# Real-World Scientific Examples This document provides comprehensive, practical examples demonstrating how to combine Claude Scientific Skills to solve real scientific problems across multiple domains. --- ## 📋 Table of Contents 1. [Drug Discovery & Medicinal Chemistry](#drug-discovery--medicinal-chemistry) 2. [Cancer Genomics & Precision Medicine](#cancer-genomics--precision-medicine) 3. [Single-Cell Transcriptomics](#single-cell-transcriptomics) 4. [Protein Structure & Function](#protein-structure--function) 5. [Chemical Safety & Toxicology](#chemical-safety--toxicology) 6. [Clinical Trial Analysis](#clinical-trial-analysis) 7. [Metabolomics & Systems Biology](#metabolomics--systems-biology) 8. [Materials Science & Chemistry](#materials-science--chemistry) 9. [Digital Pathology](#digital-pathology) 10. [Lab Automation & Protocol Design](#lab-automation--protocol-design) 11. [Agricultural Genomics](#agricultural-genomics) 12. [Neuroscience & Brain Imaging](#neuroscience--brain-imaging) 13. [Environmental Microbiology](#environmental-microbiology) 14. [Infectious Disease Research](#infectious-disease-research) 15. [Multi-Omics Integration](#multi-omics-integration) 16. [Computational Chemistry & Synthesis](#computational-chemistry--synthesis) 17. [Clinical Research & Real-World Evidence](#clinical-research--real-world-evidence) 18. [Experimental Physics & Data Analysis](#experimental-physics--data-analysis) 19. [Chemical Engineering & Process Optimization](#chemical-engineering--process-optimization) --- ## Drug Discovery & Medicinal Chemistry ### Example 1: Discovery of Novel EGFR Inhibitors for Lung Cancer **Objective**: Identify novel small molecule inhibitors of EGFR with improved properties compared to existing drugs. **Skills Used**: - `chembl-database` - Query bioactivity data - `pubchem-database` - Search compound libraries - `rdkit` - Analyze molecular properties - `datamol` - Generate analogs - `diffdock` - Molecular docking - `alphafold-database` - Retrieve protein structure - `pubmed-database` - Literature review - `cosmic-database` - Query mutations - `deepchem` - Property prediction - `scientific-visualization` - Create figures **Workflow**: ```bash # Always use available 'skills' when possible. Keep the output organized. Step 1: Query ChEMBL for known EGFR inhibitors with high potency - Search for compounds targeting EGFR (CHEMBL203) - Filter: IC50 < 50 nM, pChEMBL value > 7 - Extract SMILES strings and activity data - Export to DataFrame for analysis Step 2: Analyze structure-activity relationships - Load compounds into RDKit - Calculate molecular descriptors (MW, LogP, TPSA, HBD, HBA) - Generate Morgan fingerprints (radius=2, 2048 bits) - Perform hierarchical clustering to identify scaffolds - Visualize top scaffolds with activity annotations Step 3: Identify resistance mutations from COSMIC - Query COSMIC for EGFR mutations in lung cancer - Focus on gatekeeper mutations (T790M, C797S) - Extract mutation frequencies and clinical significance - Cross-reference with literature in PubMed Step 4: Retrieve EGFR structure from AlphaFold - Download AlphaFold prediction for EGFR kinase domain - Alternatively, use experimental structure from PDB (if available) - Prepare structure for docking (add hydrogens, optimize) Step 5: Generate novel analogs using datamol - Select top 5 scaffolds from ChEMBL analysis - Use scaffold decoration to generate 100 analogs per scaffold - Apply Lipinski's Rule of Five filtering - Ensure synthetic accessibility (SA score < 4) - Check for PAINS and unwanted substructures Step 6: Predict properties with DeepChem - Train graph convolutional model on ChEMBL EGFR data - Predict pIC50 for generated analogs - Predict ADMET properties (solubility, permeability, hERG) - Rank candidates by predicted potency and drug-likeness Step 7: Virtual screening with DiffDock - Perform molecular docking on top 50 candidates - Dock into wild-type EGFR and T790M mutant - Calculate binding energies and interaction patterns - Identify compounds with favorable binding to both forms Step 8: Search PubChem for commercial availability - Query PubChem for top 10 candidates by InChI key - Check supplier information and purchasing options - Identify close analogs if exact matches unavailable Step 9: Literature validation with PubMed - Search for any prior art on top scaffolds - Query: "[scaffold_name] AND EGFR AND inhibitor" - Summarize relevant findings and potential liabilities Step 10: Create comprehensive report - Generate 2D structure visualizations of top hits - Create scatter plots: MW vs LogP, TPSA vs potency - Produce binding pose figures for top 3 compounds - Generate table comparing properties to approved drugs (gefitinib, erlotinib) - Write scientific summary with methodology, results, and recommendations - Export to PDF with proper citations Expected Output: - Ranked list of 10-20 novel EGFR inhibitor candidates - Predicted activity and ADMET properties - Docking poses and binding analysis - Comprehensive scientific report with publication-quality figures ``` --- ### Example 2: Drug Repurposing for Rare Diseases **Objective**: Identify FDA-approved drugs that could be repurposed for treating a rare metabolic disorder. **Skills Used**: - `drugbank-database` - Query approved drugs - `opentargets-database` - Target-disease associations - `string-database` - Protein interactions - `kegg-database` - Pathway analysis - `reactome-database` - Pathway enrichment - `clinicaltrials-database` - Check ongoing trials - `fda-database` - Drug approvals and safety - `networkx` - Network analysis - `literature-review` - Systematic review **Workflow**: ```bash Step 1: Define disease pathway - Query KEGG and Reactome for disease-associated pathways - Identify key proteins and enzymes involved - Map upstream and downstream pathway components Step 2: Find protein-protein interactions - Query STRING database for interaction partners - Build protein interaction network around key disease proteins - Identify hub proteins and bottlenecks using NetworkX - Calculate centrality metrics (betweenness, closeness) Step 3: Query Open Targets for druggable targets - Search for targets associated with disease phenotype - Filter by clinical precedence and tractability - Prioritize targets with existing approved drugs Step 4: Search DrugBank for drugs targeting identified proteins - Query for approved drugs and their targets - Filter by mechanism of action relevant to disease - Retrieve drug properties and safety information Step 5: Query FDA databases for safety profiles - Check FDA adverse event database (FAERS) - Review drug labels and black box warnings - Assess risk-benefit for rare disease population Step 6: Search ClinicalTrials.gov for prior repurposing attempts - Query for disease name + drug names - Check for failed trials (and reasons for failure) - Identify ongoing trials that may compete Step 7: Perform pathway enrichment analysis - Map drug targets to disease pathways - Calculate enrichment scores with Reactome - Identify drugs affecting multiple pathway nodes Step 8: Conduct systematic literature review - Search PubMed for drug name + disease associations - Include bioRxiv for recent unpublished findings - Document any case reports or off-label use - Use literature-review skill to generate comprehensive review Step 9: Prioritize candidates - Rank by: pathway relevance, safety profile, existing evidence - Consider factors: oral availability, blood-brain barrier penetration - Assess commercial viability and patent status Step 10: Generate repurposing report - Create network visualization of drug-target-pathway relationships - Generate comparison table of top 5 candidates - Write detailed rationale for each candidate - Include mechanism of action diagrams - Provide recommendations for preclinical validation - Format as professional PDF with citations Expected Output: - Ranked list of 5-10 repurposing candidates - Network analysis of drug-target-disease relationships - Safety and efficacy evidence summary - Repurposing strategy report with next steps ``` --- ## Cancer Genomics & Precision Medicine ### Example 3: Clinical Variant Interpretation Pipeline **Objective**: Analyze a patient's tumor sequencing data to identify actionable mutations and therapeutic recommendations. **Skills Used**: - `pysam` - Parse VCF files - `ensembl-database` - Variant annotation - `clinvar-database` - Clinical significance - `cosmic-database` - Somatic mutations - `gene-database` - Gene information - `uniprot-database` - Protein impact - `drugbank-database` - Drug-gene associations - `clinicaltrials-database` - Matching trials - `opentargets-database` - Target validation - `pubmed-database` - Literature evidence - `reportlab` - Generate clinical report **Workflow**: ```bash Step 1: Parse and filter VCF file - Use pysam to read tumor VCF - Filter for high-quality variants (QUAL > 30, DP > 20) - Extract variant positions, alleles, and VAF (variant allele frequency) - Separate SNVs, indels, and structural variants Step 2: Annotate variants with Ensembl - Query Ensembl VEP API for functional consequences - Classify variants: missense, nonsense, frameshift, splice site - Extract transcript information and protein changes - Identify canonical transcripts for each gene Step 3: Query ClinVar for known pathogenic variants - Search ClinVar by genomic coordinates - Extract clinical significance classifications - Note conflicting interpretations and review status - Prioritize variants with "Pathogenic" or "Likely Pathogenic" labels Step 4: Query COSMIC for somatic cancer mutations - Search COSMIC for each variant - Extract mutation frequency across cancer types - Identify hotspot mutations (high recurrence) - Note drug resistance mutations Step 5: Retrieve gene information from NCBI Gene - Get detailed gene descriptions - Extract associated phenotypes and diseases - Identify oncogene vs tumor suppressor classification - Note gene function and biological pathways Step 6: Assess protein-level impact with UniProt - Query UniProt for protein domain information - Map variants to functional domains (kinase domain, binding site) - Check if variant affects active sites or protein stability - Retrieve post-translational modification sites Step 7: Search DrugBank for targetable alterations - Query for drugs targeting mutated genes - Filter for FDA-approved and investigational drugs - Extract mechanism of action and indications - Prioritize variants with approved targeted therapies Step 8: Query Open Targets for target-disease associations - Validate therapeutic hypotheses - Assess target tractability scores - Review clinical precedence for each gene-disease pair Step 9: Search ClinicalTrials.gov for matching trials - Build query with: cancer type + gene names + variants - Filter for: recruiting status, phase II/III trials - Extract trial eligibility criteria - Note geographic locations and contact information Step 10: Literature search for clinical evidence - PubMed query: "[gene] AND [variant] AND [cancer type]" - Focus on: case reports, clinical outcomes, resistance mechanisms - Extract relevant prognostic or predictive information Step 11: Classify variants by actionability Tier 1: FDA-approved therapy for this variant Tier 2: Clinical trial available for this variant Tier 3: Therapy approved for variant in different cancer Tier 4: Biological evidence but no approved therapy Step 12: Generate clinical genomics report - Executive summary of key findings - Table of actionable variants with evidence levels - Therapeutic recommendations with supporting evidence - Clinical trial options with eligibility information - Prognostic implications based on mutation profile - References to guidelines (NCCN, ESMO, AMP/ASCO/CAP) - Generate professional PDF using ReportLab Expected Output: - Annotated variant list with clinical significance - Tiered list of actionable mutations - Therapeutic recommendations with evidence levels - Matching clinical trials - Comprehensive clinical genomics report (PDF) ``` --- ### Example 4: Cancer Subtype Classification from Gene Expression **Objective**: Classify breast cancer subtypes using RNA-seq data and identify subtype-specific therapeutic vulnerabilities. **Skills Used**: - `pydeseq2` - Differential expression - `scanpy` - Clustering and visualization - `scikit-learn` - Machine learning classification - `gene-database` - Gene annotation - `reactome-database` - Pathway analysis - `opentargets-database` - Drug targets - `pubmed-database` - Literature validation - `matplotlib` - Visualization - `seaborn` - Heatmaps **Workflow**: ```bash Step 1: Load and preprocess RNA-seq data - Load count matrix (genes × samples) - Filter low-expression genes (mean counts < 10) - Normalize with DESeq2 size factors - Apply variance-stabilizing transformation (VST) Step 2: Classify samples using PAM50 genes - Query NCBI Gene for PAM50 classifier gene list - Extract expression values for PAM50 genes - Train Random Forest classifier on labeled training data - Predict subtypes: Luminal A, Luminal B, HER2+, Basal, Normal-like - Validate with published markers (ESR1, PGR, ERBB2, MKI67) Step 3: Perform differential expression for each subtype - Use PyDESeq2 to compare each subtype vs all others - Apply multiple testing correction (FDR < 0.05) - Filter by log2 fold change (|LFC| > 1.5) - Identify subtype-specific signature genes Step 4: Annotate differentially expressed genes - Query NCBI Gene for detailed annotations - Classify as oncogene, tumor suppressor, or other - Extract biological process and molecular function terms Step 5: Pathway enrichment analysis - Submit gene lists to Reactome API - Identify enriched pathways for each subtype (p < 0.01) - Focus on druggable pathways (kinase signaling, metabolism) - Compare pathway profiles across subtypes Step 6: Identify therapeutic targets with Open Targets - Query Open Targets for each upregulated gene - Filter by tractability score > 5 - Prioritize targets with clinical precedence - Extract associated drugs and development phase Step 7: Create comprehensive visualization - Generate UMAP projection of all samples colored by subtype - Create heatmap of PAM50 genes across subtypes - Produce volcano plots for each subtype comparison - Generate pathway enrichment dot plots - Create drug target-pathway network diagrams Step 8: Literature validation - Search PubMed for each predicted therapeutic target - Query: "[gene] AND [subtype] AND breast cancer AND therapy" - Summarize clinical evidence and ongoing trials - Note any resistance mechanisms reported Step 9: Generate subtype-specific recommendations For each subtype: - List top 5 differentially expressed genes - Identify enriched biological pathways - Recommend therapeutic strategies based on vulnerabilities - Cite supporting evidence from literature Step 10: Create comprehensive report - Classification results with confidence scores - Differential expression tables for each subtype - Pathway enrichment summaries - Therapeutic target recommendations - Publication-quality figures - Export to PDF with citations Expected Output: - Sample classification into molecular subtypes - Subtype-specific gene signatures - Pathway enrichment profiles - Prioritized therapeutic targets for each subtype - Scientific report with visualizations and recommendations ``` --- ## Single-Cell Transcriptomics ### Example 5: Single-Cell Atlas of Tumor Microenvironment **Objective**: Characterize immune cell populations in tumor microenvironment and identify immunotherapy biomarkers. **Skills Used**: - `scanpy` - Single-cell analysis - `scvi-tools` - Batch correction and integration - `cellxgene-census` - Reference data - `gene-database` - Cell type markers - `anndata` - Data structure - `arboreto` - Gene regulatory networks - `pytorch-lightning` - Deep learning - `matplotlib` - Visualization - `statistical-analysis` - Hypothesis testing **Workflow**: ```bash Step 1: Load and QC 10X Genomics data - Use Scanpy to read 10X h5 files - Calculate QC metrics: n_genes, n_counts, pct_mitochondrial - Identify mitochondrial genes (MT- prefix) - Filter cells: 200 < n_genes < 5000, pct_mt < 20% - Filter genes: expressed in at least 10 cells - Document filtering criteria and cell retention rate Step 2: Normalize and identify highly variable genes - Normalize to 10,000 counts per cell - Log-transform data (log1p) - Store raw counts in adata.raw - Identify 3,000 highly variable genes - Regress out technical variation (n_counts, pct_mt) - Scale to unit variance, clip at 10 standard deviations Step 3: Integrate with reference atlas using scVI - Download reference tumor microenvironment data from Cellxgene Census - Train scVI model on combined dataset for batch correction - Use scVI latent representation for downstream analysis - Generate batch-corrected expression matrix Step 4: Dimensionality reduction and clustering - Compute neighborhood graph (n_neighbors=15, n_pcs=50) - Calculate UMAP embedding for visualization - Perform Leiden clustering at multiple resolutions (0.3, 0.5, 0.8) - Select optimal resolution based on silhouette score Step 5: Identify cell type markers - Run differential expression for each cluster (Wilcoxon test) - Calculate marker scores (log fold change, p-value, pct expressed) - Query NCBI Gene for canonical immune cell markers: * T cells: CD3D, CD3E, CD4, CD8A * B cells: CD19, MS4A1 (CD20), CD79A * Myeloid: CD14, CD68, CD163 * NK cells: NKG7, GNLY, NCAM1 * Dendritic: CD1C, CLEC9A, LILRA4 Step 6: Annotate cell types - Assign cell type labels based on marker expression - Refine annotations with CellTypist or manual curation - Identify T cell subtypes: CD4+, CD8+, Tregs, exhausted T cells - Characterize myeloid cells: M1/M2 macrophages, dendritic cells - Create cell type proportion tables by sample/condition Step 7: Identify tumor-specific features - Compare tumor samples vs normal tissue (if available) - Identify expanded T cell clones (high proliferation markers) - Detect exhausted T cells (PDCD1, CTLA4, LAG3, HAVCR2) - Characterize immunosuppressive populations (Tregs, M2 macrophages) Step 8: Gene regulatory network inference - Use Arboreto/GRNBoost2 on each major cell type - Identify transcription factors driving cell states - Focus on exhaustion TFs: TOX, TCF7, EOMES - Build regulatory networks for visualization Step 9: Statistical analysis of cell proportions - Calculate cell type frequencies per sample - Test for significant differences between groups (responders vs non-responders) - Use statistical-analysis skill for appropriate tests (t-test, Mann-Whitney) - Calculate effect sizes and confidence intervals Step 10: Biomarker discovery for immunotherapy response - Correlate cell type abundances with clinical response - Identify gene signatures associated with response - Test signatures: T cell exhaustion, antigen presentation, inflammation - Validate with published immunotherapy response signatures Step 11: Create comprehensive visualizations - UMAP plots colored by: cell type, sample, treatment, key genes - Dot plots of canonical markers across cell types - Cell type proportion bar plots by condition - Heatmap of top differentially expressed genes per cell type - Gene regulatory network diagrams - Volcano plots for differentially abundant cell types Step 12: Generate scientific report - Methods: QC, normalization, batch correction, clustering - Results: Cell type composition, differential abundance, markers - Biomarker analysis: Predictive signatures and validation - High-quality figures suitable for publication - Export processed h5ad file and PDF report Expected Output: - Annotated single-cell atlas with cell type labels - Cell type composition analysis - Biomarker signatures for immunotherapy response - Gene regulatory networks for key cell states - Comprehensive report with publication-quality figures ``` --- ## Protein Structure & Function ### Example 6: Structure-Based Design of Protein-Protein Interaction Inhibitors **Objective**: Design small molecules to disrupt a therapeutically relevant protein-protein interaction. **Skills Used**: - `alphafold-database` - Protein structures - `pdb-database` - Experimental structures - `uniprot-database` - Protein information - `biopython` - Structure analysis - `pyrosetta` - Protein design (if available) - `rdkit` - Chemical library generation - `diffdock` - Molecular docking - `zinc-database` - Screening library - `deepchem` - Property prediction - `pymol` - Visualization (external) **Workflow**: ```bash Step 1: Retrieve protein structures - Query AlphaFold Database for both proteins in the interaction - Download PDB files and confidence scores - If available, get experimental structures from PDB database - Compare AlphaFold predictions with experimental structures (if any) Step 2: Analyze protein interaction interface - Load structures with BioPython - Identify interface residues (distance < 5Å between proteins) - Calculate interface area and binding energy contribution - Identify hot spot residues (key for binding) - Map to UniProt to get functional annotations Step 3: Characterize binding pocket - Identify cavities at the protein-protein interface - Calculate pocket volume and surface area - Assess druggability: depth, hydrophobicity, shape - Identify hydrogen bond donors/acceptors - Note any known allosteric sites Step 4: Query UniProt for known modulators - Search UniProt for both proteins - Extract information on known inhibitors or modulators - Review PTMs that affect interaction - Check disease-associated mutations in interface Step 5: Search ZINC15 for fragment library - Query ZINC for fragments matching pocket criteria: * Molecular weight: 150-300 Da * LogP: 0-3 (appropriate for PPI inhibitors) * Exclude PAINS and aggregators - Download 1,000-5,000 fragment SMILES Step 6: Virtual screening with fragment library - Use DiffDock to dock fragments into interface pocket - Rank by predicted binding affinity - Identify fragments binding to hot spot residues - Select top 50 fragments for elaboration Step 7: Fragment elaboration with RDKit - For each fragment hit, generate elaborated molecules: * Add substituents to core scaffold * Merge fragments binding to adjacent pockets * Apply medicinal chemistry filters - Generate 20-50 analogs per fragment - Filter by Lipinski's Ro5 and PPI-specific rules (MW 400-700) Step 8: Second round of virtual screening - Dock elaborated molecules with DiffDock - Calculate binding energies and interaction patterns - Prioritize molecules with: * Strong binding to hot spot residues * Multiple H-bonds and hydrophobic contacts * Favorable predicted ΔG Step 9: Predict ADMET properties with DeepChem - Train models on ChEMBL data - Predict: solubility, permeability, hERG liability - Filter for drug-like properties - Rank by overall score (affinity + ADMET) Step 10: Literature and patent search - PubMed: "[protein A] AND [protein B] AND inhibitor" - USPTO: Check for prior art on top scaffolds - Assess freedom to operate - Identify any reported PPI inhibitors for this target Step 11: Prepare molecules for synthesis - Assess synthetic accessibility (SA score < 4) - Identify commercial building blocks - Propose synthetic routes for top 10 candidates - Calculate estimated synthesis cost Step 12: Generate comprehensive design report - Interface analysis with hot spot identification - Fragment screening results - Top 10 designed molecules with predicted properties - Docking poses and interaction diagrams - Synthetic accessibility assessment - Comparison to known PPI inhibitors - Recommendations for experimental validation - Publication-quality figures and PDF report Expected Output: - Interface characterization and hot spot analysis - Ranked library of designed PPI inhibitors - Predicted binding modes and affinities - ADMET property predictions - Synthetic accessibility assessment - Comprehensive drug design report ``` --- ## Chemical Safety & Toxicology ### Example 7: Predictive Toxicology Assessment **Objective**: Assess potential toxicity and safety liabilities of drug candidates before synthesis. **Skills Used**: - `rdkit` - Molecular descriptors - `deepchem` - Toxicity prediction - `chembl-database` - Toxicity data - `pubchem-database` - Bioassay data - `drugbank-database` - Known drug toxicities - `fda-database` - Adverse events - `hmdb-database` - Metabolite prediction - `scikit-learn` - Classification models - `shap` - Model interpretability **Workflow**: ```bash Step 1: Calculate molecular descriptors - Load candidate molecules with RDKit - Calculate physicochemical properties: * MW, LogP, TPSA, rotatable bonds, H-bond donors/acceptors * Aromatic rings, sp3 fraction, formal charge - Calculate structural alerts: * PAINS patterns * Toxic functional groups (nitroaromatics, epoxides, etc.) * Genotoxic alerts (Ames mutagenicity) Step 2: Screen for known toxicophores - Search for structural alerts using SMARTS patterns: * Michael acceptors * Aldehyde/ketone reactivity * Quinones and quinone-like structures * Thioureas and isocyanates - Flag molecules with high-risk substructures Step 3: Query ChEMBL for similar compounds with toxicity data - Perform similarity search (Tanimoto > 0.7) - Extract toxicity assay results: * Cytotoxicity (IC50 values) * Hepatotoxicity markers * Cardiotoxicity (hERG inhibition) * Genotoxicity (Ames test results) - Analyze structure-toxicity relationships Step 4: Search PubChem BioAssays for toxicity screening - Query relevant assays: * Tox21 panel (cell viability, stress response, genotoxicity) * Liver toxicity assays * hERG channel inhibition - Extract activity data for similar compounds - Calculate hit rates for concerning assays Step 5: Train toxicity prediction models with DeepChem - Load Tox21 dataset from DeepChem - Train graph convolutional models for: * Nuclear receptor signaling * Stress response pathways * Genotoxicity endpoints - Validate models with cross-validation - Predict toxicity for candidate molecules Step 6: Predict hERG cardiotoxicity liability - Train DeepChem model on hERG inhibition data from ChEMBL - Predict IC50 for hERG channel - Flag compounds with predicted IC50 < 10 μM - Identify structural features associated with hERG liability Step 7: Predict hepatotoxicity risk - Train models on DILI (drug-induced liver injury) datasets - Extract features: reactive metabolites, mitochondrial toxicity - Predict hepatotoxicity risk class (low/medium/high) - Use SHAP values to explain predictions Step 8: Predict metabolic stability and metabolites - Identify sites of metabolism using RDKit SMARTS patterns - Predict CYP450 interactions - Query HMDB for potential metabolite structures - Assess if metabolites contain toxic substructures - Predict metabolic stability (half-life) Step 9: Check FDA adverse event database - Query FAERS for approved drugs similar to candidates - Extract common adverse events - Identify target organ toxicities - Calculate reporting odds ratios for serious events Step 10: Literature review of toxicity mechanisms - PubMed search: "[scaffold] AND (toxicity OR hepatotoxicity OR cardiotoxicity)" - Identify mechanistic studies on similar compounds - Note any case reports of adverse events - Review preclinical and clinical safety data Step 11: Assess ADME liabilities - Predict solubility, permeability, plasma protein binding - Identify potential drug-drug interaction risks - Assess blood-brain barrier penetration (for CNS or non-CNS drugs) - Evaluate metabolic stability Step 12: Generate safety assessment report - Executive summary of safety profile for each candidate - Red flags: structural alerts, predicted toxicities - Yellow flags: moderate concerns requiring testing - Green light: acceptable predicted safety profile - Comparison table of all candidates - Recommendations for risk mitigation: * Structural modifications to reduce toxicity * Priority in vitro assays to run * Preclinical study design recommendations - Comprehensive PDF report with: * Toxicophore analysis * Prediction model results with confidence * SHAP interpretation plots * Literature evidence * Risk assessment matrix Expected Output: - Toxicity predictions for all candidates - Structural alert analysis - hERG, hepatotoxicity, and genotoxicity risk scores - Metabolite predictions - Prioritized list with safety rankings - Comprehensive toxicology assessment report ``` --- ## Clinical Trial Analysis ### Example 8: Competitive Landscape Analysis for New Indication **Objective**: Analyze the clinical trial landscape for a specific indication to inform development strategy. **Skills Used**: - `clinicaltrials-database` - Trial registry - `fda-database` - Drug approvals - `pubmed-database` - Published results - `drugbank-database` - Approved drugs - `opentargets-database` - Target validation - `polars` - Data manipulation - `matplotlib` - Visualization - `seaborn` - Statistical plots - `reportlab` - Report generation **Workflow**: ```bash Step 1: Search ClinicalTrials.gov for all trials in indication - Query: "[disease/indication]" - Filter: All phases, all statuses - Extract fields: * NCT ID, title, phase, status * Start date, completion date, enrollment * Intervention/drug names * Primary/secondary outcomes * Sponsor and collaborators - Export to structured JSON/CSV Step 2: Categorize trials by mechanism of action - Extract drug names and intervention types - Query DrugBank for mechanism of action - Query Open Targets for target information - Classify into categories: * Small molecules vs biologics * Target class (kinase inhibitor, antibody, etc.) * Novel vs repurposing Step 3: Analyze trial phase progression - Calculate success rates by phase (I → II, II → III) - Identify terminated trials and reasons for termination - Track time from phase I start to NDA submission - Calculate median development timelines Step 4: Search FDA database for recent approvals - Query FDA drug approvals in the indication (last 10 years) - Extract approval dates, indications, priority review status - Note any accelerated approvals or breakthroughs - Review FDA drug labels for safety information Step 5: Extract outcome measures - Compile all primary endpoints used - Identify most common endpoints: * Survival (OS, PFS, DFS) * Response rates (ORR, CR, PR) * Biomarker endpoints * Patient-reported outcomes - Note emerging or novel endpoints Step 6: Analyze competitive dynamics - Identify leading companies and their pipelines - Map trials by phase for each major competitor - Note partnership and licensing deals - Assess crowded vs underserved patient segments Step 7: Search PubMed for published trial results - Query: "[NCT ID]" for each completed trial - Extract published outcomes and conclusions - Identify trends in efficacy and safety - Note any unmet needs highlighted in discussions Step 8: Analyze target validation evidence - Query Open Targets for target-disease associations - Extract genetic evidence scores - Review tractability assessments - Compare targets being pursued across trials Step 9: Identify unmet needs and opportunities - Analyze trial failures for common patterns - Identify patient populations excluded from trials - Note resistance mechanisms or limitations mentioned - Assess gaps in current therapeutic approaches Step 10: Perform temporal trend analysis - Plot trial starts over time (by phase, mechanism) - Identify increasing or decreasing interest in targets - Correlate with publication trends and scientific advances - Predict future trends in the space Step 11: Create comprehensive visualizations - Timeline of all trials (Gantt chart style) - Phase distribution pie chart - Mechanism of action breakdown - Geographic distribution of trials - Enrollment trends over time - Success rate funnels (Phase I → II → III → Approval) - Sponsor/company market share Step 12: Generate competitive intelligence report - Executive summary of competitive landscape - Total number of active programs by phase - Key players and their development stage - Standard of care and approved therapies - Emerging approaches and novel targets - Identified opportunities and white space - Risk analysis (crowded targets, high failure rates) - Strategic recommendations: * Patient population to target * Differentiation strategies * Partnership opportunities * Regulatory pathway considerations - Export as professional PDF with citations and data tables Expected Output: - Comprehensive trial database for indication - Success rate and timeline statistics - Competitive landscape mapping - Unmet need analysis - Strategic recommendations - Publication-ready report with visualizations ``` --- ## Metabolomics & Systems Biology ### Example 9: Multi-Omics Integration for Metabolic Disease **Objective**: Integrate transcriptomics, proteomics, and metabolomics to identify dysregulated pathways in metabolic disease. **Skills Used**: - `pydeseq2` - RNA-seq analysis - `pyopenms` - Mass spectrometry - `hmdb-database` - Metabolite identification - `metabolomics-workbench-database` - Public datasets - `kegg-database` - Pathway mapping - `reactome-database` - Pathway analysis - `string-database` - Protein interactions - `statsmodels` - Multi-omics correlation - `networkx` - Network analysis - `pymc` - Bayesian modeling **Workflow**: ```bash Step 1: Process RNA-seq data - Load gene count matrix - Run differential expression with PyDESeq2 - Compare disease vs control (adjusted p < 0.05, |LFC| > 1) - Extract gene symbols and fold changes - Map to KEGG gene IDs Step 2: Process proteomics data - Load LC-MS/MS results with PyOpenMS - Perform peptide identification and quantification - Normalize protein abundances - Run statistical testing (t-test or limma) - Extract significant proteins (p < 0.05, |FC| > 1.5) Step 3: Process metabolomics data - Load untargeted metabolomics data (mzML format) with PyOpenMS - Perform peak detection and alignment - Match features to HMDB database by accurate mass - Annotate metabolites with MS/MS fragmentation - Extract putative identifications (Level 2/3) - Perform statistical analysis (FDR < 0.05, |FC| > 2) Step 4: Search Metabolomics Workbench for public data - Query for same disease or tissue type - Download relevant studies - Reprocess for consistency with own data - Use as validation cohort Step 5: Map all features to KEGG pathways - Map genes to KEGG orthology (KO) terms - Map proteins to KEGG identifiers - Map metabolites to KEGG compound IDs - Identify pathways with multi-omics coverage Step 6: Perform pathway enrichment analysis - Test for enrichment in KEGG pathways - Test for enrichment in Reactome pathways - Apply Fisher's exact test with multiple testing correction - Focus on pathways with hits in ≥2 omics layers Step 7: Build protein-metabolite networks - Query STRING for protein-protein interactions - Map proteins to KEGG reactions - Connect enzymes to their substrates/products - Build integrated network with genes → proteins → metabolites Step 8: Network topology analysis with NetworkX - Calculate node centrality (degree, betweenness) - Identify hub metabolites and key enzymes - Find bottleneck reactions - Detect network modules with community detection - Identify dysregulated subnetworks Step 9: Correlation analysis across omics layers - Calculate Spearman correlations between: * Gene expression and protein abundance * Protein abundance and metabolite levels * Gene expression and metabolites (for enzyme-product pairs) - Use statsmodels for significance testing - Focus on enzyme-metabolite pairs with expected relationships Step 10: Bayesian network modeling with PyMC - Build probabilistic graphical model of pathway - Model causal relationships: gene → protein → metabolite - Incorporate prior knowledge from KEGG/Reactome - Perform inference to identify key regulatory nodes - Estimate effect sizes and uncertainties Step 11: Identify therapeutic targets - Prioritize enzymes with: * Significant changes in all three omics layers * High network centrality * Druggable target class (kinases, transporters, etc.) - Query DrugBank for existing inhibitors - Search PubMed for validation in disease models Step 12: Create comprehensive multi-omics report - Summary statistics for each omics layer - Venn diagram of overlapping pathway hits - Pathway enrichment dot plots - Integrated network visualization (color by fold change) - Correlation heatmaps (enzyme-metabolite pairs) - Bayesian network structure - Table of prioritized therapeutic targets - Biological interpretation and mechanistic insights - Generate publication-quality figures - Export PDF report with all results Expected Output: - Integrated multi-omics dataset - Dysregulated pathway identification - Multi-omics network model - Prioritized list of therapeutic targets - Comprehensive systems biology report ``` --- ## Materials Science & Chemistry ### Example 10: High-Throughput Materials Discovery for Battery Applications **Objective**: Discover novel solid electrolyte materials for lithium-ion batteries using computational screening. **Skills Used**: - `pymatgen` - Materials analysis - `matminer` - Feature engineering - `scikit-learn` - Machine learning - `pymoo` - Multi-objective optimization - `ase` - Atomic simulation - `sympy` - Symbolic math - `vaex` - Large dataset handling - `matplotlib` - Visualization - `scientific-writing` - Report generation **Workflow**: ```bash Step 1: Generate candidate materials library - Use Pymatgen to enumerate compositions: * Li-containing compounds (Li₁₋ₓM₁₊ₓX₂) * M = transition metals (Zr, Ti, Ta, Nb) * X = O, S, Se - Generate ~10,000 candidate compositions - Apply charge neutrality constraints Step 2: Filter by thermodynamic stability - Query Materials Project database via Pymatgen - Calculate formation energy from elements - Calculate energy above convex hull (E_hull) - Filter: E_hull < 50 meV/atom (likely stable) - Retain ~2,000 thermodynamically plausible compounds Step 3: Predict crystal structures - Use Pymatgen structure predictor - Generate most likely crystal structures for each composition - Consider common structure types: LISICON, NASICON, garnet, perovskite - Calculate structural descriptors Step 4: Calculate material properties with Pymatgen - Lattice parameters and volume - Density - Packing fraction - Ionic radii and bond lengths - Coordination environments Step 5: Feature engineering with matminer - Calculate compositional features: * Elemental property statistics (electronegativity, ionic radius) * Valence electron concentrations * Stoichiometric attributes - Calculate structural features: * Pore size distribution * Site disorder parameters * Partial radial distribution functions Step 6: Build ML models for Li⁺ conductivity prediction - Collect training data from literature (experimental conductivities) - Train ensemble models with scikit-learn: * Random Forest * Gradient Boosting * Neural Network - Use 5-fold cross-validation - Predict ionic conductivity for all candidates Step 7: Predict additional properties - Electrochemical stability window (ML model) - Mechanical properties (bulk modulus, shear modulus) - Interfacial resistance (estimate from structure) - Synthesis temperature (ML prediction from similar compounds) Step 8: Multi-objective optimization with PyMOO Define optimization objectives: - Maximize: ionic conductivity (>10⁻³ S/cm target) - Maximize: electrochemical window (>4.5V target) - Minimize: synthesis temperature (<800°C preferred) - Minimize: cost (based on elemental abundance) Run NSGA-II to find Pareto optimal solutions Extract top 50 candidates from Pareto front Step 9: Analyze Pareto optimal materials - Identify composition trends (which elements appear frequently) - Analyze structure-property relationships - Calculate trade-offs between objectives - Identify "sweet spot" compositions Step 10: Validate predictions with DFT calculations - Select top 10 candidates for detailed study - Set up DFT calculations (VASP-like, if available via ASE) - Calculate: * Accurate formation energies * Li⁺ migration barriers (NEB calculations) * Electronic band gap * Elastic constants - Compare DFT results with ML predictions Step 11: Literature and patent search - Search for prior art on top candidates - PubMed and Google Scholar: "[composition] AND electrolyte" - USPTO: Check for existing patents on similar compositions - Identify any experimental reports on related materials Step 12: Generate materials discovery report - Summary of screening workflow and statistics - Pareto front visualization (conductivity vs stability vs cost) - Structure visualization of top candidates - Property comparison table - Composition-property trend analysis - DFT validation results - Predicted performance vs state-of-art materials - Synthesis recommendations - IP landscape summary - Prioritized list of 5-10 materials for experimental validation - Export as publication-ready PDF Expected Output: - Screened library of 10,000+ materials - ML models for property prediction - Pareto-optimal set of 50 candidates - Detailed analysis of top 10 materials - DFT validation results - Comprehensive materials discovery report ``` --- ## Digital Pathology ### Example 11: Automated Tumor Detection in Whole Slide Images **Objective**: Develop and validate a deep learning model for automated tumor detection in histopathology images. **Skills Used**: - `histolab` - Whole slide image processing - `pathml` - Computational pathology - `pytorch-lightning` - Deep learning - `torchvision` - Image models - `scikit-learn` - Model evaluation - `pydicom` - DICOM handling - `omero-integration` - Image management - `matplotlib` - Visualization - `shap` - Model interpretability **Workflow**: ```bash Step 1: Load whole slide images with HistoLab - Load WSI files (SVS, TIFF formats) - Extract slide metadata and magnification levels - Visualize slide thumbnails - Inspect tissue area vs background Step 2: Tile extraction and preprocessing - Use HistoLab to extract tiles (256×256 pixels at 20× magnification) - Filter tiles: * Remove background (tissue percentage > 80%) * Apply color normalization (Macenko or Reinhard method) * Filter out artifacts and bubbles - Extract ~100,000 tiles per slide across all slides Step 3: Create annotations (if training from scratch) - Load pathologist annotations (if available via OMERO) - Convert annotations to tile-level labels - Categories: tumor, stroma, necrosis, normal - Balance classes through stratified sampling Step 4: Set up PathML pipeline - Create PathML SlideData objects - Define preprocessing pipeline: * Stain normalization * Color augmentation (HSV jitter) * Rotation and flipping - Split data: 70% train, 15% validation, 15% test Step 5: Build deep learning model with PyTorch Lightning - Architecture: ResNet50 or EfficientNet backbone - Add custom classification head for tissue types - Define training pipeline: * Loss function: Cross-entropy or Focal loss * Optimizer: Adam with learning rate scheduling * Augmentations: rotation, flip, color jitter, elastic deformation * Batch size: 32 * Mixed precision training Step 6: Train model - Train on tile-level labels - Monitor metrics: accuracy, F1 score, AUC - Use early stopping on validation loss - Save best model checkpoint - Training time: ~6-12 hours on GPU Step 7: Evaluate model performance - Test on held-out test set - Calculate metrics with scikit-learn: * Accuracy, precision, recall, F1 per class * Confusion matrix * ROC curves and AUC - Compute confidence intervals with bootstrapping Step 8: Slide-level aggregation - Apply model to all tiles in each test slide - Aggregate predictions: * Majority voting * Weighted average by confidence * Spatial smoothing with convolution - Generate probability heatmaps overlaid on WSI Step 9: Model interpretability with SHAP - Apply GradCAM or SHAP to explain predictions - Visualize which regions contribute to tumor classification - Generate attention maps showing model focus - Validate that model attends to relevant histological features Step 10: Clinical validation - Compare model predictions with pathologist diagnosis - Calculate inter-rater agreement (kappa score) - Identify discordant cases for review - Analyze error types: false positives, false negatives Step 11: Integration with OMERO - Upload processed slides and heatmaps to OMERO server - Attach model predictions as slide metadata - Enable pathologist review interface - Store annotations and corrections for model retraining Step 12: Generate clinical validation report - Model architecture and training details - Performance metrics with confidence intervals - Slide-level accuracy vs pathologist ground truth - Heatmap visualizations for representative cases - Analysis of failure modes - Comparison with published methods - Discussion of clinical applicability - Recommendations for deployment and monitoring - Export PDF report for regulatory submission (if needed) Expected Output: - Trained deep learning model for tumor detection - Tile-level and slide-level predictions - Probability heatmaps for visualization - Performance metrics and validation results - Model interpretation visualizations - Clinical validation report ``` --- ## Lab Automation & Protocol Design ### Example 12: Automated High-Throughput Screening Protocol **Objective**: Design and execute an automated compound screening workflow using liquid handling robots. **Skills Used**: - `pylabrobot` - Lab automation - `opentrons-integration` - Opentrons protocol - `benchling-integration` - Sample tracking - `protocolsio-integration` - Protocol documentation - `simpy` - Process simulation - `polars` - Data processing - `matplotlib` - Plate visualization - `reportlab` - Report generation **Workflow**: ```bash Step 1: Define screening campaign in Benchling - Create compound library in Benchling registry - Register all compounds with structure, concentration, location - Define plate layouts (384-well format) - Track compound source plates in inventory - Set up ELN entry for campaign documentation Step 2: Design assay protocol - Define assay steps: * Dispense cells (5000 cells/well) * Add compounds (dose-response curve, 10 concentrations) * Incubate 48 hours at 37°C * Add detection reagent (cell viability assay) * Read luminescence signal - Calculate required reagent volumes - Document protocol in Protocols.io - Share with team for review Step 3: Simulate workflow with SimPy - Model liquid handler, incubator, plate reader as resources - Simulate timing for 20 plates (7,680 wells) - Identify bottlenecks (plate reader reads take 5 min/plate) - Optimize scheduling: stagger plate processing - Validate that throughput goal is achievable (20 plates/day) Step 4: Design plate layout - Use PyLabRobot to generate plate maps: * Columns 1-2: positive controls (DMSO) * Columns 3-22: compound titrations (10 concentrations in duplicate) * Columns 23-24: negative controls (cytotoxic control) - Randomize compound positions across plates - Account for edge effects (avoid outer wells for samples) - Export plate maps to CSV Step 5: Create Opentrons protocol for cell seeding - Write Python protocol using Opentrons API 2.0 - Steps: * Aspirate cells from reservoir * Dispense 40 μL cell suspension per well * Tips: use P300 multi-channel for speed * Include mixing steps to prevent settling - Simulate protocol in Opentrons app - Test on one plate before full run Step 6: Create Opentrons protocol for compound addition - Acoustic liquid handler (Echo) or pin tool for nanoliter transfers - If using Opentrons: * Source: 384-well compound plates * Transfer 100 nL compound (in DMSO) to assay plates * Use P20 for precision * Prepare serial dilutions on deck if needed - Account for DMSO normalization (1% final) Step 7: Integrate with Benchling for sample tracking - Use Benchling API to: * Retrieve compound information (structure, batch, concentration) * Log plate creation in inventory * Create transfer records for audit trail * Link assay plates to ELN entry Step 8: Execute automated workflow - Day 1: Seed cells with Opentrons - Day 1 (4h later): Add compounds with Opentrons - Day 3: Add detection reagent (manual or automated) - Day 3 (2h later): Read plates on plate reader - Store plates at 4°C between steps Step 9: Collect and process data - Export raw luminescence data from plate reader - Load data with Polars for fast processing - Normalize data: * Subtract background (media-only wells) * Calculate % viability relative to DMSO control * Apply plate-wise normalization to correct systematic effects - Quality control: * Z' factor calculation (> 0.5 for acceptable assay) * Coefficient of variation for controls (< 10%) * Flag plates with poor QC metrics Step 10: Dose-response curve fitting - Fit 4-parameter logistic curves for each compound - Calculate IC50, Hill slope, max/min response - Use scikit-learn or scipy for curve fitting - Compute 95% confidence intervals - Flag compounds with poor curve fits (R² < 0.8) Step 11: Hit identification and triage - Define hit criteria: * IC50 < 10 μM * Max inhibition > 50% * Curve quality: R² > 0.8 - Prioritize hits by potency - Check for PAINS patterns with RDKit - Cross-reference with known aggregators/frequent hitters Step 12: Visualize results and generate report - Create plate heatmaps showing % viability - Dose-response curve plots for hits - Scatter plot: potency vs max effect - QC metric summary across plates - Structure visualization of top 20 hits - Generate campaign summary report: * Screening statistics (compounds tested, hit rate) * QC metrics and data quality assessment * Hit list with structures and IC50 values * Protocol documentation from Protocols.io * Raw data files and analysis code * Recommendations for confirmation assays - Update Benchling ELN with results - Export PDF report for stakeholders Expected Output: - Automated screening protocols (Opentrons Python files) - Executed screen of 384-well plates - Quality-controlled dose-response data - Hit list with IC50 values - Comprehensive screening report ``` --- ## Agricultural Genomics ### Example 13: GWAS for Crop Yield Improvement **Objective**: Identify genetic markers associated with drought tolerance and yield in a crop species. **Skills Used**: - `biopython` - Sequence analysis - `pysam` - VCF processing - `gwas-database` - Public GWAS data - `ensembl-database` - Plant genomics - `gene-database` - Gene annotation - `scanpy` - Population structure (adapted for genetic data) - `scikit-learn` - PCA and clustering - `statsmodels` - Association testing - `matplotlib` - Manhattan plots - `seaborn` - Visualization **Workflow**: ```bash Step 1: Load and QC genotype data - Load VCF file with pysam - Filter variants: * Call rate > 95% * Minor allele frequency (MAF) > 5% * Hardy-Weinberg equilibrium p > 1e-6 - Convert to numeric genotype matrix (0, 1, 2) - Retain ~500,000 SNPs after QC Step 2: Assess population structure - Calculate genetic relationship matrix - Perform PCA with scikit-learn (use top 10 PCs) - Visualize population structure (PC1 vs PC2) - Identify distinct subpopulations or admixture - Note: will use PCs as covariates in GWAS Step 3: Load and process phenotype data - Drought tolerance score (1-10 scale, measured under stress) - Grain yield (kg/hectare) - Days to flowering - Plant height - Quality control: * Remove outliers (> 3 SD from mean) * Transform if needed (log or rank-based for skewed traits) * Adjust for environmental covariates (field, year) Step 4: Calculate kinship matrix - Compute genetic relatedness matrix - Account for population structure and relatedness - Will use in mixed linear model to control for confounding Step 5: Run genome-wide association study - For each phenotype, test association with each SNP - Use mixed linear model (MLM) in statsmodels: * Fixed effects: SNP genotype, PCs (top 10) * Random effects: kinship matrix * Bonferroni threshold: p < 5e-8 (genome-wide significance) - Multiple testing correction: Bonferroni or FDR - Calculate genomic inflation factor (λ) to check for inflation Step 6: Identify significant associations - Extract SNPs passing significance threshold - Determine lead SNPs (most significant in each locus) - Define loci: extend ±500 kb around lead SNP - Identify independent associations via conditional analysis Step 7: Annotate significant loci - Map SNPs to genes using Ensembl Plants API - Identify genic vs intergenic SNPs - For genic SNPs: * Determine consequence (missense, synonymous, intronic, UTR) * Extract gene names and descriptions - Query NCBI Gene for gene function - Prioritize genes with known roles in stress response or development Step 8: Search GWAS Catalog for prior reports - Query GWAS Catalog for similar traits in same or related species - Check for replication of known loci - Identify novel vs known associations Step 9: Functional enrichment analysis - Extract all genes within significant loci - Perform GO enrichment analysis - Test for enrichment in KEGG pathways - Focus on pathways related to: * Drought stress response (ABA signaling, osmotic adjustment) * Photosynthesis and carbon fixation * Root development Step 10: Estimate SNP heritability and genetic architecture - Calculate variance explained by significant SNPs - Estimate SNP-based heritability (proportion of variance explained) - Assess genetic architecture: few large-effect vs many small-effect loci Step 11: Build genomic prediction model - Train genomic selection model with scikit-learn: * Ridge regression (GBLUP equivalent) * Elastic net * Random Forest - Use all SNPs (not just significant ones) - Cross-validate to predict breeding values - Assess prediction accuracy Step 12: Generate GWAS report - Manhattan plots for each trait - QQ plots to assess test calibration - Regional association plots for significant loci - Gene models overlaid on loci - Table of significant SNPs with annotations - Functional enrichment results - Genomic prediction accuracy - Biological interpretation: * Candidate genes for drought tolerance * Potential molecular mechanisms * Implications for breeding programs - Recommendations: * SNPs to use for marker-assisted selection * Genes for functional validation * Crosses to generate mapping populations - Export publication-quality PDF with all results Expected Output: - Significant SNP-trait associations - Annotated candidate genes - Functional enrichment analysis - Genomic prediction models - Comprehensive GWAS report - Recommendations for breeding programs ``` --- ## Neuroscience & Brain Imaging ### Example 14: Brain Connectivity Analysis from fMRI Data **Objective**: Analyze resting-state fMRI data to identify altered brain connectivity patterns in disease. **Skills Used**: - `neurokit2` - Neurophysiological signal processing - `nilearn` (external) - Neuroimaging analysis - `scikit-learn` - Classification and clustering - `networkx` - Graph theory analysis - `statsmodels` - Statistical testing - `torch_geometric` - Graph neural networks - `pymc` - Bayesian modeling - `matplotlib` - Brain visualization - `seaborn` - Connectivity matrices **Workflow**: ```bash Step 1: Load and preprocess fMRI data # Note: Use nilearn or similar for fMRI-specific preprocessing - Load 4D fMRI images (BOLD signal) - Preprocessing: * Motion correction (realignment) * Slice timing correction * Spatial normalization to MNI space * Smoothing (6mm FWHM Gaussian kernel) * Temporal filtering (0.01-0.1 Hz bandpass) * Nuisance regression (motion, CSF, white matter) Step 2: Define brain regions (parcellation) - Apply brain atlas (e.g., AAL, Schaefer 200-region atlas) - Extract average time series for each region - Result: 200 time series per subject (one per brain region) Step 3: Signal cleaning with NeuroKit2 - Denoise time series - Remove physiological artifacts - Apply additional bandpass filtering if needed - Identify and handle outlier time points Step 4: Calculate functional connectivity - Compute pairwise Pearson correlations between all regions - Result: 200×200 connectivity matrix per subject - Fisher z-transform correlations for group statistics - Threshold weak connections (|r| < 0.2) Step 5: Graph theory analysis with NetworkX - Convert connectivity matrices to graphs - Calculate global network metrics: * Clustering coefficient (local connectivity) * Path length (integration) * Small-worldness (balance of segregation and integration) * Modularity (community structure) - Calculate node-level metrics: * Degree centrality * Betweenness centrality * Eigenvector centrality * Participation coefficient (inter-module connectivity) Step 6: Statistical comparison between groups - Compare patients vs healthy controls - Use statsmodels for group comparisons: * Paired or unpaired t-tests for connectivity edges * FDR correction for multiple comparisons across all edges * Identify edges with significantly different connectivity - Compare global and node-level network metrics - Calculate effect sizes (Cohen's d) Step 7: Identify altered subnetworks - Threshold statistical maps (FDR < 0.05) - Identify clusters of altered connectivity - Map to functional brain networks: * Default mode network (DMN) * Salience network (SN) * Central executive network (CEN) * Sensorimotor network - Visualize altered connections on brain surfaces Step 8: Machine learning classification - Train classifier to distinguish patients from controls - Use scikit-learn Random Forest or SVM - Features: connectivity values or network metrics - Cross-validation (10-fold) - Calculate accuracy, sensitivity, specificity, AUC - Identify most discriminative features (connectivity edges) Step 9: Graph neural network analysis with Torch Geometric - Build graph neural network (GCN or GAT) - Input: connectivity matrices as adjacency matrices - Train to predict diagnosis - Extract learned representations - Visualize latent space (UMAP) - Interpret which brain regions are most important Step 10: Bayesian network modeling with PyMC - Build directed graphical model of brain networks - Estimate effective connectivity (directional influence) - Incorporate prior knowledge about anatomical connections - Perform posterior inference - Identify key driver regions in disease Step 11: Clinical correlation analysis - Correlate network metrics with clinical scores: * Symptom severity * Cognitive performance * Treatment response - Use Spearman or Pearson correlation - Identify brain-behavior relationships Step 12: Generate comprehensive neuroimaging report - Brain connectivity matrices (patients vs controls) - Statistical comparison maps on brain surface - Network metric comparison bar plots - Graph visualizations (circular or force-directed layout) - Machine learning ROC curves - Brain-behavior correlation plots - Clinical interpretation: * Which networks are disrupted? * Relationship to symptoms * Potential biomarker utility - Recommendations: * Brain regions for therapeutic targeting (TMS, DBS) * Network metrics as treatment response predictors - Export publication-ready PDF with brain visualizations Expected Output: - Functional connectivity matrices for all subjects - Statistical maps of altered connectivity - Graph theory metrics - Machine learning classification model - Brain-behavior correlations - Comprehensive neuroimaging report ``` --- ## Environmental Microbiology ### Example 15: Metagenomic Analysis of Environmental Samples **Objective**: Characterize microbial community composition and functional potential from environmental DNA samples. **Skills Used**: - `biopython` - Sequence processing - `pysam` - BAM file handling - `ena-database` - Sequence data - `uniprot-database` - Protein annotation - `kegg-database` - Pathway analysis - `etetoolkit` - Phylogenetic trees - `scikit-bio` - Microbial ecology - `networkx` - Co-occurrence networks - `statsmodels` - Diversity statistics - `matplotlib` - Visualization **Workflow**: ```bash Step 1: Load and QC metagenomic reads - Load FASTQ files with BioPython - Quality control with FastQC-equivalent: * Remove adapters and low-quality bases (Q < 20) * Filter short reads (< 50 bp) * Remove host contamination (if applicable) - Subsample to even depth if comparing samples Step 2: Taxonomic classification - Use Kraken2-like approach or query ENA database - Classify reads to taxonomic lineages - Generate abundance table: * Rows: taxa (species or OTUs) * Columns: samples * Values: read counts or relative abundance - Summarize at different levels: phylum, class, order, family, genus, species Step 3: Calculate diversity metrics with scikit-bio - Alpha diversity (within-sample): * Richness (number of species) * Shannon entropy * Simpson diversity * Chao1 estimated richness - Beta diversity (between-sample): * Bray-Curtis dissimilarity * Weighted/unweighted UniFrac distance * Jaccard distance - Rarefaction curves to assess sampling completeness Step 4: Statistical comparison of communities - Compare diversity between groups (e.g., polluted vs pristine) - Use statsmodels for: * Mann-Whitney or Kruskal-Wallis tests (alpha diversity) * PERMANOVA for beta diversity (adonis test) * LEfSe for differential abundance testing - Identify taxa enriched or depleted in each condition Step 5: Build phylogenetic tree with ETE Toolkit - Extract 16S rRNA sequences (or marker genes) - Align sequences (MUSCLE/MAFFT equivalent) - Build phylogenetic tree (neighbor-joining or maximum likelihood) - Visualize tree colored by sample or environment - Root tree with outgroup Step 6: Co-occurrence network analysis - Calculate pairwise correlations between taxa - Use Spearman correlation to identify co-occurrence patterns - Filter significant correlations (p < 0.01, |r| > 0.6) - Build co-occurrence network with NetworkX - Identify modules (communities of co-occurring taxa) - Calculate network topology metrics - Visualize network (nodes = taxa, edges = correlations) Step 7: Functional annotation - Assemble contigs from reads (if performing assembly) - Predict genes with Prodigal-like tools - Annotate genes using UniProt and KEGG - Map proteins to KEGG pathways - Generate functional profile: * Abundance of metabolic pathways * Key enzymes (nitrification, denitrification, methanogenesis) * Antibiotic resistance genes * Virulence factors Step 8: Functional diversity analysis - Compare functional profiles between samples - Calculate pathway richness and evenness - Identify enriched pathways with statistical testing - Link taxonomy to function: * Which taxa contribute to which functions? * Use shotgun data to assign functions to taxa Step 9: Search ENA for related environmental samples - Query ENA for metagenomic studies from similar environments - Download and compare to own samples - Place samples in context of global microbiome diversity - Identify unique vs ubiquitous taxa Step 10: Environmental parameter correlation - Correlate community composition with metadata: * Temperature, pH, salinity * Nutrient concentrations (N, P) * Pollutant levels (heavy metals, hydrocarbons) - Use Mantel test to correlate distance matrices - Identify environmental drivers of community structure Step 11: Biomarker discovery - Identify taxa or pathways that correlate with environmental condition - Use Random Forest to find predictive features - Validate biomarkers: * Sensitivity and specificity * Cross-validation across samples - Propose taxa as bioindicators of environmental health Step 12: Generate environmental microbiome report - Taxonomic composition bar charts (stacked by phylum/class) - Alpha and beta diversity plots (boxplots, PCoA) - Phylogenetic tree with environmental context - Co-occurrence network visualization - Functional pathway heatmaps - Environmental correlation plots - Statistical comparison tables - Biological interpretation: * Dominant taxa and their ecological roles * Functional potential of the community * Environmental factors shaping the microbiome * Biomarker taxa for monitoring - Recommendations: * Biomarkers for environmental monitoring * Functional guilds for restoration * Further sampling or sequencing strategies - Export comprehensive PDF report Expected Output: - Taxonomic profiles for all samples - Diversity metrics and statistical comparisons - Phylogenetic tree - Co-occurrence network - Functional annotation and pathway analysis - Comprehensive microbiome report ``` --- ## Infectious Disease Research ### Example 16: Antimicrobial Resistance Surveillance and Prediction **Objective**: Track antimicrobial resistance trends and predict resistance phenotypes from genomic data. **Skills Used**: - `biopython` - Sequence analysis - `pysam` - Genome assembly analysis - `ena-database` - Public genomic data - `uniprot-database` - Resistance protein annotation - `gene-database` - Resistance gene catalogs - `etetoolkit` - Phylogenetic analysis - `scikit-learn` - Resistance prediction - `networkx` - Transmission networks - `statsmodels` - Trend analysis - `matplotlib` - Epidemiological plots **Workflow**: ```bash Step 1: Collect bacterial genome sequences - Isolates from hospital surveillance program - Load FASTA assemblies with BioPython - Basic QC: * Assess assembly quality (N50, completeness) * Estimate genome size and coverage * Remove contaminated assemblies Step 2: Species identification and MLST typing - Perform in silico MLST (multi-locus sequence typing) - Extract housekeeping gene sequences - Assign sequence types (ST) - Classify isolates into clonal complexes - Identify high-risk clones (e.g., ST131 E. coli, ST258 K. pneumoniae) Step 3: Antimicrobial resistance (AMR) gene detection - Query NCBI Gene and UniProt for AMR gene databases - Screen assemblies for resistance genes: * Beta-lactamases (blaTEM, blaCTX-M, blaKPC, blaNDM) * Aminoglycoside resistance (aac, aph, ant) * Fluoroquinolone resistance (gyrA, parC mutations) * Colistin resistance (mcr-1 to mcr-10) * Efflux pumps - Calculate gene presence/absence matrix Step 4: Resistance mechanism annotation - Map detected genes to resistance classes: * Enzymatic modification (e.g., beta-lactamases) * Target modification (e.g., ribosomal methylation) * Target mutation (e.g., fluoroquinolone resistance) * Efflux pumps - Query UniProt for detailed mechanism descriptions - Link genes to antibiotic classes affected Step 5: Build phylogenetic tree with ETE Toolkit - Extract core genome SNPs - Concatenate SNP alignments - Build maximum likelihood tree - Root with outgroup or midpoint rooting - Annotate tree with: * Resistance profiles * Sequence types * Collection date and location Step 6: Genotype-phenotype correlation - Match genomic data with phenotypic susceptibility testing - For each antibiotic, correlate: * Presence of resistance genes with MIC values * Target mutations with resistance phenotype - Calculate sensitivity/specificity of genetic markers - Identify discordant cases (false positives/negatives) Step 7: Machine learning resistance prediction - Train classification models with scikit-learn: * Features: presence/absence of resistance genes + mutations * Target: resistance phenotype (susceptible/intermediate/resistant) * Models: Logistic Regression, Random Forest, Gradient Boosting - Train separate models for each antibiotic - Cross-validate (stratified 5-fold) - Calculate accuracy, precision, recall, F1 score - Feature importance: which genes are most predictive? Step 8: Temporal trend analysis - Track resistance rates over time - Use statsmodels for: * Mann-Kendall trend test * Joinpoint regression (identify change points) * Forecast future resistance rates (ARIMA) - Analyze trends for each antibiotic class - Identify emerging resistance mechanisms Step 9: Transmission network inference - Identify closely related isolates (< 10 SNPs difference) - Build transmission network with NetworkX: * Nodes: isolates * Edges: putative transmission links - Incorporate temporal and spatial data - Identify outbreak clusters - Detect super-spreaders (high degree nodes) - Analyze network topology Step 10: Search ENA for global context - Query ENA for same species from other regions/countries - Download representative genomes - Integrate into phylogenetic analysis - Assess whether local isolates are globally distributed clones - Identify region-specific vs international resistance genes Step 11: Plasmid and mobile element analysis - Identify plasmid contigs - Detect insertion sequences and transposons - Track mobile genetic elements carrying resistance genes - Identify conjugative plasmids facilitating horizontal gene transfer - Build plasmid similarity networks Step 12: Generate AMR surveillance report - Summary statistics: * Number of isolates by species, ST, location * Resistance rates for each antibiotic - Phylogenetic tree annotated with resistance profiles - Temporal trend plots (resistance % over time) - Transmission network visualizations - Prediction model performance metrics - Heatmap: resistance genes by isolate - Geographic distribution map (if spatial data available) - Interpretation: * Predominant resistance mechanisms * High-risk clones circulating * Temporal trends and emerging threats * Transmission clusters and outbreaks - Recommendations: * Infection control measures for clusters * Antibiotic stewardship priorities * Resistance genes to monitor * Laboratories to perform confirmatory testing - Export comprehensive PDF for public health reporting Expected Output: - AMR gene profiles for all isolates - Phylogenetic tree with resistance annotations - Temporal trends in resistance rates - ML models for resistance prediction from genomes - Transmission networks - Comprehensive AMR surveillance report for public health ``` --- ## Multi-Omics Integration ### Example 17: Integrative Analysis of Cancer Multi-Omics Data **Objective**: Integrate genomics, transcriptomics, proteomics, and clinical data to identify cancer subtypes and therapeutic strategies. **Skills Used**: - `pydeseq2` - RNA-seq DE analysis - `pysam` - Variant calling - `ensembl-database` - Gene annotation - `cosmic-database` - Cancer mutations - `string-database` - Protein interactions - `reactome-database` - Pathway analysis - `opentargets-database` - Drug targets - `scikit-learn` - Clustering and classification - `torch_geometric` - Graph neural networks - `umap-learn` - Dimensionality reduction - `statsmodels` - Survival analysis - `pymoo` - Multi-objective optimization **Workflow**: ```bash Step 1: Load and preprocess genomic data (WES/WGS) - Parse VCF files with pysam - Filter high-quality variants (QUAL > 30, DP > 20) - Annotate with Ensembl VEP (missense, nonsense, frameshift) - Query COSMIC for known cancer mutations - Create mutation matrix: samples × genes (binary: mutated or not) - Focus on cancer genes from COSMIC Cancer Gene Census Step 2: Process transcriptomic data (RNA-seq) - Load gene count matrix - Run differential expression with PyDESeq2 - Compare tumor vs normal (if paired samples available) - Normalize counts (TPM or FPKM) - Identify highly variable genes - Create expression matrix: samples × genes (log2 TPM) Step 3: Load proteomic data (Mass spec) - Protein abundance matrix from LC-MS/MS - Normalize protein abundances (median normalization) - Log2-transform - Filter proteins detected in < 50% of samples - Create protein matrix: samples × proteins Step 4: Load clinical data - Demographics: age, sex, race - Tumor characteristics: stage, grade, histology - Treatment: surgery, chemo, radiation, targeted therapy - Outcome: overall survival (OS), progression-free survival (PFS) - Response: complete/partial response, stable/progressive disease Step 5: Data integration and harmonization - Match sample IDs across omics layers - Ensure consistent gene/protein identifiers - Handle missing data: * Impute with KNN or median (for moderate missingness) * Remove features with > 50% missing - Create multi-omics data structure (dictionary of matrices) Step 6: Multi-omics dimensionality reduction - Concatenate all omics features (genes + proteins + mutations) - Apply UMAP with umap-learn for visualization - Alternative: PCA or t-SNE - Visualize samples in 2D space colored by: * Histological subtype * Stage * Survival (high vs low) - Identify patterns or clusters Step 7: Unsupervised clustering to identify subtypes - Perform consensus clustering with scikit-learn - Test k = 2 to 10 clusters - Evaluate cluster stability and optimal k - Assign samples to clusters (subtypes) - Visualize clustering in UMAP space Step 8: Characterize molecular subtypes For each subtype: - Differential expression analysis: * Compare subtype vs all others with PyDESeq2 * Extract top differentially expressed genes and proteins - Mutation enrichment: * Fisher's exact test for each gene * Identify subtype-specific mutations - Pathway enrichment: * Query Reactome for enriched pathways * Query KEGG for metabolic pathway differences * Identify hallmark biological processes Step 9: Build protein-protein interaction networks - Query STRING database for interactions among: * Differentially expressed proteins * Products of mutated genes - Construct PPI network with NetworkX - Identify network modules (community detection) - Calculate centrality metrics to find hub proteins - Overlay fold changes on network for visualization Step 10: Survival analysis by subtype - Use statsmodels or lifelines for survival analysis - Kaplan-Meier curves for each subtype - Log-rank test for significance - Cox proportional hazards model: * Covariates: subtype, stage, age, treatment * Estimate hazard ratios - Identify prognostic subtypes Step 11: Predict therapeutic response - Train machine learning models with scikit-learn: * Features: multi-omics data * Target: response to specific therapy (responder/non-responder) * Models: Random Forest, XGBoost, SVM - Cross-validation to assess performance - Identify features predictive of response - Calculate AUC and feature importance Step 12: Graph neural network for integrated prediction - Build heterogeneous graph with Torch Geometric: * Nodes: samples, genes, proteins, pathways * Edges: gene-protein, protein-protein, gene-pathway * Node features: expression, mutation status - Train GNN to predict: * Subtype classification * Survival risk * Treatment response - Extract learned embeddings for interpretation Step 13: Identify therapeutic targets with Open Targets - For each subtype, query Open Targets: * Input: upregulated genes/proteins * Extract target-disease associations * Prioritize by tractability score - Search for FDA-approved drugs targeting identified proteins - Identify clinical trials for relevant targets - Propose subtype-specific therapeutic strategies Step 14: Multi-objective optimization of treatment strategies - Use PyMOO to optimize treatment selection: * Objectives: 1. Maximize predicted response probability 2. Minimize predicted toxicity 3. Minimize cost * Constraints: patient eligibility, drug availability - Generate Pareto-optimal treatment strategies - Personalized treatment recommendations per patient Step 15: Generate comprehensive multi-omics report - Sample clustering and subtype assignments - UMAP visualization colored by subtype, survival, mutations - Subtype characterization: * Molecular signatures (genes, proteins, mutations) * Enriched pathways * PPI networks - Kaplan-Meier survival curves by subtype - ML model performance (AUC, confusion matrices) - Feature importance plots - Therapeutic target tables with supporting evidence - Personalized treatment recommendations - Clinical implications: * Prognostic biomarkers * Predictive biomarkers for therapy selection * Novel drug targets - Export publication-quality PDF with all figures and tables Expected Output: - Integrated multi-omics dataset - Cancer subtype classification - Molecular characterization of subtypes - Survival analysis and prognostic markers - Predictive models for treatment response - Therapeutic target identification - Personalized treatment strategies - Comprehensive integrative genomics report ``` --- ## Experimental Physics & Data Analysis ### Example 18: Analysis of Particle Physics Detector Data **Objective**: Analyze experimental data from particle detector to identify signal events and measure physical constants. **Skills Used**: - `astropy` - Units and constants - `sympy` - Symbolic mathematics - `scipy` - Statistical analysis - `scikit-learn` - Classification - `stable-baselines3` - Reinforcement learning for optimization - `matplotlib` - Visualization - `seaborn` - Statistical plots - `statsmodels` - Hypothesis testing - `dask` - Large-scale data processing - `vaex` - Out-of-core dataframes **Workflow**: ```bash Step 1: Load and inspect detector data - Load ROOT files or HDF5 with raw detector signals - Use Vaex for out-of-core processing (TBs of data) - Inspect data structure: event IDs, timestamps, detector channels - Extract key observables: * Energy deposits in calorimeters * Particle trajectories from tracking detectors * Time-of-flight measurements * Trigger information Step 2: Apply detector calibration and corrections - Load calibration constants - Apply energy calibrations to convert ADC to physical units - Correct for detector efficiency variations - Apply geometric corrections (alignment) - Use Astropy units for unit conversions (eV, GeV, MeV) - Account for dead time and detector acceptance Step 3: Event reconstruction - Cluster energy deposits to form particle candidates - Reconstruct particle trajectories (tracks) - Match tracks to calorimeter clusters - Calculate invariant masses for particle identification - Compute momentum and energy for each particle - Use Dask for parallel processing across events Step 4: Event selection and filtering - Define signal region based on physics hypothesis - Apply quality cuts: * Track quality (chi-squared, number of hits) * Fiducial volume cuts * Timing cuts (beam window) * Particle identification cuts - Estimate trigger efficiency - Calculate event weights for corrections Step 5: Background estimation - Identify background sources: * Cosmic rays * Beam-related backgrounds * Detector noise * Physics backgrounds (non-signal processes) - Simulate backgrounds using Monte Carlo (if available) - Estimate background from data in control regions - Use sideband subtraction method Step 6: Signal extraction - Fit invariant mass distributions to extract signal - Use scipy for likelihood fitting: * Signal model: Gaussian or Breit-Wigner * Background model: polynomial or exponential * Combined fit with maximum likelihood - Calculate signal significance (S/√B or Z-score) - Estimate systematic uncertainties Step 7: Machine learning event classification - Train classifier with scikit-learn to separate signal from background - Features: kinematic variables, topology, detector response - Models: Boosted Decision Trees (XGBoost), Neural Networks - Cross-validate with k-fold CV - Optimize selection criteria using ROC curves - Calculate signal efficiency and background rejection Step 8: Reinforcement learning for trigger optimization - Use Stable-Baselines3 to optimize trigger thresholds - Environment: detector simulator - Action: adjust trigger thresholds - Reward: maximize signal efficiency while controlling rate - Train PPO or SAC agent - Validate on real data Step 9: Calculate physical observables - Measure cross-sections: * σ = N_signal / (ε × L × BR) * N_signal: number of signal events * ε: detection efficiency * L: integrated luminosity * BR: branching ratio - Use Sympy for symbolic error propagation - Calculate with Astropy for proper unit handling Step 10: Statistical analysis and hypothesis testing - Perform hypothesis tests with statsmodels: * Likelihood ratio test for signal vs background-only * Calculate p-values and significance levels * Set confidence limits (CLs method) - Bayesian analysis for parameter estimation - Calculate confidence intervals and error bands Step 11: Systematic uncertainty evaluation - Identify sources of systematic uncertainty: * Detector calibration uncertainties * Background estimation uncertainties * Theoretical uncertainties (cross-sections, PDFs) * Monte Carlo modeling uncertainties - Propagate uncertainties through analysis chain - Combine statistical and systematic uncertainties - Present as error budget Step 12: Create comprehensive physics report - Event displays showing candidate signal events - Kinematic distributions (momentum, energy, angles) - Invariant mass plots with fitted signal - ROC curves for ML classifiers - Cross-section measurements with error bars - Comparison with theoretical predictions - Systematic uncertainty breakdown - Statistical significance calculations - Interpretation: * Consistency with Standard Model * Constraints on new physics parameters * Discovery potential or exclusion limits - Recommendations: * Detector improvements * Additional data needed * Future analysis strategies - Export publication-ready PDF formatted for physics journal Expected Output: - Reconstructed physics events - Signal vs background classification - Measured cross-sections and branching ratios - Statistical significance of observations - Systematic uncertainty analysis - Comprehensive experimental physics paper ``` --- ## Chemical Engineering & Process Optimization ### Example 19: Optimization of Chemical Reactor Design and Operation **Objective**: Design and optimize a continuous chemical reactor for maximum yield and efficiency while meeting safety and economic constraints. **Skills Used**: - `sympy` - Symbolic equations and reaction kinetics - `scipy` - Numerical integration and optimization - `pymoo` - Multi-objective optimization - `simpy` - Process simulation - `pymc` - Bayesian parameter estimation - `scikit-learn` - Process modeling - `stable-baselines3` - Real-time control optimization - `matplotlib` - Process diagrams - `reportlab` - Engineering reports **Workflow**: ```bash Step 1: Define reaction system and kinetics - Chemical reaction: A + B → C + D - Use Sympy to define symbolic rate equations: * Arrhenius equation: k = A × exp(-Ea/RT) * Rate law: r = k × [A]^α × [B]^β - Define material and energy balances symbolically - Include equilibrium constants and thermodynamics - Account for side reactions and byproducts Step 2: Develop reactor model - Select reactor type: CSTR, PFR, batch, or semi-batch - Write conservation equations: * Mass balance: dC/dt = (F_in × C_in - F_out × C)/V + r * Energy balance: ρCp × dT/dt = Q - ΔH_rxn × r × V * Momentum balance (pressure drop) - Include heat transfer correlations - Model mixing and mass transfer limitations Step 3: Parameter estimation with PyMC - Load experimental data from pilot reactor - Bayesian inference to estimate kinetic parameters: * Pre-exponential factor (A) * Activation energy (Ea) * Reaction orders (α, β) - Use MCMC sampling with PyMC - Incorporate prior knowledge from literature - Calculate posterior distributions and credible intervals - Assess parameter uncertainty and correlation Step 4: Model validation - Simulate reactor with estimated parameters using scipy.integrate - Compare predictions with experimental data - Calculate goodness of fit (R², RMSE) - Perform sensitivity analysis: * Which parameters most affect yield? * Identify critical operating conditions - Refine model if needed Step 5: Machine learning surrogate model - Train fast surrogate model with scikit-learn - Generate training data from detailed model (1000+ runs) - Features: T, P, residence time, feed composition, catalyst loading - Target: yield, selectivity, conversion - Models: Gaussian Process Regression, Random Forest - Validate surrogate accuracy (R² > 0.95) - Use for rapid optimization Step 6: Single-objective optimization - Maximize yield with scipy.optimize: * Decision variables: T, P, feed ratio, residence time * Objective: maximize Y = (moles C produced) / (moles A fed) * Constraints: - Temperature: 300 K ≤ T ≤ 500 K (safety) - Pressure: 1 bar ≤ P ≤ 50 bar (equipment limits) - Residence time: 1 min ≤ τ ≤ 60 min - Conversion: X_A ≥ 90% - Use Sequential Least Squares Programming (SLSQP) - Identify optimal operating point Step 7: Multi-objective optimization with PyMOO - Competing objectives: 1. Maximize product yield 2. Minimize energy consumption (heating/cooling) 3. Minimize operating cost (raw materials, utilities) 4. Maximize reactor productivity (throughput) - Constraints: - Safety: temperature and pressure limits - Environmental: waste production limits - Economic: minimum profitability - Run NSGA-II or NSGA-III - Generate Pareto front of optimal solutions - Select operating point based on preferences Step 8: Dynamic process simulation with SimPy - Model complete plant: * Reactors, separators, heat exchangers * Pumps, compressors, valves * Storage tanks and buffers - Simulate startup, steady-state, and shutdown - Include disturbances: * Feed composition variations * Equipment failures * Demand fluctuations - Evaluate dynamic stability - Calculate time to steady state Step 9: Control system design - Design feedback control loops: * Temperature control (PID controller) * Pressure control * Flow control * Level control - Tune PID parameters using Ziegler-Nichols or optimization - Implement cascade control for improved performance - Add feedforward control for disturbance rejection Step 10: Reinforcement learning for advanced control - Use Stable-Baselines3 to train RL agent: * Environment: reactor simulation (SimPy-based) * State: T, P, concentrations, flow rates * Actions: adjust setpoints, flow rates, heating/cooling * Reward: +yield -energy cost -deviation from setpoint - Train PPO or TD3 agent - Compare with conventional PID control - Evaluate performance under disturbances - Implement model-free adaptive control Step 11: Economic analysis - Calculate capital costs (CAPEX): * Reactor vessel cost (function of size, pressure rating) * Heat exchanger costs * Pumps and instrumentation * Installation costs - Calculate operating costs (OPEX): * Raw materials (A, B, catalyst) * Utilities (steam, cooling water, electricity) * Labor and maintenance - Revenue from product sales - Calculate economic metrics: * Net present value (NPV) * Internal rate of return (IRR) * Payback period * Levelized cost of production Step 12: Safety analysis - Identify hazards: * Exothermic runaway reactions * Pressure buildup * Toxic or flammable materials - Perform HAZOP-style analysis - Calculate safe operating limits: * Maximum temperature of synthesis reaction (MTSR) * Adiabatic temperature rise * Relief valve sizing - Design emergency shutdown systems - Implement safety interlocks Step 13: Uncertainty quantification - Propagate parameter uncertainties from PyMC: * How does kinetic parameter uncertainty affect yield? * Monte Carlo simulation with parameter distributions - Evaluate robustness of optimal design - Calculate confidence intervals on economic metrics - Identify critical uncertainties for further study Step 14: Generate comprehensive engineering report - Executive summary of project objectives and results - Process flow diagram (PFD) with material and energy streams - Reaction kinetics and model equations - Parameter estimation results with uncertainties - Optimization results: * Pareto front for multi-objective optimization * Recommended operating conditions * Trade-off analysis - Dynamic simulation results (startup curves, response to disturbances) - Control system design and tuning - Economic analysis with sensitivity to key assumptions - Safety analysis and hazard mitigation - Scale-up considerations: * Pilot to commercial scale * Heat and mass transfer limitations * Equipment sizing - Recommendations: * Optimal reactor design (size, type, materials of construction) * Operating conditions for maximum profitability * Control strategy * Further experimental studies needed - Technical drawings and P&ID (piping and instrumentation diagram) - Export as professional engineering report (PDF) Expected Output: - Validated reactor model with parameter uncertainties - Optimal reactor design and operating conditions - Pareto-optimal solutions for multi-objective optimization - Dynamic process simulation results - Advanced control strategies (RL-based) - Economic feasibility analysis - Safety assessment - Comprehensive chemical engineering design report ``` --- ## Summary These examples demonstrate: 1. **Cross-domain applicability**: Skills are useful across many scientific fields 2. **Skill integration**: Complex workflows combine multiple databases, packages, and analysis methods 3. **Real-world relevance**: Examples address actual research questions and clinical needs 4. **End-to-end workflows**: From data acquisition to publication-ready reports 5. **Best practices**: QC, statistical rigor, visualization, interpretation, and documentation ### How to Use These Examples 1. **Adapt to your needs**: Modify parameters, datasets, and objectives for your specific research question 2. **Combine skills creatively**: Mix and match skills from different categories 3. **Follow the structure**: Each example provides a clear step-by-step workflow 4. **Generate comprehensive output**: Aim for publication-quality figures and professional reports 5. **Cite your sources**: Always verify data and provide proper citations ### Additional Notes - Always start with: "Always use available 'skills' when possible. Keep the output organized." - For complex projects, break into manageable steps and validate intermediate results - Save checkpoints and intermediate data files - Document parameters and decisions for reproducibility - Generate README files explaining methodology - Create PDFs for stakeholder communication These examples showcase the power of combining the skills in this repository to tackle complex, real-world scientific challenges across multiple domains.