#networking #boolean #control #parametrized #experiment #models #controlling

bin+lib biodivine-pbn-control

A library for controlling parametrized Boolean networks

4 releases (2 breaking)

0.3.1 Dec 19, 2023
0.2.1 Feb 22, 2023
0.2.0 Nov 20, 2022
0.1.1 Feb 17, 2022
0.1.0 Feb 13, 2022

#455 in Network programming

MIT license

195KB
3.5K SLoC

Biodivine library for control of parametrised (partially specified) Boolean networks

A library to solve one-step, temporary and permanent source-target control of parametrised (partially-specified) Boolean networks.

The directory structure:

.
├── auxiliary_scripts    # Scripts to do & process experiments
├── models               # Base experimental models
├── results              # Raw measured results from experiments
├── results_simple       # Raw measured results from experiments for simplified phenotype control procedure
└── src                  # Library source code

Auxiliary scripts

  • analyse_results.py - A script showing quick statistics about the obtained experiment results
  • networks_sampler.py - A script generating partially-specified samples of witness models
  • plot_results.ipynb - A Jupyter notebook for visualization of the experiment results
  • run_groups.py - A script for obtaining the experiment results, running the methods from library on the generated methods. Allows timeout specification.

Models

Base models for testing the library. Contains witness models from CellCollective platform and some parametrised version of the models.

Results

The raw unprocessed outputs of experiments for both performance comparison and robustness metric of one-step/temporary/permantent source-traget control.

Results

The raw unprocessed outputs of experiments for phenotype control.

src

Source code of the library. Consists of following rust modules:

aeon module

Operations to perform base state-transition graph manipulations.

control

Implementations of control algorithm on the perturbable graph.

phenotype_control

Implementations of phenotype control algorithm on the perturbable graph.

perturbation

Data structure representing state transition graph of Boolean network which is viable for perturbations.

Dependencies

~12MB
~199K SLoC