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Build or load a genome-scale metabolic model (GSMM) using COBRApy. Covers loading from BIGG, constructing minimal models from scratch, setting medium constraints, and exporting validated .json model files.
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/aiming-lab/AutoResearchClaw --skill gsmm-builder명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
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Run Flux Balance Analysis (FBA) and related constraint-based simulations using COBRApy. Covers standard FBA, parsimonious FBA (pFBA), Flux Variability Analysis (FVA), loopless FBA, gene/reaction knockouts, and carbon source swapping. Outputs flux distributions and CSV files.
Analyse FBA flux distributions to extract biological insights. Covers gene essentiality, phenotypic phase planes, flux sampling, pathway-level aggregation, secretion product prediction, and production of publication- quality figures.
Validate a COBRApy genome-scale metabolic model for mass/charge balance, stoichiometric consistency, biomass producibility, dead-end metabolites, thermodynamic loops, and GPR rule formatting. Outputs a structured validation report with errors and warnings.
SOC 직업 분류 기준
SKILL.md 표시 중
| name | gsmm-builder |
| description | Build or load a genome-scale metabolic model (GSMM) using COBRApy. Covers loading from BIGG, constructing minimal models from scratch, setting medium constraints, and exporting validated .json model files. |
| metadata | {"category":"domain","trigger-keywords":"metabolic,metabolism,GSMM,COBRApy,COBRA,BIGG,genome-scale model,stoichiometric model,model loading,medium constraints","applicable-stages":"9,10,11,12,13","priority":"2"} |
The gsmm-builder skill constructs or loads genome-scale metabolic models
(GSMMs) in the COBRApy framework. It is the entry point for every metabolic
flux analysis pipeline. Output is a validated COBRApy Model object
serialized to a JSON file ready for downstream FBA and flux analysis.
GSMMs encode every known metabolic reaction in an organism as a stoichiometric matrix. Constraints (reaction bounds, medium composition, objective function) turn the model into a solvable linear program.
Option A: Load a curated BIGG model
import cobra
import cobra.io
# Load E. coli iJO1366 from a local SBML file
model = cobra.io.read_sbml_model("iJO1366.xml")
# Or load from a pre-downloaded JSON file
model = cobra.io.load_json_model("iJO1366.json")
print(f"Loaded {model.id}: reactions, "
)
Key BIGG model IDs:
iJO1366 — E. coli K-12 MG1655 (2583 reactions)Recon3D — Homo sapiens (13543 reactions)iMM904 — S. cerevisiae (1577 reactions)iNJ661 — M. tuberculosis (1049 reactions)Option B: Build a minimal model from scratch
from cobra import Model, Metabolite, Reaction
model = Model("toy_glycolysis")
# Define metabolites with compartments and formula
glc_e = Metabolite("glc__D_e", formula="C6H12O6", name="D-Glucose",
compartment="e")
glc_c = Metabolite("glc__D_c", formula="C6H12O6", name="D-Glucose",
compartment="c")
atp_c = Metabolite("atp_c", formula="C10H12N5O13P3", name="ATP",
compartment="c")
biomass = Metabolite("biomass", formula="", name="Biomass", compartment="c")
# Build reactions
ex_glc = Reaction("EX_glc__D_e")
ex_glc.lower_bound = -10.0 # uptake (negative = import)
ex_glc.upper_bound = 0.0
ex_glc.add_metabolites({glc_e: 1.0})
transport = Reaction("GLCt")
transport.lower_bound = -1000.0
transport.upper_bound = 1000.0
transport.add_metabolites({glc_e: -1.0, glc_c: 1.0})
# Stoichiometry: 1 glucose + ADP -> 2 ATP (simplified glycolysis)
glycolysis = Reaction("GLYCOLYSIS")
glycolysis.lower_bound = 0.0
glycolysis.upper_bound = 1000.0
glycolysis.add_metabolites({glc_c: -1.0, atp_c: 2.0})
biomass_rxn = Reaction("BIOMASS")
biomass_rxn.lower_bound = 0.0
biomass_rxn.upper_bound = 1000.0
biomass_rxn.add_metabolites({atp_c: -10.0, biomass: 1.0})
model.add_reactions([ex_glc, transport, glycolysis, biomass_rxn])
# Set biomass as the optimization target
model.objective = "BIOMASS_Ec_iJO1366_core_53p95M" # reaction ID string
# Verify objective is set
print(model.objective.to_json())
# M9 minimal medium with glucose (aerobic)
M9_MEDIUM = {
"EX_glc__D_e": -10.0, # glucose uptake, mmol/gDW/h
"EX_o2_e": -20.0, # oxygen (aerobic)
"EX_nh4_e": -1000.0, # ammonium (unlimited)
"EX_pi_e": -1000.0, # phosphate (unlimited)
"EX_so4_e": -1000.0, # sulfate (unlimited)
"EX_h2o_e": -1000.0, # water (unlimited)
"EX_h_e": -1000.0, # protons (unlimited)
}
# Apply medium: close all exchange reactions first, then open selected
medium = model.medium # returns dict of current open exchange lb magnitudes
for rxn_id, lb in M9_MEDIUM.items():
if rxn_id in model.reactions:
model.reactions.get_by_id(rxn_id).lower_bound = lb
# Anaerobic: set O2 uptake to zero
# model.reactions.get_by_id("EX_o2_e").lower_bound = 0.0
import cobra.io
cobra.io.save_json_model(model, "output/my_model.json")
cobra.io.write_sbml_model(model, "output/my_model.xml")
print("Model saved.")
| Convention | Detail |
|---|---|
| Metabolite ID format | <bigg_id>_<compartment> e.g. glc__D_c, atp_m |
| Compartment codes | c cytosol, e extracellular, m mitochondria, n nucleus |
| Exchange reaction prefix | EX_ e.g. EX_glc__D_e |
| Transport reaction prefix | t or species-specific e.g. GLCt, PGI |
| Uptake bound sign | Negative lower bound = import (e.g. lb = -10) |
| Secretion bound sign | Positive upper bound = export (e.g. ub = 1000) |
| Biomass objective | Reaction with ID containing BIOMASS or Growth |
| Aerobic medium | EX_o2_e lb = -20 (mmol/gDW/h) |
| Anaerobic medium | EX_o2_e lb = 0 |
| Default irreversible rxn | lb = 0, ub = 1000 |
| Default reversible rxn | lb = -1000, ub = 1000 |
# Download model directly from BIGG REST API
curl -O "http://bigg.ucsd.edu/static/models/iJO1366.json"
curl -O "http://bigg.ucsd.edu/static/models/Recon3D.json"
model.optimize() and check solution.status == "infeasible".biomass_rxn.lower_bound = 0.gsmm-validator before FBA.