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13
Statistical model checking in BioLab: applications to the automated analysis of TCell receptor signaling pathway
 In CMSB’08
, 2008
"... Abstract. We present an algorithm, called BioLab, for verifying temporal properties of rulebased models of cellular signalling networks. BioLab models are encoded in the BioNetGen language, and properties are expressed as formulae in probabilistic bounded linear temporal logic. Temporal logic is a ..."
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Abstract. We present an algorithm, called BioLab, for verifying temporal properties of rulebased models of cellular signalling networks. BioLab models are encoded in the BioNetGen language, and properties are expressed as formulae in probabilistic bounded linear temporal logic. Temporal logic is a formalism for representing and reasoning about propositions qualified in terms of time. Properties are then verified using sequential hypothesis testing on executions generated using stochastic simulation. BioLab is optimal, in the sense that it generates the minimum number of executions necessary to verify the given property. BioLab also provides guarantees on the probability of it generating TypeI (i.e., falsepositive) and TypeII (i.e., falsenegative) errors. Moreover, these error bounds are prespecified by the user. We demonstrate BioLab by verifying stochastic effects and bistability in the dynamics of the Tcell receptor signaling network.
Controllability of Boolean control networks via the PerronFrobenius theory
 AUTOMATICA
, 2012
"... Boolean control networks (BCNs) are recently attracting considerable interest as computational models for genetic and cellular networks. Addressing controltheoretic problems in BCNs may lead to a better understanding of the intrinsic control in biological systems, as well as to developing suitable ..."
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Cited by 13 (3 self)
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Boolean control networks (BCNs) are recently attracting considerable interest as computational models for genetic and cellular networks. Addressing controltheoretic problems in BCNs may lead to a better understanding of the intrinsic control in biological systems, as well as to developing suitable protocols for manipulating biological systems using exogenous inputs. We introduce two definitions for controllability of a BCN, and show that a necessary and sufficient condition for each form of controllability is that a certain nonnegative matrix is irreducible or primitive, respectively. Our analysis is based on a result that may be of independent interest, namely, a simple algebraic formula for the number of different control sequences that steer a BCN between given initial and final states in a given number of time steps, while avoiding a set of forbidden states.
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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Cited by 4 (0 self)
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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
Towards inference and learning in dynamic bayesian networks using generalized evidence
, 2008
"... This report introduces a novel approach to performing inference and learning in Dynamic Bayesian Networks (DBN). The traditional approach to inference and learning in DBNs involves conditioning on one or more finitelength observation sequences. In this report, we consider conditioning on what we wi ..."
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Cited by 3 (1 self)
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This report introduces a novel approach to performing inference and learning in Dynamic Bayesian Networks (DBN). The traditional approach to inference and learning in DBNs involves conditioning on one or more finitelength observation sequences. In this report, we consider conditioning on what we will call generalized evidence, which consists of a possibly infinite set of behaviors compactly encoded in the form of a formula, φ, in temporal logic. We then introduce exact algorithms for solving inference problems (i.e., computing P (Xφ)) and learning problems (i.e., computing P (Θφ)) using techniques from the field of Model Checking. The advantage of our approach is that it enables scientists to pose and solve inference and learning problems that cannot be expressed using traditional approaches. The contributions of this report include: (1) the introduction of the inference and learning problems over generalized evidence, (2) exact algorithms for solving these problems for a restricted class of DBNs, and (3) a series of case studies demonstrating the Dynamic Bayesian Networks (DBNs) are a family of probabilistic graphical models for representing stochastic processes. The inference and learning problems in DBNs involve computing posterior distributions over unobserved (aka hidden) variables or parameters, respectively, given
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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Cited by 3 (3 self)
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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.
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.
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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Cited by 1 (0 self)
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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.
Applications to the automated analysis of TCell Receptor Signaling Pathway ⋆
"... Abstract. We present an algorithm, called BioLab, for verifying temporal properties of rulebased models of cellular signalling networks. BioLab models are encoded in the BioNetGen language, and properties are expressed as formulae in probabilistic bounded linear temporal logic. Temporal logic is a ..."
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Abstract. We present an algorithm, called BioLab, for verifying temporal properties of rulebased models of cellular signalling networks. BioLab models are encoded in the BioNetGen language, and properties are expressed as formulae in probabilistic bounded linear temporal logic. Temporal logic is a formalism for representing and reasoning about propositions qualified in terms of time. Properties are then verified using sequential hypothesis testing on executions generated using stochastic simulation. BioLab is optimal, in the sense that it generates the minimum
Optimal Control of Asynchronous Boolean Networks Modeled by Petri Nets
"... Abstract. A Boolean network model is one of the models of gene regulatory networks, and is widely used in analysis and control. Although a Boolean network is a class of discretetime nonlinear systems and expresses the synchronous behavior, it is important to consider the asynchronous behavior. In t ..."
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Abstract. A Boolean network model is one of the models of gene regulatory networks, and is widely used in analysis and control. Although a Boolean network is a class of discretetime nonlinear systems and expresses the synchronous behavior, it is important to consider the asynchronous behavior. In this paper, using a Petri net, a new modeling method of asynchronous Boolean networks with control inputs is proposed. Furthermore, the optimal control problem of Petri nets expressing asynchronous Boolean networks is formulated, and a solution method is proposed. The proposed approach provides us a new control method of gene regulatory networks. 1
doi:10.1155/2010/210685 Research Article PolynomialTime Algorithm for Controllability Test of a Class of Boolean Biological Networks
"... Copyright © 2010 Koichi Kobayashi 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. In recent years, Booleannetworkmodelbased ..."
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Copyright © 2010 Koichi Kobayashi 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. In recent years, Booleannetworkmodelbased approaches to dynamical analysis of complex biological networks such as gene regulatory networks have been extensively studied. One of the fundamental problems in control theory of such networks is the problem of determining whether a given substance quantity can be arbitrarily controlled by operating the other substance quantities, which we call the controllability problem. This paper proposes a polynomialtime algorithm for solving this problem. Although the algorithm is based on a sufficient condition for controllability, it is easily computable for a wider class of largescale biological networks compared with the existing approaches. A key to this success in our approach is to give up computing Boolean operations in a rigorous way and to exploit an adjacency matrix of a directed graph induced by a Boolean network. By applying the proposed approach to a neurotransmitter signaling pathway, it is shown that it is effective. 1.