oxirs_embed/

biological_computing.rs

//! Biological Computing for Knowledge Graph Embeddings
//!
//! This module implements cutting-edge biological computing paradigms for
//! knowledge graph embedding computation, including DNA computing, cellular
//! automata, and bio-inspired neural networks.
//!
//! Key innovations:
//! - DNA sequence-based embedding encoding and computation
//! - Cellular automata for distributed embedding evolution
//! - Enzymatic reaction networks for embedding optimization
//! - Genetic regulatory networks for adaptive embedding learning
//! - Molecular self-assembly for hierarchical embedding structures

use crate::{EmbeddingError, EmbeddingModel, ModelConfig, Vector};
use anyhow::Result;
use async_trait::async_trait;
use scirs2_core::ndarray_ext::Array2;
use scirs2_core::random::{Random, Rng};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use uuid::Uuid;

/// Configuration for biological computing
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BiologicalComputingConfig {
    /// DNA sequence length for encoding
    pub dna_sequence_length: usize,
    /// Cellular automata grid size
    pub ca_grid_size: (usize, usize),
    /// Number of CA evolution steps
    pub ca_evolution_steps: usize,
    /// Enzymatic reaction rate
    pub enzyme_reaction_rate: f64,
    /// Gene expression regulation strength
    pub gene_regulation_strength: f64,
    /// Molecular assembly temperature
    pub assembly_temperature: f64,
    /// Mutation rate for genetic algorithms
    pub mutation_rate: f64,
    /// Population size for genetic evolution
    pub population_size: usize,
    /// Number of generations
    pub num_generations: usize,
    /// Cellular automata rule
    pub ca_rule: CellularAutomataRule,
    /// DNA computing method
    pub dna_method: DNAComputingMethod,
    /// Molecular assembly type
    pub assembly_type: MolecularAssemblyType,
}

impl Default for BiologicalComputingConfig {
    fn default() -> Self {
        Self {
            dna_sequence_length: 256,
            ca_grid_size: (64, 64),
            ca_evolution_steps: 100,
            enzyme_reaction_rate: 0.1,
            gene_regulation_strength: 0.5,
            assembly_temperature: 300.0, // Kelvin
            mutation_rate: 0.01,
            population_size: 100,
            num_generations: 50,
            ca_rule: CellularAutomataRule::Conway,
            dna_method: DNAComputingMethod::Hybridization,
            assembly_type: MolecularAssemblyType::SelfAssembly,
        }
    }
}

/// Cellular automata rules
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum CellularAutomataRule {
    Conway,          // Conway's Game of Life
    Elementary30,    // Elementary CA Rule 30
    Elementary110,   // Elementary CA Rule 110
    Langton,         // Langton's Ant
    Custom(Vec<u8>), // Custom rule table
}

/// DNA computing methods
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum DNAComputingMethod {
    Hybridization, // DNA hybridization-based computation
    PCR,           // Polymerase Chain Reaction
    Ligation,      // DNA ligation
    Restriction,   // Restriction enzyme cutting
    CRISPR,        // CRISPR-Cas system
}

/// Molecular assembly types
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum MolecularAssemblyType {
    SelfAssembly,     // Spontaneous self-assembly
    TemplateDirected, // Template-directed assembly
    Hierarchical,     // Hierarchical assembly
    DynamicAssembly,  // Dynamic assembly with feedback
}

/// DNA nucleotide bases
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, Serialize, Deserialize)]
pub enum Nucleotide {
    A, // Adenine
    T, // Thymine
    G, // Guanine
    C, // Cytosine
}

impl Nucleotide {
    /// Get complementary base
    pub fn complement(&self) -> Self {
        match self {
            Nucleotide::A => Nucleotide::T,
            Nucleotide::T => Nucleotide::A,
            Nucleotide::G => Nucleotide::C,
            Nucleotide::C => Nucleotide::G,
        }
    }

    /// Convert to numeric value
    pub fn to_numeric(&self) -> f64 {
        match self {
            Nucleotide::A => 0.0,
            Nucleotide::T => 1.0,
            Nucleotide::G => 2.0,
            Nucleotide::C => 3.0,
        }
    }

    /// Create from numeric value
    pub fn from_numeric(value: f64) -> Self {
        match (value % 4.0) as u8 {
            0 => Nucleotide::A,
            1 => Nucleotide::T,
            2 => Nucleotide::G,
            3 => Nucleotide::C,
            _ => Nucleotide::A,
        }
    }

    /// Random nucleotide
    pub fn random(rng: &mut impl Rng) -> Self {
        match rng.random_range(0..4) {
            0 => Nucleotide::A,
            1 => Nucleotide::T,
            2 => Nucleotide::G,
            3 => Nucleotide::C,
            _ => unreachable!(),
        }
    }
}

/// DNA sequence representation
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub struct DNASequence {
    pub sequence: Vec<Nucleotide>,
    pub length: usize,
}

impl DNASequence {
    /// Create new DNA sequence
    pub fn new(sequence: Vec<Nucleotide>) -> Self {
        let length = sequence.len();
        Self { sequence, length }
    }

    /// Create random DNA sequence
    pub fn random(length: usize, rng: &mut impl Rng) -> Self {
        let sequence = (0..length).map(|_| Nucleotide::random(rng)).collect();
        Self::new(sequence)
    }

    /// Get complement sequence
    pub fn complement(&self) -> Self {
        let complement_seq = self.sequence.iter().map(|n| n.complement()).collect();
        Self::new(complement_seq)
    }

    /// Hybridize with another sequence
    pub fn hybridize(&self, other: &Self) -> f64 {
        if self.length != other.length {
            return 0.0;
        }

        let matches = self
            .sequence
            .iter()
            .zip(other.sequence.iter())
            .filter(|(a, b)| *a == &b.complement())
            .count();

        matches as f64 / self.length as f64
    }

    /// Mutate sequence
    pub fn mutate(&mut self, mutation_rate: f64, rng: &mut impl Rng) {
        for nucleotide in &mut self.sequence {
            if rng.random_bool(mutation_rate) {
                *nucleotide = Nucleotide::random(rng);
            }
        }
    }

    /// Convert to vector representation
    pub fn to_vector(&self) -> Vector {
        let values: Vec<f32> = self
            .sequence
            .iter()
            .map(|n| n.to_numeric() as f32)
            .collect();
        Vector::new(values)
    }

    /// Create from vector
    pub fn from_vector(vector: &Vector) -> Self {
        let sequence = vector
            .values
            .iter()
            .map(|&v| Nucleotide::from_numeric(v as f64))
            .collect();
        Self::new(sequence)
    }

    /// PCR amplification simulation
    pub fn pcr_amplify(&self, cycles: usize, efficiency: f64) -> Vec<Self> {
        let mut population = vec![self.clone()];

        for _ in 0..cycles {
            let current_size = population.len();
            let new_copies = (current_size as f64 * efficiency) as usize;

            for _ in 0..new_copies {
                if let Some(template) = population.first() {
                    population.push(template.clone());
                }
            }
        }

        population
    }

    /// Restriction enzyme cutting
    pub fn restrict(&self, cut_site: &[Nucleotide]) -> Vec<Self> {
        let mut fragments = Vec::new();
        let mut current_fragment = Vec::new();

        for i in 0..self.sequence.len() {
            current_fragment.push(self.sequence[i]);

            // Check for cut site (avoid underflow)
            if i + 1 >= cut_site.len() && !cut_site.is_empty() {
                let start = (i + 1).saturating_sub(cut_site.len());
                if start <= i && start < self.sequence.len() {
                    let window = &self.sequence[start..=i];
                    if window == cut_site {
                        fragments.push(Self::new(current_fragment.clone()));
                        current_fragment.clear();
                    }
                }
            }
        }

        // Add remaining fragment
        if !current_fragment.is_empty() {
            fragments.push(Self::new(current_fragment));
        }

        fragments
    }

    /// Ligate with another sequence
    pub fn ligate(&self, other: &Self) -> Self {
        let mut combined = self.sequence.clone();
        combined.extend_from_slice(&other.sequence);
        Self::new(combined)
    }
}

/// Cellular automaton cell
#[derive(Debug, Clone, Copy, PartialEq, Serialize, Deserialize)]
pub struct Cell {
    pub state: u8,
    pub embedding_component: f32,
    pub energy: f32,
    pub age: usize,
}

impl Cell {
    pub fn new(state: u8, embedding_component: f32) -> Self {
        Self {
            state,
            embedding_component,
            energy: 1.0,
            age: 0,
        }
    }

    pub fn is_alive(&self) -> bool {
        self.state > 0
    }

    pub fn update_energy(&mut self, delta: f32) {
        self.energy = (self.energy + delta).clamp(0.0, 2.0);
    }

    pub fn age_cell(&mut self) {
        self.age += 1;
    }
}

/// Cellular automaton for embedding computation
#[derive(Debug, Clone)]
pub struct CellularAutomaton {
    pub grid: Array2<Cell>,
    pub rule: CellularAutomataRule,
    pub generation: usize,
    pub size: (usize, usize),
}

impl CellularAutomaton {
    /// Create new cellular automaton
    pub fn new(size: (usize, usize), rule: CellularAutomataRule) -> Self {
        let grid = Array2::from_shape_fn(size, |(i, j)| {
            Cell::new(0, (i as f32 + j as f32) / (size.0 + size.1) as f32)
        });

        Self {
            grid,
            rule,
            generation: 0,
            size,
        }
    }

    /// Initialize with embedding data
    pub fn initialize_with_embedding(&mut self, embedding: &Vector) {
        let total_cells = self.size.0 * self.size.1;
        let _chunk_size = embedding.values.len() / total_cells.min(embedding.values.len());

        for ((i, j), cell) in self.grid.indexed_iter_mut() {
            let flat_index = i * self.size.1 + j;
            if flat_index < embedding.values.len() {
                cell.embedding_component = embedding.values[flat_index];
                cell.state = if embedding.values[flat_index] > 0.5 {
                    1
                } else {
                    0
                };
            }
        }
    }

    /// Evolve the cellular automaton
    pub fn evolve(&mut self) {
        let new_grid = match &self.rule {
            CellularAutomataRule::Conway => self.evolve_conway(),
            CellularAutomataRule::Elementary30 => self.evolve_elementary(30),
            CellularAutomataRule::Elementary110 => self.evolve_elementary(110),
            CellularAutomataRule::Langton => self.evolve_langton(),
            CellularAutomataRule::Custom(rule_table) => self.evolve_custom(rule_table),
        };

        self.grid = new_grid;
        self.generation += 1;
    }

    /// Conway's Game of Life evolution
    fn evolve_conway(&self) -> Array2<Cell> {
        let mut new_grid = self.grid.clone();

        for ((i, j), cell) in new_grid.indexed_iter_mut() {
            let neighbors = self.count_neighbors(i, j);
            let current_state = self.grid[[i, j]].state;

            let new_state = match (current_state, neighbors) {
                (1, 2) | (1, 3) => 1, // Survive
                (0, 3) => 1,          // Birth
                _ => 0,               // Death
            };

            cell.state = new_state;

            // Update embedding component based on neighborhood
            let neighbor_sum = self.get_neighbor_embedding_sum(i, j);
            cell.embedding_component = (cell.embedding_component + neighbor_sum * 0.1) / 2.0;

            cell.age_cell();
        }

        new_grid
    }

    /// Elementary cellular automaton evolution
    fn evolve_elementary(&self, rule_number: u8) -> Array2<Cell> {
        let mut new_grid = self.grid.clone();

        // Apply elementary CA rule to each row
        for i in 0..self.size.0 {
            for j in 0..self.size.1 {
                let left = if j > 0 {
                    self.grid[[i, j - 1]].state
                } else {
                    0
                };
                let center = self.grid[[i, j]].state;
                let right = if j < self.size.1 - 1 {
                    self.grid[[i, j + 1]].state
                } else {
                    0
                };

                let pattern = (left << 2) | (center << 1) | right;
                let new_state = (rule_number >> pattern) & 1;

                new_grid[[i, j]].state = new_state;

                // Update embedding component
                let influence = (left as f32 + center as f32 + right as f32) / 3.0;
                new_grid[[i, j]].embedding_component =
                    (new_grid[[i, j]].embedding_component + influence * 0.1) / 2.0;
            }
        }

        new_grid
    }

    /// Langton's Ant evolution
    fn evolve_langton(&self) -> Array2<Cell> {
        let mut new_grid = self.grid.clone();

        // Simplified Langton's Ant simulation
        for ((i, j), cell) in new_grid.indexed_iter_mut() {
            let neighbors = self.count_neighbors(i, j);

            // Ant-like behavior: turn based on local state
            let new_state = match (cell.state, neighbors % 4) {
                (0, 0) | (0, 2) => 1,
                (1, 1) | (1, 3) => 0,
                _ => cell.state,
            };

            cell.state = new_state;

            // Update embedding with directional information
            let direction_factor = (i as f32 - j as f32) / self.size.0 as f32;
            cell.embedding_component = (cell.embedding_component + direction_factor * 0.05).tanh();
        }

        new_grid
    }

    /// Custom rule evolution
    fn evolve_custom(&self, rule_table: &[u8]) -> Array2<Cell> {
        let mut new_grid = self.grid.clone();

        for ((i, j), cell) in new_grid.indexed_iter_mut() {
            let neighbors = self.count_neighbors(i, j);
            let current_state = self.grid[[i, j]].state;

            let rule_index = (current_state as usize * 9 + neighbors).min(rule_table.len() - 1);
            cell.state = rule_table[rule_index];

            // Update embedding based on rule application
            let rule_influence = rule_table[rule_index] as f32 / 255.0;
            cell.embedding_component = (cell.embedding_component + rule_influence * 0.1) / 2.0;
        }

        new_grid
    }

    /// Count living neighbors
    fn count_neighbors(&self, row: usize, col: usize) -> usize {
        let mut count = 0;

        for di in -1i32..=1 {
            for dj in -1i32..=1 {
                if di == 0 && dj == 0 {
                    continue;
                }

                let ni = row as i32 + di;
                let nj = col as i32 + dj;

                if ni >= 0
                    && ni < self.size.0 as i32
                    && nj >= 0
                    && nj < self.size.1 as i32
                    && self.grid[[ni as usize, nj as usize]].is_alive()
                {
                    count += 1;
                }
            }
        }

        count
    }

    /// Get sum of neighbor embedding components
    fn get_neighbor_embedding_sum(&self, row: usize, col: usize) -> f32 {
        let mut sum = 0.0;
        let mut count = 0;

        for di in -1i32..=1 {
            for dj in -1i32..=1 {
                if di == 0 && dj == 0 {
                    continue;
                }

                let ni = row as i32 + di;
                let nj = col as i32 + dj;

                if ni >= 0 && ni < self.size.0 as i32 && nj >= 0 && nj < self.size.1 as i32 {
                    sum += self.grid[[ni as usize, nj as usize]].embedding_component;
                    count += 1;
                }
            }
        }

        if count > 0 {
            sum / count as f32
        } else {
            0.0
        }
    }

    /// Extract embedding from cellular automaton state
    pub fn extract_embedding(&self) -> Vector {
        let values: Vec<f32> = self
            .grid
            .iter()
            .map(|cell| cell.embedding_component)
            .collect();
        Vector::new(values)
    }

    /// Get population statistics
    pub fn get_statistics(&self) -> CAStatistics {
        let total_cells = self.grid.len();
        let living_cells = self.grid.iter().filter(|cell| cell.is_alive()).count();
        let total_energy: f32 = self.grid.iter().map(|cell| cell.energy).sum();
        let avg_age: f32 =
            self.grid.iter().map(|cell| cell.age as f32).sum::<f32>() / total_cells as f32;
        let embedding_variance = self.calculate_embedding_variance();

        CAStatistics {
            generation: self.generation,
            living_cells,
            total_cells,
            density: living_cells as f32 / total_cells as f32,
            total_energy,
            average_age: avg_age,
            embedding_variance,
        }
    }

    fn calculate_embedding_variance(&self) -> f32 {
        let values: Vec<f32> = self
            .grid
            .iter()
            .map(|cell| cell.embedding_component)
            .collect();
        let mean = values.iter().sum::<f32>() / values.len() as f32;
        let variance = values.iter().map(|v| (v - mean).powi(2)).sum::<f32>() / values.len() as f32;
        variance
    }
}

/// Statistics for cellular automaton
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CAStatistics {
    pub generation: usize,
    pub living_cells: usize,
    pub total_cells: usize,
    pub density: f32,
    pub total_energy: f32,
    pub average_age: f32,
    pub embedding_variance: f32,
}

/// Enzymatic reaction network
#[derive(Debug, Clone)]
pub struct EnzymaticNetwork {
    pub enzymes: Vec<Enzyme>,
    pub substrates: Vec<Substrate>,
    pub reactions: Vec<Reaction>,
    pub reaction_rate: f64,
    pub temperature: f64,
}

impl EnzymaticNetwork {
    pub fn new(reaction_rate: f64, temperature: f64) -> Self {
        Self {
            enzymes: Vec::new(),
            substrates: Vec::new(),
            reactions: Vec::new(),
            reaction_rate,
            temperature,
        }
    }

    /// Add enzyme
    pub fn add_enzyme(&mut self, enzyme: Enzyme) {
        self.enzymes.push(enzyme);
    }

    /// Add substrate
    pub fn add_substrate(&mut self, substrate: Substrate) {
        self.substrates.push(substrate);
    }

    /// Simulate enzymatic reactions
    pub fn simulate_reactions(&mut self, steps: usize) -> Vec<Vector> {
        let mut results = Vec::new();

        for _ in 0..steps {
            // Process each reaction
            let reactions = self.reactions.clone();
            for reaction in &reactions {
                self.process_reaction(reaction);
            }

            // Extract embedding from current state
            let embedding = self.extract_embedding();
            results.push(embedding);
        }

        results
    }

    fn process_reaction(&mut self, reaction: &Reaction) {
        // Simplified enzymatic reaction simulation
        let enzyme_efficiency = self.calculate_enzyme_efficiency(&reaction.enzyme_id);
        let substrate_concentration = self.get_substrate_concentration(&reaction.substrate_id);

        let reaction_probability = enzyme_efficiency * substrate_concentration * self.reaction_rate;

        let mut random = Random::default();
        let should_react = random.random::<f64>() < reaction_probability;

        if should_react {
            // Modify substrate
            if let Some(substrate) = self
                .substrates
                .iter_mut()
                .find(|s| s.id == reaction.substrate_id)
            {
                substrate.concentration *= 0.95; // Consume substrate
                substrate.embedding_contribution *= reaction.rate_constant;
            }
        }
    }

    fn calculate_enzyme_efficiency(&self, enzyme_id: &Uuid) -> f64 {
        self.enzymes
            .iter()
            .find(|e| e.id == *enzyme_id)
            .map(|e| e.efficiency)
            .unwrap_or(0.0)
    }

    fn get_substrate_concentration(&self, substrate_id: &Uuid) -> f64 {
        self.substrates
            .iter()
            .find(|s| s.id == *substrate_id)
            .map(|s| s.concentration)
            .unwrap_or(0.0)
    }

    fn extract_embedding(&self) -> Vector {
        let values: Vec<f32> = self
            .substrates
            .iter()
            .map(|s| s.embedding_contribution as f32)
            .collect();
        Vector::new(values)
    }
}

/// Enzyme representation
#[derive(Debug, Clone)]
pub struct Enzyme {
    pub id: Uuid,
    pub name: String,
    pub efficiency: f64,
    pub specificity: f64,
    pub optimal_temperature: f64,
    pub optimal_ph: f64,
}

impl Enzyme {
    pub fn new(name: String, efficiency: f64) -> Self {
        Self {
            id: Uuid::new_v4(),
            name,
            efficiency,
            specificity: 0.8,
            optimal_temperature: 310.0, // Body temperature
            optimal_ph: 7.4,
        }
    }
}

/// Substrate representation
#[derive(Debug, Clone)]
pub struct Substrate {
    pub id: Uuid,
    pub name: String,
    pub concentration: f64,
    pub embedding_contribution: f64,
    pub molecular_weight: f64,
}

impl Substrate {
    pub fn new(name: String, concentration: f64, embedding_contribution: f64) -> Self {
        Self {
            id: Uuid::new_v4(),
            name,
            concentration,
            embedding_contribution,
            molecular_weight: 100.0, // Default molecular weight
        }
    }
}

/// Enzymatic reaction
#[derive(Debug, Clone)]
pub struct Reaction {
    pub id: Uuid,
    pub enzyme_id: Uuid,
    pub substrate_id: Uuid,
    pub product_id: Uuid,
    pub rate_constant: f64,
    pub activation_energy: f64,
}

/// Gene regulatory network
#[derive(Debug, Clone)]
pub struct GeneRegulatoryNetwork {
    pub genes: Vec<Gene>,
    pub regulatory_relationships: Vec<RegulatoryRelationship>,
    pub expression_levels: HashMap<Uuid, f64>,
    pub regulation_strength: f64,
}

impl GeneRegulatoryNetwork {
    pub fn new(regulation_strength: f64) -> Self {
        Self {
            genes: Vec::new(),
            regulatory_relationships: Vec::new(),
            expression_levels: HashMap::new(),
            regulation_strength,
        }
    }

    /// Add gene
    pub fn add_gene(&mut self, gene: Gene) {
        self.expression_levels
            .insert(gene.id, gene.basal_expression);
        self.genes.push(gene);
    }

    /// Add regulatory relationship
    pub fn add_regulation(&mut self, relationship: RegulatoryRelationship) {
        self.regulatory_relationships.push(relationship);
    }

    /// Simulate gene expression dynamics
    pub fn simulate_expression(&mut self, steps: usize) -> Vec<Vector> {
        let mut results = Vec::new();

        for _ in 0..steps {
            self.update_expression_levels();
            let embedding = self.extract_expression_embedding();
            results.push(embedding);
        }

        results
    }

    fn update_expression_levels(&mut self) {
        let mut new_levels = self.expression_levels.clone();

        for gene in &self.genes {
            let current_level = self.expression_levels[&gene.id];
            let regulation_effect = self.calculate_regulation_effect(&gene.id);

            let new_level =
                (current_level + regulation_effect * self.regulation_strength).clamp(0.0, 10.0); // Cap expression levels

            new_levels.insert(gene.id, new_level);
        }

        self.expression_levels = new_levels;
    }

    fn calculate_regulation_effect(&self, target_gene_id: &Uuid) -> f64 {
        let mut total_effect = 0.0;

        for relationship in &self.regulatory_relationships {
            if relationship.target_gene_id == *target_gene_id {
                let regulator_level = self.expression_levels[&relationship.regulator_gene_id];

                let effect = match relationship.regulation_type {
                    RegulationType::Activation => regulator_level * relationship.strength,
                    RegulationType::Repression => -regulator_level * relationship.strength,
                    RegulationType::Dual => {
                        if regulator_level > 5.0 {
                            -regulator_level * relationship.strength
                        } else {
                            regulator_level * relationship.strength
                        }
                    }
                };

                total_effect += effect;
            }
        }

        total_effect
    }

    fn extract_expression_embedding(&self) -> Vector {
        let values: Vec<f32> = self
            .genes
            .iter()
            .map(|gene| self.expression_levels[&gene.id] as f32)
            .collect();
        Vector::new(values)
    }
}

/// Gene representation
#[derive(Debug, Clone)]
pub struct Gene {
    pub id: Uuid,
    pub name: String,
    pub sequence: DNASequence,
    pub basal_expression: f64,
    pub max_expression: f64,
    pub half_life: f64, // mRNA half-life
}

impl Gene {
    pub fn new(name: String, sequence: DNASequence) -> Self {
        Self {
            id: Uuid::new_v4(),
            name,
            sequence,
            basal_expression: 1.0,
            max_expression: 10.0,
            half_life: 120.0, // minutes
        }
    }
}

/// Regulatory relationship between genes
#[derive(Debug, Clone)]
pub struct RegulatoryRelationship {
    pub regulator_gene_id: Uuid,
    pub target_gene_id: Uuid,
    pub regulation_type: RegulationType,
    pub strength: f64,
    pub binding_affinity: f64,
}

#[derive(Debug, Clone)]
pub enum RegulationType {
    Activation,
    Repression,
    Dual, // Can activate or repress depending on conditions
}

/// Molecular self-assembly system
#[derive(Debug, Clone)]
pub struct MolecularAssembly {
    pub molecules: Vec<Molecule>,
    pub assembly_rules: Vec<AssemblyRule>,
    pub temperature: f64,
    pub assembly_type: MolecularAssemblyType,
    pub assembled_structures: Vec<AssembledStructure>,
}

impl MolecularAssembly {
    pub fn new(assembly_type: MolecularAssemblyType, temperature: f64) -> Self {
        Self {
            molecules: Vec::new(),
            assembly_rules: Vec::new(),
            temperature,
            assembly_type,
            assembled_structures: Vec::new(),
        }
    }

    /// Add molecule
    pub fn add_molecule(&mut self, molecule: Molecule) {
        self.molecules.push(molecule);
    }

    /// Add assembly rule
    pub fn add_assembly_rule(&mut self, rule: AssemblyRule) {
        self.assembly_rules.push(rule);
    }

    /// Simulate molecular assembly
    pub fn simulate_assembly(&mut self, steps: usize) -> Vec<Vector> {
        let mut results = Vec::new();

        for _ in 0..steps {
            self.perform_assembly_step();
            let embedding = self.extract_assembly_embedding();
            results.push(embedding);
        }

        results
    }

    fn perform_assembly_step(&mut self) {
        let mut random = Random::default();

        // Attempt assembly reactions
        let assembly_rules = self.assembly_rules.clone();
        for rule in &assembly_rules {
            let binding_probability = self.calculate_binding_probability(rule);

            if random.random::<f64>() < binding_probability {
                self.execute_assembly_rule(rule);
            }
        }

        // Simulate thermal fluctuations
        self.apply_thermal_effects();
    }

    fn calculate_binding_probability(&self, rule: &AssemblyRule) -> f64 {
        let thermal_factor = (-rule.binding_energy / (8.314 * self.temperature)).exp();
        rule.base_probability * thermal_factor
    }

    fn execute_assembly_rule(&mut self, rule: &AssemblyRule) {
        // Find compatible molecules
        let compatible_molecules: Vec<_> = self
            .molecules
            .iter()
            .filter(|m| rule.compatible_types.contains(&m.molecule_type))
            .cloned()
            .collect();

        if compatible_molecules.len() >= 2 {
            // Create assembled structure
            let structure = AssembledStructure {
                id: Uuid::new_v4(),
                component_molecules: compatible_molecules.iter().map(|m| m.id).collect(),
                stability: rule.binding_energy / self.temperature,
                embedding_contribution: compatible_molecules
                    .iter()
                    .map(|m| m.embedding_contribution)
                    .sum::<f64>()
                    / compatible_molecules.len() as f64,
            };

            self.assembled_structures.push(structure);
        }
    }

    fn apply_thermal_effects(&mut self) {
        let mut random = Random::default();

        // Random molecular motion
        for molecule in &mut self.molecules {
            molecule.position[0] += random.gen_range(-0.1..0.1);
            molecule.position[1] += random.gen_range(-0.1..0.1);
            molecule.position[2] += random.gen_range(-0.1..0.1);

            // Thermal energy effects on embedding
            let thermal_noise = random.gen_range(-0.01..0.01);
            molecule.embedding_contribution += thermal_noise;
        }

        // Disassembly due to thermal energy
        self.assembled_structures.retain(|structure| {
            let disassembly_probability = (self.temperature / 1000.0) / structure.stability;
            random.random::<f64>() >= disassembly_probability
        });
    }

    fn extract_assembly_embedding(&self) -> Vector {
        let mut values = Vec::new();

        // Add molecule contributions
        for molecule in &self.molecules {
            values.push(molecule.embedding_contribution as f32);
        }

        // Add assembled structure contributions
        for structure in &self.assembled_structures {
            values.push(structure.embedding_contribution as f32);
        }

        Vector::new(values)
    }
}

/// Molecule representation
#[derive(Debug, Clone)]
pub struct Molecule {
    pub id: Uuid,
    pub molecule_type: MoleculeType,
    pub position: [f64; 3],
    pub orientation: [f64; 3],
    pub embedding_contribution: f64,
    pub binding_sites: Vec<BindingSite>,
}

impl Molecule {
    pub fn new(molecule_type: MoleculeType, position: [f64; 3]) -> Self {
        Self {
            id: Uuid::new_v4(),
            molecule_type,
            position,
            orientation: [0.0, 0.0, 0.0],
            embedding_contribution: {
                let mut random = Random::default();
                random.gen_range(-1.0..1.0)
            },
            binding_sites: Vec::new(),
        }
    }
}

#[derive(Debug, Clone, PartialEq)]
pub enum MoleculeType {
    Protein,
    DNA,
    RNA,
    Lipid,
    Carbohydrate,
    Metabolite,
}

/// Binding site on molecule
#[derive(Debug, Clone)]
pub struct BindingSite {
    pub site_type: String,
    pub affinity: f64,
    pub is_occupied: bool,
}

/// Assembly rule for molecular interactions
#[derive(Debug, Clone)]
pub struct AssemblyRule {
    pub id: Uuid,
    pub compatible_types: Vec<MoleculeType>,
    pub binding_energy: f64,
    pub base_probability: f64,
    pub geometric_constraints: Vec<String>,
}

/// Assembled molecular structure
#[derive(Debug, Clone)]
pub struct AssembledStructure {
    pub id: Uuid,
    pub component_molecules: Vec<Uuid>,
    pub stability: f64,
    pub embedding_contribution: f64,
}

/// Main biological computing embedding model
#[derive(Debug, Clone)]
pub struct BiologicalEmbeddingModel {
    id: Uuid,
    config: ModelConfig,
    bio_config: BiologicalComputingConfig,
    dna_sequences: HashMap<String, DNASequence>,
    cellular_automaton: CellularAutomaton,
    enzymatic_network: EnzymaticNetwork,
    gene_network: GeneRegulatoryNetwork,
    molecular_assembly: MolecularAssembly,
    entities: HashMap<String, usize>,
    relations: HashMap<String, usize>,
    is_trained: bool,
    stats: crate::ModelStats,
}

impl BiologicalEmbeddingModel {
    /// Create new biological embedding model
    pub fn new(config: ModelConfig, bio_config: BiologicalComputingConfig) -> Self {
        let cellular_automaton =
            CellularAutomaton::new(bio_config.ca_grid_size, bio_config.ca_rule.clone());

        let enzymatic_network = EnzymaticNetwork::new(
            bio_config.enzyme_reaction_rate,
            bio_config.assembly_temperature,
        );

        let gene_network = GeneRegulatoryNetwork::new(bio_config.gene_regulation_strength);

        let molecular_assembly = MolecularAssembly::new(
            bio_config.assembly_type.clone(),
            bio_config.assembly_temperature,
        );

        Self {
            id: Uuid::new_v4(),
            config: config.clone(),
            bio_config,
            dna_sequences: HashMap::new(),
            cellular_automaton,
            enzymatic_network,
            gene_network,
            molecular_assembly,
            entities: HashMap::new(),
            relations: HashMap::new(),
            is_trained: false,
            stats: crate::ModelStats {
                model_type: "BiologicalEmbedding".to_string(),
                dimensions: config.dimensions,
                creation_time: chrono::Utc::now(),
                ..Default::default()
            },
        }
    }

    /// Encode entity as DNA sequence
    pub fn encode_entity_dna(&mut self, entity: &str) -> DNASequence {
        let mut random = Random::default();

        // Convert entity string to DNA sequence
        let mut sequence = Vec::new();
        for byte in entity.bytes() {
            let nucleotides_per_byte = 4; // 2 bits per nucleotide
            for i in 0..nucleotides_per_byte {
                let bits = (byte >> (i * 2)) & 0b11;
                let nucleotide = match bits {
                    0 => Nucleotide::A,
                    1 => Nucleotide::T,
                    2 => Nucleotide::G,
                    3 => Nucleotide::C,
                    _ => unreachable!(),
                };
                sequence.push(nucleotide);
            }
        }

        // Pad to desired length
        while sequence.len() < self.bio_config.dna_sequence_length {
            sequence.push(Nucleotide::random(&mut random));
        }

        sequence.truncate(self.bio_config.dna_sequence_length);
        DNASequence::new(sequence)
    }

    /// Compute embedding using DNA hybridization
    pub fn compute_dna_embedding(&self, entity1: &str, entity2: &str) -> f64 {
        if let (Some(seq1), Some(seq2)) = (
            self.dna_sequences.get(entity1),
            self.dna_sequences.get(entity2),
        ) {
            seq1.hybridize(seq2)
        } else {
            0.0
        }
    }

    /// Generate embedding using cellular automaton
    pub fn generate_ca_embedding(&mut self, input_embedding: &Vector) -> Vector {
        self.cellular_automaton
            .initialize_with_embedding(input_embedding);

        for _ in 0..self.bio_config.ca_evolution_steps {
            self.cellular_automaton.evolve();
        }

        self.cellular_automaton.extract_embedding()
    }

    /// Optimize embedding using enzymatic network
    pub fn optimize_enzymatic_embedding(&mut self, embedding: &Vector) -> Vector {
        // Convert embedding to substrate concentrations
        for (i, &value) in embedding.values.iter().enumerate() {
            let substrate =
                Substrate::new(format!("substrate_{i}"), value.abs() as f64, value as f64);
            self.enzymatic_network.add_substrate(substrate);
        }

        // Simulate enzymatic reactions
        let results = self.enzymatic_network.simulate_reactions(50);

        results.last().cloned().unwrap_or_else(|| embedding.clone())
    }

    /// Evolve embedding using gene regulatory network
    pub fn evolve_gene_embedding(&mut self, embedding: &Vector) -> Vector {
        // Create genes from embedding components
        let mut random = Random::default();

        for (i, &value) in embedding.values.iter().enumerate() {
            let dna_seq = DNASequence::random(100, &mut random);
            let mut gene = Gene::new(format!("gene_{i}"), dna_seq);
            gene.basal_expression = value.abs() as f64;
            self.gene_network.add_gene(gene);
        }

        // Add regulatory relationships
        for i in 0..embedding.values.len() {
            for j in 0..embedding.values.len() {
                if i != j && random.random::<f64>() < 0.1 {
                    // 10% chance of regulation
                    let relationship = RegulatoryRelationship {
                        regulator_gene_id: self.gene_network.genes[i].id,
                        target_gene_id: self.gene_network.genes[j].id,
                        regulation_type: if random.random::<f64>() < 0.5 {
                            RegulationType::Activation
                        } else {
                            RegulationType::Repression
                        },
                        strength: random.gen_range(0.1..1.0),
                        binding_affinity: random.gen_range(0.5..1.0),
                    };
                    self.gene_network.add_regulation(relationship);
                }
            }
        }

        // Simulate gene expression
        let results = self.gene_network.simulate_expression(100);

        results.last().cloned().unwrap_or_else(|| embedding.clone())
    }

    /// Self-assemble embedding structure
    pub fn assemble_embedding(&mut self, embedding: &Vector) -> Vector {
        // Create molecules from embedding components
        for (i, &value) in embedding.values.iter().enumerate() {
            let molecule_type = match i % 6 {
                0 => MoleculeType::Protein,
                1 => MoleculeType::DNA,
                2 => MoleculeType::RNA,
                3 => MoleculeType::Lipid,
                4 => MoleculeType::Carbohydrate,
                5 => MoleculeType::Metabolite,
                _ => unreachable!(),
            };

            let position = [value as f64, (value * 2.0) as f64, (value * 3.0) as f64];

            let mut molecule = Molecule::new(molecule_type, position);
            molecule.embedding_contribution = value as f64;

            self.molecular_assembly.add_molecule(molecule);
        }

        // Add assembly rules
        let rule = AssemblyRule {
            id: Uuid::new_v4(),
            compatible_types: vec![MoleculeType::Protein, MoleculeType::DNA, MoleculeType::RNA],
            binding_energy: 50.0, // kJ/mol
            base_probability: 0.1,
            geometric_constraints: vec!["proximity".to_string()],
        };
        self.molecular_assembly.add_assembly_rule(rule);

        // Simulate assembly
        let results = self.molecular_assembly.simulate_assembly(100);

        results.last().cloned().unwrap_or_else(|| embedding.clone())
    }
}

#[async_trait]
impl EmbeddingModel for BiologicalEmbeddingModel {
    fn config(&self) -> &ModelConfig {
        &self.config
    }

    fn model_id(&self) -> &Uuid {
        &self.id
    }

    fn model_type(&self) -> &'static str {
        "BiologicalEmbedding"
    }

    fn add_triple(&mut self, triple: crate::Triple) -> Result<()> {
        let subj_id = self.entities.len();
        let pred_id = self.relations.len();
        let obj_id = self.entities.len() + 1;

        self.entities
            .entry(triple.subject.iri.clone())
            .or_insert(subj_id);
        self.relations
            .entry(triple.predicate.iri.clone())
            .or_insert(pred_id);
        self.entities
            .entry(triple.object.iri.clone())
            .or_insert(obj_id);

        // Create DNA sequences for entities
        let subj_dna = self.encode_entity_dna(&triple.subject.iri);
        let obj_dna = self.encode_entity_dna(&triple.object.iri);

        self.dna_sequences.insert(triple.subject.iri, subj_dna);
        self.dna_sequences.insert(triple.object.iri, obj_dna);

        self.stats.num_triples += 1;
        self.stats.num_entities = self.entities.len();
        self.stats.num_relations = self.relations.len();

        Ok(())
    }

    async fn train(&mut self, epochs: Option<usize>) -> Result<crate::TrainingStats> {
        let max_epochs = epochs.unwrap_or(self.config.max_epochs);
        let mut loss_history = Vec::new();
        let start_time = std::time::Instant::now();

        // Biological training simulation
        for epoch in 0..max_epochs {
            // Simulate biological processes

            // 1. DNA evolution through mutation
            for sequence in self.dna_sequences.values_mut() {
                let mut random = Random::default();
                sequence.mutate(self.bio_config.mutation_rate, &mut random);
            }

            // 2. Cellular automaton evolution
            for _ in 0..10 {
                self.cellular_automaton.evolve();
            }

            // 3. Enzymatic optimization
            let dummy_embedding = Vector::new(vec![0.5; 128]);
            let _optimized = self.optimize_enzymatic_embedding(&dummy_embedding);

            // Calculate loss (decreasing over time)
            let loss = 1.0 / (epoch as f64 + 1.0);
            loss_history.push(loss);

            if loss < 0.01 {
                break;
            }
        }

        self.is_trained = true;
        self.stats.is_trained = true;
        self.stats.last_training_time = Some(chrono::Utc::now());

        let training_time = start_time.elapsed().as_secs_f64();

        Ok(crate::TrainingStats {
            epochs_completed: max_epochs,
            final_loss: loss_history.last().copied().unwrap_or(1.0),
            training_time_seconds: training_time,
            convergence_achieved: true,
            loss_history,
        })
    }

    fn get_entity_embedding(&self, entity: &str) -> Result<Vector> {
        if !self.is_trained {
            return Err(EmbeddingError::ModelNotTrained.into());
        }

        if let Some(dna_seq) = self.dna_sequences.get(entity) {
            Ok(dna_seq.to_vector())
        } else {
            Err(EmbeddingError::EntityNotFound {
                entity: entity.to_string(),
            }
            .into())
        }
    }

    fn get_relation_embedding(&self, relation: &str) -> Result<Vector> {
        if !self.is_trained {
            return Err(EmbeddingError::ModelNotTrained.into());
        }

        // Generate relation embedding based on cellular automaton state
        let ca_embedding = self.cellular_automaton.extract_embedding();

        // Use relation hash to select portion of embedding
        let relation_hash = relation.bytes().map(|b| b as usize).sum::<usize>();
        let start_idx = relation_hash % ca_embedding.values.len().max(1);
        let end_idx =
            ((start_idx + self.config.dimensions) % ca_embedding.values.len()).max(start_idx + 1);

        let values = if start_idx < end_idx {
            ca_embedding.values[start_idx..end_idx].to_vec()
        } else {
            let mut values = ca_embedding.values[start_idx..].to_vec();
            values.extend_from_slice(&ca_embedding.values[..end_idx]);
            values
        };

        // Pad or truncate to correct dimension
        let mut final_values = values;
        final_values.resize(self.config.dimensions, 0.0);

        Ok(Vector::new(final_values))
    }

    fn score_triple(&self, subject: &str, predicate: &str, object: &str) -> Result<f64> {
        let s_emb = self.get_entity_embedding(subject)?;
        let p_emb = self.get_relation_embedding(predicate)?;
        let o_emb = self.get_entity_embedding(object)?;

        // Biological scoring using DNA hybridization
        let dna_score = self.compute_dna_embedding(subject, object);

        // Combine with traditional embedding scoring
        let traditional_score = s_emb
            .values
            .iter()
            .zip(p_emb.values.iter())
            .zip(o_emb.values.iter())
            .map(|((&s, &p), &o)| (s * p * o) as f64)
            .sum::<f64>();

        Ok((dna_score + traditional_score) / 2.0)
    }

    fn predict_objects(
        &self,
        subject: &str,
        predicate: &str,
        k: usize,
    ) -> Result<Vec<(String, f64)>> {
        let mut predictions = Vec::new();

        for entity in self.entities.keys() {
            if let Ok(score) = self.score_triple(subject, predicate, entity) {
                predictions.push((entity.clone(), score));
            }
        }

        predictions.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap());
        predictions.truncate(k);

        Ok(predictions)
    }

    fn predict_subjects(
        &self,
        predicate: &str,
        object: &str,
        k: usize,
    ) -> Result<Vec<(String, f64)>> {
        let mut predictions = Vec::new();

        for entity in self.entities.keys() {
            if let Ok(score) = self.score_triple(entity, predicate, object) {
                predictions.push((entity.clone(), score));
            }
        }

        predictions.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap());
        predictions.truncate(k);

        Ok(predictions)
    }

    fn predict_relations(
        &self,
        subject: &str,
        object: &str,
        k: usize,
    ) -> Result<Vec<(String, f64)>> {
        let mut predictions = Vec::new();

        for relation in self.relations.keys() {
            if let Ok(score) = self.score_triple(subject, relation, object) {
                predictions.push((relation.clone(), score));
            }
        }

        predictions.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap());
        predictions.truncate(k);

        Ok(predictions)
    }

    fn get_entities(&self) -> Vec<String> {
        self.entities.keys().cloned().collect()
    }

    fn get_relations(&self) -> Vec<String> {
        self.relations.keys().cloned().collect()
    }

    fn get_stats(&self) -> crate::ModelStats {
        self.stats.clone()
    }

    fn save(&self, _path: &str) -> Result<()> {
        Ok(())
    }

    fn load(&mut self, _path: &str) -> Result<()> {
        Ok(())
    }

    fn clear(&mut self) {
        self.entities.clear();
        self.relations.clear();
        self.dna_sequences.clear();
        self.is_trained = false;
        self.stats = crate::ModelStats::default();
    }

    fn is_trained(&self) -> bool {
        self.is_trained
    }

    async fn encode(&self, texts: &[String]) -> Result<Vec<Vec<f32>>> {
        let mut encoded = Vec::new();

        for text in texts {
            // Encode as DNA sequence first
            let mut temp_model = self.clone();
            let dna_seq = temp_model.encode_entity_dna(text);
            let embedding = dna_seq.to_vector();

            // Apply biological transformations
            let ca_embedding = temp_model.generate_ca_embedding(&embedding);

            encoded.push(ca_embedding.values);
        }

        Ok(encoded)
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_nucleotide_operations() {
        let a = Nucleotide::A;
        let t = Nucleotide::T;

        assert_eq!(a.complement(), t);
        assert_eq!(t.complement(), a);
        assert_eq!(a.to_numeric(), 0.0);
        assert_eq!(Nucleotide::from_numeric(0.0), a);
    }

    #[test]
    fn test_dna_sequence() {
        let seq1 = DNASequence::new(vec![
            Nucleotide::A,
            Nucleotide::T,
            Nucleotide::G,
            Nucleotide::C,
        ]);
        let seq2 = DNASequence::new(vec![
            Nucleotide::T,
            Nucleotide::A,
            Nucleotide::C,
            Nucleotide::G,
        ]);

        assert_eq!(seq1.length, 4);
        assert_eq!(seq1.hybridize(&seq2), 1.0); // Perfect complement

        let vector = seq1.to_vector();
        assert_eq!(vector.values.len(), 4);
    }

    #[test]
    fn test_cellular_automaton() {
        let mut ca = CellularAutomaton::new((10, 10), CellularAutomataRule::Conway);
        let embedding = Vector::new(vec![0.5; 100]);

        ca.initialize_with_embedding(&embedding);
        ca.evolve();

        assert_eq!(ca.generation, 1);
        let stats = ca.get_statistics();
        assert_eq!(stats.total_cells, 100);
    }

    #[test]
    fn test_enzymatic_network() {
        let mut network = EnzymaticNetwork::new(0.1, 300.0);

        let enzyme = Enzyme::new("test_enzyme".to_string(), 0.8);
        let substrate = Substrate::new("test_substrate".to_string(), 1.0, 0.5);

        network.add_enzyme(enzyme);
        network.add_substrate(substrate);

        let results = network.simulate_reactions(10);
        assert_eq!(results.len(), 10);
    }

    #[test]
    fn test_gene_regulatory_network() {
        let mut network = GeneRegulatoryNetwork::new(0.5);
        let mut random = Random::default();

        let dna_seq = DNASequence::random(100, &mut random);
        let gene = Gene::new("test_gene".to_string(), dna_seq);
        network.add_gene(gene);

        let results = network.simulate_expression(10);
        assert_eq!(results.len(), 10);
    }

    #[test]
    fn test_molecular_assembly() {
        let mut assembly = MolecularAssembly::new(MolecularAssemblyType::SelfAssembly, 300.0);

        let molecule = Molecule::new(MoleculeType::Protein, [0.0, 0.0, 0.0]);
        assembly.add_molecule(molecule);

        let results = assembly.simulate_assembly(10);
        assert_eq!(results.len(), 10);
    }

    #[tokio::test]
    async fn test_biological_embedding_model() {
        let model_config = ModelConfig::default();
        let bio_config = BiologicalComputingConfig::default();
        let mut model = BiologicalEmbeddingModel::new(model_config, bio_config);

        let triple = crate::Triple::new(
            crate::NamedNode::new("http://example.org/alice").unwrap(),
            crate::NamedNode::new("http://example.org/knows").unwrap(),
            crate::NamedNode::new("http://example.org/bob").unwrap(),
        );

        model.add_triple(triple).unwrap();
        assert_eq!(model.get_entities().len(), 2);
        assert_eq!(model.get_relations().len(), 1);
        assert_eq!(model.dna_sequences.len(), 2);
    }

    #[test]
    fn test_dna_encoding() {
        let model_config = ModelConfig::default();
        let bio_config = BiologicalComputingConfig::default();
        let mut model = BiologicalEmbeddingModel::new(model_config, bio_config);

        let dna_seq = model.encode_entity_dna("test_entity");
        assert_eq!(dna_seq.length, 256);
    }

    #[test]
    fn test_pcr_amplification() {
        let mut random = Random::default();
        let seq = DNASequence::random(50, &mut random);

        let amplified = seq.pcr_amplify(5, 2.0);
        assert!(amplified.len() > 1);
    }

    #[test]
    fn test_restriction_cutting() {
        let seq = DNASequence::new(vec![
            Nucleotide::A,
            Nucleotide::T,
            Nucleotide::G,
            Nucleotide::C,
            Nucleotide::G,
            Nucleotide::C, // Cut site
            Nucleotide::T,
            Nucleotide::A,
        ]);

        let cut_site = vec![Nucleotide::G, Nucleotide::C];
        let fragments = seq.restrict(&cut_site);

        assert!(!fragments.is_empty());
    }
}