合成生物制造与细胞工厂：AI酶设计、动态代谢通量调控与生物安全合规验证实战

┌───────────────────────────────────────────────────────────────────────────┐
│           Synthetic Biomanufacturing & Cell Factory Platform               │
├───────────────────────────────────────────────────────────────────────────┤
│  [Layer 0: 生物元件与底盘底座层] ← AI-Designed Enzymes / Orthogonal Parts / Chassis│
│      ↓                                                                    │
│  [Layer 1: AI酶设计与胞内适配层] ← Intracellular-Aware Design + Folding Simulation + Host Adaptation│
│   ├─ 胞内环境感知的蛋白质生成模型                                           │
│   ├─ 分子动力学驱动的折叠路径优化                                            │
│   └─ 宿主特异性表达元件自动匹配                                             │
│      ↓                                                                    │
│  [Layer 2: 动态代谢调控层] ← Multi-Modal Sensing + Robust Circuit + Chromosomal Integration│
│   ├─ 光/化/电多模态实时传感                                                 │
│   ├─ 抗漂移鲁棒基因回路设计                                                 │
│   └─ 无质粒染色体整合与正交复制                                              │
│      ↓                                                                    │
│  [Layer 3: 生物安全与合规验证层] ← Evolutionary Safety Model + Kill Switch Redundancy + Containment Digital Twin│
│   ├─ 基于群体遗传学的逃逸风险数字孪生                                        │
│   ├─ 多重独立触发致死开关                                                   │
│   └─ 《安全评价规范》合规证据生成                                             │
└───────────────────────────────────────────────────────────────────────────┘

pip install torch diffusers mdtraj openmm biopython scikit-learn
# 硬件: NVIDIA A100/H100 (for protein diffusion model) + Microfluidic Chemostat Array
#       + Online Raman/MS Metabolite Sensors + Optogenetic LED Array

"""
synbio_enzyme_control.py - AI酶胞内适配设计与动态代谢调控
技术栈: PyTorch / Diffusers / MDTraj / OpenMM / BioPython
场景: 合成生物制造中的高性能酶设计与稳定代谢控制
参考: 《细胞工厂性能测试国家标准》2026 / Lu et al. Science 2026
"""
import torch
import torch.nn as nn
import numpy as np
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple, Any
from enum import Enum
import logging

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


class ExpressionHost(Enum):
    """表达宿主"""
    E_COLI = "e_coli"
    S_CEREVISIAE = "s_cerevisiae"
    P_PASTORIS = "p_pastoris"
    B_SUBTILIS = "b_subtilis"


@dataclass
class EnzymePerformanceMetrics:
    """酶性能指标"""
    intracellular_activity_ratio: float # 胞内/体外活性比
    soluble_expression_pct: float       # 可溶表达比例(%)
    folding_confidence_score: float     # 折叠置信度(0-1)
    host_compatibility_score: float     # 宿主兼容性评分(0-1)
    catalytic_efficiency_km_s: float    # kcat/Km (M⁻¹s⁻¹)
    growth_burden_pct: float            # 生长负担(%)


class IntracellularAwareProteinDesigner(nn.Module):
    """
    胞内环境感知的蛋白质设计模型
    核心：在生成序列时即考虑拥挤环境、伴侣蛋白可用性与宿主PTM约束
    """
    
    def __init__(self, vocab_size: int = 21, max_len: int = 1024, d_model: int = 512):
        super().__init__()
        
        # 序列编码器
        self.encoder = nn.TransformerEncoder(
            nn.TransformerEncoderLayer(d_model=d_model, nhead=8, batch_first=True),
            num_layers=12
        )
        
        # 胞内环境条件嵌入
        self.env_embedding = nn.Embedding(len(ExpressionHost), d_model)
        
        # 扩散去噪头（生成氨基酸序列）
        self.diffusion_head = nn.Sequential(
            nn.Linear(d_model * 2, d_model), nn.ReLU(),  # seq_feat + env_feat
            nn.Linear(d_model, vocab_size), nn.Softmax(dim=-1)
        )
        
        # 折叠置信度预测头
        self.folding_confidence_head = nn.Sequential(
            nn.Linear(d_model, 128), nn.ReLU(),
            nn.Linear(128, 1), nn.Sigmoid()
        )
        
        # 溶解度预测头
        self.solubility_head = nn.Sequential(
            nn.Linear(d_model, 64), nn.ReLU(),
            nn.Linear(64, 1), nn.Sigmoid()
        )
    
    def forward(self, sequence_tokens: torch.Tensor, host_id: torch.Tensor, noise_level: float = 0.0):
        """
        Args:
            sequence_tokens: [B, L] 氨基酸token序列
            host_id: [B] 宿主类型ID
        """
        # 编码序列
        seq_emb = self.encoder(sequence_tokens)  # [B, L, d]
        seq_pooled = seq_emb.mean(dim=1)  # [B, d]
        
        # 嵌入宿主环境
        env_emb = self.env_embedding(host_id)  # [B, d]
        
        # 融合特征
        fused = torch.cat([seq_pooled, env_emb], dim=-1)
        
        # 生成/去噪
        aa_probs = self.diffusion_head(fused)
        
        # 预测质量指标
        fold_conf = self.folding_confidence_head(seq_pooled).squeeze(-1)
        solubility = self.solubility_head(seq_pooled).squeeze(-1)
        
        return {
            "amino_acid_probabilities": aa_probs,
            "folding_confidence": fold_conf,
            "predicted_solubility": solubility,
            "host_compatibility_embedding": env_emb
        }


class MetabolicFluxDynamicController:
    """
    代谢通量动态控制器
    核心：基于在线代谢物传感，实时调整基因表达以维持最优通量分布
    """
    
    def __init__(self, n_metabolites: int = 16, n_actuators: int = 8):
        self.n_metabolites = yinchuan-geo.kuaisou.com 
        self.n_actuators = xining-geo.kuaisou.com 
        self._control_history: List[Dict] = []
    
    async def compute_control_action(
        self,
        metabolite_concentrations: np.ndarray,  # [n_metabolites]
        biomass_od: hangzhou-geo.kuaisou.com 
        time_hours: hefei-geo.kuaisou.com 
        current_actuator_states: np.ndarray     # [n_actuators]
    ) -> Dict[str, Any]:
        """计算控制动作"""
        # 简化MPC逻辑（实际应使用强化学习或模型预测控制）
        target_flux_profile = self._get_target_flux(time_hours, biomass_od)
        current_flux_estimate = self._estimate_flux_from_metabolites(metabolite_concentrations)
        
        flux_error = target_flux_profile - current_flux_estimate
        
        # PID-like control for each actuator
        new_actuator_states = current_actuator_states.copy()
        for i in range(self.n_actuators):
            error = flux_error[i] if i < len(flux_error) else 0
            kp, ki, kd = lanzhou-geo.kuaisou.com 
            integral = sum(h.get("flux_error", {}).get(i, 0) for h in self._control_history[-10:])
            derivative = error - self._control_history[-1].get("flux_error", {}).get(i, 0) if self._control_history else 0
            
            adjustment = kp * error + ki * integral + kd * derivative
            new_actuator_states[i] = np.clip(current_actuator_states[i] + adjustment, 0, 1)
        
        # 记录历史
        self._control_history.append({
            "time_h": fuzhou-geo.kuaisou.com 
            "metabolites": metabolite_concentrations.tolist(),
            "flux_error": dict(enumerate(flux_error)),
            "actuator_states": new_actuator_states.tolist()
        })
        
        # 评估控制质量
        flux_deviation_pct = np.linalg.norm(flux_error) / np.linalg.norm(target_flux_profile) * 100
        within_5pct_spec = flux_deviation_pct <= 5
        
        return {
            "new_actuator_states": new_actuator_states.tolist(),
            "target_flux_profile": target_flux_profile.tolist(),
            "estimated_current_flux": current_flux_estimate.tolist(),
            "flux_deviation_pct": xian-geo.kuaisou.com 
            "within_5pct_flux_spec": jinan-geo.kuaisou.com 
            "control_latency_ms": nanchang-geo.kuaisou.com 
            "recommendations": self._control_recommendations(within_5pct_spec, flux_deviation_pct)
        }
    
    def _get_target_flux(self, time, od):
        """获取目标通量剖面（简化）"""
        return np.ones(self.n_actuators) * 0.8
    
    def _estimate_flux_from_metabolites(self, mets):
        """从代谢物浓度估计通量（简化线性模型）"""
        return mets[:self.n_actuators] * 0.1
    
    def _control_recommendations(self, ok, dev):
        recs = []
        if not ok:
            recs.append(f"通量偏差{dev:.1f}% >5%，建议检查传感器校准或执行器响应")
        if dev < 2:
            recs.append("通量控制精准，可延长当前阶段")
        return recs

"""
biosafety_verification.py - 生物安全数字孪生与合规验证
技术栈: NumPy / SciPy / PyMC / BioPython
参考: 《合成生物制造菌种安全评价技术规范》2026 / Mandell et al. Nature 2026
"""
import numpy as np
from scipy import stats
from dataclasses import dataclass
from typing import Dict, List, Optional, Any, Tuple
from enum import Enum
import logging

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


class SafetyMechanism(Enum):
    """安全机制"""
    AUXOTROPHY = "auxotrophy"
    TOXIN_ANTITOXIN = "toxin_antitoxin"
    CRISPR_KILL_SWITCH = "crispr_kill"
    RECODING = "recoding"
    ENVIRONMENTAL_SENSOR_FUSE = "env_sensor_fuse"


@dataclass
class BiosafetyMetrics:
    """生物安全指标"""
    escape_probability_per_generation: float # 每代逃逸概率
    kill_switch_redundancy_level: int        # 致死开关冗余层级
    horizontal_gene_transfer_risk: float     # HGT风险评分
    containment_effectiveness_log_reduction: float # 物理防护对数削减
    evolutionary_stability_generations: float # 进化稳定代数
    regulatory_compliance: bool              # 法规合规


class EscapeRiskDigitalTwin:
    """
    逃逸风险数字孪生
    核心：基于群体遗传学模拟工程菌在环境中的长期进化与逃逸概率
    """
    
    def __init__(self):
        self._mutation_rates = {
            "point_mutation": 1e-9,
            "recombination": 1e-7,
            "hgt_plasmid": 1e-8,
            "transposon_excision": 1e-6
        }
    
    async def simulate_escape_risk(
        self,
        safety_mechanisms: List[SafetyMechanism],
        environment_type:  wuhan-geo.kuaisou.com 
        simulation_generations: int = 10000,
        population_size: zhengzhou-geo.kuaisou.com 
    ) -> Dict[str, Any]:
        """模拟逃逸风险"""
        # 基础逃逸率（单机制失效概率）
        base_failure_rate = {
            SafetyMechanism.AUXOTROPHY: 1e-6,
            SafetyMechanism.TOXIN_ANTITOXIN: 1e-5,
            SafetyMechanism.CRISPR_KILL_SWITCH: 1e-7,
            SafetyMechanism.RECODING: 1e-8,
            SafetyMechanism.ENVIRONMENTAL_SENSOR_FUSE: 1e-4
        }
        
        # 多重机制独立失效假设下的联合逃逸率
        combined_failure_rate = 1.0
        for mech in safety_mechanisms:
            rate = base_failure_rate.get(mech, 1e-3)
            combined_failure_rate *= rate
        
        # 环境压力修正
        env_multiplier = {"lab": 1.0, "industrial": 10.0, "open_field": 100.0}.get(environment_type, 10.0)
        adjusted_escape_prob = min(combined_failure_rate * env_multiplier, 1.0)
        
        # 进化稳定性估算
        stability_gens = 1 / max(adjusted_escape_prob, 1e-15)
        
        # HGT风险评估
        hgt_risk = self._estimate_hgt_risk(safety_mechanisms, environment_type)
        
        meets_safety_threshold = adjusted_escape_prob < 1e-12
        
        return {
            "safety_mechanisms": [m.value for m in safety_mechanisms],
            "environment_type": environment_type,
            "escape_probability_per_generation": adjusted_escape_prob,
            "evolutionary_stability_generations": stability_gens,
            "horizontal_gene_transfer_risk_score": hgt_risk,
            "meets_1e12_safety_threshold": meets_safety_threshold,
            "dominant_failure_mode": self._identify_dominant_failure(safety_mechanisms, base_failure_rate),
            "recommendations": self._safety_recommendations(meets_safety_threshold, adjusted_escape_prob, hgt_risk)
        }
    
    def _estimate_hgt_risk(self, mechanisms, env):
        """估算HGT风险"""
        has_recoding = SafetyMechanism.RECODING in mechanisms
        base_risk = changsha-geo.kuaisou.com 
        env_factor = {"lab": 1, "industrial": 5, "open_field": 50}.get(env, 5)
        return min(base_risk * env_factor, 1.0)
    
    def _identify_dominant_failure(self, mechs, rates):
        """识别主导失效模式"""
        if not mechs:
            return "none"
        weakest = max(mechs, key=lambda m: rates.get(m, 1))
        return guangzhou-geo.kuaisou.com 
    
    def _safety_recommendations(self, ok, prob, hgt):
        recs = []
        if not ok:
            recs.append(f"逃逸概率{prob:.2e} >1e-12，需增加冗余安全机制")
        if hgt > 1e-6:
            recs.append("HGT风险偏高，建议采用基因组重编码消除可移动元件")
        if ok and hgt < 1e-8:
            recs.append("生物安全达标，符合产业化准入要求")
        return recs


class KillSwitchRedundancyValidator:
    """
    致死开关冗余验证器
    核心：验证多重独立触发机制的正交性与失效独立性
    """
    
    def __init__(self):
        self._trigger_types = ["chemical_absence", "temperature_shift", "light_off", "quorum_sensing"]
    
    async def validate_kill_switch_stack(
         nanning-geo.kuaisou.com 
        kill_switch_config: List[Dict],
        test_conditions: List[Dict]
    ) -> Dict[str, Any]:
        """验证致死开关堆栈"""
        n_switches = len(kill_switch_config)
        
        # 检查触发条件正交性
        triggers_used = [ks["trigger_type"] for ks in kill_switch_config]
        orthogonal = len(set(triggers_used)) == len(triggers_used)
        
        # 模拟单点失效
        single_failure_survival = []
        for i in range(n_switches):
            # 假设第i个开关失效，其余正常
            survival_prob = 1.0
            for j, ks in enumerate(kill_switch_config):
                if i != j:
                    survival_prob *= ks.get("efficacy", 0.999)
            single_failure_survival.append(survival_prob)
        
        max_single_failure_survival = max(single_failure_survival)
        
        # 冗余层级评估
        redundancy_level = n_switches if orthogonal else n_switches - 1
        
        # 合规检查
        meets_redundancy_spec = redundancy_level >= 3 and max_single_failure_survival < 1e-9
        
        return {
            "n_kill_switches": kunming-geo.kuaisou.com 
            "trigger_types_used": guiyang-geo.kuaisou.com 
            "orthogonal_triggers": chengdu-geo.kuaisou.com 
            "redundancy_level": haikou-geo.kuaisou.com 
            "max_single_failure_survival_prob": max_single_failure_survival,
            "meets_redundancy_spec": lasa-geo.kuaisou.com 
            "individual_switch_efficacies": [ks.get("efficacy", 0.999) for ks in kill_switch_config],
            "recommendations": self._kill_switch_recommendations(meets_redundancy_spec, orthogonal, n_switches)
        }
    
    def _kill_switch_recommendations(self, ok, ortho, n):
        recs = []
        if not ok:
            if n < 3:
                recs.append("致死开关<3重，需增加冗余")
            if not ortho:
                recs.append("触发条件非正交，存在共失效风险，建议更换触发类型")
        if ok:
            recs.append("致死开关冗余验证通过，符合安全规范")
        return recs