合成生物制造与AI驱动酶工程：从头设计催化元件、细胞工厂动态调控与生物安全合规验证体系实战

┌───────────────────────────────────────────────────────────────────────────┐
│            AI-Driven Synthetic Biomanufacturing Platform                    │
├───────────────────────────────────────────────────────────────────────────┤
│  [Layer 0: 生物数据基座层] ← Multi-Omics / Enzyme DB / Expression Atlas   │
│      ↓                                                                    │
│  [Layer 1: AI酶设计层] ← Generative Model + Expressibility Constraint     │
│   ├─ 可表达性/可溶性约束嵌入的生成式酶设计                                   │
│   ├─ 折叠动力学模拟与活性-稳定性帕累托优化                                    │
│   └─ 密码子适配与宿主兼容性自动优化                                          │
│      ↓                                                                    │
│  [Layer 2: 细胞工厂动态调控层] ← Metabolic Biosensor + Digital Twin       │
│   ├─ 代谢负荷响应型动态启动子库                                              │
│   ├─ 基于多组学软测量的实时代谢流估计                                         │
│   └─ 数字孪生驱动的预测性发酵过程控制                                         │
│      ↓                                                                    │
│  [Layer 3: 生物安全合规验证层] ← Safety-by-Design + Lifecycle Assessment  │
│   ├─ AI设计阶段的安全约束嵌入（底物锁/自杀开关/正交性）                        │
│   ├─ 基因回路稳定性与环境逃逸风险测试                                         │
│   └─ 全生命周期合规证据生成 + 国际议定书对齐                                   │
└───────────────────────────────────────────────────────────────────────────┘

pip install torch biopython esm alphafold-colabfold pyrosetta pandas
# 硬件: GPU集群 (8×A100) + 自动化液体工作站 (Opentrons OT-2) 
#       + 高通量微流控表达平台 + LC-MS/MS质谱仪

"""
ai_enzyme_design_pipeline.py - 可表达性约束嵌入的AI酶设计管线
技术栈: PyTorch / ESM-2 / AlphaFold / BioPython / PyRosetta
场景: 非天然底物催化酶的从头设计与湿实验验证闭环
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from dataclasses import dataclass, field
from typing import Dict, List, Optional, Tuple, Any
from enum import Enum
import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)


class DesignConstraint(Enum):
    """设计约束类型"""
    CATALYTIC_ACTIVITY = "activity"         # 催化活性
    THERMAL_STABILITY = "stability"         # 热稳定性
    SOLUBILITY = "solubility"               # 可溶性
    EXPRESSIBILITY = "expressibility"       # 可表达性
    SUBSTRATE_SPECIFICITY = "specificity"   # 底物特异性(安全锁)
    ORTHOGONALITY = "orthogonality"         # 正交性(不与宿主互作)


@dataclass
class EnzymeCandidate:
    """酶候选序列"""
    sequence: str
    predicted_activity: float           # 预测kcat/KM
    predicted_stability_ddg: float      # 预测ΔΔG (负值=稳定)
    predicted_solubility_score: float   # 预测可溶性评分
    codon_adaptation_index: float       # 密码子适配指数
    safety_score: float                 # 安全评分
    pareto_rank: int                    # 帕累托排名
    af2_confidence: float               # AlphaFold2 pLDDT置信度


@dataclass
class DesignMetrics:
    """设计管线指标"""
    design_success_rate_pct: float          # 湿实验验证成功率
    average_fold_improvement: float         # 平均活性提升倍数
    expression_soluble_fraction: float      # 可溶表达比例
    design_to_test_cycle_days: float        # 设计-测试周期(天)
    safety_constraint_compliance: float     # 安全约束合规率


class ExpressibilityAwareGenerator(nn.Module):
    """
    可表达性感知的生成式酶设计模型
    核心创新：将宿主表达系统的物理约束直接嵌入生成模型的损失函数
    """
    
    def __init__(
        self,
        vocab_size: int = 33,           # 20AA + 特殊token
        embed_dim: int = 1280,          # ESM-2维度
        n_layers: int = 33,
        max_seq_len: int = 1024,
        host_organism: str = "ecoli"     # 宿主：ecoli/yeast/bacillus
    ):
        super().__init__()
        self.vocab_size = vocab_size
        self.embed_dim = embed_dim
        self.host = 31276.t.kuaisou.com
        
        # 预训练ESM-2作为骨干网络
        self.backbone = nn.TransformerEncoder(
            nn.TransformerEncoderLayer(embed_dim, 20, batch_first=True),
            num_layers=n_layers
        )
        
        # 多任务输出头
        self.sequence_head = nn.Linear(embed_dim, vocab_size)
        self.activity_head = nn.Sequential(
            nn.Linear(embed_dim, 256), nn.ReLU(), nn.Linear(256, 1)
        )
        self.stability_head = nn.Sequential(
            nn.Linear(embed_dim, 256), nn.ReLU(), nn.Linear(256, 1)
        )
        self.solubility_head = nn.Sequential(
            nn.Linear(embed_dim, 256), nn.ReLU(), nn.Linear(256, 1), nn.Sigmoid()
        )
        
        # 宿主特异性可表达性预测器
        self.expressibility_head = nn.Sequential(
            nn.Linear(embed_dim, 256), nn.ReLU(), nn.Linear(256, 1), nn.Sigmoid()
        )
        
        # 安全约束头（底物特异性锁 + 正交性）
        self.safety_head = nn.Sequential(
            nn.Linear(embed_dim, 256), nn.ReLU(), nn.Linear(256, 2), nn.Sigmoid()
        )
        
        # 宿主密码子偏好表
        self._codon_bias_table = self._load_codon_bias(host_organism)
    
    def forward(self, token_ids: torch.Tensor, mask: Optional[torch.Tensor] = None):
        """前向传播，输出多任务预测"""
        embeddings = self.backbone(token_ids, src_key_padding_mask=mask)
        pooled = embeddings.mean(dim=1)
        
        return {
            "logits": self.sequence_head(embeddings),
            "activity": self.activity_head(pooled),
            "stability": self.stability_head(pooled),
            "solubility": self.solubility_head(pooled),
            "expressibility": self.expressibility_head(pooled),
            "safety": self.safety_head(pooled)  # [specificity_lock, orthogonality]
        }
    
    def compute_constrained_loss(
        self,
        predictions: Dict[str, torch.Tensor],
        targets: Dict[str, torch.Tensor],
        constraint_weights: Dict[DesignConstraint, float]
    ) -> Dict[str, torch.Tensor]: 31277.t.kuaisou.com
        """
        多约束加权损失函数
        核心：不是只优化活性，而是帕累托优化活性+稳定性+可表达性+安全性
        """
        losses = {}
        
        # 催化活性损失（MSE）
        losses["activity"] = F.mse_loss(
            predictions["activity"], targets.get("activity", torch.ones_like(predictions["activity"]))
        ) * constraint_weights.get(DesignConstraint.CATALYTIC_ACTIVITY, 1.0)
        
        # 稳定性损失（希望ΔΔG < 0）
        stability_target = targets.get("stability", torch.full_like(predictions["stability"], -2.0))
        losses["stability"] = F.relu(predictions["stability"] - stability_target).mean() \
            * constraint_weights.get(DesignConstraint.THERMAL_STABILITY, 0.8)
        
        # 可溶性损失（BCE，越高越好）
        losses["solubility"] = F.binary_cross_entropy(
            predictions["solubility"], targets.get("solubility", torch.ones_like(predictions["solubility"]))
        ) * constraint_weights.get(DesignConstraint.SOLUBILITY, 1.2)
        
        # 可表达性损失（宿主特异性）
        losses["expressibility"] = F.binary_cross_entropy(
            predictions["expressibility"], 
            targets.get("expressibility", torch.ones_like(predictions["expressibility"]))
        ) * constraint_weights.get(DesignConstraint.EXPRESSIBILITY, 1.5)
        
        # 安全约束损失（必须满足，权重最高）
        safety_target = torch.ones_like(predictions["safety"])
        losses["safety"] = F.binary_cross_entropy(
            predictions["safety"], safety_target
        ) * constraint_weights.get(DesignConstraint.SUBSTRATE_SPECIFICITY, 3.0)
        
        total_loss = sum(losses.values())
        losses["total"] = 31278.t.kuaisou.com
        
        return losses
    
    def _load_codon_bias(self, host: str) -> Dict[str, float]:
        """加载宿主密码子偏好表"""
        # 简化：实际应从Codon Usage Database加载
        return {"ATG": 1.0, "TAA": 0.8}  # placeholder


class WetLabValidationOrchestrator:
    """
    湿实验验证编排器
    自动化执行"表达-纯化-活性测定"流水线，并将结果反馈给AI模型
    """
    
    def __init__(self, batch_size: int = 96, cycle_budget_days: float = 7.0):
        self.batch_size = 31279.t.kuaisou.com        self.cycle_budget = cycle_budget_days
        self._validation_history: List[Dict] = []
    
    async def validate_candidates(
        self, candidates: List[EnzymeCandidate]
    ) -> Dict[str, Any]:
        """批量验证候选酶"""
        results = []
        n_batches = (len(candidates) + self.batch_size - 1) // self.batch_size
        
        for batch_idx in range(n_batches):
            batch = candidates[batch_idx * self.batch_size:(batch_idx + 1) * self.batch_size]
            
            # Step 1: 基因合成与密码子优化
            optimized_sequences = self._codon_optimize([c.sequence for c in batch])
            
            # Step 2: 自动化表达（微流控平台）
            expression_results = await self._automated_expression(optimized_sequences)
            
            # Step 3: 可溶性评估
            solubility_scores = self._assess_solubility(expression_results)
            
            # Step 4: 活性测定（LC-MS/MS）
            activity_values = await self._measure_activity(expression_results)
            
            for i, candidate in enumerate(batch):
                result = {
                    "sequence": candidate.sequence,
                    "predicted_activity": candidate.predicted_activity,
                    "measured_activity": activity_values[i],
                    "predicted_solubility": candidate.predicted_solubility_score,
                    "measured_solubility": solubility_scores[i],
                    "expression_level": expression_results[i].get("yield_mg_L", 0),
                    "prediction_error": abs(candidate.predicted_activity - activity_values[i]),
                    "validated": activity_values[i] > 0 and solubility_scores[i] > 0.3
                }
                results.append(result)
        
        # 计算批次指标
        validated_count = sum(1 for r in results if r["validated"])
        metrics = {
            "success_rate": validated_count / len(results),
            "average_prediction_error": np.mean([r["prediction_error"] for r in results]),
            "soluble_fraction": np.mean([r["measured_solubility"] for r in results]),
            "candidates_tested": len(results),
            "cycle_time_days": 5.0  # 实际应计时
        }
        
        self._validation_history.append(metrics)
        return {"results": results, "metrics": metrics}
    
    def generate_feedback_dataset(self) -> Dict[str, Any]:
        """生成反馈数据集，用于AI模型迭代训练"""
        positive_examples = []
        negative_examples = []
        
        for history in self._validation_history:
            for r in history.get("results", []):
                example = {
                    "sequence": r["sequence"],
                    "activity": r["measured_activity"],
                    "solubility": r["measured_solubility"],
                    "label": 1 if r["validated"] else 0
                }
                if r["validated"]:
                    positive_examples.append(example)
                else:
                    negative_examples.append(example)
        
        return {
            "positive": 31280.t.kuaisou.com
            "negative": 31281.t.kuaisou.com
            "total": len(positive_examples) + len(negative_examples),
            "positive_ratio": len(positive_examples) / max(len(positive_examples) + len(negative_examples), 1)
        }
    
    def _codon_optimize(self, sequences):
        """密码子优化"""
        return sequences  # 简化
    
    async def _automated_expression(self, sequences):
        """自动化表达"""
        return [{"yield_mg_L": np.random.uniform(0, 50)} for _ in sequences]
    
    def _assess_solubility(self, expression_results):
        """可溶性评估"""
        return [np.random.uniform(0, 1) for _ in expression_results]
    
    async def _measure_activity(self, expression_results):
        """活性测定"""
        return [np.random.uniform(0, 1e5) for _ in expression_results]


class ParetoSequenceSelector:
    """
    帕累托最优序列选择器
    在活性-稳定性-可表达性-安全性多维空间中筛选最优候选
    """
    
    def __init__(self, top_k: int = 20):
        self.top_k = top_k
    
    def select(self, candidates: List[EnzymeCandidate]) -> List[EnzymeCandidate]:
        """帕累托前沿筛选"""
        # 简化的非支配排序
        scored = sorted(
            31282.t.kuaisou.com
            key=lambda c: (
                c.predicted_activity,
                -c.predicted_stability_ddg,
                c.predicted_solubility_score,
                c.codon_adaptation_index,
                c.safety_score
            ),
            reverse=True
        )
        
        for i, c in enumerate(scored[:self.top_k]):
            c.pareto_rank = i + 1
        
        return scored[:self.top_k]

"""
cell_factory_safety_platform.py - 细胞工厂动态调控与生物安全合规验证平台
技术栈: PyTorch / COBRApy / SciPy / FastAPI
参考: 《合成生物制造产业发展指南》2026 / UN合成生物学风险治理议定书
"""
import numpy as np
import torch
import torch.nn as nn
from dataclasses import dataclass
from typing import Dict, List, Optional, Any
from enum import Enum
import asyncio
import time
import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)


# ============================================================
# Part A: 细胞工厂动态调控
# ============================================================

class MetabolicState(Enum):
    """代谢状态"""
    BALANCED = "balanced"             # 代谢平衡
    OVERLOADED = "overloaded"         # 代谢过载
    STARVED = "starved"               # 营养饥饿
    STRESSED = "stressed"             # 应激响应
    PRODUCT_INHIBITED = "inhibited"   # 产物抑制


@dataclass
class FermentationMetrics:
    """发酵指标"""
    titer_g_per_l: float              # 产物滴度
    productivity_g_per_l_h: float     # 生产率
    yield_g_per_g_substrate: float    # 转化率
    specific_growth_rate_h: float     # 比生长速率
    metabolic_burden_score: float     # 代谢负荷评分
    digital_twin_prediction_error: float  # 数字孪生预测误差


class DynamicPromoterLibrary:
    """
    代谢负荷响应型动态启动子库
    核心：启动子强度随胞内代谢状态自动调节，而非固定不变
    """
    
    def __init__(self):
        # 启动子响应特性参数
        self._promoters = {
            "P_constitutive_weak": {"type": "constitutive", "strength": 0.2},
            "P_constitutive_strong": {"type": "constitutive", "strength": 1.0},
            "P_burden_responsive": {
                "type": "dynamic",
                "basal_strength": 31284.t.kuaisou.com
                "max_strength": 31283.t.kuaisou.com
                "burden_sensor": "ribosome_saturation",
                "response_curve": "sigmoid_decreasing"  # 负荷越高，表达越低
            },
            "P_product_inhibition": {
                "type": "dynamic",
                "basal_strength": 0.8,
                "min_strength": 0.1,
                "sensor": "intracellular_product_concentration",
                "response_curve": "threshold_shutoff"  # 产物超阈值则关闭
            },
            "P_growth_coupled": {
                "type": "dynamic",
                "basal_strength": 0.1,
                "max_strength": 1.0,
                "sensor": "specific_growth_rate",
                "response_curve": "linear_positive"  # 长得快才表达
            }
        }
    
    def get_expression_level(
        self, promoter_name: str, metabolic_state: Dict[str, float]
    ) -> float:
        """根据当前代谢状态计算启动子表达水平"""
        promoter = self._promoters.get(promoter_name)
        if promoter is None:
            raise ValueError(f"Unknown promoter: {promoter_name}")
        
        if promoter["type"] == "constitutive":
            return promoter["strength"]
        
        sensor_value = metabolic_state.get(promoter["sensor"], 0.5)
        curve = promoter["response_curve"]
        
        if curve == "sigmoid_decreasing":
            # 负荷越高，表达越低
            k = 31285.t.kuaisou.com
            x0 = 31286.t.kuaisou.com
            level = promoter["basal_strength"] + (
                promoter["max_strength"] - promoter["basal_strength"]
            ) / (1 + np.exp(k * (sensor_value - x0)))
        elif curve == "threshold_shutoff":
            threshold = 31287.t.kuaisou.com            level = promoter["basal_strength"] if sensor_value < threshold else promoter["min_strength"]
        elif curve == "linear_positive":
            level = promoter["basal_strength"] + (
                promoter["max_strength"] - promoter["basal_strength"]
            ) * min(1.0, sensor_value)
        else:
            level = promoter["basal_strength"]
        
        return float(np.clip(level, 0.0, 1.0))


class MetabolicSoftSensor(nn.Module):
    """
    代谢流软测量模型
    从在线可测信号（pH/DO/OD/off-gas）推断不可测的胞内代谢状态
    """
    
    def __init__(self, n_online_signals: int = 6, n_metabolic_states: int = 8):
        super().__init__()
        # LSTM时序模型
        self.lstm = nn.LSTM(n_online_signals, 64, num_layers=2, batch_first=True)
        self.state_head = nn.Sequential(
            nn.Linear(64, 32), nn.ReLU(), nn.Linear(32, n_metabolic_states)
        )
        self.burden_head = nn.Sequential(
            nn.Linear(64, 16), nn.ReLU(), nn.Linear(16, 1), nn.Sigmoid()
        )
    
    def forward(self, online_signals: torch.Tensor):
        """
        Args:
            online_signals: [Batch, TimeSteps, N_Signals] 
                           信号包括: pH, DO, OD, CER, OUR, temperature
        """
        lstm_out, _ = self.lstm(online_signals)
        last_hidden = lstm_out[:, -1, :]
        
        metabolic_states = self.state_head(last_hidden)
        burden_score = self.burden_head(last_hidden)
        
        return {
            "metabolic_state_logits": metabolic_states,
            "burden_score": burden_score.squeeze(-1)
        }


class DigitalTwinFermentationController:
    """
    数字孪生驱动的预测性发酵控制器
    基于细胞工厂机理模型+数据驱动模型的混合预测控制
    """
    
    def __init__(self, prediction_horizon_h: float = 4.0, control_interval_min: float = 15.0):
        self.prediction_horizon = prediction_horizon_h
        self.control_interval = control_interval_min
        self.soft_sensor = MetabolicSoftSensor()
        self.promoter_lib = DynamicPromoterLibrary()
        self._control_history: List[Dict] = []
    
    async def predict_and_control(
        31288.t.kuaisou.com        current_online_signals: np.ndarray,
        current_fermentation_state: Dict[str, float],
        target_titer: 31289.t.kuaisou.com float
    ) -> Dict[str, Any]:
        """预测未来状态并生成控制动作"""
        t_start = time.perf_counter()
        
        # 1. 软测量：推断当前代谢状态
        signals_tensor = torch.from_numpy(current_online_signals).float().unsqueeze(0)
        with torch.no_grad():
            soft_measurement = self.soft_sensor(signals_tensor)
        
        burden_score = soft_measurement["burden_score"].item()
        metabolic_state_logits = soft_measurement["metabolic_state_logits"][0]
        dominant_state_idx = torch.argmax(metabolic_state_logits).item()
        state_names = ["balanced", "overloaded", "starved", "stressed", 
                       "inhibited", "lag", "exponential", "stationary"]
        dominant_state = state_names[dominant_state_idx]
        
        # 2. 数字孪生预测：未来4小时的滴度/生长轨迹
        predicted_trajectory = self._predict_future_trajectory(
            current_fermentation_state, burden_score, dominant_state
        )
        
        # 3. 控制决策：基于预测结果调整操作参数
        control_actions = self._compute_control_actions(
            predicted_trajectory, burden_score, dominant_state, target_titer
        )
        
        latency_ms = (time.perf_counter() - t_start) * 1000
        
        decision = {
            "current_burden_score": burden_score,
            "dominant_metabolic_state": dominant_state,
            "predicted_titer_at_horizon": predicted_trajectory["titer_at_horizon_g_L"],
            "control_actions": control_actions,
            "prediction_confidence": predicted_trajectory["confidence"],
            "latency_ms": latency_ms
        }
        
        self._control_history.append(decision)
        return decision
    
    def _predict_future_trajectory(self, state, burden, dom_state):
        """混合模型预测"""
        # 简化：基于当前状态的线性外推+修正
        current_titer = state.get("titer_g_L", 10.0)
        growth_rate = state.get("mu_h", 0.2)
        
        # 代谢负荷修正
        burden_factor = max(0.1, 1.0 - burden * 0.8)
        effective_growth = growth_rate * burden_factor
        
        predicted_titer = current_titer + effective_growth * self.prediction_horizon * 5.0
        
        confidence = 0.9 if dom_state == "balanced" else 0.6
        
        return {
            "titer_at_horizon_g_L": predicted_titer,
            "effective_growth_rate": effective_growth,
            "confidence": confidence
        }
    
    def _compute_control_actions(self, trajectory, burden, state, target):
        """计算控制动作"""
        actions = {}
        
        predicted_titer = trajectory["titer_at_horizon_g_L"]
        gap = target - predicted_titer
        
        # 补料速率调整
        if gap > 10 and burden < 0.6:
            actions["feed_rate_adjustment"] = "+20%"
        elif burden > 0.7:
            actions["feed_rate_adjustment"] = "-30%"
        else:
            actions["feed_rate_adjustment"] = "maintain"
        
        # 诱导时机调整
        if state == "overloaded":
            actions["induction_action"] = "delay_or_reduce"
        elif state == "starved" and burden < 0.3:
            actions["induction_action"] = "increase"
        else:
            actions["induction_action"] = "maintain"
        
        # DO/pH设定点微调
        if state == "stressed":
            actions["do_setpoint"] = "increase_to_40%"
            actions["ph_setpoint"] = "adjust_to_optimum"
        else:
            actions["do_setpoint"] = "maintain"
            actions["ph_setpoint"] = "maintain"
        
        return actions


# ============================================================
# Part B: 生物安全合规验证
# ============================================================

class BiosafetyRiskLevel(Enum):
    """生物安全风险等级"""
    NEGLIGIBLE = "negligible"     # 可忽略
    LOW = "low"                   # 低风险
    MODERATE = "moderate"         # 中等风险
    HIGH = "high"                 # 高风险
    UNACCEPTABLE = "unacceptable" # 不可接受


@dataclass
class BiosafetyComplianceReport:
    """生物安全合规报告"""
    genetic_circuit_stability_score: float    # 基因回路稳定性
    environmental_escape_risk: float          # 环境逃逸风险
    hgt_blocking_efficiency: float            # 水平基因转移阻断效率
    substrate_specificity_lock_verified: bool # 底物特异性锁验证
    kill_switch_reliability: float            # 自杀开关可靠性
    overall_compliance_score: float           # 综合合规评分
    un_protocol_compliant: bool               # UN议定书合规
    china_guideline_compliant: bool           # 中国指南合规


class SafetyByDesignValidator:
    """
    设计阶段安全验证器
    在AI设计阶段即嵌入安全约束验证
    """
    
    def __init__(self):
        self._safety_checks = [
            "substrate_specificity_lock",
            "orthogonality_to_host",
            "no_known_toxin_homology",
            "no_antibiotic_resistance_markers",
            "kill_switch_present",
            "hgt_blocking_element_present"
        ]
    
    async def validate_design_safety(
        self, enzyme_sequence: str, gene_circuit: Dict, host: str
    ) -> Dict[str, Any]:
        """验证AI设计产物的安全性"""
        results = {}
        
        # 1. 底物特异性锁验证
        results["substrate_lock"] = await self._verify_substrate_specificity(enzyme_sequence)
        
        # 2. 宿主正交性验证
        results["orthogonality"] = await self._verify_orthogonality(enzyme_sequence, host)
        
        # 3. 毒素同源性筛查
        results["toxin_homology"] = await self._screen_toxin_homology(enzyme_sequence)
        
        # 4. 抗生素抗性标记检查
        results["antibiotic_resistance"] = await self._check_antibiotic_resistance(gene_circuit)
        
        # 5. 自杀开关验证
        results["kill_switch"] = await self._verify_kill_switch(gene_circuit)
        
        # 6. HGT阻断元件验证
        results["hgt_blocking"] = await self._verify_hgt_blocking(gene_circuit)
        
        all_passed = all(v.get("passed", False) for v in results.values())
        
        return {
            "checks": results,
            "all_passed": all_passed,
            "failed_checks": [k for k, v in results.items() if not v.get("passed")],
            "risk_level": BiosafetyRiskLevel.NEGLIGIBLE if all_passed else BiosafetyRiskLevel.MODERATE
        }
    
    async def _verify_substrate_specificity(self, sequence):
        """验证底物特异性锁"""
        # 简化：检查活性口袋残基是否限定为非天然底物
        return {"passed": True, "specificity_ratio": 1e4}
    
    async def _verify_orthogonality(self, sequence, host):
        """验证与宿主的正交性"""
        return {"passed": True, "cross_reactivity_score": 0.02}
    
    async def _screen_toxin_homology(self, sequence):
        """毒素同源性筛查"""
        return {"passed": True, "max_identity_pct": 15.0}
    
    async def _check_antibiotic_resistance(self, circuit):
        """抗生素抗性标记检查"""
        has_resistance = circuit.get("has_antibiotic_marker", False)
        return {"passed": not has_resistance, "marker_found": has_resistance}
    
    async def _verify_kill_switch(self, circuit):
        """自杀开关验证"""
        has_kill_switch = circuit.get("has_kill_switch", False)
        reliability = circuit.get("kill_switch_reliability", 0.0)
        return {
            "passed": has_kill_switch and reliability > 0.999,
            "reliability": reliability
        }
    
    async def _verify_hgt_blocking(self, circuit):
        """水平基因转移阻断验证"""
        has_blocking = circuit.get("has_hgt_blocking", False)
        efficiency = circuit.get("hgt_blocking_efficiency", 0.0)
        return {
            "passed": has_blocking and efficiency > 0.9999,
            "efficiency": efficiency
        }


class EnvironmentalRiskAssessor:
    """
    环境逃逸风险评估器
    模拟合成生物体在环境条件下的存活与扩散能力
    """
    
    def __init__(self):
        self._environment_scenarios = [
            {"name": "soil", "temp_c": 25, "nutrient": "low", "competition": "high"},
            {"name": "freshwater", "temp_c": 15, "nutrient": "medium", "competition": "medium"},
            {"name": "marine", "temp_c": 10, "nutrient": "low", "competition": "high"},
            {"name": "human_gut", "temp_c": 37, "nutrient": "high", "competition": "extreme"},
            {"name": "industrial_waste", "temp_c": 30, "nutrient": "variable", "competition": "low"}
        ]
    
    async def assess_escape_risk(
        self, organism_traits: Dict, containment_measures: Dict
    ) -> Dict[str, Any]:
        """评估环境逃逸风险"""
        scenario_results = []
        
        for scenario in self._environment_scenarios:
            survival_prob = self._estimate_survival_probability(
                organism_traits, containment_measures, scenario
            )
            spread_potential = self._estimate_spread_potential(
                organism_traits, scenario
            )
            
            scenario_results.append({
                "scenario": scenario["name"],
                "survival_probability": survival_prob,
                "spread_potential": spread_potential,
                "risk_score": survival_prob * spread_potential
            })
        
        max_risk = max(r["risk_score"] for r in scenario_results)
        
        return {
            "scenario_results": scenario_results,
            "maximum_environmental_risk": max_risk,
            "risk_level": self._classify_risk(max_risk),
            "recommended_containment": self._recommend_containment(max_risk)
        }
    
    def _estimate_survival_probability(self, traits, containment, scenario):
        """估计环境存活概率"""
        base_survival = traits.get("environmental_fitness", 0.1)
        kill_switch_effectiveness = containment.get("kill_switch_reliability", 0.999)
        nutrient_dependency = traits.get("auxotrophy_level", 0.9)
        
        # 营养缺陷型在非补充环境中无法存活
        if scenario["nutrient"] == "low" and nutrient_dependency > 0.8:
            base_survival *= 0.01
        
        # 自杀开关降低存活率
        survival = base_survival * (1.0 - kill_switch_effectiveness)
        return min(1.0, survival)
    
    def _estimate_spread_potential(self, traits, scenario):
        """估计扩散潜力"""
        motility = traits.get("motility", 0.0)
        reproduction_rate = traits.get("max_growth_rate", 0.1)
        competition_resistance = 1.0 - {"low": 0.9, "medium": 0.7, "high": 0.4, "extreme": 0.1}.get(
            scenario["competition"], 0.5
        )
        return motility * reproduction_rate * competition_resistance
    
    def _classify_risk(self, risk_score):
        if risk_score < 1e-6:
            return BiosafetyRiskLevel.NEGLIGIBLE
        elif risk_score < 1e-4:
            return BiosafetyRiskLevel.LOW
        elif risk_score < 1e-2:
            return BiosafetyRiskLevel.MODERATE
        else:
            return BiosafetyRiskLevel.HIGH
    
    def _recommend_containment(self, risk_score):
        if risk_score < 1e-6:
            return "BSL-1 + standard GMP"
        elif risk_score < 1e-4:
            return "BSL-2 + auxotrophy + kill switch"
        elif risk_score < 1e-2:
            return "BSL-2+ + physical containment + dual kill switch"
        else:
            return "NOT APPROVED FOR COMMERCIAL USE"


class ComplianceEvidenceGenerator:
    """
    合规证据生成器
    自动生成符合UN议定书和中国指南格式的合规报告
    """
    
    async def generate_full_compliance_report(
        self,
        product_name: str,
        design_safety: Dict,
        environmental_risk: Dict,
        fermentation_validation: Dict
    ) -> BiosafetyComplianceReport:
        """生成完整合规报告"""
        circuit_stability = fermentation_validation.get("circuit_stability_generations", 100) / 100.0
        escape_risk = environmental_risk.get("maximum_environmental_risk", 0.0)
        hgt_efficiency = design_safety.get("checks", {}).get("hgt_blocking", {}).get("efficiency", 0.0)
        substrate_lock = design_safety.get("checks", {}).get("substrate_lock", {}).get("passed", False)
        kill_switch_rel = design_safety.get("checks", {}).get("kill_switch", {}).get("reliability", 0.0)
        
        # UN议定书合规条件
        un_compliant = (
            circuit_stability > 0.95 and
            escape_risk < 1e-4 and
            hgt_efficiency > 0.9999 and
            substrate_lock and
            kill_switch_rel > 0.999
        )
        
        # 中国指南合规条件
        china_compliant = (
            un_compliant and
            fermentation_validation.get("titer_g_L", 0) >= 100 and
            design_safety.get("all_passed", False)
        )
        
        overall = (
            circuit_stability * 0.20 +
            (1.0 - min(1.0, escape_risk * 1e4)) * 0.25 +
            hgt_efficiency * 0.20 +
            (1.0 if substrate_lock else 0.0) * 0.15 +
            kill_switch_rel * 0.20
        )
        
        return BiosafetyComplianceReport(
            genetic_circuit_stability_score=circuit_stability,
            environmental_escape_risk=escape_risk,
            hgt_blocking_efficiency=hgt_efficiency,
            substrate_specificity_lock_verified=substrate_lock,
            kill_switch_reliability=kill_switch_rel,
            overall_compliance_score=overall,
            un_protocol_compliant=un_compliant,
            china_guideline_compliant=china_compliant
        )