Bayesian robustness in the control of gene regulatory networks

Ranadip Pal, Aniruddha Datta, Edward R. Dougherty

Research output: Contribution to journalArticlepeer-review

28 Scopus citations


The errors originating in the data extraction process, gene selection and network inference prevent the transition probabilities of a gene regulatory network from being accurately estimated. Thus, it is important to study the effect of modeling errors on the final outcome of an intervention strategy and to design robust intervention strategies. Two major approaches applied to the design of robust policies in general are the minimax (worst case) approach and the Bayesian approach. The minimax control approach is typically conservative because it gives too much importance to the scenarios which hardly occur in practice. Consequently, in this paper, we formulate the Bayesian approach for the control of gene regulatory networks. We characterize the errors emanating from the data extraction and inference processes and compare the performance of the minimax and Bayesian designs based on these uncertainties.

Original languageEnglish
Pages (from-to)3667-3678
Number of pages12
JournalIEEE Transactions on Signal Processing
Issue number9
StatePublished - 2009


  • Bayesian robustness
  • Gene regulatory networks
  • Intervention
  • Parameter estimation
  • Robust control


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