Abstract
Genome-scale metabolic models (GSMMs) of human cells are predictive tools with great potential for revealing important aspects of cell physiology, disease as well as for the diagnosis and treatment of diseases caused by the deregulation of metabolism. In the past decade, there have been notable efforts to reconstruct models of human metabolism, with five generic GSMMs currently available. Maintaining references to biological databases is important to allow seamless integration of models themselves and with experimental data. Still, the incorporation of external identifiers is often missed in the model reconstruction process.
In this work, we review the most relevant human GSMMs, analyze the presence of external database identifiers, extract available metabolite annotation and identifiers and create an internal database of metabolites. Using a graph-based system loaded with information from the most relevant omics data repositories, we attempt to cluster similar metabolites through database cross-referencing. With this approach, we have successfully enriched the metabolite annotation of several older GSMMs and identified common entities that could be leveraged in the future towards the creation of a unified consensus model of human metabolism.
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Acknowledgments
This work is co-funded by the North Portugal Regional Operational Programme, under the “Portugal 2020”, through the European Regional Development Fund (ERDF), within project SISBI- RefaNORTE-01-0247-FEDER-003381. This study was also supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684) and BioTecNorte operation (NORTE-01-0145-FEDER-000004) funded by European Regional Development Fund under the scope of Norte2020 - Programa Operacional Regional do Norte. The authors also thank the PhD scholarships funded by national funds through Fundação para a Ciência e Tecnologia, with references: SFRH/BD/118657/2016 (V.V.), SFRH/BD/ 133248/2017 (J.F.) and SFRH/BD/131916/2017 (R.R.).
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Vieira, V., Ferreira, J., Rodrigues, R., Rocha, M. (2019). Metabolite Integration Pipeline for the Improvement of Human Metabolic Models. In: Fdez-Riverola, F., Mohamad, M., Rocha, M., De Paz, J., González, P. (eds) Practical Applications of Computational Biology and Bioinformatics, 12th International Conference. PACBB2018 2018. Advances in Intelligent Systems and Computing, vol 803. Springer, Cham. https://doi.org/10.1007/978-3-319-98702-6_23
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DOI: https://doi.org/10.1007/978-3-319-98702-6_23
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