Load canonical inputs (GEM, PPI, regmap, CRISPR, comparators) with provenance tags into a new session. Optional disease_context and phenotype_keywords set the session's disease + phenotype-of-interest for downstream tools.

Build a symbol<->entrez crosswalk between PPI hits and model genes. Symbols without a table entrez (or absent from the model) are resolved via the id-map resolver (model gene table + symbol map + MyGene); each entry carries authority/confidence/matched_by provenance.

Fail-closed positive-control gate: is the GEM structurally capable of detecting a known dependency?

Extract a localized metabolic subnetwork, never routing through currency metabolites.

Couple validated nodes as soft flux constraints; gate on provenance-independent comparator agreement.

Run FBA/pFBA/FVA/MOMA on the (optionally PPI-integrated) model. interaction_constraints=[{reaction_or_gene,effect,scale}] apply soft signed capacity constraints (reversible viral-sequestration encoding, no gene deletion). Returns objective value + top fluxes (FBA/pFBA/MOMA) or per-reaction ranges (FVA).

Reconcile identifiers to canonical ids so the pipeline never relies on the LLM guessing an accession. Metabolites/reactions -> MetaNetX MNXref (BiGG/KEGG/ChEBI/HMDB/... -> MNXM*/MNXR* + xrefs); genes -> the model's Entrez id space via the gene table + symbol map + MyGene (MNXref does NOT map genes). Every mapping carries {authority, confidence, matched_by}: exact id matches are high-confidence, fuzzy name hits low.

Boltz-2 structural gate: classify a bait/prey pair physical/regulatory/context_only from ipTM + flux relevance.

High-throughput LLM extraction of span-grounded {source,target,sign,tier,section,disease_specificity,phenotype_relevance} claims with provenance+confidence. Optional disease_context + phenotype_keywords (both fall back to the session) disambiguate references and rank on-phenotype claims — soft signals, never filters.

Compile literature.extract_claim's (source,target,sign) triples into the InteractionConstraint list metabolic.simulate/couple_and_test consume. Deterministic server-side policy (not agent judgement): prefer the literal target when it resolves into the model gene universe (role_used=target); else fall back to the source as a confidence-penalized proxy (role_used=source_proxy); else drop the claim (role_used counts + drop reasons returned). Each compiled constraint carries resolution_confidence so a fuzzy alias-tier resolution is visibly weaker evidence than an exact model-id hit.

Extract the signed regulatory subgraph around seed nodes (representative disease-map module).

Compile the subgraph to Boolean rules and find baseline attractors.

Add LLM edges, re-simulate, and gate llm_adds_value vs no_added_value; also returns a continuous attractor_distance + permuted_edge_null_fraction.

Deterministic stopping rule for iterative edge integration: given the per-round augment_compare scores, decide continue | converged | noise_indistinguishable | budget_exhausted (a fail-closed hard stop).

Render an interactive HTML of the regulatory subnetwork with LLM-added edges highlighted.

Render the 3-layer viral->enzyme->reaction interactive HTML with ipTM tiers and flux overlay.

DEFAULT metabolic visualizer: overlay compiled constraints on an Escher curated pathway map (biochemically-correct layout); automatically falls back to the cytoscape subnetwork renderer when no curated map covers the model. Returns {backend, out_path, n_reactions_on_map, map_used, fallback_reason}.

Run the full fail-closed DAG for a session; returns negative_result_report or target_handoff. Set iterative_integration=True to have the SERVER run the edge-integration loop (batched admission, self-stopping via integration.convergence_check) instead of a single-shot augment; integration_params={edge_batch_size,min_gain,null_alpha,patience,max_rounds}. default_visualization=True (default) renders the extracted regulatory subnetwork to OUTPUT_DIR as a standard output of every run.