/influence_maximization_BNs

Code to generalize methods for influence maximization to Boolean networks

Primary LanguageJupyter Notebook

Influence maximization in Boolean networks

Code to generalize methods (e.g. mean-field approximation) for influence maximization to Boolean networks.

Tutorials:

  • Tutorial_Drosophila_SPN.ipynb shows how to run the individual based mean-field approximation (IBMFA), calculate entropy, and retrieve driver sets for biological networks (e.g. the drosophila single-cell SPN).
  • Tutorial_RBN.ipynb shows how to run the IBMFA, calculate entropy, and retrieve driver sets for random Boolean networks (RBNs).

Scripts:

  • mean_field_computations.py (code to run IBMFA)
  • entropy_computations.py (code to calculate entropy of network configurations)
  • driver_sets.py (code to find driver sets towards fixed points of a network)
  • simulations.py (code to run and analyze simulations)
  • brute_force_computations.py (code to run and analyze brute-force calculations for small networks)
  • RBN_computations.py (code for additional functions needed for RBNs)
  • modules.py (utility functions related to influence pathways and code for general threshold networks)
  • utils.py (utility functions for the IBMFA)

Original notebooks:

Note: the scripts above were created from the functions originally used in the jupyter notebooks below. If there's a bug in the above code, you may refer to the original function defined in the notebook to help troubleshoot.

  • See information_diffusion.ipynb for results on specific genetic regulatory networks (GRNs).
  • See dynamic_game_theory.ipynb for results on random Boolean networks (RBNs) and the Cell Collective repository (http://cellcollective.org/) [1]
  • See Mean-field Approximation in Boolean Networks.ipynb for figure results used in the paper.
  • See network_attractors.ipynb for results on attractors and control kernels. These calculations require code from https://zenodo.org/record/5172898 [2]

The corresponding paper has been submitted for publication.

References:

[1] Tom´aˇs Helikar, Bryan Kowal, Sean McClenathan, Mitchell Bruckner, Thaine Rowley, Alex Madrahimov, Ben Wicks, Manish Shrestha, Kahani Limbu, and Jim A Rogers. The cell collective: toward an open and collaborative approach to systems biology. BMC systems biology, 6(1):1–14, 2012.

[2] Borriello, E., Daniels, B.C. The basis of easy controllability in Boolean networks. Nat Commun 12, 5227 (2021). https://doi.org/10.1038/s41467-021-25533-3