TY - JOUR
T1 - Adaptive intervention in probabilistic boolean networks
AU - Layek, Ritwik
AU - Datta, Aniruddha
AU - Pal, Ranadip
AU - Dougherty, Edward R.
N1 - Copyright:
Copyright 2010 Elsevier B.V., All rights reserved.
PY - 2009/8
Y1 - 2009/8
N2 - Motivation: A basic problem of translational systems biology is to utilize gene regulatory networks as a vehicle to design therapeutic intervention strategies to beneficially alter network and, therefore, cellular dynamics. One strain of research has this problem from the perspective of control theory via the design of optimal Markov chain decision processes, mainly in the framework of probabilistic Boolean networks (PBNs). Full optimization assumes that the network is accurately modeled and, to the extent that model inference is inaccurate, which can be expected for gene regulatory networks owing to the combination of model complexity and a paucity of time-course data, the designed intervention strategy may perform poorly. We desire intervention strategies that do not assume accurate full-model inference. Results: This article demonstrates the feasibility of applying on-line adaptive control to improve intervention performance in genetic regulatory networks modeled by PBNs. It shows via simulations that when the network is modeled by a member of a known family of PBNs, an adaptive design can yield improved performance in terms of the average cost. Two algorithms are presented, one better suited for instantaneously random PBNs and the other better suited for context-sensitive PBNs with low switching probability between the constituent BNs.
AB - Motivation: A basic problem of translational systems biology is to utilize gene regulatory networks as a vehicle to design therapeutic intervention strategies to beneficially alter network and, therefore, cellular dynamics. One strain of research has this problem from the perspective of control theory via the design of optimal Markov chain decision processes, mainly in the framework of probabilistic Boolean networks (PBNs). Full optimization assumes that the network is accurately modeled and, to the extent that model inference is inaccurate, which can be expected for gene regulatory networks owing to the combination of model complexity and a paucity of time-course data, the designed intervention strategy may perform poorly. We desire intervention strategies that do not assume accurate full-model inference. Results: This article demonstrates the feasibility of applying on-line adaptive control to improve intervention performance in genetic regulatory networks modeled by PBNs. It shows via simulations that when the network is modeled by a member of a known family of PBNs, an adaptive design can yield improved performance in terms of the average cost. Two algorithms are presented, one better suited for instantaneously random PBNs and the other better suited for context-sensitive PBNs with low switching probability between the constituent BNs.
UR - http://www.scopus.com/inward/record.url?scp=68549104407&partnerID=8YFLogxK
U2 - 10.1093/bioinformatics/btp349
DO - 10.1093/bioinformatics/btp349
M3 - Article
C2 - 19505946
AN - SCOPUS:68549104407
VL - 25
SP - 2042
EP - 2048
JO - Bioinformatics
JF - Bioinformatics
SN - 1367-4803
IS - 16
ER -