Abstract
The biology is a research field well known for its huge quantity and diversity of data. Today, these data are still recognized as heterogeneous and fragmented. Despite the fact that several initiatives of biological knowledge representation have been realized, biologists and bioinformaticians do not have a formal representation that, at the level of the entire organism, can help them to organize such a diversity and quantity of data. Recently, in the context of the whole cell modeling approach, the systemic mathematical models have proven to be a powerful tool for understanding the bacterial cell behavior. We advocate that an ontology built on the principles that govern the design of such models, can help to organize the biological data. In this article, we describe the first step in the conception of an ontology dedicated to biological data organization at the level of the entire organism and for molecular scales i.e., the choice of concepts and relations compliant with principles at work in the systemic mathematical models.
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References
Gene Ontology Consortium: The gene ontology resource: 20 years and still GOing strong. Nucleic Acids Res. 47(D1), D330–D338 (2019)
Demir, E., Cary, M.P., Paley, S., et al.: The BioPAX community standard for pathway data sharing. Nat. Bbiotechnol. 28(9), 935–942 (2010)
Goelzer, A., Muntel, J., Chubukov, V., et al.: Quantitative prediction of genome-wide resource allocation in bacteria. Metab. Eng. 32, 232–243 (2015)
Hartwell, L.H., Hopfield, J.J., Leibler, S., et al.: From molecular to modular cell biology. Nature 402(6761), C47–C52 (1999)
Henry, V., Saïs, F., Inizan, O., et al.: BiPOm: a rule-based ontology to represent and infer molecule knowledge from a biological process-centered viewpoint. BMC Bioinform. 21(1), 1–18 (2020)
Henry, V.J., Goelzer, A., Ferré, A., et al.: The bacterial interlocked process ONtology (BiPON): a systemic multi-scale unified representation of biological processes in prokaryotes. J. Biomed. Semant. 8(1), 1–16 (2017)
Hucka, M., Finney, A., Sauro, H.M., et al.: The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19(4), 524–531 (2003)
Joyce, A.R., Palsson, B.Ø.: The model organism as a system: integrating ‘omics’ data sets. Nat. Rev. Mol. Cell Biol. 7(3), 198–210 (2006)
Karr, J.R., Sanghvi, J.C., Macklin, D.N., et al.: A whole-cell computational model predicts phenotype from genotype. Cell 150(2), 389–401 (2012)
López de Maturana, E., Alonso, L., Alarcón, P., et al.: Challenges in the integration of omics and non-omics data. Genes 10(3), 238 (2019)
Michaelis, L., Menten, M.L., et al.: Die kinetik der invertinwirkung. Biochem. z 49(333–369), 352 (1913)
Nicolas, J.: Artificial intelligence and bioinformatics. In: Marquis, P., Papini, O., Prade, H. (eds.) A Guided Tour of Artificial Intelligence Research, pp. 209–264. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-06170-8_7
Ramon, C., Gollub, M.G., Stelling, J.: Integrating-omics data into genome-scale metabolic network models: principles and challenges. Essays Biochem. 62(4), 563–574 (2018)
Reuter, J.A., Spacek, D.V., Snyder, M.P.: High-throughput sequencing technologies. Mol. Cell 58(4), 586–597 (2015)
Voit, E.O.: Computational Analysis of Biochemical Systems: A Practical Guide for Biochemists and Molecular Biologists. Cambridge University Press, Cambridge (2000)
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Inizan, O., Fromion, V., Goelzer, A., Saïs, F., Symeonidou, D. (2022). An Ontology to Structure Biological Data: The Contribution of Mathematical Models. In: Garoufallou, E., Ovalle-Perandones, MA., Vlachidis, A. (eds) Metadata and Semantic Research. MTSR 2021. Communications in Computer and Information Science, vol 1537. Springer, Cham. https://doi.org/10.1007/978-3-030-98876-0_5
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