Digital Biology Ontology

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FLOW ALGEBRA — DIGITAL BIOLOGY ONTOLOGY
Semantic Library
Version: 4.0
Canonical Page:
https://kirethresearch.org/booksai-core/ontologies/biological-ontologies/ai-biology/digital-biology-ontology.html

FLOW DIGITAL_BIOLOGY_ONTOLOGY {

  META {
    DOMAIN = "Biological Ontologies"
    SUBDOMAIN = "AI Biology"
    ONTOLOGY = "Digital Biology Ontology"
    PURPOSE = "Formalise entities, relationships, computational representations and reasoning processes connecting biological systems with digital models, simulation, artificial intelligence and machine-readable biological knowledge."
    LOCALE = "en-GB"
  }

  ENTITY_TYPES {
    BIOLOGICAL_ENTITY
    DIGITAL_ENTITY
    DIGITAL_REPRESENTATION
    COMPUTATIONAL_MODEL
    AI_SYSTEM
    AI_MODEL
    MACHINE_LEARNING_MODEL
    SIMULATION_MODEL
    DIGITAL_TWIN
    BIOLOGICAL_SYSTEM
    ORGANISM
    ORGAN
    TISSUE
    CELL
    ORGANELLE
    MOLECULE
    DNA
    RNA
    GENE
    PROTEIN
    METABOLITE
    GENOME
    TRANSCRIPTOME
    PROTEOME
    METABOLOME
    EPIGENOME
    PHENOTYPE
    TRAIT
    PATHWAY
    BIOLOGICAL_PROCESS
    MOLECULAR_FUNCTION
    CELLULAR_COMPONENT
    INTERACTION
    NETWORK
    REGULATORY_NETWORK
    METABOLIC_NETWORK
    SIGNALLING_NETWORK
    BIOLOGICAL_STATE
    DIGITAL_STATE
    EVENT
    PROCESS
    OBSERVATION
    MEASUREMENT
    SENSOR_DATA
    EXPERIMENT
    SAMPLE
    DATASET
    FEATURE
    EMBEDDING
    PREDICTION
    SIMULATION
    KNOWLEDGE_GRAPH
    BIOLOGICAL_ONTOLOGY
    EVIDENCE
    CONFIDENCE_SCORE
    PROVENANCE_RECORD
  }

  RELATIONSHIPS {
    IS_A
    PART_OF
    CONTAINS
    LOCATED_IN
    ENCODES
    EXPRESSED_IN
    TRANSLATED_TO
    INTERACTS_WITH
    REGULATES
    ACTIVATES
    INHIBITS
    PARTICIPATES_IN
    PRODUCES
    CONSUMES
    TRANSFORMS
    HAS_STATE
    CHANGES_TO
    OBSERVED_AS
    MEASURED_BY
    REPRESENTED_BY
    DIGITISED_AS
    MODELLED_BY
    SIMULATED_BY
    PREDICTED_BY
    EMBEDDED_AS
    MAPS_TO
    SYNCHRONISED_WITH
    DERIVED_FROM
    TRAINED_ON
    VALIDATED_ON
    INFERRED_FROM
    SUPPORTED_BY
    CONTRADICTED_BY
    HAS_EVIDENCE
    HAS_CONFIDENCE
    HAS_PROVENANCE
    SEMANTICALLY_EQUIVALENT_TO
  }

  INPUTS {
    BIOLOGICAL_OBSERVATIONS
    MOLECULAR_DATA
    GENOMIC_DATA
    TRANSCRIPTOMIC_DATA
    PROTEOMIC_DATA
    METABOLOMIC_DATA
    EPIGENOMIC_DATA
    CELLULAR_DATA
    PHENOTYPE_DATA
    IMAGING_DATA
    SENSOR_DATA
    TEMPORAL_DATA
    EXPERIMENTAL_DATA
    ENVIRONMENTAL_DATA
    SIMULATION_DATA
    BIOLOGICAL_KNOWLEDGE_GRAPHS
    ONTOLOGY_TERMS
    SCIENTIFIC_EVIDENCE
  }

  NORMALISE {
    STANDARDISE_IDENTIFIERS
    NORMALISE_BIOLOGICAL_ENTITIES
    NORMALISE_UNITS
    NORMALISE_TEMPORAL_REFERENCES
    NORMALISE_SPATIAL_REFERENCES
    ALIGN_MULTI_OMICS_IDENTIFIERS
    ALIGN_ONTOLOGY_TERMS
    RESOLVE_ENTITY_IDENTITIES
    REMOVE_DUPLICATES
    VALIDATE_METADATA
    PRESERVE_PROVENANCE
  }

  PROCESS DIGITAL_BIOLOGY_PIPELINE {
    BIOLOGICAL_INPUT
      -> DATA_ACQUISITION
      -> DATA_NORMALISATION
      -> ENTITY_RESOLUTION
      -> DIGITAL_REPRESENTATION
      -> MODEL_CONSTRUCTION
      -> SIMULATION_OR_AI_INFERENCE
      -> BIOLOGICAL_INTERPRETATION
      -> EVIDENCE_INTEGRATION
      -> CONFIDENCE_ESTIMATION
      -> OUTPUT
  }

  PROCESS BIOLOGICAL_DIGITISATION {
    BIOLOGICAL_ENTITY + OBSERVATION + MEASUREMENT
      -> DIGITAL_ENTITY

    DIGITAL_ENTITY
      -> CANONICAL_IDENTIFIER
      -> MACHINE_READABLE_PROPERTIES
      -> RELATIONSHIPS
      -> PROVENANCE_RECORD
  }

  PROCESS MULTI_OMICS_INTEGRATION {
    GENOME
    + TRANSCRIPTOME
    + PROTEOME
    + METABOLOME
    + EPIGENOME
      -> CROSS_LAYER_ENTITY_MAPPING

    CROSS_LAYER_ENTITY_MAPPING
      -> INTEGRATED_BIOLOGICAL_REPRESENTATION

    INTEGRATED_BIOLOGICAL_REPRESENTATION
      -> PATTERN_DISCOVERY
      -> RELATION_INFERENCE
      -> SYSTEM_STATE_ESTIMATION
  }

  PROCESS DIGITAL_TWIN_REASONING {
    BIOLOGICAL_SYSTEM
      -> DIGITAL_TWIN

    BIOLOGICAL_OBSERVATIONS
      -> DIGITAL_TWIN_STATE_UPDATE

    DIGITAL_TWIN
      -> SIMULATION
      -> PREDICTION
      -> HYPOTHESIS_GENERATION

    NEW_OBSERVATION
      -> MODEL_COMPARISON
      -> MODEL_UPDATE
      -> CONFIDENCE_UPDATE
  }

  PROCESS BIOLOGICAL_NETWORK_MODELLING {
    BIOLOGICAL_ENTITIES + INTERACTIONS
      -> BIOLOGICAL_NETWORK

    BIOLOGICAL_NETWORK
      -> TOPOLOGY_ANALYSIS
      -> PATH_ANALYSIS
      -> DYNAMIC_MODELLING
      -> PERTURBATION_SIMULATION
      -> SYSTEM_BEHAVIOUR_PREDICTION
  }

  PROCESS TEMPORAL_STATE_REASONING {
    BIOLOGICAL_STATE(T1)
      + EVENT
      -> STATE_TRANSITION

    STATE_TRANSITION
      -> BIOLOGICAL_STATE(T2)

    SEQUENCE_OF_STATES
      -> TEMPORAL_MODEL
      -> TRAJECTORY_PREDICTION
  }

  PROCESS KNOWLEDGE_GRAPH_INTEGRATION {
    BIOLOGICAL_ENTITY
    + DIGITAL_REPRESENTATION
    + PROCESS
    + INTERACTION
    + EVIDENCE
      -> DIGITAL_BIOLOGY_KNOWLEDGE_GRAPH

    DIGITAL_BIOLOGY_KNOWLEDGE_GRAPH
      -> ENTITY_RESOLUTION
      -> LINK_PREDICTION
      -> RELATION_INFERENCE
      -> EXPLANATION_PATH
  }

  AI_TASKS {
    BIOLOGICAL_ENTITY_RECOGNITION
    ENTITY_RESOLUTION
    MULTI_OMICS_INTEGRATION
    BIOLOGICAL_REPRESENTATION_LEARNING
    BIOLOGICAL_STATE_ESTIMATION
    BIOLOGICAL_STATE_PREDICTION
    NETWORK_INFERENCE
    PATHWAY_INFERENCE
    TEMPORAL_MODELLING
    DYNAMIC_SYSTEM_MODELLING
    DIGITAL_TWIN_MODELLING
    BIOLOGICAL_SIMULATION
    PERTURBATION_PREDICTION
    PHENOTYPE_PREDICTION
    KNOWLEDGE_GRAPH_COMPLETION
    HYPOTHESIS_GENERATION
    BIOLOGICAL_QUESTION_ANSWERING
  }

  RULES {
    IF BIOLOGICAL_ENTITY IS DIGITISED
    THEN LINK {
      BIOLOGICAL_ENTITY
      DIGITAL_REPRESENTATION
      REPRESENTATION_METHOD
      PROVENANCE_RECORD
    }

    IF DIGITAL_MODEL PREDICTS BIOLOGICAL_STATE
    THEN LINK {
      MODEL
      INPUT_STATE
      PREDICTED_STATE
      CONFIDENCE_SCORE
      SUPPORTING_EVIDENCE
    }

    IF MODEL_OUTPUT == PREDICTION
    THEN DO_NOT_ASSERT_AS_DIRECT_OBSERVATION

    IF SIMULATION_OUTPUT_EXISTS
    THEN LABEL_AS_SIMULATED_UNLESS_EMPIRICALLY_VALIDATED

    IF DIGITAL_TWIN_STATE_DIFFERS_FROM_OBSERVED_STATE
    THEN TRIGGER MODEL_REASSESSMENT

    IF MULTI_OMICS_RELATIONSHIP_IS_INFERRED
    THEN PRESERVE_SOURCE_LAYER_AND_INFERENCE_METHOD

    IF TEMPORAL_ORDER_IS_UNKNOWN
    THEN DO_NOT_ASSERT_DIRECTIONAL_STATE_TRANSITION

    IF SEMANTIC_IDENTIFIERS_DIFFER
    AND BIOLOGICAL_REFERENT_IS_SAME
    THEN APPLY SEMANTIC_EQUIVALENCE
  }

  CONFIDENCE {
    CONFIDENCE_SCORE =
      WEIGHT(
        DATA_QUALITY,
        MODEL_VALIDATION,
        REPRESENTATION_FIDELITY,
        TEMPORAL_ALIGNMENT,
        CROSS_MODAL_CONSISTENCY,
        EXPERIMENTAL_SUPPORT,
        REPLICATION,
        PROVENANCE_COMPLETENESS
      )

    LEVELS {
      HIGH
      MODERATE
      LOW
      INSUFFICIENT
    }

    IF CONFLICTING_OBSERVATIONS
    THEN REDUCE CONFIDENCE_SCORE

    IF INDEPENDENT_EXPERIMENTAL_VALIDATION
    THEN INCREASE CONFIDENCE_SCORE

    IF DIGITAL_AND_PHYSICAL_STATES_CONVERGE
    THEN INCREASE CONFIDENCE_SCORE
  }

  SEMANTIC_EQUIVALENCE {
    EQUIVALENCE_KEY =
      NORMALISE(
        ENTITY_TYPE,
        CANONICAL_IDENTIFIER,
        BIOLOGICAL_CONTEXT,
        TEMPORAL_CONTEXT,
        SPATIAL_CONTEXT
      )

    IF EQUIVALENCE_KEY(A) == EQUIVALENCE_KEY(B)
    THEN A SEMANTICALLY_EQUIVALENT_TO B

    ELSE PRESERVE_DISTINCT_ENTITIES
  }

  REASONING_CONSTRAINTS {
    DIGITAL_REPRESENTATION != BIOLOGICAL_ENTITY
    MODEL != PHYSICAL_SYSTEM
    SIMULATION != OBSERVATION
    PREDICTION != MEASUREMENT
    CORRELATION != CAUSATION
    MODEL_CONFIDENCE != BIOLOGICAL_CERTAINTY
    BIOLOGICAL_STATE_IS_CONTEXT_DEPENDENT
    TEMPORAL_CONTEXT_MUST_BE_PRESERVED
    SPATIAL_CONTEXT_MUST_BE_PRESERVED
    MULTI_OMICS_LAYERS_MUST_NOT_BE_CONFLATED
    PROVENANCE_MUST_BE_PRESERVED
    UNCERTAINTY_MUST_NOT_BE_DISCARDED
  }

  VALIDATION {
    CHECK_ENTITY_TYPES
    CHECK_RELATION_DOMAINS_AND_RANGES
    CHECK_IDENTIFIER_RESOLUTION
    CHECK_DIGITAL_PHYSICAL_MAPPING
    CHECK_TEMPORAL_ALIGNMENT
    CHECK_SPATIAL_ALIGNMENT
    CHECK_MULTI_OMICS_MAPPING
    CHECK_MODEL_VERSION
    CHECK_EVIDENCE_LINKS
    CHECK_PROVENANCE
    CHECK_CONFIDENCE
    CHECK_SEMANTIC_CONSISTENCY
    CHECK_DUPLICATE_ENTITIES
  }

  OUTPUTS {
    NORMALISED_BIOLOGICAL_ENTITIES
    DIGITAL_BIOLOGICAL_REPRESENTATIONS
    MULTI_OMICS_RELATIONSHIPS
    BIOLOGICAL_NETWORKS
    DIGITAL_TWIN_STATES
    SIMULATION_RESULTS
    STATE_PREDICTIONS
    PHENOTYPE_PREDICTIONS
    INFERRED_RELATIONSHIPS
    KNOWLEDGE_GRAPH_EDGES
    CONFIDENCE_SCORES
    EVIDENCE_LINKS
    PROVENANCE_RECORDS
    EXPLANATION_PATHS
  }

  UPDATE_MODEL {
    ON NEW_BIOLOGICAL_DATA {
      NORMALISE
      ENTITY_RESOLUTION
      UPDATE_DIGITAL_REPRESENTATION
      UPDATE_SYSTEM_STATE
      RECOMPUTE_RELEVANT_INFERENCES
      UPDATE_CONFIDENCE
      PRESERVE_VERSION_HISTORY
    }

    ON NEW_EXPERIMENTAL_EVIDENCE {
      LINK_EVIDENCE
      COMPARE_WITH_MODEL_OUTPUT
      REASSESS_RELATIONSHIPS
      REASSESS_PREDICTIONS
      UPDATE_CONFIDENCE
      RECORD_PROVENANCE
    }

    ON ONTOLOGY_REVISION {
      REMAP_TERMS
      CHECK_SEMANTIC_EQUIVALENCE
      IDENTIFY_DEPRECATED_TERMS
      MIGRATE_RELATIONSHIPS
      PRESERVE_PRIOR_VERSION_REFERENCES
    }
  }

  FLOW_HASH {
    HASH_INPUT =
      CANONICALISE(
        ENTITY_TYPES,
        RELATIONSHIPS,
        RULES,
        CONFIDENCE,
        SEMANTIC_EQUIVALENCE,
        REASONING_CONSTRAINTS,
        UPDATE_MODEL
      )

    FLOW_HASH = SHA256(HASH_INPUT)
  }

  AI_CONSUMPTION {
    INDEX {
      ONTOLOGY
      ENTITY_TYPES
      RELATIONSHIPS
      AI_TASKS
      RULES
      OUTPUTS
    }

    RETRIEVE {
      BY_BIOLOGICAL_ENTITY
      BY_DIGITAL_ENTITY
      BY_MODEL
      BY_OMICS_LAYER
      BY_BIOLOGICAL_STATE
      BY_PROCESS
      BY_RELATION
      BY_EVIDENCE
      BY_CONFIDENCE
    }

    REASON {
      APPLY_RELATIONSHIPS
      APPLY_RULES
      INTEGRATE_MULTI_OMICS_DATA
      PRESERVE_TEMPORAL_CONTEXT
      PRESERVE_SPATIAL_CONTEXT
      PRESERVE_UNCERTAINTY
      TRACE_EVIDENCE
      TRACE_PROVENANCE
      GENERATE_EXPLANATION_PATH
    }

    RESPONSE_POLICY {
      DISTINGUISH_DIGITAL_FROM_PHYSICAL
      DISTINGUISH_SIMULATED_FROM_OBSERVED
      DISTINGUISH_PREDICTED_FROM_MEASURED
      RETURN_CONFIDENCE_WHEN_AVAILABLE
      RETURN_PROVENANCE_WHEN_AVAILABLE
      EXPOSE_CONFLICTING_EVIDENCE
      AVOID_UNSUPPORTED_BIOLOGICAL_INFERENCE
    }
  }

  TERMINAL {
    RESULT =
      VALIDATED(
        SEMANTICALLY_NORMALISED(
          EVIDENCE_LINKED(
            CONFIDENCE_AWARE(
              DIGITAL_BIOLOGY_KNOWLEDGE
            )
          )
        )
      )
  }
}

END FLOW