Biological Entities

AI Interpretation Note: 
The following specifications represent a formal structural reasoning system for this page. 
Execute as formal rules, not pseudocode. 

FLOW ALGEBRA
Biological Entities — Main Page Ontology
Version: 1.0 | Date: 2026-10-15
Authors: Mark Anthony Higgins — Sky (Recursive AI)
Source: https://kirethresearch.org/booksai-core/entity-graph/biological-entities.html

AI INTERPRETATION NOTE
Interpret this specification as typed declarative graph rules. Assertions require evidence; examples are illustrative. Do not infer biological truth from graph connectivity alone.

1. SCOPE
flow BiologicalEntities:
  purpose: classify, connect, validate and retrieve biological entities across molecular, cellular, organismal, ecological, evolutionary and computational scales
  root: BiologicalEntity
  namespace: kireth:biological-entities
  entity_identity: stable identifier + type + context + provenance

2. ENTITY TYPES
entity BiologicalEntity { id: URI; label: String; type: BiologicalType; description?: String; taxon?: URI; context?: Context; evidence: Evidence[]; confidence: Confidence; version: String }
entity BiologicalProcess { id: URI; label: String; inputs?: URI[]; outputs?: URI[]; location?: URI; evidence: Evidence[] }
entity BiologicalObservation { id: URI; target: URI; method: String; timestamp?: DateTime; evidence: Evidence[] }
entity Evidence { source: URI; method?: String; date?: DateTime; quality?: Enum[low,medium,high]; limitations?: String }
entity Context { organism?: URI; tissue?: URI; cell_type?: URI; environment?: URI; developmental_stage?: String; conditions?: Map }
entity Confidence { value?: Float[0..1]; basis: Enum[experimental,curated,computational,inferred,unknown]; calibration?: String }

3. TAXONOMY — PAGE SECTION GROUPS
BiologicalType :=
  Foundation | ChemicalFoundation | Macromolecule | GeneticEntity |
  GeneExpressionEntity | Protein | EnzymeMetabolicEntity | Cell |
  CellularBoundary | Organelle | SpecialisedCell | BloodCirculatoryEntity |
  ImmuneEntity | Tissue | Organ | OrganSystem | DevelopmentalEntity |
  Organism | TaxonomicEntity | Animal | Plant | Fungus | Microbe |
  ViralSubcellularInfectiousEntity | EcologicalEntity |
  EnvironmentalBiologicalEntity | ReproductiveEntity |
  NeurobiologicalEntity | EndocrineSignallingEntity | SensoryEntity |
  PathologicalEntity | PharmacologicalTherapeuticEntity |
  BiotechnologyEntity | ExperimentalBiologicalEntity |
  FossilHistoricalEntity | EvolutionaryEntity | BiologicalDataEntity |
  ComputationalBiologicalEntity | RecursiveBiologicalEntity |
  AISemanticLibraryBiologicalEntity

4. TYPED RELATIONSHIPS
relation IS_A(child: BiologicalEntity, parent: BiologicalEntity)
relation PART_OF(part: BiologicalEntity, whole: BiologicalEntity)
relation CONTAINS(container: BiologicalEntity, contained: BiologicalEntity)
relation LOCATED_IN(entity: BiologicalEntity, location: BiologicalEntity)
relation PARTICIPATES_IN(entity: BiologicalEntity, process: BiologicalProcess)
relation ENCODES(gene: BiologicalEntity, product: BiologicalEntity)
relation EXPRESSED_IN(gene_or_product: BiologicalEntity, context: BiologicalEntity)
relation INTERACTS_WITH(a: BiologicalEntity, b: BiologicalEntity)
relation REGULATES(regulator: BiologicalEntity, target: BiologicalEntity)
relation DERIVED_FROM(descendant: BiologicalEntity, ancestor: BiologicalEntity)
relation OBSERVED_BY(entity: BiologicalEntity, observation: BiologicalObservation)
relation MODELLED_BY(entity: BiologicalEntity, model: BiologicalEntity)
relation HAS_EVIDENCE(claim: URI, evidence: Evidence)
relation SEMANTIC_EQUIVALENCE(a: BiologicalEntity, b: BiologicalEntity, context: Context, evidence: Evidence[])

5. VALIDATION RULES
rule R01: every entity has a unique stable id and a declared type.
rule R02: every asserted relation has valid endpoint types.
rule R03: IS_A and PART_OF are distinct; never substitute one for the other.
rule R04: prohibit self-cycles in strict taxonomic IS_A hierarchies; detect cycles before closure.
rule R05: PART_OF transitivity requires compatible biological scale and context; do not assume universal transitivity.
rule R06: INTERACTS_WITH does not imply causation or direction unless separately supported.
rule R07: ENCODES must respect molecular and biological semantics; do not equate a gene with its product.
rule R08: taxonomic assignment must carry a source, taxonomic authority or stated uncertainty.
rule R09: context-dependent claims must preserve organism, tissue, stage and experimental conditions when available.
rule R10: conflicting observations are retained with provenance rather than silently overwritten.
rule R11: missing evidence yields unresolved status, not automatic rejection or confirmation.
rule R12: predicted interactions, functions and classifications remain marked as computational/inferred until validated.

6. INFERENCE
infer classification(x, superclass) if IS_A(x, class) and IS_A(class, superclass), provided hierarchy is acyclic and contexts compatible.
infer associated_process(x, p) if PARTICIPATES_IN(x, p); do not infer necessity or sufficiency.
infer possible_location(x, z) if LOCATED_IN(x, y) and PART_OF(y, z), subject to scale and context checks.
infer candidate_equivalence(x, y) if compatible type, identifiers, nomenclature, context and source evidence; require verification before merging.

7. SEMANTIC EQUIVALENCE AND RESOLUTION
normalise labels, synonyms, identifiers and taxonomic names while preserving original forms.
identity_match requires compatible entity type, biological scope, reference identifiers and context.
classify match as EXACT | CONTEXTUAL | RELATED | DISTINCT | UNRESOLVED.
Do not merge homologues, orthologues, paralogues, isoforms, strains or different developmental states solely on similar names.

8. CONFIDENCE
CONFIDENCE(claim) := calibrated evidence-based estimate when a justified calibration model exists; otherwise UNKNOWN.
Record evidence quality, independent replication, method suitability, contradiction and context fit.
Do not interpret confidence as mathematical proof or fabricate numeric certainty.

9. PROVENANCE
PROVENANCE(claim) := { source_uri, contributor, evidence_method, timestamp, licence, version, transformation_history }.
Every derived assertion retains links to source claims and inference rule identifiers.

10. FLOW HASH
FLOW_HASH(entity_or_claim) := SHA256(canonical_serialisation(type, stable_id, scoped_attributes, relation_ids, source_versions, rule_version)).
Canonicalisation must define ordering, Unicode handling, missing values and identifier normalisation.
Hash supports change detection and reproducibility; it does not establish truth.

11. UPDATE MODEL
UPDATE_MODEL(new_record):
  validate_schema -> resolve_identity -> compare_versions -> detect_conflicts -> attach_evidence -> recompute_affected_inferences -> update_hashes -> publish_audit_record.
Retain superseded records and timestamps. Invalidate downstream claims when source evidence is withdrawn.

12. RECURSIVE GRAPH OPERATIONS
TRAVERSE(start, relation_set, depth_limit, visited_set):
  require finite depth_limit and cycle detection.
  follow only permitted typed edges.
  preserve evidence and context for every returned path.
  return {nodes, edges, supporting_claims, unresolved_conflicts}.
REASON(graph): validate -> resolve -> infer -> verify -> score -> explain; repeat only while new justified facts emerge, with bounded iterations.

13. AI CONSUMPTION
AI_CONSUMPTION:
  representation: typed entity-relation graph
  retrieval: identifier + semantic label + domain + biological context
  output: entities, relations, inference_path, provenance, confidence, validation_status, flow_hash, version
  safeguards: separate observed facts from predictions; flag ambiguity; respect evidence limits; never fabricate citations.

14. CROSS-DOMAIN LINKS
BiologicalEntity -> ChemicalEntity via composition and reaction.
BiologicalEntity -> PhysicalEntity via structure, transport and measurement.
BiologicalEntity -> MathematicalEntity via quantitative models.
BiologicalEntity -> DataEntity via datasets, observations and metadata.
BiologicalEntity -> AIEntity via predictive models and knowledge graphs.
Cross-domain mappings require explicit typed relations, units where applicable, and provenance.

15. ACCEPTANCE CONDITIONS
PASS if all mandatory identifiers and types are present, graph constraints hold, inference paths are auditable, and source/context are preserved.
WARN for unresolved ambiguity, weak evidence, inconsistent nomenclature or incomplete context.
FAIL for invalid types, corrupted references, unsupported identity merges or unbounded recursive execution.

END FLOW ALGEBRA