alterlab-cobrapy

COBRApy - 基于约束的重建与分析

概述

COBRApy 是一个用于代谢模型的基于约束的重建与分析（COBRA）的 Python 库，是系统生物学研究的重要工具。使用全基因组规模代谢模型、进行细胞代谢计算模拟、开展代谢工程分析，并预测表型行为。

何时使用此技能

当用户想要执行以下操作时，使用此技能：

• 加载全基因组规模代谢模型（SBML/JSON/YAML），并使用 FBA/pFBA 模拟生长。
• 预测敲除表型、必需基因或最小培养基。
• 使用 FVA、通量采样或产量包络探索通量空间。
• 构建、补缺或整理基于约束的模型。

不适用场景

场景	改用
查找通路或反应注释，而非进行模拟	alterlab-kegg、alterlab-reactome
查询反应的酶动力学参数（kcat、Km）	alterlab-brenda
分析代谢网络图结构（中心性、社群），而非通量	alterlab-networkx
代谢组学测量数据及其鉴定	alterlab-metabolomics-wb、alterlab-hmdb
非通量平衡问题的多目标优化	alterlab-pymoo

安装与要求

uv pip install cobra              # 0.32.x as of 2026-09
uv pip install 'cobra[array]'     # scipy extra, for array-based model handling
uv pip install 'cobra[chrr]'      # hopsy, enables the fast CHRR sampler

要求： 依赖 optlang 和求解器。默认通过 swiglpk 附带 GLPK；CPLEX 和 Gurobi 为可选项，在全基因组规模模型上的速度快得多。pandas 是硬性依赖（版本固定为 < 3）；matplotlib/seaborn 仅用于绘图。通量采样和并行敲除使用 multiprocessing，因此在 Windows/macOS spawn 模式下，脚本需使用 if __name__ == "__main__": 保护。

内置模型名称。 load_model 仅识别三个内置别名："textbook"（E. coli 核心模型 e_coli_core）、"iJO1366"（完整的 E. coli 全基因组规模模型）和 "salmonella"。任何其他标识符（例如 "ecoli"、"e_coli_core"）都不是内置别名，会触发对 BiGG/BioModels 的远程查询，因此需要网络连接且可能失败。对于自己的模型，请使用 read_sbml_model("path.xml")。

核心功能

COBRApy 提供全面的工具，涵盖多个关键领域：

1. 模型管理

从存储库或文件加载现有模型：

from cobra.io import load_model

# Load bundled test models
model = load_model("textbook")  # E. coli core model
model = load_model("iJO1366")   # Full E. coli genome-scale model (BiGG)
model = load_model("salmonella")

# Load from files
from cobra.io import read_sbml_model, load_json_model, load_yaml_model
model = read_sbml_model("path/to/model.xml")
model = load_json_model("path/to/model.json")
model = load_yaml_model("path/to/model.yml")

以多种格式保存模型：

from cobra.io import write_sbml_model, save_json_model, save_yaml_model
write_sbml_model(model, "output.xml")  # Preferred format
save_json_model(model, "output.json")  # For Escher compatibility
save_yaml_model(model, "output.yml")   # Human-readable

2. 模型结构与组件

访问并检查模型组件：

# Access components
model.reactions      # DictList of all reactions
model.metabolites    # DictList of all metabolites
model.genes          # DictList of all genes

# Get specific items by ID or index
reaction = model.reactions.get_by_id("PFK")
metabolite = model.metabolites[0]

# Inspect properties
print(reaction.reaction)        # Stoichiometric equation
print(reaction.bounds)          # Flux constraints
print(reaction.gene_reaction_rule)  # GPR logic
print(metabolite.formula)       # Chemical formula
print(metabolite.compartment)   # Cellular location

3. 通量平衡分析（FBA）

执行标准 FBA 模拟：

# Basic optimization
solution = model.optimize()
print(f"Objective value: {solution.objective_value}")
print(f"Status: {solution.status}")

# Access fluxes
print(solution.fluxes["PFK"])
print(solution.fluxes.head())

# Fast optimization (objective value only)
objective_value = model.slim_optimize()

# Change objective
model.objective = "ATPM"
solution = model.optimize()

简约 FBA（最小化总通量）：

from cobra.flux_analysis import pfba
solution = pfba(model)

几何 FBA（寻找中心解）：

from cobra.flux_analysis import geometric_fba
solution = geometric_fba(model)

4. 通量变异性分析（FVA）

确定所有反应的通量范围：

from cobra.flux_analysis import flux_variability_analysis

# Standard FVA
fva_result = flux_variability_analysis(model)

# FVA at 90% optimality
fva_result = flux_variability_analysis(model, fraction_of_optimum=0.9)

# Loopless FVA (eliminates thermodynamically infeasible loops)
fva_result = flux_variability_analysis(model, loopless=True)

# FVA for specific reactions
fva_result = flux_variability_analysis(
    model,
    reaction_list=["PFK", "FBA", "PGI"]
)

5. 基因与反应敲除研究

执行敲除分析：

from cobra.flux_analysis import (
    single_gene_deletion,
    single_reaction_deletion,
    double_gene_deletion,
    double_reaction_deletion
)

# Single deletions
gene_results = single_gene_deletion(model)
reaction_results = single_reaction_deletion(model)

# Double deletions (uses multiprocessing)
double_gene_results = double_gene_deletion(
    model,
    processes=4  # Number of CPU cores
)

# Manual knockout using context manager
with model:
    model.genes.get_by_id("b0008").knock_out()
    solution = model.optimize()
    print(f"Growth after knockout: {solution.objective_value}")
# Model automatically reverts after context exit

6. 生长培养基与最小培养基

管理生长培养基：

# View current medium
print(model.medium)

# Modify medium (must reassign entire dict)
medium = model.medium
medium["EX_glc__D_e"] = 10.0  # Set glucose uptake
medium["EX_o2_e"] = 0.0       # Anaerobic conditions
model.medium = medium

# Calculate minimal media
from cobra.medium import minimal_medium

# Minimize total import flux
min_medium = minimal_medium(model, minimize_components=False)

# Minimize number of components (uses MILP, slower)
min_medium = minimal_medium(
    model,
    minimize_components=True,
    open_exchanges=True
)

7. 通量采样

对可行通量空间进行采样：

from cobra.sampling import sample

# method defaults to "auto" in current cobrapy; the three options are:
#   "chrr"  - coordinate hit-and-run with rounding, via the C++ hopsy library
#             (install with cobra[chrr]); usually the fastest and best-mixing
#   "optgp" - parallel, wants large n (>1000) to be efficient
#   "achr"  - artificial centering hit-and-run, better for small n
samples = sample(model, n=1000)                              # auto
samples = sample(model, n=1000, method="optgp", processes=4)  # explicit OptGP
samples = sample(model, n=1000, method="achr")

# Validate samples
from cobra.sampling import OptGPSampler
sampler = OptGPSampler(model, processes=4)
sampler.sample(1000)
validation = sampler.validate(sampler.samples)
print(validation.value_counts())  # Should be all 'v' for valid

8. 产量包络

计算表型相平面：

from cobra.flux_analysis import production_envelope

# Standard production envelope
envelope = production_envelope(
    model,
    reactions=["EX_glc__D_e", "EX_o2_e"],
    objective="EX_ac_e"  # Acetate production
)

# With carbon yield
envelope = production_envelope(
    model,
    reactions=["EX_glc__D_e", "EX_o2_e"],
    carbon_sources="EX_glc__D_e"
)

# Visualize (use matplotlib or pandas plotting)
import matplotlib.pyplot as plt
envelope.plot(x="EX_glc__D_e", y="EX_o2_e", kind="scatter")
plt.show()

9. 缺口填补

添加反应以使模型变得可行：

import cobra
from cobra.flux_analysis import gapfill

# Build a universal model of candidate reactions to draw from.
# In practice, load a curated reaction database (e.g. the BiGG
# universal reactions JSON via load_json_model) or assemble reactions
# into an empty cobra.Model — there is no load_model("universal").
universal = cobra.Model("universal_reactions")
# ...populate `universal` with candidate cobra.Reaction objects...

with model:
    # Create a gap for demonstration
    model.remove_reactions([model.reactions.PGI])

    # Find the minimal set of reactions from `universal` to restore feasibility
    solution = gapfill(model, universal, demand_reactions=False)
    for reaction in solution[0]:
        print(reaction.id)

注意：gapfill 返回一个反应列表组成的列表（当 iterations > 1 时，每次迭代返回一个列表），因此应遍历 solution[0]，而不是直接打印 solution。

10. 模型构建

从头构建模型：

from cobra import Model, Reaction, Metabolite

# Create model
model = Model("my_model")

# Create metabolites
atp_c = Metabolite("atp_c", formula="C10H12N5O13P3",
                   name="ATP", compartment="c")
adp_c = Metabolite("adp_c", formula="C10H12N5O10P2",
                   name="ADP", compartment="c")
pi_c = Metabolite("pi_c", formula="HO4P",
                  name="Phosphate", compartment="c")

# Create reaction
reaction = Reaction("ATPASE")
reaction.name = "ATP hydrolysis"
reaction.subsystem = "Energy"
reaction.lower_bound = 0.0
reaction.upper_bound = 1000.0

# Add metabolites with stoichiometry
reaction.add_metabolites({
    atp_c: -1.0,
    adp_c: 1.0,
    pi_c: 1.0
})

# Add gene-reaction rule
reaction.gene_reaction_rule = "(gene1 and gene2) or gene3"

# Add to model
model.add_reactions([reaction])

# Add boundary reactions
model.add_boundary(atp_c, type="exchange")
model.add_boundary(adp_c, type="demand")

# Set objective
model.objective = "ATPASE"

常见工作流

工作流 1：加载模型并预测生长

from cobra.io import load_model

# Load model
model = load_model("iJO1366")

# Run FBA
solution = model.optimize()
print(f"Growth rate: {solution.objective_value:.3f} /h")

# Show active pathways
print(solution.fluxes[solution.fluxes.abs() > 1e-6])

工作流 2：基因敲除筛选

from cobra.io import load_model
from cobra.flux_analysis import single_gene_deletion

# Load model
model = load_model("iJO1366")

# Perform single gene deletions
results = single_gene_deletion(model)

# Find essential genes (growth < threshold)
essential_genes = results[results["growth"] < 0.01]
print(f"Found {len(essential_genes)} essential genes")

# Find genes with minimal impact
neutral_genes = results[results["growth"] > 0.9 * solution.objective_value]

工作流 3：培养基优化

from cobra.io import load_model
from cobra.medium import minimal_medium

# Load model
model = load_model("iJO1366")

# Calculate minimal medium for 50% of max growth
target_growth = model.slim_optimize() * 0.5
min_medium = minimal_medium(
    model,
    target_growth,
    minimize_components=True
)

print(f"Minimal medium components: {len(min_medium)}")
print(min_medium)

工作流 4：通量不确定性分析

from cobra.io import load_model
from cobra.flux_analysis import flux_variability_analysis
from cobra.sampling import sample

# Load model
model = load_model("iJO1366")

# First check flux ranges at optimality
fva = flux_variability_analysis(model, fraction_of_optimum=1.0)

# For reactions with large ranges, sample to understand distribution
samples = sample(model, n=1000)

# Analyze specific reaction
reaction_id = "PFK"
import matplotlib.pyplot as plt
samples[reaction_id].hist(bins=50)
plt.xlabel(f"Flux through {reaction_id}")
plt.ylabel("Frequency")
plt.show()

工作流 5：临时变更的上下文管理器

使用上下文管理器进行临时修改：

# Model remains unchanged outside context
with model:
    # Temporarily change objective
    model.objective = "ATPM"

    # Temporarily modify bounds
    model.reactions.EX_glc__D_e.lower_bound = -5.0

    # Temporarily knock out genes
    model.genes.b0008.knock_out()

    # Optimize with changes
    solution = model.optimize()
    print(f"Modified growth: {solution.objective_value}")

# All changes automatically reverted
solution = model.optimize()
print(f"Original growth: {solution.objective_value}")

核心概念

DictList 对象

模型使用 DictList 对象来存储反应、代谢物和基因，其行为兼具列表和字典的特性：

# Access by index
first_reaction = model.reactions[0]

# Access by ID
pfk = model.reactions.get_by_id("PFK")

# Query methods
atp_reactions = model.reactions.query("atp")

通量约束

反应边界定义可行的通量范围：

• 不可逆：lower_bound = 0, upper_bound > 0
• 可逆：lower_bound < 0, upper_bound > 0
• 使用 .bounds 同时设置两个边界，以避免不一致

基因-反应规则（GPR）

将基因与反应关联起来的布尔逻辑：

# AND logic (both required)
reaction.gene_reaction_rule = "gene1 and gene2"

# OR logic (either sufficient)
reaction.gene_reaction_rule = "gene1 or gene2"

# Complex logic
reaction.gene_reaction_rule = "(gene1 and gene2) or (gene3 and gene4)"

交换反应

表示代谢物输入/输出的特殊反应：

• 按惯例以 EX_ 为前缀命名
• 正通量 = 分泌，负通量 = 摄取
• 通过 model.medium 字典管理

最佳实践

1. 使用上下文管理器进行临时修改，以避免状态管理问题
2. 在分析前验证模型，使用 model.slim_optimize() 确保可行
3. 检查优化后的求解状态——optimal 表示求解成功
4. 在热力学可行性很重要时使用无环 FVA
5. 在 FVA 中适当设置 fraction_of_optimum，以探索次优空间
6. 并行处理计算开销较大的操作（采样、双重敲除）
7. 优先使用 SBML 格式进行模型交换和长期存储
8. 仅需目标值时使用 slim_optimize()，以提升性能
9. 验证通量样本，确保数值稳定性

故障排除

无可行解：检查培养基约束、反应边界和模型一致性
优化速度慢：通过 model.solver 尝试不同的求解器（GLPK、CPLEX、Gurobi）
解无界：确认交换反应具有适当的上界
导入错误：确保文件格式正确且 SBML 标识符有效

参考资料

如需了解详细工作流和 API 模式，请参阅：

• references/workflows.md - 全面的分步工作流示例
• references/api_quick_reference.md - 常用函数签名和模式

官方文档：https://cobrapy.readthedocs.io/en/latest/

属于 AlterLab Academic Skills 套件。