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IDENTIFICATION OF GENETIC NETWORKS FROM A SMALL NUMBER OF GENE EXPRESSION PATTERNS UNDER THE BOOLEAN NETWORK MODEL
 PACIFIC SYMPOSIUM ON BIOCOMPUTING 4:1728 (1999)
, 1999
"... ... for inferring genetic network architectures from state transition tables which correspond to time series of gene expression patterns, using the Boolean network model. Their results of computational experiments suggested that a small number of state transition (INPUT/OUTPUT) pairs are sufficient ..."
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Cited by 254 (17 self)
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in order to infer the original Boolean network correctly. This paper gives a mathematical proof for their observation. Precisely, this paper devises a much simpler algorithm for the same problem and proves that, if the indegree of each node (i.e., the number of input nodes to each node) is bounded by a
Robustness and Information Propagation in Attractors of Random Boolean Networks
, 2012
"... Attractors represent the longterm behaviors of Random Boolean Networks. We study how the amount of information propagated between the nodes when on an attractor, as quantified by the average pairwise mutual information (IA), relates to the robustness of the attractor to perturbations (RA). We find ..."
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Attractors represent the longterm behaviors of Random Boolean Networks. We study how the amount of information propagated between the nodes when on an attractor, as quantified by the average pairwise mutual information (IA), relates to the robustness of the attractor to perturbations (RA). We find
Elementary Gates for Quantum Computation
, 1995
"... We show that a set of gates that consists of all onebit quantum gates (U(2)) and the twobit exclusiveor gate (that maps Boolean values (x, y)to(x, x⊕y)) is universal in the sense that all unitary operations on arbitrarily many bits n (U(2 n)) can be expressed as compositions of these gates. We in ..."
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Cited by 280 (11 self)
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constructions of quantum computational networks. We derive upper and lower bounds on the exact number of elementary gates required to build up a variety of two and threebit quantum gates, the asymptotic number required for nbit DeutschToffoli gates, and make some observations about the number required
attractors of
"... Dynamics of a stochastic spatially extended system predicted by comparing deterministic and stochastic ..."
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Dynamics of a stochastic spatially extended system predicted by comparing deterministic and stochastic
An AttractorBased Complexity Measurement for Boolean Recurrent Neural Networks
, 2013
"... We provide a novel refined attractorbased complexity measurement for Boolean recurrent neural networks that represents an assessment of their computational power in terms of the significance of their attractor dynamics. This complexity measurement is achieved by first proving a computational equiva ..."
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We provide a novel refined attractorbased complexity measurement for Boolean recurrent neural networks that represents an assessment of their computational power in terms of the significance of their attractor dynamics. This complexity measurement is achieved by first proving a computational
A REDUCTION METHOD FOR BOOLEAN NETWORK MODELS PROVEN TO CONSERVE ATTRACTORS
"... Abstract. Boolean models, wherein each component is characterized with a binary (ON or OFF) variable, have been widely employed for dynamic modeling of biological regulatory networks. However, the exponential dependencse of the size of the state space of these models on the number of nodes in the ne ..."
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in the network can be a daunting prospect for attractor analysis of largescale systems. We have previously proposed a network reduction technique for Boolean models and demonstrated its applicability on two biological systems, namely, the abscisic acid signal transduction network as well as the TLGL leukemia
Algorithms and Complexity Analyses for Control of Singleton Attractors in Boolean Networks
, 2008
"... 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
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
Time Out of Joint: Attractors in Asynchronous Random Boolean Networks
 Proceedings of the Fourth European Conference on Artificial Life (ECAL97
, 1997
"... Random Boolean networks (RBNs) are complex systems composed of many simple components which have been much analysed and shown to have many robust generic properties. Some synchronous versions have been influential as highly abstract models of specific biological systems, but for many biological phen ..."
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Cited by 71 (0 self)
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Random Boolean networks (RBNs) are complex systems composed of many simple components which have been much analysed and shown to have many robust generic properties. Some synchronous versions have been influential as highly abstract models of specific biological systems, but for many biological
Results 1  10
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27,518