| name | tooluniverse-systems-biology |
| description | Systems biology and pathway analysis integrating Reactome, KEGG, WikiPathways, BioCarta, NCI-Nature Pathway Interaction Database. Multi-database pathway enrichment, protein-pathway relationships, network reasoning. Use for pathway analysis on a gene list, multi-source pathway concordance, and systems-level interpretation across databases. |
| disable-model-invocation | true |
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:
- Gene Set Analysis: Identify enriched pathways from RNA-seq, proteomics, or screen results
- Protein Function: Discover pathways and processes a protein participates in
- Pathway Discovery: Find pathways related to diseases, processes, or phenotypes
- Systems Integration: Connect genes → pathways → processes → diseases
- Model Discovery: Find computational systems biology models (SBML)
- 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
| Database | Strengths |
|---|
| Reactome | Detailed mechanistic pathways with reactions; human-curated |
| KEGG | Metabolic maps, disease pathways, drug targets |
| WikiPathways | Emerging and community-curated pathways |
| Pathway Commons | Meta-database aggregating multiple sources |
| BioModels | Mathematical/computational SBML models |
| Enrichr | Statistical over-representation analysis |
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
| Tool | Input | Use |
|---|
ReactomeAnalysis_pathway_enrichment | identifiers (newline-separated symbols), page_size | FDR-corrected Reactome enrichment (recommended) |
enrichr_gene_enrichment_analysis | gene_list (array), libs (array) | Over-representation with KEGG/Reactome/WikiPathways |
STRING_functional_enrichment | protein_ids (array), species, category | Functional enrichment from PPI networks |
intact_get_interactions | identifier (UniProt accession) | Binary protein interactions with evidence |
- 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
- Map UniProt ID to Reactome pathways
- Get all pathways this protein appears in
- 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
| Tool | Key Params | Coverage |
|---|
kegg_search_pathway | keyword | Reference, metabolic, disease pathways |
kegg_get_pathway_info | pathway_id (e.g., "hsa04930") | Detailed genes/compounds for a pathway |
WikiPathways_search | query, organism | Community-curated, emerging pathways |
PathwayCommons_search | action="search_pathways", keyword | Meta-database aggregating multiple sources |
biomodels_search | query, limit | SBML computational models |
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
- Retrieve top-level pathways for specified organism
- Display pathway categories (metabolism, signaling, disease, etc.)
- 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):
| Tool | Correct Parameter | Common Mistake |
|---|
Reactome_map_uniprot_to_pathways | uniprot_id | id |
PathwayCommons_search | action + keyword (both required) | omitting action |
enrichr_gene_enrichment_analysis | gene_list (array) | string |
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
| Type | Effect on Km | Effect on Vmax | Lineweaver-Burk pattern |
|---|
| Competitive | Increases (Km_app = Km * (1 + [I]/Ki)) | Unchanged | Lines intersect on y-axis |
| Uncompetitive | Decreases | Decreases | Parallel lines |
| Noncompetitive (pure) | Unchanged | Decreases (Vmax_app = Vmax / (1 + [I]/Ki)) | Lines intersect on x-axis |
| Mixed | Changes | Decreases | Lines intersect in quadrant II or III |
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:
- 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).
- 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.
- 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.
- Temperature: Standard assays at 25C or 37C. Thermophilic enzymes need 50-80C. Psychrophilic enzymes denature above 30C.
- Reducing environment: Many enzymes need DTT or beta-mercaptoethanol to maintain active-site cysteines in reduced form.
- Substrate: Wrong isomer (D- vs L-), wrong oxidation state, or degraded substrate. Use fresh substrate and verify by a positive control enzyme.
- Inhibitors in buffer: EDTA chelates essential metals. Phosphate competes at phospho-binding sites. Detergents can denature.
- 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 published SBML models for the organism.
- 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.
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
Best for: Gene set analysis, protein function investigation, pathway discovery, systems-level biology
