TY - JOUR
T1 - Bayesian robustness in the control of gene regulatory networks
AU - Pal, Ranadip
AU - Datta, Aniruddha
AU - Dougherty, Edward R.
N1 - Funding Information:
Manuscript received December 07, 2008; accepted April 17, 2009. First published May 12, 2009; current version published August 12, 2009. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Xiaodong Cai. This research was supported in part by the National Science Foundation under Grants ECS-0355227, CCF-0514644, and ECCS-0701531 and in part by the National Cancer Institute under Grant CA90301.
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
KW - Bayesian robustness
KW - Gene regulatory networks
KW - Intervention
KW - Parameter estimation
KW - Robust control
UR - http://www.scopus.com/inward/record.url?scp=69349084710&partnerID=8YFLogxK
U2 - 10.1109/TSP.2009.2022872
DO - 10.1109/TSP.2009.2022872
M3 - Article
AN - SCOPUS:69349084710
VL - 57
SP - 3667
EP - 3678
JO - IEEE Transactions on Signal Processing
JF - IEEE Transactions on Signal Processing
SN - 1053-587X
IS - 9
ER -