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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

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 Used

ReactomeAnalysis_pathway_enrichment (recommended — returns FDR-corrected results with Reactome pathway IDs):

• Input : identifiers (newline-separated gene symbols, e.g., "PIK3CA\nAKT1\nMTOR"), page_size (int)
• Output : Enriched pathways with p-values, FDR, entity counts, Reactome stable IDs

enrichr_gene_enrichment_analysis :

• Input :
  • gene_list: Array of gene symbols (e.g., ["TP53", "BRCA1", "EGFR"])
  • libs: Array of library names (e.g., ["KEGG_2021_Human", "Reactome_2022", "WikiPathways_2024_Human"])
• Output : Array of enriched pathways with p-values, adjusted p-values, genes
• Use : Statistical over-representation analysis

STRING_get_network (protein interaction networks):

• Input : identifiers (gene symbol), species (9606 for human), limit (max interactors)
• Output : Interaction edges with combined scores, evidence types

STRING_functional_enrichment (functional enrichment from PPI networks):

• Input : protein_ids (array of gene symbols), species (9606), category ("KEGG", "Process", "Component", "Function", "Reactome", "WikiPathways")
• Output : Enriched functional categories with FDR values

intact_get_interactions (detailed binary protein interactions):

• Input : identifier (UniProt accession)
• Output : Interaction records with experimental evidence types

Workflow

1. Submit gene list to Enrichr
2. Query KEGG pathway library for human
3. Get enriched pathways sorted by significance
4. Extract:
   • Pathway names and IDs
   • P-values (raw and adjusted)
   • Genes from input list in each pathway
   • Enrichment scores

Decision Logic

• Significance threshold : Adjusted p-value < 0.05 (default)
• Minimum genes : At least 2 genes from input list in pathway
• Report top pathways : Show 10-20 most significant
• Empty results : If no enrichment → note "no significant pathways" (don't fail)

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 Used

KEGG Search

kegg_search_pathway :

• Input : keyword (e.g., "diabetes", "apoptosis")
• Output : Array of pathway IDs and descriptions
• Coverage : Reference pathways, metabolism, diseases

kegg_get_pathway_info :

• Input : pathway_id (e.g., "hsa04930")
• Output : Pathway details, genes, compounds
• Use : Get detailed information for specific pathway

WikiPathways Search

WikiPathways_search :

• Input :
  • query: Keyword or gene symbol
  • organism: Species filter (e.g., "Homo sapiens")
• Output : Array of pathway matches with IDs, names, URLs
• Coverage : Community-curated, includes emerging pathways

Pathway Commons Search

pc_search_pathways :

• Input :
  • action: "search_pathways"
  • keyword: Search term
  • datasource: Optional filter (e.g., "reactome", "kegg")
  • limit: Max results (default: 10)
• Output : Total hits and array of pathways with source attribution
• Coverage : Meta-database aggregating multiple sources

BioModels Search

biomodels_search :

• Input :
  • query: Keyword for computational models
  • limit: Max results
• Output : Array of SBML models with IDs, names, publications
• Coverage : Mathematical/computational systems biology models

Workflow

1. Search KEGG pathways by keyword
2. Search WikiPathways with organism filter
3. Search Pathway Commons (aggregates multiple sources)
4. Search BioModels for computational models
5. Compile results from all sources
6. Note overlaps and source-specific pathways

Decision Logic

• Parallel queries : Search all databases simultaneously (independent)
• Empty from one source : Continue with other sources (common for specialized keywords)
• Result consolidation : Group by pathway concept, note which databases contain each
• Model availability : BioModels may be empty for many processes - 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

Report Format

Progressive Markdown Report :

• Create report file first
• Add sections progressively
• Each section self-contained (handles empty gracefully)

Required Sections :

1. Header : Analysis parameters (genes, protein, keyword, organism)
2. Phase 1 Results : Pathway enrichment (if gene list)
3. Phase 2 Results : Protein-pathway mapping (if protein ID)
4. Phase 3 Results : Keyword search across databases (if keyword)
5. Phase 4 Results : Top-level pathway catalog (always)

Per-Database Subsections :

• Database name and result count
• Table of pathways with key metadata
• Note if database returns no results
• Links or IDs for follow-up

Data Tables

Enrichment Results : | Pathway | P-value | Adjusted P-value | Genes | | ... | ... | ... | ... |

Protein Pathways : | Pathway Name | Pathway ID | Species | | ... | ... | ... |

Keyword Search : | Pathway/Model ID | Name | Source/Database | | ... | ... | ... |

Tool Parameter Reference

Critical Parameter Notes (from testing):

Response Format Notes :

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

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]"

Common Use Patterns

Pattern 1: Differential Expression Analysis

Input: Gene list from RNA-seq (upregulated genes)
Workflow: Phase 1 (Enrichment) → Phase 4 (Context)
Output: Enriched pathways explaining expression changes

Pattern 2: Protein Function Investigation

Input: UniProt ID of protein of interest
Workflow: Phase 2 (Protein mapping) → Phase 3 (Keyword with protein name)
Output: All pathways involving protein + related pathways

Pattern 3: Disease Pathway Exploration

Input: Disease name or process keyword
Workflow: Phase 3 (Keyword search) → Phase 4 (Context)
Output: Pathways from multiple databases related to disease

Pattern 4: Comprehensive Multi-Input

Input: Gene list + protein ID + keyword
Workflow: All phases
Output: Complete systems view with enrichment, specific mappings, and context

Quality Checks

Data Completeness

• At least one analysis phase completed successfully
• Each database result includes source attribution
• Empty results explicitly noted (not silently omitted)
• P-values reported with appropriate precision
• Pathway IDs provided for follow-up analysis

Biological Validity

• Enrichment p-values show significance threshold
• Protein mappings consistent with known function
• Keyword results relevant to query
• Cross-database results show expected overlaps

Report Quality

• All sections present even if "no data"
• Tables formatted consistently
• Source databases clearly attributed
• Follow-up recommendations if data sparse

Limitations & Known Issues

Database-Specific

• 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 can have duplicates; check source
• BioModels : Sparse for many processes; often returns no results
• Enrichr : Requires gene symbols (not IDs); case-sensitive

Technical

• Response formats : Different databases use different response structures (handled in implementation)
• Rate limits : Some databases have rate limits for heavy usage
• Version differences : Pathway databases updated at different rates

Analysis

• Enrichment bias : Pathway enrichment depends on pathway size and annotation completeness
• Organism specificity : Not all databases cover all organisms equally
• Pathway definitions : Same biological process may be modeled differently across databases

Summary

Systems Biology & Pathway Analysis Skill provides comprehensive pathway analysis by integrating:

1. ✅ Statistical pathway enrichment (Enrichr)
2. ✅ Protein-pathway mapping (Reactome)
3. ✅ Multi-database keyword search (KEGG, WikiPathways, Pathway Commons, BioModels)
4. ✅ Hierarchical pathway context (Reactome top-level)

Outputs : Markdown report with pathway tables, enrichment statistics, and cross-database comparisons

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

Weekly Installs

131

Repository

mims-harvard/to…universe

GitHub Stars

1.2K

First Seen

Feb 19, 2026

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