Convert Python ODE systems to SBML (Level 3 Version 2) and PEtab bundles
Project description
Convert Python ODE systems to SBML (Level 3 Version 2) and PEtab bundles.
ode2sbml is a Python package that converts ordinary differential equation (ODE) systems
written in Python into valid SBML Level 3 Version 2
files and optionally PEtab bundles for parameter estimation.
Installation
pip install ode2sbml
Or install from source:
git clone https://github.com/bibymaths/ode2sbml.git
cd ode2sbml
pip install -e .
Dependencies (installed automatically):
python-libsbml >= 5.20sympy >= 1.12sbmlmath >= 0.3petab >= 0.4click >= 8.0rich >= 13.0numpy >= 1.24pydantic >= 2.0
Quick Start
Convention A — scipy-style function
from ode2sbml import from_function, to_sbml
def lotka_volterra(t, y, p):
x, y_val = y
alpha, beta, delta, gamma = p
dx = alpha * x - beta * x * y_val
dy = -gamma * y_val + delta * x * y_val
return [dx, dy]
model = from_function(
func=lotka_volterra,
state_vars=["x", "y"],
param_names=["alpha", "beta", "delta", "gamma"],
initial_conditions={"x": 10.0, "y": 5.0},
parameter_values={"alpha": 1.1, "beta": 0.4, "delta": 0.1, "gamma": 0.4},
model_id="lotka_volterra",
model_name="Lotka-Volterra",
)
to_sbml(model, "lotka_volterra.xml")
Convention B — dict-of-formulas
from ode2sbml import from_dict, to_sbml
model_spec = {
"species": {"S": 990.0, "I": 10.0, "R": 0.0},
"parameters": {"beta": 0.3, "gamma": 0.1},
"odes": {
"S": "-beta*S*I",
"I": "beta*S*I - gamma*I",
"R": "gamma*I",
},
"compartments": {"host": 1.0},
}
model = from_dict(model_spec, model_id="sir", model_name="SIR Epidemic Model")
to_sbml(model, "sir_model.xml")
Convention C — SymPy symbolic system
import sympy as sp
from ode2sbml import from_sympy, to_sbml
x, y, z = sp.symbols("x y z")
sigma, rho, b = sp.symbols("sigma rho b")
odes = {
x: sigma * (y - x),
y: x * (rho - z) - y,
z: x * y - b * z,
}
params = {sigma: 10.0, rho: 28.0, b: 8.0 / 3.0}
inits = {x: 0.0, y: 1.0, z: 0.0}
model = from_sympy(odes=odes, params=params, inits=inits,
model_id="lorenz", model_name="Lorenz System")
to_sbml(model, "lorenz.xml")
PEtab Export
from ode2sbml import from_dict, to_petab
model = from_dict(model_spec, model_id="sir", model_name="SIR")
yaml_path = to_petab(model, outdir="petab_bundle/", observables={
"obs_I": {"formula": "I", "noise_formula": "0.1 * I"},
})
print(f"PEtab bundle written. Manifest: {yaml_path}")
CLI
# Convert a Python model file to SBML
ode2sbml convert model.py --output model.xml --format sbml
# Convert to PEtab bundle
ode2sbml convert model.py --output ./bundle/ --format petab
# Validate an SBML file
ode2sbml validate model.xml
Supported SBML Features
| Feature | Supported |
|---|---|
| SBML Level 3 Version 2 | ✅ |
| Compartments | ✅ |
| Species (state variables) | ✅ |
| Parameters | ✅ |
| Rate rules (ODEs) | ✅ |
| Assignment rules | ✅ |
| Events (with triggers) | ✅ |
| Unit definitions | ✅ |
| Model notes | ✅ |
| MathML via sbmlmath | ✅ |
| libsbml consistency check | ✅ |
| PEtab bundle export | ✅ |
| JSON serialization (IR) | ✅ |
| Reactions / stoichiometry | ❌ (use RateRules) |
| Spatial SBML | ❌ |
API Reference
Parsers
from_function(func, state_vars, param_names, initial_conditions, parameter_values, ...)— Convention Afrom_dict(model_dict, ...)— Convention Bfrom_sympy(odes, params, inits, ...)— Convention C
Exporters
to_sbml(model, filepath)— Write SBML Level 3 Version 2 XMLto_petab(model, outdir, observables, ...)— Write PEtab bundle
Validators
validate_sbml_file(filepath)— Validate from filevalidate_sbml_string(xml_str)— Validate from stringvalidate_sbml_doc(doc)— Validate libsbml document
IR Classes
ODEModel,Compartment,Species,Parameter,RateRule,AssignmentRule,EventTrigger
References
- Hucka M. et al. (2003). The systems biology markup language (SBML). Bioinformatics.
- Schmiester L. et al. (2021). PEtab. PLOS Computational Biology.
- Müller W. et al. (2021). yaml2sbml. JOSS.
- Watanabe L. et al. (2019). SBMLtoODEpy. JOSS.
- SBML Level 3 Version 2 Specification
- libsbml Python API
- sbmlmath documentation
- PEtab documentation
License
MIT — see LICENSE.
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