Domain Extensions

Kwasa-Kwasa provides specialized extensions for various scientific domains, allowing researchers to leverage domain-specific knowledge and tools within the unified framework.

Overview

Domain extensions provide:

Available Domain Extensions

1. Genomics Extension

The genomics extension provides comprehensive tools for DNA, RNA, and protein analysis.

Key Features

Basic Usage

import genomics

// Create sequence objects
item dna_seq = genomics.DNASequence("ATGCGATCGTAGC")
item rna_seq = genomics.RNASequence("AUGCGAUCGUAGC")

// Basic analysis
item gc_content = dna_seq.gc_content()
item complement = dna_seq.complement()
item reverse_complement = dna_seq.reverse_complement()

// Find open reading frames
item orfs = dna_seq.find_orfs(min_length=100)

// Translate to protein
item protein = rna_seq.translate()

Advanced Features

// Multiple sequence alignment
item sequences = [
    genomics.DNASequence("ATGCGATCG"),
    genomics.DNASequence("ATGCGATGG"),
    genomics.DNASequence("ATGCGATAG")
]

item alignment = genomics.align_multiple(sequences, algorithm="muscle")
item phylo_tree = genomics.build_phylogeny(alignment, method="neighbor_joining")

// Variant analysis
item reference = genomics.load_reference("hg38.fa")
item sample_reads = genomics.load_reads("sample.fastq")
item variants = genomics.call_variants(reference, sample_reads)

// Gene expression analysis
item expression_data = genomics.load_expression("expression_matrix.csv")
item de_genes = genomics.differential_expression(
    expression_data, 
    condition_a="control", 
    condition_b="treatment"
)

Specialized Data Types

// Genomic intervals
item interval = genomics.GenomicInterval("chr1", 1000, 2000, strand="+")
item gene_annotation = genomics.GeneAnnotation(
    gene_id="ENSG00000001",
    symbol="BRCA1",
    intervals=[interval]
)

// Variant representations
item snv = genomics.SNV("chr1", 12345, "A", "T")
item indel = genomics.Indel("chr2", 67890, "ATG", "A")

// Population genetics
item population = genomics.Population([
    genomics.Individual("IND001", genotype="AA"),
    genomics.Individual("IND002", genotype="AT"),
    genomics.Individual("IND003", genotype="TT")
])

item allele_freq = population.allele_frequency()
item hardy_weinberg = population.test_hardy_weinberg()

2. Chemistry Extension

The chemistry extension enables molecular structure analysis and chemical informatics.

Key Features

Basic Usage

import chemistry

// Create molecules from SMILES
item ethanol = chemistry.Molecule.from_smiles("CCO")
item aspirin = chemistry.Molecule.from_smiles("CC(=O)OC1=CC=CC=C1C(=O)O")

// Basic properties
item mol_weight = ethanol.molecular_weight()
item formula = ethanol.molecular_formula()
item atom_count = ethanol.atom_count()

// Functional group analysis
item functional_groups = aspirin.identify_functional_groups()
item aromatic_rings = aspirin.count_aromatic_rings()

Advanced Chemical Analysis

// Chemical reactions
item reactants = [
    chemistry.Molecule.from_smiles("CCO"),      // Ethanol
    chemistry.Molecule.from_smiles("CC(=O)O")   // Acetic acid
]

item products = chemistry.predict_reaction(reactants, reaction_type="esterification")

// Molecular similarity
item similarity = chemistry.tanimoto_similarity(aspirin, ethanol)
item cluster = chemistry.cluster_molecules(
    [aspirin, ethanol, /* more molecules */],
    similarity_threshold=0.7
)

// Property prediction
item logp = chemistry.predict_logp(aspirin)
item solubility = chemistry.predict_solubility(aspirin)
item toxicity = chemistry.predict_toxicity(aspirin)

Drug Discovery Tools

// ADMET prediction
item admet = chemistry.predict_admet(aspirin)
print("Absorption: {}", admet.absorption)
print("Distribution: {}", admet.distribution)
print("Metabolism: {}", admet.metabolism)
print("Excretion: {}", admet.excretion)
print("Toxicity: {}", admet.toxicity)

// Lead optimization
item lead_compound = chemistry.Molecule.from_smiles("...")
item optimized = chemistry.optimize_lead(
    lead_compound,
    objectives=["potency", "selectivity", "admet"],
    constraints=["molecular_weight < 500", "logp < 5"]
)

3. Mass Spectrometry Extension

Specialized tools for mass spectrometry data analysis.

Key Features

Basic Usage

import massspec

// Load spectrum data
item spectrum = massspec.load_spectrum("sample.mzML")

// Basic processing
item processed = spectrum
    .smooth(method="savitzky_golay")
    .baseline_correct()
    .normalize()

// Peak detection
item peaks = processed.find_peaks(
    intensity_threshold=1000,
    snr_threshold=3.0
)

Advanced MS Analysis

// Metabolomics workflow
item samples = massspec.load_samples("metabolomics_data/")
item aligned = massspec.align_peaks(samples)
item identified = massspec.identify_metabolites(
    aligned,
    database="HMDB",
    mass_tolerance=0.005
)

// Statistical analysis
item pca_result = massspec.pca_analysis(identified)
item differential = massspec.differential_analysis(
    identified,
    group_a="control",
    group_b="treatment"
)

// Proteomics analysis
item protein_spectrum = massspec.load_proteomics("proteins.mgf")
item peptides = massspec.database_search(
    protein_spectrum,
    database="uniprot_human",
    enzyme="trypsin"
)

4. Physics Extension

Tools for computational physics and data analysis.

Key Features

Basic Usage

import physics

// Classical mechanics
item particle = physics.Particle(mass=1.0, charge=1.0, position=[0, 0, 0])
item force_field = physics.ElectricField(strength=1000, direction=[1, 0, 0])

item trajectory = physics.simulate_trajectory(
    particle,
    force_field,
    time_steps=1000,
    dt=0.001
)

// Quantum mechanics
item hamiltonian = physics.quantum.Hamiltonian(
    kinetic_energy=physics.quantum.KineticEnergy(),
    potential=physics.quantum.HarmonicOscillator(frequency=1.0)
)

item eigenvalues, eigenvectors = hamiltonian.solve()

Advanced Physics Analysis

// Statistical mechanics
item system = physics.statistical.CanonicalEnsemble(
    temperature=300,  // Kelvin
    particles=1000
)

item partition_function = system.partition_function()
item thermodynamic_props = system.thermodynamic_properties()

// Signal processing
item signal = physics.signals.load_signal("detector_data.csv")
item filtered = signal
    .bandpass_filter(low_freq=10, high_freq=1000)
    .denoise(method="wavelet")

item fft_result = signal.fourier_transform()
item peaks = fft_result.find_peaks()

5. Materials Science Extension

Tools for materials analysis and property prediction.

Key Features

Basic Usage

import materials

// Crystal structure
item crystal = materials.Crystal.from_cif("structure.cif")
item space_group = crystal.space_group()
item density = crystal.density()

// Property prediction
item band_gap = materials.predict_band_gap(crystal)
item elastic_modulus = materials.predict_elastic_modulus(crystal)

Cross-Domain Integration

One of the key strengths of Kwasa-Kwasa is the ability to integrate analysis across domains:

Genomics-Chemistry Integration

import genomics
import chemistry

// Analyze drug-target interactions
item target_protein = genomics.Protein.from_sequence("MKTAYIAK...")
item drug_compound = chemistry.Molecule.from_smiles("CC(C)CC1=CC=C(C=C1)C(C)C(=O)O")

// Predict binding affinity
item binding_site = target_protein.predict_binding_site()
item affinity = chemistry.predict_binding_affinity(drug_compound, binding_site)

// Analyze sequence-structure-function relationships
proposition DrugTargetBinding:
    motion HighAffinity("Drug shows high binding affinity to target")
    motion SpecificBinding("Drug binds specifically to target site")
    
    within binding_analysis:
        given affinity > 8.0:  // pKd
            support HighAffinity
        given specificity_score > 0.9:
            support SpecificBinding

Chemistry-Physics Integration

import chemistry
import physics

// Molecular dynamics simulation
item molecule = chemistry.Molecule.from_smiles("CCO")
item force_field = physics.molecular.AMBER99()

item md_simulation = physics.molecular.MDSimulation(
    molecule=molecule,
    force_field=force_field,
    temperature=300,
    pressure=1.0
)

item trajectory = md_simulation.run(simulation_time=1000)  // ps
item properties = trajectory.analyze_properties()

Multi-Domain Evidence Integration

evidence_integrator MultiDomainAnalysis:
    sources:
        - genomic_evidence: genomics.SequenceAnalysis
        - chemical_evidence: chemistry.StructureAnalysis
        - physical_evidence: physics.PropertyMeasurement
    
    integration_rules:
        - correlation_analysis(genomic_evidence, chemical_evidence)
        - physical_validation(chemical_evidence, physical_evidence)
        - consistency_check(all_sources)
    
    output:
        confidence_score: weighted_average
        recommendations: expert_system_rules

Extension Development

Creating Custom Extensions

// Define a custom domain extension
extension CustomDomain:
    name: "custom_analysis"
    version: "1.0.0"
    
    // Define custom data types
    datatype CustomData:
        fields:
            - measurement: Float
            - uncertainty: Float
            - metadata: Dict[String, Any]
        
        methods:
            - validate() -> Boolean
            - normalize() -> CustomData
            - compare(other: CustomData) -> Float
    
    // Define custom functions
    funxn custom_analysis(data: List[CustomData]) -> AnalysisResult:
        // Implementation here
        
    // Define integration points
    integration:
        with genomics:
            - sequence_custom_correlation
        with chemistry:
            - structure_custom_mapping

Extension Registration

// Register the extension
register_extension(CustomDomain)

// Use the extension
import custom_analysis

item data = custom_analysis.CustomData(
    measurement=42.0,
    uncertainty=0.1,
    metadata={"source": "experiment_1"}
)

item results = custom_analysis.custom_analysis([data])

Best Practices

1. Domain-Specific Validation

// Always validate domain-specific data
funxn validate_genomic_sequence(sequence: String) -> Boolean:
    item valid_bases = {"A", "T", "G", "C"}
    for each base in sequence:
        given base not in valid_bases:
            return false
    return true

// Use domain-specific error types
exception InvalidSequenceError:
    message: "Invalid nucleotide sequence"
    invalid_character: String
    position: Integer

2. Leverage Domain Knowledge

// Use domain-specific patterns
within protein_sequence:
    given matches(".*RGD.*"):  // Integrin binding motif
        add_annotation("integrin_binding")
    given matches(".*KDEL$"):  // ER retention signal
        add_annotation("er_retention")

3. Cross-Domain Consistency

// Ensure consistency across domains
proposition ConsistentAnalysis:
    motion ChemicallyConsistent("Chemical analysis consistent with physical properties")
    motion BiologicallyRelevant("Results are biologically plausible")
    
    within multi_domain_results:
        given chemical_props.solubility matches biological_function.membrane_transport:
            support ChemicallyConsistent
        given predicted_activity < experimental_toxicity:
            support BiologicallyRelevant

Performance Considerations

Memory Management

Parallel Processing

Integration Optimization

Next Steps