BiPOm: a rule-based ontology to represent and infer molecule knowledge from a biological process-centered viewpoint

Background Managing 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 compose...

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Published in:BMC bioinformatics Vol. 21; no. 1; pp. 1 - 18
Main Authors: Henry, Vincent, Saïs, Fatiha, Inizan, Olivier, Marchadier, Elodie, Dibie, Juliette, Goelzer, Anne, Fromion, Vincent
Format: Journal Article
Language:English
Published: London BioMed Central 23.07.2020
Springer Nature B.V
BMC
Subjects:
Algorithms
Bioinformatics
Biological activity
Biomedical and Life Sciences
Cognition & reasoning
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Computer Science
Cultural heritage
Formal Languages and Automata Theory
Knowledge
Knowledge management
Knowledge representation
Knowledge-based analysis
Life Sciences
Logical rules
Metabolic processes
Metabolism
Methodology
Methodology Article
Microarrays
Molecular biology
Ontology
Semantic web
Semantics
Substrates
Subsystems
Web Ontology Language-OWL
Ontology
Logical rules
Metabolic processes
ISSN:1471-2105, 1471-2105
Online Access:Get full text
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Summary:Background Managing 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. Results We introduce BiPOm ( B iological i nterlocked P rocess O ntology for m etabolism) 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. Conclusion BiPOm 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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PMCID: PMC7376860
ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-020-03637-9