cobrapy

Name: cobrapy
Author: K-Dense-AI

792installs3.1kforks

How to install cobrapy?

k-dense-ai/scientific-agent-skills/cobrapy

$npx -y skills add K-Dense-AI/scientific-agent-skills --skill cobrapy

Installs into the current project.

Summary

Constraint-based metabolic modeling (COBRA). FBA, FVA, gene knockouts, flux sampling, SBML models, for systems biology and metabolic engineering analysis.

Files · 1

View on GitHub

SKILL.md

1	# COBRApy - Constraint-Based Reconstruction and Analysis
2
3	## Overview
4
5	COBRApy is a Python library for constraint-based reconstruction and analysis (COBRA) of metabolic models, essential for systems biology research. Work with genome-scale metabolic models, perform computational simulations of cellular metabolism, conduct metabolic engineering analyses, and predict phenotypic behaviors.
6
7	Version note: Examples target cobra 0.31.1 on PyPI (import `cobra`). Docs: [cobrapy.readthedocs.io](https://cobrapy.readthedocs.io/en/latest/). Repo: [opencobra/cobrapy](https://github.com/opencobra/cobrapy).
8
9	## When to Use This Skill
10
11	Use this skill when:
12	- Loading, building, or exporting genome-scale metabolic models (SBML, JSON, YAML)
13	- Running FBA, pFBA, FVA, or flux sampling on COBRA models
14	- Performing gene or reaction knockout screens and production envelope analysis
15	- Designing or optimizing growth media and exchange constraints
16	- Gap-filling infeasible models or validating model consistency
17
18	## Installation
19
20	```bash
21	uv pip install "cobra==0.31.1"
22	```
23
24	MATLAB model I/O (optional):
25
26	```bash
27	uv pip install "cobra[array]==0.31.1"
28	```
29
30	COBRApy uses [optlang](https://optlang.readthedocs.io/) for solvers. GLPK installs automatically via `swiglpk`. For large MILPs/QPs, cobra 0.29+ adds a hybrid solver (HIGHS/OSQP); `model.solver = "osqp"` now routes through hybrid and may error on plain LPs in a future release—prefer `model.solver = "hybrid"` when available.
31
32	## Core Capabilities
33
34	COBRApy provides comprehensive tools organized into several key areas:
35
36	### 1. Model Management
37
38	Load existing models from repositories or files:
39	```python
40	from cobra.io import load_model
41
42	# Bundled locally (no network): textbook, iJO1366, salmonella
43	model = load_model("textbook") # alias for e_coli_core (95 reactions)
44	model = load_model("e_coli_core") # same core E. coli model
45	model = load_model("iJO1366") # genome-scale E. coli (bundled)
46	model = load_model("salmonella") # Salmonella iYS1720 (bundled)
47
48	# Remote (BiGG / BioModels; requires network, cached after first fetch)
49	model = load_model("iML1515") # E. coli genome-scale on BiGG
50
51	# Load from files
52	from cobra.io import read_sbml_model, load_json_model, load_yaml_model
53	model = read_sbml_model("path/to/model.xml")
54	model = load_json_model("path/to/model.json")
55	model = load_yaml_model("path/to/model.yml")
56	```
57
58	Save models in various formats:
59	```python
60	from cobra.io import write_sbml_model, save_json_model, save_yaml_model
61	write_sbml_model(model, "output.xml") # Preferred format
62	save_json_model(model, "output.json") # For Escher compatibility
63	save_yaml_model(model, "output.yml") # Human-readable
64	```
65
66	### 2. Model Structure and Components
67
68	Access and inspect model components:
69	```python
70	# Access components
71	model.reactions # DictList of all reactions
72	model.metabolites # DictList of all metabolites
73	model.genes # DictList of all genes
74
75	# Get specific items by ID or index
76	reaction = model.reactions.get_by_id("PFK")
77	metabolite = model.metabolites[0]
78
79	# Inspect properties
80	print(reaction.reaction) # Stoichiometric equation
81	print(reaction.bounds) # Flux constraints
82	print(reaction.gene_reaction_rule) # GPR logic
83	print(metabolite.formula) # Chemical formula
84	print(metabolite.compartment) # Cellular location
85	```
86
87	### 3. Flux Balance Analysis (FBA)
88
89	Perform standard FBA simulation:
90	```python
91	# Basic optimization
92	solution = model.optimize()
93	print(f"Objective value: {solution.objective_value}")
94	print(f"Status: {solution.status}")
95
96	# Access fluxes
97	print(solution.fluxes["PFK"])
98	print(solution.fluxes.head())
99
100	# Fast optimization (objective value only)
101	objective_value = model.slim_optimize()
102
103	# Change objective
104	model.objective = "ATPM"
105	solution = model.optimize()
106	```
107
108	Parsimonious FBA (minimize total flux):
109	```python
110	from cobra.flux_analysis import pfba
111	solution = pfba(model)
112	```
113
114	Geometric FBA (find central solution):
115	```python
116	from cobra.flux_analysis import geometric_fba
117	solution = geometric_fba(model)
118	```
119
120	### 4. Flux Variability Analysis (FVA)
121
122	Determine flux ranges for all reactions:
123	```python
124	from cobra.flux_analysis import flux_variability_analysis
125
126	# Standard FVA
127	fva_result = flux_variability_analysis(model)
128
129	# FVA at 90% optimality
130	fva_result = flux_variability_analysis(model, fraction_of_optimum=0.9)
131
132	# Loopless FVA (eliminates thermodynamically infeasible loops)
133	fva_result = f

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cobrapy

792installs3.1kforks

View on GitHub

How to install cobrapy?

k-dense-ai/scientific-agent-skills/cobrapy

$npx -y skills add K-Dense-AI/scientific-agent-skills --skill cobrapy

Installs into the current project.

Summary

Constraint-based metabolic modeling (COBRA). FBA, FVA, gene knockouts, flux sampling, SBML models, for systems biology and metabolic engineering analysis.

Files · 1

View on GitHub

SKILL.md

1	# COBRApy - Constraint-Based Reconstruction and Analysis
2
3	## Overview
4
5	COBRApy is a Python library for constraint-based reconstruction and analysis (COBRA) of metabolic models, essential for systems biology research. Work with genome-scale metabolic models, perform computational simulations of cellular metabolism, conduct metabolic engineering analyses, and predict phenotypic behaviors.
6
7	Version note: Examples target cobra 0.31.1 on PyPI (import `cobra`). Docs: [cobrapy.readthedocs.io](https://cobrapy.readthedocs.io/en/latest/). Repo: [opencobra/cobrapy](https://github.com/opencobra/cobrapy).
8
9	## When to Use This Skill
10
11	Use this skill when:
12	- Loading, building, or exporting genome-scale metabolic models (SBML, JSON, YAML)
13	- Running FBA, pFBA, FVA, or flux sampling on COBRA models
14	- Performing gene or reaction knockout screens and production envelope analysis
15	- Designing or optimizing growth media and exchange constraints
16	- Gap-filling infeasible models or validating model consistency
17
18	## Installation
19
20	```bash
21	uv pip install "cobra==0.31.1"
22	```
23
24	MATLAB model I/O (optional):
25
26	```bash
27	uv pip install "cobra[array]==0.31.1"
28	```
29
30	COBRApy uses [optlang](https://optlang.readthedocs.io/) for solvers. GLPK installs automatically via `swiglpk`. For large MILPs/QPs, cobra 0.29+ adds a hybrid solver (HIGHS/OSQP); `model.solver = "osqp"` now routes through hybrid and may error on plain LPs in a future release—prefer `model.solver = "hybrid"` when available.
31
32	## Core Capabilities
33
34	COBRApy provides comprehensive tools organized into several key areas:
35
36	### 1. Model Management
37
38	Load existing models from repositories or files:
39	```python
40	from cobra.io import load_model
41
42	# Bundled locally (no network): textbook, iJO1366, salmonella
43	model = load_model("textbook") # alias for e_coli_core (95 reactions)
44	model = load_model("e_coli_core") # same core E. coli model
45	model = load_model("iJO1366") # genome-scale E. coli (bundled)
46	model = load_model("salmonella") # Salmonella iYS1720 (bundled)
47
48	# Remote (BiGG / BioModels; requires network, cached after first fetch)
49	model = load_model("iML1515") # E. coli genome-scale on BiGG
50
51	# Load from files
52	from cobra.io import read_sbml_model, load_json_model, load_yaml_model
53	model = read_sbml_model("path/to/model.xml")
54	model = load_json_model("path/to/model.json")
55	model = load_yaml_model("path/to/model.yml")
56	```
57
58	Save models in various formats:
59	```python
60	from cobra.io import write_sbml_model, save_json_model, save_yaml_model
61	write_sbml_model(model, "output.xml") # Preferred format
62	save_json_model(model, "output.json") # For Escher compatibility
63	save_yaml_model(model, "output.yml") # Human-readable
64	```
65
66	### 2. Model Structure and Components
67
68	Access and inspect model components:
69	```python
70	# Access components
71	model.reactions # DictList of all reactions
72	model.metabolites # DictList of all metabolites
73	model.genes # DictList of all genes
74
75	# Get specific items by ID or index
76	reaction = model.reactions.get_by_id("PFK")
77	metabolite = model.metabolites[0]
78
79	# Inspect properties
80	print(reaction.reaction) # Stoichiometric equation
81	print(reaction.bounds) # Flux constraints
82	print(reaction.gene_reaction_rule) # GPR logic
83	print(metabolite.formula) # Chemical formula
84	print(metabolite.compartment) # Cellular location
85	```
86
87	### 3. Flux Balance Analysis (FBA)
88
89	Perform standard FBA simulation:
90	```python
91	# Basic optimization
92	solution = model.optimize()
93	print(f"Objective value: {solution.objective_value}")
94	print(f"Status: {solution.status}")
95
96	# Access fluxes
97	print(solution.fluxes["PFK"])
98	print(solution.fluxes.head())
99
100	# Fast optimization (objective value only)
101	objective_value = model.slim_optimize()
102
103	# Change objective
104	model.objective = "ATPM"
105	solution = model.optimize()
106	```
107
108	Parsimonious FBA (minimize total flux):
109	```python
110	from cobra.flux_analysis import pfba
111	solution = pfba(model)
112	```
113
114	Geometric FBA (find central solution):
115	```python
116	from cobra.flux_analysis import geometric_fba
117	solution = geometric_fba(model)
118	```
119
120	### 4. Flux Variability Analysis (FVA)
121
122	Determine flux ranges for all reactions:
123	```python
124	from cobra.flux_analysis import flux_variability_analysis
125
126	# Standard FVA
127	fva_result = flux_variability_analysis(model)
128
129	# FVA at 90% optimality
130	fva_result = flux_variability_analysis(model, fraction_of_optimum=0.9)
131
132	# Loopless FVA (eliminates thermodynamically infeasible loops)
133	fva_result = f

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