Optimal infinite horizon control for probabilistic Boolean networks

Pal, Ranadip; Datta, Aniruddha; Dougherty, Edward R.

doi:10.1109/acc.2006.1655433

Cited by 139 publications

(123 citation statements)

References 11 publications

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“…In the last decade, many results on control of BNs and PBNs have been obtained so far (see, e.g., [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32]). Especially, analysis and control methods using the semi-tensor product (STP) method have been widely studied (see, e.g., [33][34][35][36][37][38][39][40][41][42][43]).…”

Section: Introductionmentioning

confidence: 99%

Optimization-Based Approaches to Control of Probabilistic Boolean Networks

Kobayashi

Hiraishi

2017

Algorithms

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Abstract:Control of gene regulatory networks is one of the fundamental topics in systems biology. In the last decade, control theory of Boolean networks (BNs), which is well known as a model of gene regulatory networks, has been widely studied. In this review paper, our previously proposed methods on optimal control of probabilistic Boolean networks (PBNs) are introduced. First, the outline of PBNs is explained. Next, an optimal control method using polynomial optimization is explained. The finite-time optimal control problem is reduced to a polynomial optimization problem. Furthermore, another finite-time optimal control problem, which can be reduced to an integer programming problem, is also explained.

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Section: Introductionmentioning

confidence: 99%

Optimization-Based Approaches to Control of Probabilistic Boolean Networks

Kobayashi

Hiraishi

2017

Algorithms

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“…where b 0 is the known initial belief state, and the discount factor γ places a premium on minimizing the costs of early interventions as opposed to later ones, which is sensible from a medical perspective [6]. The classical results proved in [30] for MDPs can be used here.…”

Section: Infinite-horizon Controlmentioning

confidence: 99%

Control of Gene Regulatory Networks With Noisy Measurements and Uncertain Inputs

Imani

Braga-Neto

2018

IEEE Trans. Control Netw. Syst.

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Abstract-This paper is concerned with the problem of stochastic control of gene regulatory networks (GRNs) observed indirectly through noisy measurements and with uncertainty in the intervention inputs. The partial observability of the gene states and uncertainty in the intervention process are accounted for by modeling GRNs using the partially-observed Boolean dynamical system (POBDS) signal model with noisy gene expression measurements. Obtaining the optimal infinite-horizon control strategy for this problem is not attainable in general, and we apply reinforcement learning and Gaussian process techniques to find a near-optimal solution. The POBDS is first transformed to a directly-observed Markov Decision Process in a continuous belief space, and the Gaussian process is used for modeling the cost function over the belief and intervention spaces. Reinforcement learning then is used to learn the cost function from the available gene expression data. In addition, we employ sparsification, which enables the control of large partially-observed GRNs. The performance of the resulting algorithm is studied through a comprehensive set of numerical experiments using synthetic gene expression data generated from a melanoma gene regulatory network.

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“…Recent studies on the design of network intervention strategies in PBNs have mainly focused on two basic categories of intervention approaches: external control [46] and structural intervention [38]. External control involves stationary control policies to flip (or not flip) the expression state of a control gene to steer the system away from "undesirable" dynamics corresponding to aberrant cellular phenotypes to "desirable" network behavior.…”

Section: A Review Of Probabilistic Boolean Network (Pbn) and Networkmentioning

confidence: 99%

Quantifying the Objective Cost of Uncertainty in Complex Dynamical Systems

Yoon

Qian

Dougherty

2013

IEEE Trans. Signal Process.

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Real-world problems often involve complex systems that cannot be perfectly modeled or identified, and many engineering applications aim to design operators that can perform reliably in the presence of such uncertainty. In this paper, we propose a novel Bayesian framework for objective-based uncertainty quantification (UQ), which quantifies the uncertainty in a given system based on the expected increase of the operational cost that it induces. This measure of uncertainty, called MOCU (mean objective cost of uncertainty), provides a practical way of quantifying the effect of various types of system uncertainties on the operation of interest.Furthermore, the proposed UQ framework provides a general mathematical basis for designing robust operators, and it can be applied to diverse applications, including robust filtering, classification, and control. We demonstrate the utility and effectiveness of the proposed framework by applying it to the problem of robust structural intervention of gene regulatory networks, an important application in translational genomics. Index TermsMean objective cost of uncertainty (MOCU), objective-based uncertainty quantification (UQ), robust operator design, robust network intervention.Byung-Jun Yoon is with the

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Optimal infinite horizon control for probabilistic Boolean networks

Cited by 139 publications

References 11 publications

Optimization-Based Approaches to Control of Probabilistic Boolean Networks

Optimization-Based Approaches to Control of Probabilistic Boolean Networks

Control of Gene Regulatory Networks With Noisy Measurements and Uncertain Inputs

Quantifying the Objective Cost of Uncertainty in Complex Dynamical Systems

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