Installation and basic usage

Local installation

BoNesis requires Python ≥ 3.9.

Installation using pip

pip install bonesis

Installation Using conda

conda install -c potassco -c colomoto bonesis

Docker and online versions

BoNesis is shipped with the CoLoMoTo Docker distribution, which provides a pre-installed Jupyter notebook environment together with many tools related to modeling, simulation, and analysis of Boolean networks. See prior link for usage instructions.

You can try BoNesis without any installation on https://mybinder.org/v2/gh/colomoto/colomoto-docker/mybinder/latest, thanks to myBinder services. Note that the computing resources are limited and the storage is not persistent.

Main usage

BoNesis is primarily a Python module, named bonesis, intended to be used in scripts and notebooks.

For a first glance at BoNesis features, see Feature tour of BoNesis and Overview.

Command-line tools

Alongside the Python API, the following command-line tools are provided. Use --help option for usage detail.

bonesis-reprogramming

Marker reprogramming of minimal trap spaces (attractors of most permissive dynamics).

Example. identifying permanent reprogramming strategies involving at most 3 combined mutations to enforce that all attractors (minimal trap spaces) have components PhA fixed to 1 and PhB fixed to 0.

$ bonesis-reprogramming model.bnet '{"PhA": 1, "PhB": 0}' 3

See bonesis-reprogramming --help for complete documentation.

bonesis-attractors

Listing of fixed points and minimal trap spaces from ensembles of Boolean networks: a configuration or subspace is outputted if there exists at least one Boolean network in the ensemble for which it is a minimal trap space.

Example

$ bonesis-attractors partial_bn.aeon

See bonesis-attractors --help for complete documentation.

Note

For enumerating fixed points or minimal trap spaces of a single Boolean network, it is much more efficient to use mpbn.