Optimal Control of Probabilistic Boolean Networks: An Information-Theoretic Approach

The primary challenge with biological sciences is to control gene regulatory networks (GRNs), thereby creating therapeutic intervention methods that alter network dynamics in the desired manner. The optimal control of GRNs with probabilistic Boolean control networks (PBCNs) as the underlying structu...

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Autores principales:	K. Sonam, Sarang Sutavani, S. R. Wagh, N. M. Singh
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Information-theoretic control Markov decision processes (MDPs) optimal control parallel processing probabilistic Boolean control networks (PBCNs) Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/11173f0426d848b8a24461be1bfbf18c
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spelling	oai:doaj.org-article:11173f0426d848b8a24461be1bfbf18c2021-12-02T00:00:43ZOptimal Control of Probabilistic Boolean Networks: An Information-Theoretic Approach2169-353610.1109/ACCESS.2021.3130118https://doaj.org/article/11173f0426d848b8a24461be1bfbf18c2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9625026/https://doaj.org/toc/2169-3536The primary challenge with biological sciences is to control gene regulatory networks (GRNs), thereby creating therapeutic intervention methods that alter network dynamics in the desired manner. The optimal control of GRNs with probabilistic Boolean control networks (PBCNs) as the underlying structure is a solution to this challenge. Owing to the exponential growth in network size with the increase in the number of genes, we need an optimal control approach that scales to large systems without imposing any limitations on network dynamics. Furthermore, we are encouraged to use the graphics processing unit (GPU) to reduce time complexity utilizing the easily available and enhanced computational resources. The optimal control of PBCNs in the Markovian framework is developed in this paper employing an information-theoretic approach which includes Kullback-Leibler (KL) divergence. We convert the nonlinear optimal control problem of PBCN to a linear problem by using the exponential transformation of the cost function, also known as the desirability function. The linear formulation enables us to compute an optimal control using the path integral (PI) method. Furthermore, we offer sampling-based methodologies for approximating PI and therefore optimizing PBCN control. The sampling-based method can be implemented in parallel, which solves the optimal control problem for large PBCNs.K. SonamSarang SutavaniS. R. WaghN. M. SinghIEEEarticleInformation-theoretic controlMarkov decision processes (MDPs)optimal controlparallel processingprobabilistic Boolean control networks (PBCNs)Electrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 157068-157082 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Information-theoretic control Markov decision processes (MDPs) optimal control parallel processing probabilistic Boolean control networks (PBCNs) Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Information-theoretic control Markov decision processes (MDPs) optimal control parallel processing probabilistic Boolean control networks (PBCNs) Electrical engineering. Electronics. Nuclear engineering TK1-9971 K. Sonam Sarang Sutavani S. R. Wagh N. M. Singh Optimal Control of Probabilistic Boolean Networks: An Information-Theoretic Approach
description	The primary challenge with biological sciences is to control gene regulatory networks (GRNs), thereby creating therapeutic intervention methods that alter network dynamics in the desired manner. The optimal control of GRNs with probabilistic Boolean control networks (PBCNs) as the underlying structure is a solution to this challenge. Owing to the exponential growth in network size with the increase in the number of genes, we need an optimal control approach that scales to large systems without imposing any limitations on network dynamics. Furthermore, we are encouraged to use the graphics processing unit (GPU) to reduce time complexity utilizing the easily available and enhanced computational resources. The optimal control of PBCNs in the Markovian framework is developed in this paper employing an information-theoretic approach which includes Kullback-Leibler (KL) divergence. We convert the nonlinear optimal control problem of PBCN to a linear problem by using the exponential transformation of the cost function, also known as the desirability function. The linear formulation enables us to compute an optimal control using the path integral (PI) method. Furthermore, we offer sampling-based methodologies for approximating PI and therefore optimizing PBCN control. The sampling-based method can be implemented in parallel, which solves the optimal control problem for large PBCNs.
format	article
author	K. Sonam Sarang Sutavani S. R. Wagh N. M. Singh
author_facet	K. Sonam Sarang Sutavani S. R. Wagh N. M. Singh
author_sort	K. Sonam
title	Optimal Control of Probabilistic Boolean Networks: An Information-Theoretic Approach
title_short	Optimal Control of Probabilistic Boolean Networks: An Information-Theoretic Approach
title_full	Optimal Control of Probabilistic Boolean Networks: An Information-Theoretic Approach
title_fullStr	Optimal Control of Probabilistic Boolean Networks: An Information-Theoretic Approach
title_full_unstemmed	Optimal Control of Probabilistic Boolean Networks: An Information-Theoretic Approach
title_sort	optimal control of probabilistic boolean networks: an information-theoretic approach
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/11173f0426d848b8a24461be1bfbf18c
work_keys_str_mv	AT ksonam optimalcontrolofprobabilisticbooleannetworksaninformationtheoreticapproach AT sarangsutavani optimalcontrolofprobabilisticbooleannetworksaninformationtheoreticapproach AT srwagh optimalcontrolofprobabilisticbooleannetworksaninformationtheoreticapproach AT nmsingh optimalcontrolofprobabilisticbooleannetworksaninformationtheoreticapproach
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Optimal Control of Probabilistic Boolean Networks: An Information-Theoretic Approach

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