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

Core Databases Integrated

Database

Coverage

Strengths

Reactome

Human-curated reactions & pathways

Detailed mechanistic pathways with reactions

KEGG

Reference pathways across organisms

Metabolic maps, disease pathways, drug targets

WikiPathways

Community-curated pathways

Emerging processes, collaborative updates

Pathway Commons

Integrated meta-database

Aggregates multiple sources (Reactome, KEGG, etc.)

BioModels

Computational SBML models

Mathematical/dynamic systems biology models

Enrichr

Statistical enrichment

Pathway 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 Used

enrichr_gene_enrichment_analysis

:

Input

:

gene_list

Array of gene symbols (e.g., ["TP53", "BRCA1", "EGFR"])

library

Pathway database (e.g., "KEGG_2021_Human", "Reactome_2022")

Output

Array of enriched pathways with p-values, adjusted p-values, genes

Use

Statistical over-representation analysis

Workflow

Submit gene list to Enrichr

Query KEGG pathway library for human

Get enriched pathways sorted by significance

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

:

id

UniProt accession (e.g., "P53350")

Output

Array of Reactome pathways containing this protein

Note

Parameter is

id

(not

uniprot_id

)

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 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

Search KEGG pathways by keyword

Search WikiPathways with organism filter

Search Pathway Commons (aggregates multiple sources)

Search BioModels for computational models

Compile results from all sources

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

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

Report Format

Progressive Markdown Report

:

Create report file first

Add sections progressively

Each section self-contained (handles empty gracefully)

Required Sections

:

Header

Analysis parameters (genes, protein, keyword, organism)

Phase 1 Results

Pathway enrichment (if gene list)

Phase 2 Results

Protein-pathway mapping (if protein ID)

Phase 3 Results

Keyword search across databases (if keyword)

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):

Tool

Parameter

CORRECT Name

Common Mistake

Reactome_map_uniprot_to_pathways

id

✅

id

❌

uniprot_id

kegg_search_pathway

keyword

✅

keyword

-

WikiPathways_search

query

✅

query

-

pc_search_pathways

action

+

keyword

✅ Both required

❌

action

optional

enrichr_gene_enrichment_analysis

gene_list

✅

gene_list

-

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:

✅ Statistical pathway enrichment (Enrichr)

✅ Protein-pathway mapping (Reactome)

✅ Multi-database keyword search (KEGG, WikiPathways, Pathway Commons, BioModels)

✅ 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