TY - JOUR
T1 - Optimal infinite-horizon control for probabilistic Boolean networks
AU - Pal, Ranadip
AU - Datta, Aniruddha
AU - Dougherty, Edward R.
N1 - Funding Information:
Manuscript received March 31, 2005; revised October 10, 2005. This work was supported in part by the National Science Foundation under Grants ECS-0355227 and CCF-0514644 and in part by the Translational Genomics Research Institute. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Javier Garcia-Frias.
PY - 2006/6
Y1 - 2006/6
N2 - External control of a genetic regulatory network is used for the purpose of avoiding undesirable states, such as those associated with disease. Heretofore, intervention has focused on finite-horizon control, i.e., control over a small number of stages. This paper considers the design of optimal infinite-horizon control for context-sensitive probabilistic Boolean networks (PBNs). It can also be applied to instantaneously random PBNs. The stationary policy obtained is independent of time and dependent on the current state. This paper concentrates on discounted problems with bounded cost per stage and on average-cost-per-stage problems. These formulations are used to generate stationary policies for a PBN constructed from melanoma gene-expression data. The results show that the stationary policies obtained by the two different formulations are capable of shifting the probability mass of the stationary distribution from undesirable states to desirable ones.
AB - External control of a genetic regulatory network is used for the purpose of avoiding undesirable states, such as those associated with disease. Heretofore, intervention has focused on finite-horizon control, i.e., control over a small number of stages. This paper considers the design of optimal infinite-horizon control for context-sensitive probabilistic Boolean networks (PBNs). It can also be applied to instantaneously random PBNs. The stationary policy obtained is independent of time and dependent on the current state. This paper concentrates on discounted problems with bounded cost per stage and on average-cost-per-stage problems. These formulations are used to generate stationary policies for a PBN constructed from melanoma gene-expression data. The results show that the stationary policies obtained by the two different formulations are capable of shifting the probability mass of the stationary distribution from undesirable states to desirable ones.
KW - Altering steady state
KW - Genetic network intervention
KW - Infinite-horizon control
KW - Optimal control of probabilistic Boolean networks
UR - http://www.scopus.com/inward/record.url?scp=33744466566&partnerID=8YFLogxK
U2 - 10.1109/TSP.2006.873740
DO - 10.1109/TSP.2006.873740
M3 - Article
AN - SCOPUS:33744466566
SN - 1053-587X
VL - 54
SP - 2375
EP - 2387
JO - IEEE Transactions on Signal Processing
JF - IEEE Transactions on Signal Processing
IS - 6 II
ER -