Systems Biology & Pathway Analysis

Comprehensive pathway and systems biology analysis integrating multiple curated databases to provide multi-dimensional view of biological systems, pathway enrichment, and protein-pathway relationships.

When to Use This Skill

Triggers:

• "Analyze pathways for this gene list"
• "What pathways is [protein] involved in?"
• "Find pathways related to [keyword/process]"
• "Perform pathway enrichment analysis"
• "Map proteins to biological pathways"
• "Find computational models for [process]"
• "Systems biology analysis of [genes/proteins]"

Use Cases:

1. Gene Set Analysis: Identify enriched pathways from RNA-seq, proteomics, or screen results
2. Protein Function: Discover pathways and processes a protein participates in
3. Pathway Discovery: Find pathways related to diseases, processes, or phenotypes
4. Systems Integration: Connect genes → pathways → processes → diseases
5. Model Discovery: Find computational systems biology models (SBML)
6. Cross-Database Validation: Compare pathway annotations across multiple sources

COMPUTE, DON'T DESCRIBE

When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.

Domain Reasoning: Enrichment vs Causation

Pathway analysis answers: which biological processes are enriched in my gene list? But enrichment is not causation. A pathway being enriched means your gene list overlaps it more than expected by chance. Ask: is the enrichment driven by a few hub genes, or by many genes distributed across the pathway? A pathway with 3 input genes but 200 annotated members is less informative than one where 15 of 40 members are in your list.

LOOK UP DON'T GUESS: pathway membership, gene-to-pathway assignments, and enrichment statistics. Do not assume a gene is in a pathway — use Reactome, KEGG, or Enrichr to verify. Pathway databases disagree on membership; cross-validate key findings across at least two sources.

Core Databases Integrated

Workflow Overview

Input → Phase 1: Enrichment → Phase 2: Protein Mapping → Phase 3: Keyword Search → Phase 4: Top Pathways → Report

Phase 1: Pathway Enrichment Analysis

When: Gene list provided (from experiments, screens, differentially expressed genes)

Objective: Identify biological pathways statistically over-represented in gene list

Tools & Workflow

1. Submit gene list to Enrichr/Reactome. 2. Sort by adjusted p-value < 0.05. 3. Report top 10-20 pathways with IDs, p-values, and overlapping genes. If no enrichment, note explicitly.

Phase 2: Protein-Pathway Mapping

When: Protein UniProt ID provided

Objective: Map protein to all known pathways it participates in

Tools Used

Reactome_map_uniprot_to_pathways:

• Input:
• uniprot_id: UniProt accession (e.g., "P53350")
• Output: Array of Reactome pathways containing this protein

Reactome_get_pathway_reactions:

• Input:
• stId: Reactome pathway stable ID (e.g., "R-HSA-73817")
• Output: Array of reactions and subpathways
• Use: Get mechanistic details of pathways

Workflow

1. Map UniProt ID to Reactome pathways
2. Get all pathways this protein appears in
3. For top pathway (or user-specified):

• Retrieve detailed reactions and subpathways
• Extract event names, types (Reaction vs Pathway)
• Note disease associations if present

Decision Logic

• Multiple pathways: Report all pathways, prioritize by hierarchical level
• Top pathway details: Get detailed reactions for 1-3 most relevant
• Versioned IDs: Reactome uses unversioned IDs - strip version if present
• Empty results: Check if protein ID valid; suggest alternative databases if Reactome empty

Phase 3: Keyword-Based Pathway Search

When: User provides keyword or biological process name

Objective: Search multiple pathway databases to find relevant pathways

Tools

Search all databases in parallel. Group results by pathway concept. BioModels often returns empty — this is normal.

Phase 4: Top-Level Pathway Catalog

When: Always included to provide context

Objective: Show major biological systems/pathways for organism

Tools Used

Reactome_list_top_pathways:

• Input: species (e.g., "Homo sapiens")
• Output: Array of top-level pathway categories
• Use: Provides hierarchical pathway organization

Workflow

1. Retrieve top-level pathways for specified organism
2. Display pathway categories (metabolism, signaling, disease, etc.)
3. Serve as reference for pathway hierarchy

Decision Logic

• Always show: Provides context even if other phases empty
• Organism-specific: Filter by species of interest
• Hierarchical view: These are parent pathways with many subpathways

Output Structure

Create a markdown report progressively: header → Phase 1 enrichment results → Phase 2 protein mapping → Phase 3 keyword search → Phase 4 top pathway catalog. Note empty results explicitly; never silently omit them. Include pathway IDs for follow-up.

Tool Parameter Reference

Critical Parameter Notes (from testing):

Response Format Notes:

• Reactome: Returns list directly (not wrapped in {status, data})
• Pathway Commons: Returns dict with total_hits and pathways
• Others: Standard {status: "success", data: [...]} format

Domain Reasoning: Enzyme Kinetics & Metabolic Analysis

LOOK UP DON'T GUESS: Km values, kcat values, cofactor requirements, and optimal pH/temperature for specific enzymes. Use BindingDB_search_by_target, ChEMBL_get_molecule, BRENDA_get_enzyme_info (requires BRENDA_EMAIL + BRENDA_PASSWORD env vars; free academic registration at brenda-enzymes.org) (if available), or EuropePMC_search_articles to retrieve published kinetic parameters. Do not estimate Km from first principles.

Michaelis-Menten Kinetics

The foundational model: v = Vmax * [S] / (Km + [S])

• Km = substrate concentration at half-maximal velocity. NOT binding affinity (Km = (koff + kcat) / kon).
• Vmax = maximum velocity = kcat * [E_total]. Proportional to enzyme concentration.
• kcat = turnover number = molecules of substrate converted per enzyme per second.
• Catalytic efficiency = kcat / Km. The "best" enzymes approach the diffusion limit (~10^8 M^-1 s^-1).

To determine Km and Vmax from data: use Lineweaver-Burk (1/v vs 1/[S]), Eadie-Hofstee (v vs v/[S]), or nonlinear regression (preferred — avoids distortion from reciprocal transforms). See enzyme_kinetics.py in skills/tooluniverse-computational-biophysics/scripts/.

Allosteric Regulation & Cooperative Binding

Not all enzymes follow Michaelis-Menten. Sigmoidal v-vs-[S] curves indicate cooperativity.

• Hill equation: v = Vmax * [S]^nH / (K0.5^nH + [S]^nH)
• Hill coefficient (nH): nH = 1 (no cooperativity), nH > 1 (positive, e.g., hemoglobin O2 binding nH ~ 2.8), nH < 1 (negative cooperativity).
• K0.5: substrate concentration at half-maximal velocity (analogous to Km but not identical for cooperative systems).
• Allosteric activators shift the curve LEFT (lower K0.5). Allosteric inhibitors shift it RIGHT (higher K0.5) or reduce Vmax.

Enzyme Inhibition Types

To determine Ki: measure v at multiple [I] and [S], fit to the appropriate model. The enzyme_kinetics.py script handles competitive, uncompetitive, and noncompetitive inhibition calculations.

Troubleshooting "No Activity" Results

When a purified enzyme shows no catalytic activity, systematically check:

1. Oligomeric state: Many enzymes are obligate dimers/tetramers. Dilute protein may dissociate. Check with SEC, native PAGE, or DLS. Concentrate sample or add stabilizing agents (glycerol, specific ions).
2. Cofactors: Metal ions (Zn2+, Mg2+, Mn2+), coenzymes (NAD+, FAD, PLP), or prosthetic groups may be lost during purification. LOOK UP the enzyme's cofactor requirements and supplement the assay buffer.
3. pH: Most enzymes have a sharp pH optimum. Even 1 pH unit off can reduce activity 10-fold. Buffer at the literature-reported optimal pH.
4. Temperature: Standard assays at 25C or 37C. Thermophilic enzymes need 50-80C. Psychrophilic enzymes denature above 30C.
5. Reducing environment: Many enzymes need DTT or beta-mercaptoethanol to maintain active-site cysteines in reduced form.
6. Substrate: Wrong isomer (D- vs L-), wrong oxidation state, or degraded substrate. Use fresh substrate and verify by a positive control enzyme.
7. Inhibitors in buffer: EDTA chelates essential metals. Phosphate competes at phospho-binding sites. Detergents can denature.
8. Protein folding: Inclusion body protein may be misfolded even after refolding. Check by CD spectroscopy or thermal shift assay.

Metabolic Flux Analysis Reasoning

Metabolic flux analysis (MFA) quantifies the rates of metabolic reactions in vivo, not just enzyme activities in vitro.

Key concepts:

• Steady-state assumption: At metabolic steady state, the rate of production of each intermediate equals its rate of consumption. This gives a system of linear equations: S * v = 0, where S is the stoichiometric matrix and v is the flux vector.
• Flux Balance Analysis (FBA): When the system is underdetermined (more reactions than metabolites), FBA uses linear programming to optimize an objective function (e.g., maximize biomass production). Use biomodels_search to find a published SBML paper's model for the organism, or — for a model that is already curated, versioned, and ready to run FBA on without further cleanup — use BiGG (below).
• 13C-MFA: Uses isotope labeling to experimentally constrain intracellular fluxes. The labeling pattern of metabolites reveals which pathways carried flux.
• Control coefficients: How much does a 1% change in enzyme activity change the pathway flux? Most enzymes have near-zero flux control coefficients — flux is usually controlled by a few rate-limiting steps plus substrate supply.

LOOK UP DON'T GUESS: stoichiometric coefficients, pathway topology, and published flux distributions. Use KEGG (kegg_get_pathway_info), Reactome (Reactome_get_pathway_reactions), and BioModels (biomodels_search) for these data.

Genome-Scale Metabolic Models & FBA (BiGG)

BiGG Models (bigg.ucsd.edu) is a curated database of 108 genome-scale metabolic reconstructions (bacteria, archaea, eukaryotes — E. coli, yeast, human Recon3D, and more), each already in FBA-ready COBRA format: every reaction carries flux lower_bound/upper_bound and a gene_reaction_rule, unlike a biomodels_search hit (which is a paper's raw SBML file that may need cleanup before it will run). Use BiGG when the goal is to actually run FBA, not just read about a pathway.

Tools (each has its own operation enum value defaulted in its schema — you don't need to set it manually, just call the tool named for what you want):

Workflow — find a model, inspect it, get it FBA-ready:

1. BiGG_list_models → each entry has bigg_id, organism, reaction_count, metabolite_count, gene_count. For a quick/small worked example, e_coli_core (E. coli core metabolism: 95 reactions, 72 metabolites, 137 genes) is the standard teaching model; for genome-scale work pick a full reconstruction like iJO1366 (E. coli) or Recon3D (human).
2. BiGG_get_model(model_id="e_coli_core") → publication DOI, genome accession (ncbi_accession:NC_000913.3 for E. coli), file sizes, last-updated date — check currency before citing.
3. BiGG_get_model_reactions(model_id="e_coli_core") → list of {bigg_id, name, organism} per reaction (e.g. ACKr = "Acetate kinase"). Use this to find candidate reaction IDs before drilling in.
4. BiGG_get_reaction(reaction_id="ACKr", model_id="e_coli_core") → full stoichiometry as a metabolites array (each with bigg_id, name, signed stoichiometry), plus gene_reaction_rule (empty string/None for some reactions — a real absence, not a fetch failure) and database_links (RHEA, etc.).
5. BiGG_get_metabolite(metabolite_id="g3p_c", model_id="e_coli_core") → name, formula, compartment_bigg_id/compartment_name, and database_links for cross-referencing to KEGG/ChEBI. Note some fields (e.g. formulae) come back None for some entries — report what's actually present rather than assuming every field is populated.
6. BiGG_search(query="glucose", search_type="metabolites") → ranked hits across the universal namespace by default (model_bigg_id: "Universal") as well as model-specific IDs; use search_type="models" to find organisms, "genes" to resolve a gene symbol to its BiGG gene ID.
7. BiGG_download_model(model_id="e_coli_core", format="json") → the complete COBRA model: top-level metabolites/reactions/genes/compartments arrays, each reaction with lower_bound, upper_bound, gene_reaction_rule, and a metabolites dict of {metabolite_id: stoichiometry} — this is the object to hand to a COBRApy Model for FBA, not BiGG_get_model's metadata-only response. format="sbml" returns the same model as an SBML XML string instead, for tools that expect that format.

Gotchas (from live testing): model_id defaults to "universal" for BiGG_get_reaction/BiGG_get_metabolite — pass the specific model ID when you want model-scoped stoichiometry/bounds rather than the universal (model-agnostic) entry. BiGG_search's default search_type is "reactions" — set it explicitly for metabolite/gene/model searches. BiGG_get_model returns only counts and metadata; BiGG_download_model is the one that returns an actually runnable model.

Reaction-Level Biochemistry (Rhea)

Rhea (rhea-db.org, SIB) is an expert-curated knowledgebase of individual biochemical reactions — each has a ChEBI-based equation, a curation status (approved, reviewed, etc.), a mass/charge balance flag, and cross-referenced EC number(s). Use it when the question is about ONE reaction's participants, stoichiometry, and enzyme classification, rather than a whole model's fluxes (BiGG, above) or a pathway diagram (Reactome/KEGG).

Tools:

Workflow (real values from live testing): Rhea_search_reactions(query="glucose") → RHEA:14293 ("D-glucose + NAD(+) = D-glucono-1,5-lactone + NADH + H(+)", EC 1.1.1.47/1.1.1.118/1.1.1.359 among others) → Rhea_get_reaction(rhea_id="RHEA:14293") → status_curation: "approved", balanced: true, reactants [{chebi_id: "CHEBI:4167", name: "D-glucose", stoichiometry: "1"}, {chebi_id: "CHEBI:57540", name: "NAD+", ...}]. Cross-reference a Rhea chebi_id against tooluniverse-chemical-compound-retrieval's PubChem/ChEBI tools for the compound's structure, and a Rhea ec_numbers value against BRENDA (see Domain Reasoning above) for kinetic constants on that same reaction.

Fallback Strategies

Enrichment Analysis

• Primary: Enrichr with KEGG library
• Fallback: Try alternative libraries (Reactome, GO Biological Process)
• If all fail: Note "enrichment analysis unavailable" and continue

Protein Mapping

• Primary: Reactome protein-pathway mapping
• Fallback: Use keyword search with protein name
• If empty: Check if protein ID valid; suggest checking gene symbol

Keyword Search

• Primary: Search all databases (KEGG, WikiPathways, Pathway Commons, BioModels)
• Fallback: If all empty, broaden keyword (e.g., "diabetes" → "glucose")
• If still empty: Note "no pathways found for [keyword]"

Limitations & Known Issues

• Reactome: Strong human coverage; limited for non-model organisms
• KEGG: Requires keyword match; may miss synonyms
• WikiPathways: Variable curation quality; check pathway version dates
• Pathway Commons: Aggregation may have duplicates; check source attribution
• BioModels: Sparse for many processes; often returns no results
• Enrichr: Requires gene symbols (not IDs); case-sensitive
• BiGG: Curated reconstructions only exist for 108 organisms (mostly bacteria/model organisms; not every species of interest has one) — check BiGG_search(search_type="models") before assuming coverage; BiGG_get_reaction/BiGG_get_metabolite default to the "universal" (model-agnostic) namespace unless model_id is passed explicitly

GO Term Lookup, KEGG BRITE Classification, and Causal-Statement Mining (GOAPI / KEGG BRITE / INDRA)

Four more small, previously-undocumented tool families, live-tested below. Two of them overlap in purpose with tools already used elsewhere in ToolUniverse — read the overlap notes before reaching for these as a first choice.

GOAPI: direct GO term/gene-annotation lookup

GOAPI_get_term, GOAPI_get_gene_functions, GOAPI_get_genes_by_function (go_api_tools.json) hit the GO API directly. Overlap note: this is a different tool family from GO_get_annotations_for_gene / GO_get_term_by_id / GO_get_term_details (gene_ontology_tools.json), which several other skills — tooluniverse-target-research, tooluniverse-gene-enrichment, tooluniverse-structural-variant-analysis — already document for the same purpose. Prefer those existing tools if you're already using them elsewhere in a workflow; reach for GOAPI when its CURIE-based gene addressing (below) or its reverse "genes by function" lookup is specifically what you need.

• GOAPI_get_term(go_id="GO:0006281") → term label + full definition + PMID xrefs (verified live: "GO:0006281" = "DNA repair", correct definition returned).
• GOAPI_get_gene_functions(gene_id=..., rows=, aspect=) → gotcha, verified live: gene_id must be a CURIE ("HGNC:11998", "UniProtKB:P04637", "MGI:MGI:98834"), not a bare gene symbol — gene_id="TP53" fails with a 400, gene_id="UniProtKB:P04637" succeeds and returns real GO terms with evidence_type/evidence_label/provided_by/references per annotation (e.g. p53 → "negative regulation of cell population proliferation", IMP evidence from UniProt, PMID:10962037).
• GOAPI_get_genes_by_function(go_id=...) → the reverse direction: real gene list annotated with a GO term, each with gene_id (CURIE), gene_label, taxon_id.

KEGG BRITE: hierarchical functional classification

KEGG_list_brite_hierarchies (no params) and KEGG_get_brite_hierarchy(hierarchy_id=...) (kegg_brite_tools.json) — genuinely new, no existing coverage. BRITE is KEGG's classification-tree layer (e.g. enzymes grouped by EC hierarchy, drugs by target class), distinct from kegg_search_pathway/kegg_get_pathway_info already in this skill's Phase 3, which return pathway maps rather than classification trees.

• KEGG_list_brite_hierarchies() → real response: 156 hierarchy files (e.g. br08901 = "KEGG pathway maps").
• KEGG_get_brite_hierarchy(hierarchy_id="ko01000") → real nested children tree of enzyme classes (e.g. "1. Oxidoreductases" → "1.1 Acting on the CH-OH group of donors" → ...). Use when you need the classification structure itself (e.g. "what are all the kinase subclasses"), not a single pathway's gene/compound list.

INDRA: automated literature-mined causal statements

INDRA_get_statements, INDRA_get_evidence_count, INDRA_get_statement_by_hash (indra_tools.json) — genuinely new. INDRA text-mines causal relationships (Activation/Inhibition/Phosphorylation/Complex/etc.) directly from PubMed abstracts and assembles them with evidence counts — this is automated extraction with variable precision, not manually curated causal edges like SIGNOR's (documented in tooluniverse-protein-interactions). Treat an INDRA statement as a literature-mining lead to verify, not a confirmed mechanism, especially when evidence_shown is low relative to total_evidence.

• INDRA_get_evidence_count(agent="TP53") → real count: 25,846 total evidence items across all TP53 statements (verified live; slow-ish, ~3-20s).
• INDRA_get_statements(agent="EGFR", limit=3) → gotcha: this call can take 30-90+ seconds — give it a generous timeout, don't assume a hang means failure. Real response: statements typed Phosphorylation/Complex/etc., each with hash, evidence_shown, and per-evidence pmid+source sentence (e.g. a real Phosphorylation statement backed by 2 shown evidence sentences from PMID 31635022 and 37443708, out of 74,891 total evidence for EGFR overall — most evidence is never shown per call, use INDRA_get_evidence_count first to gauge how well-studied a relationship is).
• INDRA_get_statement_by_hash(hash=...) → full single-statement detail once you have a hash from the above (real example: hash -35357180905875939, an EGFR Inhibition statement with 1,376 evidence items).

Known-broken tool, verified live: ReactomeInteractors

ReactomeInteractors_get_protein_interactors, ReactomeInteractors_get_entity_pathways, ReactomeInteractors_search_entity (reactome_interactors_tools.json) — all 3 tools currently return HTTP 521 on every call, confirmed both via tu test/tu run and an independent direct curl to Reactome's own interactors endpoint (reactome.org/ContentService/interactors/...), which also returned 521. This is Reactome's interactors service being unreachable at the origin, not a ToolUniverse bug — it may be transient, so don't assume it's permanently gone, but do not fabricate protein-interactor or entity-pathway results from this family if you hit the same error; report it as "ReactomeInteractors service currently unreachable (HTTP 521)" and fall back to STRING_functional_enrichment, intact_get_interactions, or (for causal edges) SIGNOR/INDRA above instead. Note this is a different tool family from the already-documented Reactome_map_uniprot_to_pathways/Reactome_get_pathway_reactions (Phase 2, above) and from Reactome_get_interactor (singular, in reactome_tools.json, used by tooluniverse-spatial-omics-analysis) — three separate Reactome-branded tool families exist in the registry; this section covers only the ReactomeInteractors_* one.

Best for: Gene set analysis, protein function investigation, pathway discovery, systems-level biology