Probabilistic Boolean Networks

Probabilistic Boolean Networks are a class of models of genetic regulatory networks, first introduced in [1,2]. Our work has focused on the relationships between network structure and dynamics [6], steady-state analysis [5,8], and relationships to other model classes, such as Dynamic Bayesian Networks [7].  We have also worked on model inference from experimental data [9] and intervention strategies for driving the network towards desirable states [3,4]. This and the work of multiple other authors was included in the book on Probabilistic Boolean Networks [10] published by SIAM Press in 2010 with long-term colleague Dr. Edward Dougherty (Texas A&M). Recently, PBNs have been used, together with single cell expression data, for identifying the best intervention targets to induce transdifferentiation between two cell types [11].


  1. I. Shmulevich, E. R. Dougherty, S. Kim, W. Zhang, “Probabilistic Boolean Networks: A Rule-based Uncertainty Model for Gene Regulatory Networks,” Bioinformatics, Vol. 18, No. 2, pp. 261-274, 2002.
  2. I. Shmulevich, E.R. Dougherty, and W. Zhang, “From Boolean to probabilistic Boolean networks as models of genetic regulatory networks,” Proceedings of the IEEE, Vol. 90, No. 11, pp. 1778-1792, 2002.
  3. I. Shmulevich, E. R. Dougherty, W. Zhang, “Gene Perturbation and Intervention in Probabilistic Boolean Networks,” Bioinformatics, Vol. 18, No. 10, pp. 1319-1331, 2002.
  4. I. Shmulevich, E.R. Dougherty, and W. Zhang, “Control of stationary behavior in Probabilistic Boolean Networks by means of structural intervention,” Journal of Biological Systems, Vol. 10, No. 4, pp. 431-445, 2002.
  5. I. Shmulevich, I. Gluhovsky, R. Hashimoto, E. R. Dougherty, and W. Zhang, “Steady-State Analysis of Genetic Regulatory Networks Modeled by Probabilistic Boolean Networks,” Comparative and Functional Genomics, Vol. 4, No. 6, pp. 601-608, 2003.
  6. E. R. Dougherty and I. Shmulevich, “Mappings Between Probabilistic Boolean Networks,” Signal Processing, Vol. 83, No. 4, pp. 799-809, 2003.
  7. H. Lähdesmäki, S. Hautaniemi, I. Shmulevich, O. Yli-Harja, “Relationships Between Probabilistic Boolean Networks and Dynamic Bayesian Networks as Models of Gene Regulatory Networks,” Signal Processing, Vol. 86, No. 4, pp. 814-834, 2006.
  8. M. Brun, E. R. Dougherty, I. Shmulevich, “Steady-State Probabilities for Attractors in Probabilistic Boolean Networks,” Signal Processing, Vol. 85, No. 4, pp. 1993-2013, 2005.
  9. R. F. Hashimoto, S. Kim, I. Shmulevich, W. Zhang, M. L. Bittner, E. R. Dougherty, “Growing genetic regulatory networks from seed genes,” Bioinformatics, Vol. 20, No. 8, pp. 1241-1247, 2004.
  10. I. Shmulevich and E. R. Dougherty, Probabilistic Boolean Networks: The Modeling and Control of Gene Regulatory Networks, SIAM Press, 2009.
  11. B. Tercan, B. Aguilar, S. Huang, E. R. Dougherty, I. Shmulevich, “Probabilistic Boolean Networks Predict Transcription Factor Targets to Induce Transdifferentiation,” iScience, Vol. 25, No. 9, 104951, 2022.