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Influence maximization in Boolean networks

Author

Listed:
  • Thomas Parmer

    (Indiana University)

  • Luis M. Rocha

    (Binghamton University (State University of New York)
    Instituto Gulbenkian de Ciência)

  • Filippo Radicchi

    (Indiana University)

Abstract

The optimization problem aiming at the identification of minimal sets of nodes able to drive the dynamics of Boolean networks toward desired long-term behaviors is central for some applications, as for example the detection of key therapeutic targets to control pathways in models of biological signaling and regulatory networks. Here, we develop a method to solve such an optimization problem taking inspiration from the well-studied problem of influence maximization for spreading processes in social networks. We validate the method on small gene regulatory networks whose dynamical landscapes are known by means of brute-force analysis. We then systematically study a large collection of gene regulatory networks. We find that for about 65% of the analyzed networks, the minimal driver sets contain less than 20% of their nodes.

Suggested Citation

  • Thomas Parmer & Luis M. Rocha & Filippo Radicchi, 2022. "Influence maximization in Boolean networks," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-31066-0
    DOI: 10.1038/s41467-022-31066-0
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    References listed on IDEAS

    as
    1. Martin Summer, 2013. "Financial Contagion and Network Analysis," Annual Review of Financial Economics, Annual Reviews, vol. 5(1), pages 277-297, November.
    2. Yang-Yu Liu & Jean-Jacques Slotine & Albert-László Barabási, 2011. "Controllability of complex networks," Nature, Nature, vol. 473(7346), pages 167-173, May.
    3. Fisher, M.L. & Nemhauser, G.L. & Wolsey, L.A., 1978. "An analysis of approximations for maximizing submodular set functions," LIDAM Reprints CORE 341, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    4. Bai, Wen-Jie & Zhou, Tao & Wang, Bing-Hong, 2007. "Immunization of susceptible–infected model on scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 384(2), pages 656-662.
    5. Fisher, M.L. & Nemhauser, G.L. & Wolsey, L.A., 1978. "An analysis of approximations for maximizing submodular set functions - 1," LIDAM Reprints CORE 334, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    6. Enrico Borriello & Bryan C. Daniels, 2021. "The basis of easy controllability in Boolean networks," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
    7. Maliackal Poulo Joy & Donald E. Ingber & Sui Huang, 2007. "Chaotic Mean Field Dynamics Of A Boolean Network With Random Connectivity," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 18(09), pages 1459-1473.
    8. Saeed Osat & Ali Faqeeh & Filippo Radicchi, 2017. "Optimal percolation on multiplex networks," Nature Communications, Nature, vol. 8(1), pages 1-7, December.
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