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 -