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Project description
pySBML
pySBML takes SBML models and makes them simpler :heart:
Installation
Installation is as easy as pip install pysbml.
Transform this
Compartments
| name | size | is_constant |
|---|---|---|
| C | 1.0 | True |
Variables
| name | amount | conc | constant | substance_units | compartment | only_substance_units | boundary_condition |
|---|---|---|---|---|---|---|---|
| S1 | None | 0.00015 | False | substance | C | False | False |
| S2 | None | 0.0 | False | substance | C | False | False |
Parameters
| name | value | is_constant | unit |
|---|---|---|---|
| k1 | 1.0 | True |
Reactions
| name | body | args | stoichiometry | local pars |
|---|---|---|---|---|
| reaction1 | C * k1 * S1 | [C, k1, S1] | {'S1': -1.0, 'S2': 1.0} | {} |
Into this
Parameters
| name | value | unit |
|---|---|---|
| k1 | 1.0 | None |
| C | 1.0 | None |
Variables
| name | value | unit |
|---|---|---|
| S1 | 0.00015 | None |
| S2 | 0.0 | None |
Reactions
| name | fn | stoichiometry |
|---|---|---|
| reaction1 | S1*k1 | {'S1': -1.0, 'S2': 1.0} |
Development setup
We recommend using uv
uv
- Install
uvas described in the docs. - Run
uv sync --all-extras --all-groupsto install dependencies locally
Tool family 🏠
pySBML is part of a larger family of tools that are designed with a similar set of abstractions. Check them out!
- MxlPy is a Python package for mechanistic learning (Mxl)
- MxlBricks is built on top of
MxlPyto build mechanistic models composed of pre-defined reactions (bricks) - MxlModels supplies flat, single-file versions of MxlBricks models for easy inspection
- MxlWeb brings simulation of mechanistic models to the browser!
- Parameteriser looks up kinetic parameters from BRENDA and other databases
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