https://github.com/jcrozum/biobalm
The biologist's Boolean attractor landscape mapper, building Waddington landscapes from Boolean networks.
https://github.com/jcrozum/biobalm
answer-set-programming attractors biology boolean-networks control-theory discrete-dynamics feedback-vertex-set network-science petri-nets systems-biology waddington-landscape
Last synced: 24 days ago
JSON representation
The biologist's Boolean attractor landscape mapper, building Waddington landscapes from Boolean networks.
- Host: GitHub
- URL: https://github.com/jcrozum/biobalm
- Owner: jcrozum
- License: mit
- Created: 2022-10-11T16:52:00.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2025-08-18T16:28:14.000Z (about 2 months ago)
- Last Synced: 2025-08-18T18:26:13.241Z (about 2 months ago)
- Topics: answer-set-programming, attractors, biology, boolean-networks, control-theory, discrete-dynamics, feedback-vertex-set, network-science, petri-nets, systems-biology, waddington-landscape
- Language: Python
- Homepage: https://jcrozum.github.io/biobalm/
- Size: 69.2 MB
- Stars: 3
- Watchers: 3
- Forks: 1
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
[](https://pypi.org/project/biobalm/)
[](https://jcrozum.github.io/biobalm/)
[](https://github.com/jcrozum/biobalm/actions?query=workflow%3Atest)
[](https://github.com/jcrozum/biobalm/issues)
[](https://github.com/jcrozum/biobalm/blob/main/LICENSE)
# Boolean Attractor Landscape Mapper (`biobalm`)
`biobalm` is a Python library for exploring the attractor landscape of large-scale Boolean networks with hundreds or thousands of variables. It combines symbolic (BDD) and automated (ASP) reasoning to efficiently construct a *succession diagram* of a Boolean network: an inclusion-based acyclic graph of the network's trap spaces. biobalm can then use this succession diagram to accelerate attractor search and infer control strategies for target trap spaces.
> `biobalm` is accompanied by an analysis artefact that benchmarks it against `pystablemotifs`, `mts-nfvs` and `aeon.py`. The artefact also compares the succession diagrams generated by published biological Boolean networks to random network ensembles and finds significant differences in structure. The artefact is available at [Zenodo](https://doi.org/10.5281/zenodo.13854760) and [Github](https://github.com/jcrozum/balm-analysis).
### Installation
`biobalm` is on PyPI:
```
pip install biobalm
```
The base installation should enable all core functionalities (generate succession diagrams and control strategies, find attractor seeds states and attractor sets). Optionally, you can also use `pint` during attractor identification as a static analysis step:
- Native binaries of `pint` can be obtained [here](https://loicpauleve.name/pint/doc/#Binaries).
You can also install the latest version of biobalm directly from github:
```
pip install git+https://github.com/jcrozum/biobalm.git@main
```
### Referencing biobalm
The manuscript introducing and benchmarking `biobalm` is freely available in [*Bioinformatics*](https://doi.org/10.1093/bioinformatics/btaf280).
Please cite the following if you use `biobalm` in your publication:
```
@article{trinhBiobalmMappingAttractorLandscape2025,
title = {Mapping the attractor landscape of {{Boolean}} networks with biobalm},
author = {Trinh, Van-Giang and Park, Kyu Hyong and Pastva, Samuel and Rozum, Jordan C},
year = {2025},
month = may,
journal = {Bioinformatics},
volume = {41},
number = {5},
pages = {btaf280},
issn = {1367-4811},
doi = {10.1093/bioinformatics/btaf280}
}
```
### Using biobalm
To learn more about how biobalm functions, API documentation is available [online](https://jcrozum.github.io/biobalm/). You can also explore the analysis artefact mentioned above for more in-depth examples of biobalm usage.
Usage examples are also available in the `example` directory. First, a simple usage example is provided in Jupyter notebook `example/tutorial.ipynb`. You can also run `python3 example/attractors.py PATH_TO_NETWORK` to compute all attractor seeds of a Boolean network. This scripts demonstrates basic configuration options for the attractor detection process (limits the succession diagram size and shows how to deal with succession diagrams that exceed this node limit).