simbio 1.0.0b3

A Python-based package for simulation of Chemical Reaction Networks (CRNs).

simbio

A Python-based package for simulation of Chemical Reaction Networks (CRNs). It extends poincare, a package for modelling dynamical systems, to add functionality for CRNs.

Usage

To create a system with two species $A$ and $B$ and a reaction converting $2A \rightarrow B$ with rate 1:

>>> from simbio import Compartment, Species, RateLaw, initial
>>> class Model(Compartment):
...    A: Species = initial(default=1)
...    B: Species = initial(default=0)
...    r = RateLaw(
...        reactants=[2 * A],
...        products=[B],
...        rate_law=1,
...    )

This corresponds to the following system of equations

$$ \begin{cases} \frac{dA}{dt} = -2 \ \frac{dB}{dt} = +1 \end{cases} $$

with initial conditions

$$ \begin{cases} A(0) = 1 \ B(0) = 0 \end{cases} $$

In CRNs, we usually deal with mass-action reactions. Using MassAction instead of Reaction automatically adds the reactants to the rate law:

>>> from simbio import MassAction
>>> class MassActionModel(Compartment):
...    A: Species = initial(default=1)
...    B: Species = initial(default=0)
...    r = MassAction(
...        reactants=[2 * A],
...        products=[B],
...        rate=1,
...    )

generating the following equations:

$$ \begin{cases} \frac{dA}{dt} = -2 A^2 \ \frac{dB}{dt} = +1 A^2 \end{cases} $$

To simulate the system, use the Simulator.solve which outputs a pandas.DataFrame:

>>> from simbio import Simulator
>>> Simulator(MassActionModel).solve(save_at=range(5))
             A         B
time
0     1.000000  0.000000
1     0.333266  0.333367
2     0.199937  0.400032
3     0.142798  0.428601
4     0.111061  0.444470

For more details into SimBio's capabilities, we recommend reading poincaré's README.

SBML

SimBio can import models from Systems Biology Markup Language (SBML) files:

>>> from simbio.io import sbml
>>> sbml.load("repressilator.sbml")
Elowitz2000 - Repressilator
-----------------------------------------------------------------------------------
type          total  names
----------  -------  --------------------------------------------------------------
variables         6  PX, PY, PZ, X, Y, Z
parameters       17  cell, beta, alpha0, alpha, eff, n, KM, tau_mRNA, tau_prot, ...
equations        12  Reaction1, Reaction2, Reaction3, Reaction4, Reaction5, ...

or download them from the BioModels repository:

>>> from simbio.io import biomodels
>>> biomodels.load("BIOMD12")
Elowitz2000 - Repressilator
-----------------------------------------------------------------------------------
type          total  names
----------  -------  --------------------------------------------------------------
variables         6  PX, PY, PZ, X, Y, Z
parameters       17  cell, beta, alpha0, alpha, eff, n, KM, tau_mRNA, tau_prot, ...
equations        12  Reaction1, Reaction2, Reaction3, Reaction4, Reaction5, ...

Install

Using pixi, install from PyPI with:

pixi add --pypi simbio

or install the latest development version from GitHub with:

pixi add --pypi simbio@https://github.com/dyscolab/simbio.git

Otherwise, use pip or your pip-compatible package manager:

pip install simbio  # from PyPI
pip install git+https://github.com/dyscolab/simbio.git  # from GitHub

Development

This project is managed by pixi. You can install it for development using:

git clone https://github.com/dyscolab/simbio
cd simbio
pixi run pre-commit-install

Pre-commit hooks are used to lint and format the project.

Testing

Run tests using:

pixi run test

Publishing to PyPI

When a tagged commit is pushed to GitHub, the GitHub Action defined in .github/workflows/ci.yml builds and publishes the package to PyPI.

Tag a commit and push the tags with:

git tag <my-tag>
git push --tags

Trusted publishing must be enabled once in PyPI Publishing. Fill the following values in the form:

PyPI Project Name: simbio
            Owner: maurosilber
  Repository name: simbio
    Workflow name: ci.yml
 Environment name: pypi