 (2020) D407–D415.
[4] D. Waltemath, O. Wolkenhauer, How modeling standards, software, and initiatives support
reproducibility in systems biology and systems medicine, IEEE Transactions on Biomedical
Engineering 63 (2016) 1999–2006.
[5] R. Henkel, O. Wolkenhauer, D. Waltemath, Combining computational models, semantic
annotations and simulation experiments in a graph database, Database 2015 (2015) bau130.
[6] R. Henkel, L. Endler, A. Peters, N. Le Novère, D. Waltemath, Ranked retrieval of computa-
tional biology models, BMC bioinformatics 11 (2010) 423.
[7] R. Henkel, R. Hoehndorf, T. Kacprowski, C. Knüpfer, W. Liebermeister, D. Waltemath,
Notions of similarity for systems biology models, Briefings in Bioinformatics (2016)
bbw090.
[8] M. Scharm, O. Wolkenhauer, D. Waltemath, An algorithm to detect and communicate
the differences in computational models describing biological systems, Bioinformatics 32
(2015) 563–570.
[9] D. Waltemath, R. Henkel, R. Hälke, M. Scharm, O. Wolkenhauer, Improving the reuse of
computational models through version control, Bioinformatics 29 (2013) 742–748.
[10] M. D. Wilkinson, M. Dumontier, I. J. Aalbersberg, G. Appleton, M. Axton, A. Baak,
N. Blomberg, J.-W. Boiten, L. B. da Silva Santos, P. E. Bourne, et al., The fair guiding
principles for scientific data management and stewardship, Scientific data 3 (2016).
[11] R. Alm, D. Waltemath, M. Wolfien, O. Wolkenhauer, R. Henkel, Annotation-based feature
extraction from sets of sbml models, Journal of biomedical semantics 6 (2015) 20.
[12] F. Lambusch, D. Waltemath, O. Wolkenhauer, K. Sandkuhl, C. Rosenke, R. Henkel, Iden-
tifying frequent patterns in biochemical reaction networks: a workflow, Database 2018
(2018).
[13] L. Gütebier, T. Bleimehl, R. Henkel, J. Munro, S. Müller, A. Morgner, J. Laenge, A. Pachauer,
A. Erdl, J. Weimar, K. Walther Langendorf, V. Vialard, T. Liebig, M. Preusse, D. Waltemath,
A. Jarasch, CovidGraph: a graph to fight COVID-19, Bioinformatics 38 (2022) 4843–4845.
doi:1 0 . 1 0 9 3 / b i o i n f o r m a t i c s / b t a c 5 9 2.