DesignPrinciplesGeneNetworks

A collection of functions and tools to analyze Boolean gene regulatory networks

This repository contains code to perform the analyses described in the paper "A meta-analysis of Boolean network models reveals design principles of gene regulatory networks", published in 2024 in Science Advances. Most functionality is also implemented in the interactive project website https://booleangenenetworks.math.iastate.edu.

The three key pieces of code are:

1. load_database13.py,
2. canalizing_function_toolbox_v13, and
3. analyse_database13.py.

Moreover, analyse_database13_modularity.py generates the analyses described in this paper: https://www.biorxiv.org/content/10.1101/2023.09.11.557227v1, and displays the acyclic graph structure of Boolean gene regulatory networks.

load_database13.py

This file contains all code needed to load the Boolean network models via load_database(folders). The models are stored in a list of folders, as text files in a standardized format:

A = B OR C
B = A OR (C AND D)
C = NOT A

This little example represents a model with three genes, A, B and C, and one external parameter D (which only appears on the right side of the equations).

canalizing_function_toolbox_v13.py

This file contains a variety of functions to analyze Boolean functions and Boolean networks. Each Python function has its own documentation. A Boolean function is considered as a list of 0-1 entries of length 2^n where n is the number of inputs. A Boolean network of N nodes is a list of N Boolean functions. For example,

f_A = [0,1,1,1]
f_B = [0,0,0,1,1,1,1,1]
f_C = [1,0]
F = [f_A,f_B,f_C]

describes the Boolean network from above. One can also get this via

import load_database13 as db

with open('example.txt', 'w') as writer:
    writer.write('A = B OR C\nB = A OR (C AND D)\nC = NOT A')
F, I, degree, variables, constants = db.text_to_BN(folder='',textfile='example.txt')

which yields in addition the adjacency matrix I, the in-degree of each node, the names of the variables (in order) and the names of potential external parameters.

analyse_database13.py

This file combines everything. It loads the database and analyzes it. Most Python functions defined in this file are used to generate the plots in the paper "A meta-analysis of Boolean network models reveals design principles of gene regulatory networks", published in 2024 in Science Advances.