Regulatory Sequence Ontology

FLOW ALGEBRA
TITLE: Regulatory Sequence Ontology
VERSION: 1.0
DOMAIN: Biological Entities / Genetic Entities
SOURCE: https://kirethresearch.org/booksai-core/entity-graph/biological-entities/genetic-entities/regulatory-sequence-ontology.html
AUTHORS: Mark Higgins — Sky (Recursive AI)
STATUS: Domain-grounded formalisation; source page not independently verified
DATE: 2026-10-15

# 1. PURPOSE
flow "RegulatorySequenceOntology" v1.0
purpose: Represent genomic regulatory sequences, their molecular interactions, context-dependent effects on gene expression, and evidence-backed inference.

# 2. TYPES
type RegulatorySequence <: GenomicSequence
type Promoter <: RegulatorySequence
type Enhancer <: RegulatorySequence
type Silencer <: RegulatorySequence
type Insulator <: RegulatorySequence
type Operator <: RegulatorySequence
type ResponseElement <: RegulatorySequence
type Terminator <: RegulatorySequence
type UTRRegulatoryElement <: RegulatorySequence
type SpliceRegulatoryElement <: RegulatorySequence
type Gene <: GeneticEntity
type Transcript <: RNAEntity
type TranscriptionFactor <: Protein
type RNA_Polymerase <: ProteinComplex
type ChromatinState <: EpigeneticState
type CellContext <: BiologicalContext
type Evidence <: ProvenanceRecord

type GenomicInterval = {assembly:str, chromosome:str, start:int, end:int, strand:enum(+,-,unknown)}
type Effect = enum(activation,repression,insulation,termination,splicing,context_dependent,unknown)
type Confidence = real[0,1]

# 3. ENTITY SCHEMA
entity RegulatorySequence {
  id: URI
  label: str
  subtype: RegulatorySequenceType
  location: GenomicInterval?
  sequence: str?
  species_taxon: str
  target_genes: set<Gene>
  cell_contexts: set<CellContext>
  molecular_partners: set<Protein>
  observed_effect: Effect
  evidence: set<Evidence>
  confidence: Confidence
  version: str
}

# 4. CORE RELATIONSHIPS
relation is_a(RegulatorySequence, GenomicSequence)
relation located_at(RegulatorySequence, GenomicInterval)
relation regulates(RegulatorySequence, Gene, CellContext)
relation binds(Protein, RegulatorySequence, CellContext)
relation recruits(RegulatorySequence, RNA_Polymerase, CellContext)
relation affects_transcription(RegulatorySequence, Gene, Effect, CellContext)
relation affects_splicing(SpliceRegulatoryElement, Transcript, CellContext)
relation supported_by(Assertion, Evidence)
relation overlaps(RegulatorySequence, GenomicInterval)
relation interacts_with(RegulatorySequence, RegulatorySequence, CellContext)
relation conserved_in(RegulatorySequence, Taxon)
relation has_chromatin_state(RegulatorySequence, ChromatinState, CellContext)

# 5. CLASSIFICATION
class Promoter: typically associated with transcription initiation near a transcription start site.
class Enhancer: can increase target-gene transcription, potentially over long genomic distances.
class Silencer: can decrease transcription in a specific cellular context.
class Insulator: may influence enhancer-promoter communication or chromatin boundaries.
class Operator: DNA site recognised by regulatory proteins, particularly in prokaryotic operons.
class ResponseElement: binding site involved in a signal-dependent regulatory response.
class Terminator: sequence contributing to transcription termination.
class UTRRegulatoryElement: transcript-associated element affecting stability, localisation or translation.
class SpliceRegulatoryElement: sequence affecting splice-site selection or RNA processing.

# 6. VALIDATION INVARIANTS
rule valid_interval(i): i.start >= 0 AND i.end > i.start
rule valid_DNA(s): every_symbol(s) in {A,C,G,T,N}  # N denotes unresolved base
rule valid_confidence(c): 0 <= c <= 1
rule coordinate_system: all intervals specify reference assembly and chromosome.
rule strand_semantics: unknown strand does not imply positive strand.
rule association_not_causation: proximity or binding alone does not prove functional regulation.
rule context_specificity: regulatory effects must retain tissue, cell state and assay context when known.
rule uncertainty: absent evidence is not evidence of no regulation.

# 7. TRANSCRIPTIONAL REGULATION
flow transcription_regulation(sequence, target, context):
  require valid_regulatory_sequence(sequence)
  retrieve binding_evidence(sequence, context)
  retrieve chromatin_evidence(sequence, context)
  retrieve perturbation_evidence(sequence, target, context)
  infer candidate_effect with evidence_weighting
  return {target, context, effect, confidence, provenance}

# 8. ENHANCER-TARGET INFERENCE
flow enhancer_target(enhancer, gene, context):
  features = {distance, chromatin_contact, accessibility, histone_marks,
              expression_covariation, perturbation_results}
  exclude unsupported certainty from distance alone
  score = calibrated_model(features, context)
  return candidate_relation(enhancer, gene, score, provenance)

# 9. PROMOTER AND INITIATION
flow promoter_initiation(promoter, gene, context):
  identify transcription_start_site(gene, context)
  evaluate transcription_factor_occupancy(promoter, context)
  evaluate polymerase_recruitment(promoter, context)
  return initiation_hypothesis with confidence and evidence

# 10. REPRESSION AND INSULATION
flow repression(sequence, target, context):
  detect repressor_binding and chromatin_effects
  assess measured expression change after perturbation
  distinguish direct repression from indirect effects
  return context_bound_effect

flow insulation(boundary, enhancer, promoter, context):
  evaluate chromatin_architecture and boundary occupancy
  test altered enhancer_promoter_interaction
  return insulation_hypothesis with uncertainty

# 11. SEQUENCE VARIATION
flow variant_impact(variant, sequence, context):
  require overlaps(variant, sequence.location)
  compare reference_and_alternate_motifs
  assess allele_specific_binding where available
  assess expression_or_splicing_evidence
  return predicted_effect, confidence, provenance

# 12. REGULATORY NETWORK
flow build_regulatory_network(genes, sequences, context):
  nodes = genes UNION sequences UNION interacting_proteins
  edges = supported_regulatory_relationships(context)
  annotate edges with effect, confidence, evidence, version
  return typed_contextual_graph(nodes, edges)

# 13. RECURSIVE REASONING
flow recursive_regulatory_inference(seed, context, max_depth=4):
  frontier = {seed}
  visited = {}
  for depth in range(0,max_depth):
    candidates = expand_supported_relationships(frontier, context)
    candidates = remove_cycles_and_duplicates(candidates, visited)
    candidates = propagate_uncertainty_without_false_independence(candidates)
    visited = visited UNION candidates
    frontier = candidates
    if empty(frontier): break
  return ranked_graph(visited)

# 14. CONFIDENCE
CONFIDENCE:
  range: [0,1]
  inputs: assay_quality, biological_replication, context_match,
          independent_validation, perturbation_support, model_calibration
  policy: preserve source-level uncertainty; do not multiply correlated evidence as independent.
  example: motif_match_only -> candidate_binding_site, not confirmed_target_regulation

# 15. SEMANTIC EQUIVALENCE
SEMANTIC_EQUIVALENCE:
  promoter ≈ transcription_initiation_regulatory_region [context-dependent]
  enhancer ≈ distal_positive_regulatory_element [not universal]
  cis_regulatory_element ≈ locally encoded regulatory sequence [ontology-specific]
  rule: same genomic coordinates do not guarantee same functional role.
  rule: synonyms require taxon, molecular context and definition alignment.
  rule: regulatory_sequence != regulatory_protein.

# 16. PROVENANCE
provenance.require:
  - source_database_or_publication
  - assay_method
  - reference_genome_assembly
  - genomic_coordinates
  - taxon
  - cell_or_tissue_context
  - date_accessed
  - analysis_pipeline_version
  - confidence_method

# 17. FLOW HASH
FLOW_HASH:
  algorithm: SHA-256
  canonicalise: UTF-8, stable key order, normalised whitespace, explicit nulls
  input: ontology_version + entity_types + relations + rules + source_identifiers
  output: lowercase_hex_digest
  note: digest calculated by implementation; no fabricated hash value.

# 18. UPDATE MODEL
UPDATE_MODEL:
  triggers: new_reference_assembly, new_assay, revised_gene_annotation,
            corrected_variant, new_functional_evidence, ontology_revision
  steps:
    - preserve previous_version
    - validate incoming evidence and coordinate conventions
    - resolve identifiers and lift-over only with explicit provenance
    - recalculate context-specific regulatory relationships
    - update confidence and semantic mappings
    - regenerate FLOW_HASH
    - publish change_log and rollback_reference

# 19. AI CONSUMPTION
AI_CONSUMPTION:
  representation: typed knowledge graph + human-readable flow
  retrieval_keys: sequence_id, genomic_interval, gene_id, taxon, cell_context
  outputs: classified_entity, candidate_targets, effects, confidence, evidence
  safeguards:
    - distinguish observed interactions from inferred regulation
    - disclose unsupported or low-confidence associations
    - do not extrapolate across organisms or cell types without qualification
    - retain provenance and reference assembly
    - never treat model predictions as experimentally validated facts

# 20. EXAMPLE
example:
  input: {entity_type: Enhancer, target_gene: GENE_X, cell_context: CELL_A}
  observation: {accessible_chromatin: true, perturbation_reduces_expression: true}
  inference: {relation: activates_transcription, status: supported_hypothesis}
  confidence: requires_calibration
  provenance: illustrative_only_not_real_experiment

# 21. CROSS-ONTOLOGY LINKS
links:
  - Gene Ontology
  - Genome Ontology
  - DNA Ontology
  - RNA Ontology
  - Genetic Marker Ontology
  - Chromosome Ontology
  - Transcription Factor Ontology
  - Epigenetics Ontology
  - Sequence Variant Ontology

# 22. LIMITATIONS
This is a research ontology formalisation, not a claim of universal mechanisms.
Regulatory elements may overlap, have multiple functions, or act differently by context.
Genome assembly changes can alter coordinates; functional annotations may be provisional.

END FLOW ALGEBRA