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

Area of Science:

• Systems Biology
• Bioinformatics
• Ontology Engineering

Background:

• Managing complex biological knowledge is challenging.
• Systemic representations offer a promising approach for whole-cell scale understanding.
• Formalizing cellular processes as interlocked subsystems is crucial.

Purpose of the Study:

• To introduce BiPOm (Biological interlocked Process Ontology for metabolism).
• To provide a formal, rule-based knowledge representation for cellular components.
• To shift knowledge anchorage from molecules to biological processes.

Main Methods:

• Developed BiPOm using a limited set of classes and properties.
• Formalized relationships between enzymes, activities, substrates, products, and molecular states.
• Utilized automatic reasoning with logical rules for deducing molecular information.

Main Results:

• BiPOm represents metabolic processes as interlocked subsystems.
• Explicitly formalized enzyme-substrate-product relationships and molecular activity.
• Demonstrated automatic deduction of molecular types and properties.

Conclusions:

• BiPOm offers a formal, rule-based system for relating cellular components.
• It enables a holistic view of the cell as a system.
• Facilitates knowledge organization by prioritizing biological processes over individual molecules.