BiPOm

BiPOm represents metabolic processes as a rule-based ontology to formalize and infer relationships among enzymes, enzymatic activities, substrates, products, and molecular active states within interlocked cellular subsystems at the whole-cell scale.

Key Features:

• Systemic Representation: Models the cell as networks of interrelated, interlocked subsystems rather than isolated molecular components.
• Formalization and Ontology: Captures relationships between enzymes, their activities, substrates, products, and the active states of molecules using a formal ontology.
• Rule-Based Inference: Leverages logical rules to enable automatic reasoning and the deduction of molecular types and properties from existing data.
• Integration with Existing Data Sources: Can be populated using information extracted from established databases or bio-ontologies.
• Minimal Class/Property Model: Employs a limited set of classes and properties to simplify representation of metabolic processes.

Scientific Applications:

• Systems biology modeling: Representation of whole-cell metabolic organization and interconnections among cellular subsystems.
• Metabolic pathway analysis: Modeling and analysis of pathways through formalized process-centric representations.
• Enzyme and molecular inference: Deduction of enzyme functions, molecular types, and substrate–product relationships via logical reasoning.
• Data integration and knowledge inference: Integration of information from databases and bio-ontologies to support automated knowledge deduction.

Methodology:

Represents metabolic processes as interlocked subsystems using a limited set of classes and properties and applies logical, rule-based inference for automatic deduction of molecular types and properties from data.