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Algorithms for Inference, Analysis and Control of Boolean Networks
"... Abstract. Boolean networks (BNs) are known as a mathematical model of genetic networks. In this paper, we overview algorithmic aspects of inference, analysis and control of BNs while focusing on the authors’ works. For inference of BN, we review results on the sample complexity required to uniquely ..."
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Abstract. Boolean networks (BNs) are known as a mathematical model of genetic networks. In this paper, we overview algorithmic aspects of inference, analysis and control of BNs while focusing on the authors’ works. For inference of BN, we review results on the sample complexity required to uniquely identify a BN. For analysis of BN, we review efficient algorithms for identifying singleton attractors. For control of BN, we review NPhardness results and dynamic programming algorithms for general and special cases. 1
Generating stochastic gene regulatory networks consistent with pathway information and steadystate behavior
 Biomedical Engineering, IEEE Transactions on
, 2012
"... Abstract—We present a procedure to generate a stochastic genetic regulatory network model consistent with pathway information. Using the stochastic dynamics of Markov chains, we produce a model constrained by the prior knowledge despite the sometimes incomplete, time independent, and often conflicti ..."
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Abstract—We present a procedure to generate a stochastic genetic regulatory network model consistent with pathway information. Using the stochastic dynamics of Markov chains, we produce a model constrained by the prior knowledge despite the sometimes incomplete, time independent, and often conflicting nature of these pathways. We apply the Markov theory to study the model’s long run behavior and introduce a biologically important transformation to aid in comparison with real biological outcome prediction in the steadystate domain. Our technique produces biologically faithful models without the need for rate kinetics, detailed timing information, or complex inference procedures. To demonstrate the method, we produce a model using 28 pathways from the biological literature pertaining to the transcription factor family nuclear factorκB. Predictions from this model in the steadystate domain are then validated against nine mice knockout experiments. Index Terms—Gene regulatory networks, pathways, systems biology, stochastic modeling. I.
Optimal control of gene regulatory networks with effectiveness of multiple drugs: a boolean network approach,”
 BioMed Research International,
, 2013
"... Developing control theory of gene regulatory networks is one of the significant topics in the field of systems biology, and it is expected to apply the obtained results to gene therapy technologies in the future. In this paper, a control method using a Boolean network (BN) is studied. A BN is widel ..."
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Developing control theory of gene regulatory networks is one of the significant topics in the field of systems biology, and it is expected to apply the obtained results to gene therapy technologies in the future. In this paper, a control method using a Boolean network (BN) is studied. A BN is widely used as a model of gene regulatory networks, and gene expression is expressed by a binary value (0 or 1). In the control problem, we assume that the concentration level of a part of genes is arbitrarily determined as the control input. However, there are cases that no gene satisfying this assumption exists, and it is important to consider structural control via external stimuli. Furthermore, these controls are realized by multiple drugs, and it is also important to consider multiple effects such as duration of effect and side effects. In this paper, we propose a BN model with two types of the control inputs and an optimal control method with duration of drug effectiveness. First, a BN model and duration of drug effectiveness are discussed. Next, the optimal control problem is formulated and is reduced to an integer linear programming problem. Finally, numerical simulations are shown.
State reduction for network intervention in probabilistic Boolean networks
 Bioinformatics
, 2010
"... Motivation: A key goal of studying biological systems is to design therapeutic intervention strategies. Probabilistic Boolean networks (PBNs) constitute a mathematical model which enables modeling, predicting and intervening in their longrun behavior using Markov chain theory. The longrun dynamics ..."
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Motivation: A key goal of studying biological systems is to design therapeutic intervention strategies. Probabilistic Boolean networks (PBNs) constitute a mathematical model which enables modeling, predicting and intervening in their longrun behavior using Markov chain theory. The longrun dynamics of a PBN, as represented by its steadystate distribution (SSD), can guide the design of effective intervention strategies for the modeled systems. A major obstacle for its application is the large state space of the underlying Markov chain, which poses a serious computational challenge. Hence, it is critical to reduce the model complexity of PBNs for practical applications. Results: We propose a strategy to reduce the state space of the underlying Markov chain of a PBN based on a criterion that the reduction least distorts the proportional change of stationary masses for critical states, for instance, the network attractors. In comparison
An integer programming approach to control problems in probabilistic boolean networks
 in Proc. 2010 American Control Conference, 2010
"... AbstractIn this paper, control problems of probabilistic Boolean networks (PBNs) are discussed. A PBN is one of the significant models in biological networks such as gene regulatory networks. Although there are some results in control of PBNs, it is necessary to compute the state transition diagra ..."
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AbstractIn this paper, control problems of probabilistic Boolean networks (PBNs) are discussed. A PBN is one of the significant models in biological networks such as gene regulatory networks. Although there are some results in control of PBNs, it is necessary to compute the state transition diagram with 2 n nodes for a given PBN with n states. To avoid this computation, an integer programmingbased approach is proposed. In the proposed method, PBNs are transformed into a linear system with binary variables, and the control problem is reduced to an integer linear programming problem, which can be computed relatively easier than the existing methods using the state transition diagram.
Quantifying the Objective Cost of Uncertainty in Complex Dynamical Systems
, 2013
"... Realworld 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 objectivebased uncertai ..."
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Realworld 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 objectivebased 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 Terms Mean objective cost of uncertainty (MOCU), objectivebased uncertainty quantification (UQ), robust operator design, robust network intervention.
Verification and Optimal Control of ContextSensitive Probabilistic Boolean Networks Using Model Checking and Polynomial Optimization
"... One of the significant topics in systems biology is to develop control theory of gene regulatory networks (GRNs). In typical control of GRNs, expression of some genes is inhibited (activated) by manipulating external stimuli and expression of other genes. It is expected to apply control theory of G ..."
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One of the significant topics in systems biology is to develop control theory of gene regulatory networks (GRNs). In typical control of GRNs, expression of some genes is inhibited (activated) by manipulating external stimuli and expression of other genes. It is expected to apply control theory of GRNs to gene therapy technologies in the future. In this paper, a control method using a Boolean network (BN) is studied. A BN is widely used as a model of GRNs, and gene expression is expressed by a binary value (ON or OFF). In particular, a contextsensitive probabilistic Boolean network (CSPBN), which is one of the extended models of BNs, is used. For CSPBNs, the verification problem and the optimal control problem are considered. For the verification problem, a solution method using the probabilistic model checker PRISM is proposed. For the optimal control problem, a solution method using polynomial optimization is proposed. Finally, a numerical example on the WNT5A network, which is related to melanoma, is presented. The proposed methods provide us useful tools in control theory of GRNs.
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"... All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. ..."
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Research Article Algorithms and Complexity Analyses for Control of Singleton Attractors in Boolean Networks
"... A Boolean network (BN) is a mathematical model of genetic networks. We propose several algorithms for control of singleton attractors in BN. We theoretically estimate the averagecase time complexities of the proposed algorithms, and confirm them by computer experiments. The results suggest the impo ..."
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A Boolean network (BN) is a mathematical model of genetic networks. We propose several algorithms for control of singleton attractors in BN. We theoretically estimate the averagecase time complexities of the proposed algorithms, and confirm them by computer experiments. The results suggest the importance of gene ordering. Especially, setting internal nodes ahead yields shorter computational time than setting external nodes ahead in various types of algorithms. We also present a heuristic algorithm which does not look for the optimal solution but for the solution whose computational time is shorter than that of the exact algorithms. Copyright © 2008 Morihiro Hayashida et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1.
Impact Factor: 2.78 · DOI: 10.1109/TAC.2013.2294821 CITATIONS
, 2016
"... All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. ..."
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All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.