A comparison study of optimal and suboptimal intervention policies for gene regulatory networks in the presence of uncertainty

Abstract Perfect knowledge of the underlying state transition probabilities is necessary for designing an optimal intervention strategy for a given Markovian genetic regulatory network. However, in many practical situations, the complex nature of the network and/or identification costs limit the ava...
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Gespeichert in:

Autor*in:	Yousefi, Mohammadmahdi R [verfasserIn] Dougherty, Edward R [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2014

Schlagwörter:	Optimal intervention Markovian gene regulatory networks Probabilistic Boolean networks; Uncertainty; Prior knowledge; Bayesian control

Übergeordnetes Werk:	Enthalten in: EURASIP journal on bioinformatics and systems biology - Heidelberg : Springer, 2006, 2014(2014), 1 vom: 03. Apr.
Übergeordnetes Werk:	volume:2014 ; year:2014 ; number:1 ; day:03 ; month:04

Links:	Volltext

DOI / URN:	10.1186/1687-4153-2014-6

Katalog-ID:	SPR032018959

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520			\|a Abstract Perfect knowledge of the underlying state transition probabilities is necessary for designing an optimal intervention strategy for a given Markovian genetic regulatory network. However, in many practical situations, the complex nature of the network and/or identification costs limit the availability of such perfect knowledge. To address this difficulty, we propose to take a Bayesian approach and represent the system of interest as an uncertainty class of several models, each assigned some probability, which reflects our prior knowledge about the system. We define the objective function to be the expected cost relative to the probability distribution over the uncertainty class and formulate an optimal Bayesian robust intervention policy minimizing this cost function. The resulting policy may not be optimal for a fixed element within the uncertainty class, but it is optimal when averaged across the uncertainly class. Furthermore, starting from a prior probability distribution over the uncertainty class and collecting samples from the process over time, one can update the prior distribution to a posterior and find the corresponding optimal Bayesian robust policy relative to the posterior distribution. Therefore, the optimal intervention policy is essentially nonstationary and adaptive.
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author_variant	m r y mr mry e r d er erd
matchkey_str	article:16874153:2014----::cmaiosuyfpiaaduotmlnevninoiisognrgltrnto
hierarchy_sort_str	2014
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allfields	10.1186/1687-4153-2014-6 doi (DE-627)SPR032018959 (SPR)1687-4153-2014-6-e DE-627 ger DE-627 rakwb eng 570 004 ASE 42.13 bkl 42.03 bkl 42.11 bkl Yousefi, Mohammadmahdi R verfasserin aut A comparison study of optimal and suboptimal intervention policies for gene regulatory networks in the presence of uncertainty 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Perfect knowledge of the underlying state transition probabilities is necessary for designing an optimal intervention strategy for a given Markovian genetic regulatory network. However, in many practical situations, the complex nature of the network and/or identification costs limit the availability of such perfect knowledge. To address this difficulty, we propose to take a Bayesian approach and represent the system of interest as an uncertainty class of several models, each assigned some probability, which reflects our prior knowledge about the system. We define the objective function to be the expected cost relative to the probability distribution over the uncertainty class and formulate an optimal Bayesian robust intervention policy minimizing this cost function. The resulting policy may not be optimal for a fixed element within the uncertainty class, but it is optimal when averaged across the uncertainly class. Furthermore, starting from a prior probability distribution over the uncertainty class and collecting samples from the process over time, one can update the prior distribution to a posterior and find the corresponding optimal Bayesian robust policy relative to the posterior distribution. Therefore, the optimal intervention policy is essentially nonstationary and adaptive. Optimal intervention (dpeaa)DE-He213 Markovian gene regulatory networks (dpeaa)DE-He213 Probabilistic Boolean networks; Uncertainty; Prior knowledge; Bayesian control (dpeaa)DE-He213 Dougherty, Edward R verfasserin aut Enthalten in EURASIP journal on bioinformatics and systems biology Heidelberg : Springer, 2006 2014(2014), 1 vom: 03. Apr. (DE-627)511637225 (DE-600)2233385-X 1687-4153 nnns volume:2014 year:2014 number:1 day:03 month:04 https://dx.doi.org/10.1186/1687-4153-2014-6 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 42.13 ASE 42.03 ASE 42.11 ASE AR 2014 2014 1 03 04
spelling	10.1186/1687-4153-2014-6 doi (DE-627)SPR032018959 (SPR)1687-4153-2014-6-e DE-627 ger DE-627 rakwb eng 570 004 ASE 42.13 bkl 42.03 bkl 42.11 bkl Yousefi, Mohammadmahdi R verfasserin aut A comparison study of optimal and suboptimal intervention policies for gene regulatory networks in the presence of uncertainty 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Perfect knowledge of the underlying state transition probabilities is necessary for designing an optimal intervention strategy for a given Markovian genetic regulatory network. However, in many practical situations, the complex nature of the network and/or identification costs limit the availability of such perfect knowledge. To address this difficulty, we propose to take a Bayesian approach and represent the system of interest as an uncertainty class of several models, each assigned some probability, which reflects our prior knowledge about the system. We define the objective function to be the expected cost relative to the probability distribution over the uncertainty class and formulate an optimal Bayesian robust intervention policy minimizing this cost function. The resulting policy may not be optimal for a fixed element within the uncertainty class, but it is optimal when averaged across the uncertainly class. Furthermore, starting from a prior probability distribution over the uncertainty class and collecting samples from the process over time, one can update the prior distribution to a posterior and find the corresponding optimal Bayesian robust policy relative to the posterior distribution. Therefore, the optimal intervention policy is essentially nonstationary and adaptive. Optimal intervention (dpeaa)DE-He213 Markovian gene regulatory networks (dpeaa)DE-He213 Probabilistic Boolean networks; Uncertainty; Prior knowledge; Bayesian control (dpeaa)DE-He213 Dougherty, Edward R verfasserin aut Enthalten in EURASIP journal on bioinformatics and systems biology Heidelberg : Springer, 2006 2014(2014), 1 vom: 03. Apr. (DE-627)511637225 (DE-600)2233385-X 1687-4153 nnns volume:2014 year:2014 number:1 day:03 month:04 https://dx.doi.org/10.1186/1687-4153-2014-6 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 42.13 ASE 42.03 ASE 42.11 ASE AR 2014 2014 1 03 04
allfields_unstemmed	10.1186/1687-4153-2014-6 doi (DE-627)SPR032018959 (SPR)1687-4153-2014-6-e DE-627 ger DE-627 rakwb eng 570 004 ASE 42.13 bkl 42.03 bkl 42.11 bkl Yousefi, Mohammadmahdi R verfasserin aut A comparison study of optimal and suboptimal intervention policies for gene regulatory networks in the presence of uncertainty 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Perfect knowledge of the underlying state transition probabilities is necessary for designing an optimal intervention strategy for a given Markovian genetic regulatory network. However, in many practical situations, the complex nature of the network and/or identification costs limit the availability of such perfect knowledge. To address this difficulty, we propose to take a Bayesian approach and represent the system of interest as an uncertainty class of several models, each assigned some probability, which reflects our prior knowledge about the system. We define the objective function to be the expected cost relative to the probability distribution over the uncertainty class and formulate an optimal Bayesian robust intervention policy minimizing this cost function. The resulting policy may not be optimal for a fixed element within the uncertainty class, but it is optimal when averaged across the uncertainly class. Furthermore, starting from a prior probability distribution over the uncertainty class and collecting samples from the process over time, one can update the prior distribution to a posterior and find the corresponding optimal Bayesian robust policy relative to the posterior distribution. Therefore, the optimal intervention policy is essentially nonstationary and adaptive. Optimal intervention (dpeaa)DE-He213 Markovian gene regulatory networks (dpeaa)DE-He213 Probabilistic Boolean networks; Uncertainty; Prior knowledge; Bayesian control (dpeaa)DE-He213 Dougherty, Edward R verfasserin aut Enthalten in EURASIP journal on bioinformatics and systems biology Heidelberg : Springer, 2006 2014(2014), 1 vom: 03. Apr. (DE-627)511637225 (DE-600)2233385-X 1687-4153 nnns volume:2014 year:2014 number:1 day:03 month:04 https://dx.doi.org/10.1186/1687-4153-2014-6 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 42.13 ASE 42.03 ASE 42.11 ASE AR 2014 2014 1 03 04
allfieldsGer	10.1186/1687-4153-2014-6 doi (DE-627)SPR032018959 (SPR)1687-4153-2014-6-e DE-627 ger DE-627 rakwb eng 570 004 ASE 42.13 bkl 42.03 bkl 42.11 bkl Yousefi, Mohammadmahdi R verfasserin aut A comparison study of optimal and suboptimal intervention policies for gene regulatory networks in the presence of uncertainty 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Perfect knowledge of the underlying state transition probabilities is necessary for designing an optimal intervention strategy for a given Markovian genetic regulatory network. However, in many practical situations, the complex nature of the network and/or identification costs limit the availability of such perfect knowledge. To address this difficulty, we propose to take a Bayesian approach and represent the system of interest as an uncertainty class of several models, each assigned some probability, which reflects our prior knowledge about the system. We define the objective function to be the expected cost relative to the probability distribution over the uncertainty class and formulate an optimal Bayesian robust intervention policy minimizing this cost function. The resulting policy may not be optimal for a fixed element within the uncertainty class, but it is optimal when averaged across the uncertainly class. Furthermore, starting from a prior probability distribution over the uncertainty class and collecting samples from the process over time, one can update the prior distribution to a posterior and find the corresponding optimal Bayesian robust policy relative to the posterior distribution. Therefore, the optimal intervention policy is essentially nonstationary and adaptive. Optimal intervention (dpeaa)DE-He213 Markovian gene regulatory networks (dpeaa)DE-He213 Probabilistic Boolean networks; Uncertainty; Prior knowledge; Bayesian control (dpeaa)DE-He213 Dougherty, Edward R verfasserin aut Enthalten in EURASIP journal on bioinformatics and systems biology Heidelberg : Springer, 2006 2014(2014), 1 vom: 03. Apr. (DE-627)511637225 (DE-600)2233385-X 1687-4153 nnns volume:2014 year:2014 number:1 day:03 month:04 https://dx.doi.org/10.1186/1687-4153-2014-6 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 42.13 ASE 42.03 ASE 42.11 ASE AR 2014 2014 1 03 04
allfieldsSound	10.1186/1687-4153-2014-6 doi (DE-627)SPR032018959 (SPR)1687-4153-2014-6-e DE-627 ger DE-627 rakwb eng 570 004 ASE 42.13 bkl 42.03 bkl 42.11 bkl Yousefi, Mohammadmahdi R verfasserin aut A comparison study of optimal and suboptimal intervention policies for gene regulatory networks in the presence of uncertainty 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Perfect knowledge of the underlying state transition probabilities is necessary for designing an optimal intervention strategy for a given Markovian genetic regulatory network. However, in many practical situations, the complex nature of the network and/or identification costs limit the availability of such perfect knowledge. To address this difficulty, we propose to take a Bayesian approach and represent the system of interest as an uncertainty class of several models, each assigned some probability, which reflects our prior knowledge about the system. We define the objective function to be the expected cost relative to the probability distribution over the uncertainty class and formulate an optimal Bayesian robust intervention policy minimizing this cost function. The resulting policy may not be optimal for a fixed element within the uncertainty class, but it is optimal when averaged across the uncertainly class. Furthermore, starting from a prior probability distribution over the uncertainty class and collecting samples from the process over time, one can update the prior distribution to a posterior and find the corresponding optimal Bayesian robust policy relative to the posterior distribution. Therefore, the optimal intervention policy is essentially nonstationary and adaptive. Optimal intervention (dpeaa)DE-He213 Markovian gene regulatory networks (dpeaa)DE-He213 Probabilistic Boolean networks; Uncertainty; Prior knowledge; Bayesian control (dpeaa)DE-He213 Dougherty, Edward R verfasserin aut Enthalten in EURASIP journal on bioinformatics and systems biology Heidelberg : Springer, 2006 2014(2014), 1 vom: 03. Apr. (DE-627)511637225 (DE-600)2233385-X 1687-4153 nnns volume:2014 year:2014 number:1 day:03 month:04 https://dx.doi.org/10.1186/1687-4153-2014-6 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 42.13 ASE 42.03 ASE 42.11 ASE AR 2014 2014 1 03 04
language	English
source	Enthalten in EURASIP journal on bioinformatics and systems biology 2014(2014), 1 vom: 03. Apr. volume:2014 year:2014 number:1 day:03 month:04
sourceStr	Enthalten in EURASIP journal on bioinformatics and systems biology 2014(2014), 1 vom: 03. Apr. volume:2014 year:2014 number:1 day:03 month:04
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topic_facet	Optimal intervention Markovian gene regulatory networks Probabilistic Boolean networks; Uncertainty; Prior knowledge; Bayesian control
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authorswithroles_txt_mv	Yousefi, Mohammadmahdi R @@aut@@ Dougherty, Edward R @@aut@@
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author	Yousefi, Mohammadmahdi R
spellingShingle	Yousefi, Mohammadmahdi R ddc 570 bkl 42.13 bkl 42.03 bkl 42.11 misc Optimal intervention misc Markovian gene regulatory networks misc Probabilistic Boolean networks; Uncertainty; Prior knowledge; Bayesian control A comparison study of optimal and suboptimal intervention policies for gene regulatory networks in the presence of uncertainty
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topic_title	570 004 ASE 42.13 bkl 42.03 bkl 42.11 bkl A comparison study of optimal and suboptimal intervention policies for gene regulatory networks in the presence of uncertainty Optimal intervention (dpeaa)DE-He213 Markovian gene regulatory networks (dpeaa)DE-He213 Probabilistic Boolean networks; Uncertainty; Prior knowledge; Bayesian control (dpeaa)DE-He213
topic	ddc 570 bkl 42.13 bkl 42.03 bkl 42.11 misc Optimal intervention misc Markovian gene regulatory networks misc Probabilistic Boolean networks; Uncertainty; Prior knowledge; Bayesian control
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title	A comparison study of optimal and suboptimal intervention policies for gene regulatory networks in the presence of uncertainty
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title_full	A comparison study of optimal and suboptimal intervention policies for gene regulatory networks in the presence of uncertainty
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title_sort	comparison study of optimal and suboptimal intervention policies for gene regulatory networks in the presence of uncertainty
title_auth	A comparison study of optimal and suboptimal intervention policies for gene regulatory networks in the presence of uncertainty
abstract	Abstract Perfect knowledge of the underlying state transition probabilities is necessary for designing an optimal intervention strategy for a given Markovian genetic regulatory network. However, in many practical situations, the complex nature of the network and/or identification costs limit the availability of such perfect knowledge. To address this difficulty, we propose to take a Bayesian approach and represent the system of interest as an uncertainty class of several models, each assigned some probability, which reflects our prior knowledge about the system. We define the objective function to be the expected cost relative to the probability distribution over the uncertainty class and formulate an optimal Bayesian robust intervention policy minimizing this cost function. The resulting policy may not be optimal for a fixed element within the uncertainty class, but it is optimal when averaged across the uncertainly class. Furthermore, starting from a prior probability distribution over the uncertainty class and collecting samples from the process over time, one can update the prior distribution to a posterior and find the corresponding optimal Bayesian robust policy relative to the posterior distribution. Therefore, the optimal intervention policy is essentially nonstationary and adaptive.
abstractGer	Abstract Perfect knowledge of the underlying state transition probabilities is necessary for designing an optimal intervention strategy for a given Markovian genetic regulatory network. However, in many practical situations, the complex nature of the network and/or identification costs limit the availability of such perfect knowledge. To address this difficulty, we propose to take a Bayesian approach and represent the system of interest as an uncertainty class of several models, each assigned some probability, which reflects our prior knowledge about the system. We define the objective function to be the expected cost relative to the probability distribution over the uncertainty class and formulate an optimal Bayesian robust intervention policy minimizing this cost function. The resulting policy may not be optimal for a fixed element within the uncertainty class, but it is optimal when averaged across the uncertainly class. Furthermore, starting from a prior probability distribution over the uncertainty class and collecting samples from the process over time, one can update the prior distribution to a posterior and find the corresponding optimal Bayesian robust policy relative to the posterior distribution. Therefore, the optimal intervention policy is essentially nonstationary and adaptive.
abstract_unstemmed	Abstract Perfect knowledge of the underlying state transition probabilities is necessary for designing an optimal intervention strategy for a given Markovian genetic regulatory network. However, in many practical situations, the complex nature of the network and/or identification costs limit the availability of such perfect knowledge. To address this difficulty, we propose to take a Bayesian approach and represent the system of interest as an uncertainty class of several models, each assigned some probability, which reflects our prior knowledge about the system. We define the objective function to be the expected cost relative to the probability distribution over the uncertainty class and formulate an optimal Bayesian robust intervention policy minimizing this cost function. The resulting policy may not be optimal for a fixed element within the uncertainty class, but it is optimal when averaged across the uncertainly class. Furthermore, starting from a prior probability distribution over the uncertainty class and collecting samples from the process over time, one can update the prior distribution to a posterior and find the corresponding optimal Bayesian robust policy relative to the posterior distribution. Therefore, the optimal intervention policy is essentially nonstationary and adaptive.
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title_short	A comparison study of optimal and suboptimal intervention policies for gene regulatory networks in the presence of uncertainty
url	https://dx.doi.org/10.1186/1687-4153-2014-6
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up_date	2024-07-04T02:10:04.399Z
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A comparison study of optimal and suboptimal intervention policies for gene regulatory networks in the presence of uncertainty

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