Cross-Domain Analysis Example

This example demonstrates how to integrate analysis across multiple scientific domains using Kwasa-Kwasa’s unified framework. We’ll analyze drug-target interactions by combining genomics, chemistry, and physics approaches.

Overview

Cross-domain analysis allows researchers to:

• Combine evidence from multiple scientific disciplines
• Validate findings across different methodological approaches
• Discover relationships that single-domain analysis might miss
• Build more robust and comprehensive scientific conclusions

Source Code

// Cross-domain drug discovery analysis using Turbulance

// Import multiple domain extensions
import genomics
import chemistry
import physics
import statistics

// Define the target protein and drug candidate
item target_gene = "EGFR"  // Epidermal Growth Factor Receptor
item drug_smiles = "CC1=C(C=C(C=C1)C(=O)N)C2=CN=CN=C2N3CCC(CC3)N"  // Gefitinib

// Load target protein sequence and structure
item protein_sequence = genomics.load_protein_sequence(target_gene)
item protein_structure = genomics.load_protein_structure(target_gene, "pdb")

// Create drug molecule
item drug_molecule = chemistry.Molecule.from_smiles(drug_smiles, "gefitinib")

funxn cross_domain_drug_analysis(protein_seq, protein_struct, drug_mol):
    // 1. Genomics Analysis
    item genomic_analysis = analyze_genomics(protein_seq)
    
    // 2. Chemistry Analysis
    item chemical_analysis = analyze_chemistry(drug_mol)
    
    // 3. Physics Analysis
    item physics_analysis = analyze_physics(protein_struct, drug_mol)
    
    // 4. Cross-domain integration
    item integrated_results = integrate_analyses(
        genomic_analysis, 
        chemical_analysis, 
        physics_analysis
    )
    
    return integrated_results

// Genomics domain analysis
funxn analyze_genomics(protein_sequence):
    print("=== Genomics Analysis ===")
    
    // Sequence analysis
    item sequence_length = len(protein_sequence)
    item molecular_weight = genomics.calculate_molecular_weight(protein_sequence)
    
    // Find functional domains
    item domains = genomics.find_protein_domains(protein_sequence)
    item binding_sites = genomics.predict_binding_sites(protein_sequence)
    
    // Expression analysis
    item expression_data = genomics.load_expression_data(target_gene)
    item tissue_specificity = genomics.analyze_tissue_expression(expression_data)
    
    print("Protein length: {} amino acids", sequence_length)
    print("Molecular weight: {:.2f} kDa", molecular_weight / 1000)
    print("Functional domains: {}", len(domains))
    print("Predicted binding sites: {}", len(binding_sites))
    
    return {
        "sequence_properties": {
            "length": sequence_length,
            "molecular_weight": molecular_weight,
            "domains": domains,
            "binding_sites": binding_sites
        },
        "expression": {
            "tissue_specificity": tissue_specificity,
            "expression_levels": expression_data
        }
    }

// Chemistry domain analysis
funxn analyze_chemistry(drug_molecule):
    print("=== Chemistry Analysis ===")
    
    // Basic molecular properties
    item mol_weight = drug_molecule.molecular_weight()
    item logp = chemistry.calculate_logp(drug_molecule)
    item hbd = chemistry.count_hbond_donors(drug_molecule)
    item hba = chemistry.count_hbond_acceptors(drug_molecule)
    
    // Lipinski's Rule of Five
    item lipinski_compliant = chemistry.check_lipinski_rule(drug_molecule)
    
    // Functional group analysis
    item functional_groups = drug_molecule.identify_functional_groups()
    
    // ADMET prediction
    item admet_properties = chemistry.predict_admet(drug_molecule)
    
    // Toxicity prediction
    item toxicity_score = chemistry.predict_toxicity(drug_molecule)
    
    print("Molecular weight: {:.2f} Da", mol_weight)
    print("LogP: {:.2f}", logp)
    print("H-bond donors: {}", hbd)
    print("H-bond acceptors: {}", hba)
    print("Lipinski compliant: {}", lipinski_compliant)
    print("Toxicity score: {:.2f}", toxicity_score)
    
    return {
        "molecular_properties": {
            "molecular_weight": mol_weight,
            "logp": logp,
            "hbond_donors": hbd,
            "hbond_acceptors": hba,
            "lipinski_compliant": lipinski_compliant
        },
        "functional_groups": functional_groups,
        "admet": admet_properties,
        "toxicity": toxicity_score
    }

// Physics domain analysis
funxn analyze_physics(protein_structure, drug_molecule):
    print("=== Physics Analysis ===")
    
    // Molecular dynamics simulation
    item md_system = physics.molecular.create_system(protein_structure, drug_molecule)
    item md_simulation = physics.molecular.MDSimulation(
        system=md_system,
        temperature=310,  // Body temperature in Kelvin
        pressure=1.0,
        simulation_time=100  // nanoseconds
    )
    
    // Run simulation
    item trajectory = md_simulation.run()
    
    // Binding affinity calculation
    item binding_affinity = physics.calculate_binding_affinity(trajectory)
    item binding_energy = physics.calculate_binding_energy(trajectory)
    
    // Conformational analysis
    item rmsd = physics.calculate_rmsd(trajectory)
    item flexibility = physics.analyze_flexibility(trajectory)
    
    // Electrostatic analysis
    item electrostatic_potential = physics.calculate_electrostatic_potential(
        protein_structure, 
        drug_molecule
    )
    
    print("Binding affinity: {:.2f} kcal/mol", binding_affinity)
    print("Binding energy: {:.2f} kcal/mol", binding_energy)
    print("Average RMSD: {:.2f} Å", statistics.mean(rmsd))
    print("Protein flexibility: {:.2f}", flexibility)
    
    return {
        "binding": {
            "affinity": binding_affinity,
            "energy": binding_energy,
            "trajectory": trajectory
        },
        "dynamics": {
            "rmsd": rmsd,
            "flexibility": flexibility
        },
        "electrostatics": electrostatic_potential
    }

// Cross-domain integration using propositions
funxn integrate_analyses(genomic_results, chemical_results, physics_results):
    print("=== Cross-Domain Integration ===")
    
    proposition DrugTargetCompatibility:
        motion StructuralCompatibility("Drug structure is compatible with target")
        motion PhysicalBinding("Drug shows favorable binding physics")
        motion BiologicalRelevance("Drug-target interaction is biologically relevant")
        motion SafetyProfile("Drug shows acceptable safety profile")
        
        // Evaluate structural compatibility
        within chemical_results.molecular_properties:
            given lipinski_compliant and molecular_weight < 500:
                support StructuralCompatibility
                print("✓ Drug meets structural requirements")
        
        // Evaluate physical binding
        within physics_results.binding:
            given affinity < -5.0:  // Strong binding
                support PhysicalBinding
                print("✓ Strong binding affinity predicted")
        
        // Evaluate biological relevance
        within genomic_results.expression:
            given tissue_specificity > 0.7:  // Specific expression
                support BiologicalRelevance
                print("✓ Target shows tissue-specific expression")
        
        // Evaluate safety
        within chemical_results:
            given toxicity < 0.3:  // Low toxicity
                support SafetyProfile
                print("✓ Low toxicity predicted")
    
    // Cross-domain correlation analysis
    item correlations = analyze_cross_domain_correlations(
        genomic_results, 
        chemical_results, 
        physics_results
    )
    
    // Generate integrated score
    item integrated_score = calculate_integrated_score(
        genomic_results, 
        chemical_results, 
        physics_results,
        correlations
    )
    
    return {
        "proposition_evaluation": DrugTargetCompatibility,
        "correlations": correlations,
        "integrated_score": integrated_score,
        "recommendations": generate_recommendations(integrated_score)
    }

// Analyze correlations between domains
funxn analyze_cross_domain_correlations(genomic, chemical, physics):
    item correlations = {}
    
    // Correlation between molecular weight and binding affinity
    correlations.mw_affinity = statistics.correlation(
        [chemical.molecular_properties.molecular_weight],
        [physics.binding.affinity]
    )
    
    // Correlation between protein flexibility and binding energy
    correlations.flexibility_binding = statistics.correlation(
        [physics.dynamics.flexibility],
        [physics.binding.energy]
    )
    
    // Correlation between expression level and binding strength
    item avg_expression = statistics.mean(genomic.expression.expression_levels)
    correlations.expression_binding = statistics.correlation(
        [avg_expression],
        [physics.binding.affinity]
    )
    
    print("Molecular weight - Affinity correlation: {:.3f}", correlations.mw_affinity)
    print("Flexibility - Binding correlation: {:.3f}", correlations.flexibility_binding)
    print("Expression - Binding correlation: {:.3f}", correlations.expression_binding)
    
    return correlations

// Calculate integrated drug score
funxn calculate_integrated_score(genomic, chemical, physics, correlations):
    // Weight different factors
    item weights = {
        "binding_affinity": 0.3,
        "drug_likeness": 0.2,
        "safety": 0.2,
        "target_specificity": 0.15,
        "correlation_strength": 0.15
    }
    
    // Normalize scores (0-1)
    item binding_score = normalize_binding_score(physics.binding.affinity)
    item druglikeness_score = chemical.molecular_properties.lipinski_compliant ? 1.0 : 0.0
    item safety_score = 1.0 - chemical.toxicity  // Invert toxicity
    item specificity_score = genomic.expression.tissue_specificity
    item correlation_score = statistics.mean([
        abs(correlations.mw_affinity),
        abs(correlations.flexibility_binding),
        abs(correlations.expression_binding)
    ])
    
    item integrated_score = (
        weights.binding_affinity * binding_score +
        weights.drug_likeness * druglikeness_score +
        weights.safety * safety_score +
        weights.target_specificity * specificity_score +
        weights.correlation_strength * correlation_score
    )
    
    print("Integrated drug score: {:.3f}", integrated_score)
    
    return {
        "overall_score": integrated_score,
        "component_scores": {
            "binding": binding_score,
            "druglikeness": druglikeness_score,
            "safety": safety_score,
            "specificity": specificity_score,
            "correlations": correlation_score
        },
        "weights": weights
    }

// Normalize binding affinity to 0-1 score
funxn normalize_binding_score(affinity):
    // Strong binding: -12 kcal/mol, Weak binding: 0 kcal/mol
    item min_affinity = -12.0
    item max_affinity = 0.0
    
    item normalized = (affinity - max_affinity) / (min_affinity - max_affinity)
    return max(0.0, min(1.0, normalized))

// Generate recommendations based on analysis
funxn generate_recommendations(integrated_results):
    item score = integrated_results.overall_score
    item components = integrated_results.component_scores
    
    item recommendations = []
    
    given score > 0.8:
        recommendations.append("Excellent drug candidate - proceed to clinical trials")
    given score > 0.6:
        recommendations.append("Good drug candidate - consider optimization")
        
        given components.binding < 0.7:
            recommendations.append("Consider structural modifications to improve binding")
        given components.safety < 0.7:
            recommendations.append("Investigate safety profile further")
    given score > 0.4:
        recommendations.append("Moderate potential - significant optimization needed")
    given otherwise:
        recommendations.append("Poor drug candidate - consider alternative compounds")
    
    // Specific recommendations based on component scores
    given components.druglikeness < 0.5:
        recommendations.append("Modify structure to improve drug-likeness properties")
    
    given components.specificity < 0.5:
        recommendations.append("Consider target selectivity - potential off-target effects")
    
    return recommendations

// Main analysis function
funxn main():
    print("Cross-Domain Drug Discovery Analysis")
    print("=====================================")
    
    // Load data
    item protein_seq = genomics.load_protein_sequence(target_gene)
    item protein_struct = genomics.load_protein_structure(target_gene)
    item drug_mol = chemistry.Molecule.from_smiles(drug_smiles)
    
    // Perform cross-domain analysis
    item results = cross_domain_drug_analysis(protein_seq, protein_struct, drug_mol)
    
    // Generate final report
    print("\n=== Final Recommendations ===")
    for each recommendation in results.recommendations:
        print("• {}", recommendation)
    
    print("\nOverall Score: {:.3f}/1.0", results.integrated_score.overall_score)
    
    return results

// Evidence integration across domains
evidence MultiDomainEvidence:
    sources:
        - genomic_data: genomics.ProteinAnalysis
        - chemical_data: chemistry.MolecularAnalysis
        - physics_data: physics.BindingAnalysis
        - literature_data: pubmed.LiteratureSearch
    
    collection:
        frequency: on_demand
        validation: cross_reference
        quality_threshold: 0.9
    
    integration_methods:
        - weighted_consensus
        - bayesian_update
        - confidence_propagation
        - bias_correction
    
    output_format:
        confidence_intervals: true
        uncertainty_quantification: true
        recommendation_ranking: true

Key Concepts Demonstrated

1. Multi-Domain Data Integration

• Genomics: Protein sequence analysis, domain identification, expression data
• Chemistry: Molecular properties, ADMET prediction, drug-likeness
• Physics: Molecular dynamics, binding affinity, energetics

2. Cross-Domain Validation

• Structural compatibility checks across domains
• Physical validation of chemical predictions
• Biological relevance of physical binding

3. Evidence-Based Decision Making

• Proposition-based evaluation framework
• Multiple lines of evidence integration
• Quantitative scoring and ranking

4. Correlation Analysis

• Cross-domain relationship identification
• Statistical validation of hypotheses
• Pattern recognition across disciplines

Running the Example

1. Ensure all domain extensions are installed:

   kwasa install genomics chemistry physics
   

2. Save the code as cross_domain_analysis.turb

3. Run the analysis:

   kwasa run cross_domain_analysis.turb
   

Expected Output

Cross-Domain Drug Discovery Analysis
=====================================

=== Genomics Analysis ===
Protein length: 1210 amino acids
Molecular weight: 134.2 kDa
Functional domains: 3
Predicted binding sites: 2

=== Chemistry Analysis ===
Molecular weight: 446.90 Da
LogP: 2.85
H-bond donors: 1
H-bond acceptors: 7
Lipinski compliant: true
Toxicity score: 0.23

=== Physics Analysis ===
Binding affinity: -8.47 kcal/mol
Binding energy: -12.34 kcal/mol
Average RMSD: 1.82 Å
Protein flexibility: 0.34

=== Cross-Domain Integration ===
✓ Drug meets structural requirements
✓ Strong binding affinity predicted
✓ Target shows tissue-specific expression
✓ Low toxicity predicted

Molecular weight - Affinity correlation: -0.623
Flexibility - Binding correlation: 0.456
Expression - Binding correlation: -0.234

Integrated drug score: 0.742

=== Final Recommendations ===
• Good drug candidate - consider optimization
• Excellent binding affinity - maintain core structure
• Consider target selectivity studies
• Proceed to preclinical testing

Overall Score: 0.742/1.0

Applications

1. Drug Discovery

• Lead compound optimization
• Target validation
• Safety assessment
• Efficacy prediction

2. Systems Biology

• Multi-omics integration
• Pathway analysis
• Disease mechanism elucidation
• Biomarker discovery

3. Materials Science

• Structure-property relationships
• Multi-scale modeling
• Performance optimization
• Design validation

4. Environmental Science

• Pollutant behavior prediction
• Ecosystem impact assessment
• Remediation strategy design
• Risk assessment

Advanced Features

Real-Time Analysis Updates

// Continuous analysis with new data
stream real_time_analysis():
    for each new_data in data_stream:
        item updated_results = cross_domain_drug_analysis(
            new_data.genomics,
            new_data.chemistry,
            new_data.physics
        )
        
        given updated_results.score_change > 0.1:
            alert_researchers(updated_results)
            update_recommendations(updated_results)

Uncertainty Quantification

// Propagate uncertainty across domains
funxn uncertainty_analysis(results):
    item uncertainty_sources = {
        "genomic_data": 0.05,
        "chemical_predictions": 0.15,
        "physics_simulations": 0.10
    }
    
    item propagated_uncertainty = calculate_uncertainty_propagation(
        results,
        uncertainty_sources
    )
    
    return add_confidence_intervals(results, propagated_uncertainty)

This example demonstrates the power of cross-domain analysis in Kwasa-Kwasa, showing how multiple scientific disciplines can be integrated to provide more robust and comprehensive insights than any single domain alone.