pySBML

pySBML takes SBML models and makes them simpler ❤️

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 uv as described in the docs.
• Run uv sync --all-extras --all-groups to 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 MxlPy to 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