Booleannet - Boolean network simulations

booleannet is a training tool that makes use of existing Boolean network models, methods and algorithms.

It installs the bnet command line tool that implements a number of subcommands.

Additional help and documentation at: https://www.booleannet.com/

The software was first published as Boolean network simulations for life scientists by István Albert, Juilee Thakar, Song Li, Ranran Zhang, and Réka Albert in Source Code for Biology and Medicine (2008). The field has moved on and developed quite a bit since the paper. The booleannet package is now a tool better suited for learning and exploring Boolean networks.

Environment setup

booleannet works with many libraries, but not everyone needs all of them, so optional dependencies are not installed automatically. We recommend pixi as a virtual environment manager. Here is a minimal example of how to set up a pixi environment:

pixi init
pixi add python=3.12 pip graphviz
pixi run pip install git+https://github.com/hklarner/pyboolnet@3.0.16
pixi shell

Your environment is now set up with initial dependencies to use booleannet.

Install booleannet

Inside the environment, install booleannet with:

pip install --upgrade booleannet

Run bnet with no arguments to see the available subcommands:

Usage: bnet [OPTIONS] COMMAND [ARGS]...

  BooleanNet command line tools.

Options:
  --help  Show this message and exit.

Commands:
  graphviz  Generates a Graphviz graph from a model.
  models    List model summaries, or print one model in the chosen format.
  simulate  Run a synchronous or asynchronous simulation.

Add --help to any subcommand to see all available options.

models: manage known models

The bnet models subcommand operates on models from the Biodivine Boolean Models (BBM) Benchmark Dataset.

# List all models
bnet models | head

prints models by increasing number of variables:

id   name                                                   var    in    reg
165  EGGSHELL-PATTERNING-PHENOMOENOLOGICAL                    4     4     16
170  DROSOPHILA-GAP-B                                         4     3     15
007  CORTICAL-AREA-DEVELOPMENT                                5     0     14
109  ASYMMETRIC-CELL-DIVISION-A                               5     0     15
169  DROSOPHILA-GAP-A                                         5     2     17
171  DROSOPHILA-GAP-C                                         5     2     20
172  DROSOPHILA-GAP-D                                         5     2     12
184  P53-MDM2-NETWORK                                         5     1     15
189  TRP-BIOSYNTHESIS                                         5     1     13
...

To get the rules for a specific model:

# Get rules for model 7
bnet models 7

prints:

Coup_fti* = not (Fgf8 or Sp8) or not (Sp8 or Fgf8)
Emx2* = Coup_fti and not (Fgf8 or Sp8 or Pax6)
Fgf8* = Fgf8 and Sp8 and not Emx2
Pax6* = Sp8 and not (Emx2 or Coup_fti)
Sp8* = Fgf8 and not Emx2

You can also get the rules for a model by name:

# Get rules for model by name
bnet models CORTICAL-AREA-DEVELOPMENT

Get the rules in other formats:

# Get model 7 in BNet format
bnet models 7 -f bnet

simulate: run a model

Suppose your model.txt contains:

B* = A or C
C* = A and not D
D* = B and C

Then you can run the model with:

# Rules from a file. Sets initial state to A and B (random). Runs for 5 steps.
bnet simulate model.txt A=1 B=? -n 5

On row per iteration, and one column per node. The first row is the initial state. 1 is on, . is off. It sets a random initial state for any node that is not set explicitly.

# Random initial state for C, D
A B C D
1 1 . 1
1 1 . .
1 1 1 .
1 1 1 1
1 1 . 1
1 1 . .

You can send a file via stdin and pipe into the simulation:

bnet models CORTICAL-AREA-DEVELOPMENT | bnet simulate Pax6=0 Emx2=1 Fgf8=1 Sp8=0 Coup_fti=1 -n 4

prints:

Coup_fti Emx2 Fgf8 Pax6 Sp8
1 1 1 . .
. . . . .
1 . . . .
1 1 . . .
1 1 . . .

The default mode is sync. The -m async option uses random order asynchronous updates. You can pass the initial conditions from a file with the --init option.

graphviz: visualize a model

# If you have a model in a file
bnet graphviz -i model.txt

# You can pipe the rule to graphviz
bnet models CORTICAL-AREA-DEVELOPMENT | bnet graphviz

Convert BBMB to JSON

This is used internally to transform the BBMB model repository to a single JSON file.

Skips a few large models that make the file too large.

python booleannet/bbm2json.py \
       --summary ~/src/biodivine-boolean-models/models/summary.csv 
       --models ~/src/biodivine-boolean-models/models 
       --skip 253,248,79,261,256
       --output models.json.gz