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 system

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 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 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 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 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 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): 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: 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 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, path

tooluniverse-systems-biology