ArticleBMC bioinformatics2020
BiPOm: a rule-based ontology to represent and infer molecule knowledge from a biological process-centered viewpoint.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- The Immunopeptidomics Ontology (ImPO).Database : the journal of biological databases and curation · 2024Article
- The Identification of Key Genes and Biological Pathways in Heart Failure by Integrated Bioinformatics Analysis.Computational and mathematical methods in medicine · 2021Article
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Authors and funding
7 authors.
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Abstract
backgroundManaging and organizing biological knowledge remains a major challenge, due to the complexity of living systems. Recently, systemic representations have been promising in tackling such a challenge at the whole-cell scale. In such representations, the cell is considered as a system composed of interlocked subsystems. The need is now to define a relevant formalization of the systemic description of cellular processes.
resultsWe introduce BiPOm (Biological interlocked Process Ontology for metabolism) an ontology to represent metabolic processes as interlocked subsystems using a limited number of classes and properties. We explicitly formalized the relations between the enzyme, its activity, the substrates and the products of the reaction, as well as the active state of all involved molecules. We further showed that the information of molecules such as molecular types or molecular properties can be deduced by automatic reasoning using logical rules. The information necessary to populate BiPOm can be extracted from existing databases or existing bio-ontologies.
conclusionBiPOm provides a formal rule-based knowledge representation to relate all cellular components together by considering the cellular system as a whole. It relies on a paradigm shift where the anchorage of knowledge is rerouted from the molecule to the biological process. AVAILABILITY: BiPOm can be downloaded at https://github.com/SysBioInra/SysOnto.
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