Using the message passing algorithm on discrete data to detect faults in boolean regulatory networks Academic Article uri icon


  • 2014 Mohanty et al.; licensee BioMed Central. Background: An important problem in systems biology is to model gene regulatory networks which can then be utilized to develop novel therapeutic methods for cancer treatment. Knowledge about which proteins/genes are dysregulated in a regulatory network, such as in the Mitogen Activated Protein Kinase (MAPK) Network, can be used not only to decide upon which therapy to use for a particular case of cancer, but also help in discovering effective targets for new drugs. Results: In this work we demonstrate how one can start from a model signal transduction network derived from prior knowledge, and infer from gene expression data the probable locations of dysregulations in the network. Our model is based on Boolean networks, and the inference problem is solved using a version of the message passing algorithm. We have done simulation experiments on synthetic data to verify the efficacy of the algorithm as compared to the results from the much more computationally intensive Markov Chain Monte-Carlo methods. We also applied the model to analyze data collected from fibroblasts, thereby demonstrating how this model can be used on real world data.

published proceedings


altmetric score

  • 1.5

author list (cited authors)

  • Mohanty, A. K., Datta, A., & Venkatraj, V.

citation count

  • 1

complete list of authors

  • Mohanty, Anwoy Kumar||Datta, Aniruddha||Venkatraj, Vijayanagaram

publication date

  • January 2014