Results 21 to 30 of about 20,901 (253)
Reducing Boolean Networks with Backward Boolean Equivalence [PDF]
, 2021 Boolean Networks (BNs) are established models to qualitatively describe biological systems. The analysis of BNs might be infeasible for medium to large BNs due to the state-space explosion problem. We propose a novel reduction technique called \emph{Backward Boolean Equivalence} (BBE), which preserves some properties of interest of BNs.
Georgios Argyris +4 more
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Random Networks with Quantum Boolean Functions
Mathematics, 2021 We propose quantum Boolean networks, which can be classified as deterministic reversible asynchronous Boolean networks. This model is based on the previously developed concept of quantum Boolean functions.
Mario Franco +3 more
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Quotients of Probabilistic Boolean Networks [PDF]
IEEE Transactions on Automatic Control, 2022 A probabilistic Boolean network (PBN) is a discrete-time system composed of a collection of Boolean networks between which the PBN switches in a stochastic manner. This paper focuses on the study of quotients of PBNs. Given a PBN and an equivalence relation on its state set, we consider a probabilistic transition system that is generated by the PBN ...
Rui Li 0007 +2 more
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An Evaluation of Methods for Inferring Boolean Networks from Time-Series Data. [PDF]
PLoS ONE, 2013 Regulatory networks play a central role in cellular behavior and decision making. Learning these regulatory networks is a major task in biology, and devising computational methods and mathematical models for this task is a major endeavor in ...
Natalie Berestovsky, Luay Nakhleh
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Symmetrizable Boolean networks
Information Sciences, 2023 In this work, we provide a procedure that allows us to transform certain kinds of deterministic Boolean networks on minterm or maxterm functions into symmetric ones, so inferring that such symmetrizable networks can present only periodic points of periods 1 or 2. In particular, we deal with generalized parallel (or synchronous) dynamical systems (GPDS)
Juan A. Aledo +4 more
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An ASP-based Approach for Attractor Enumeration in Synchronous and Asynchronous Boolean Networks [PDF]
Electronic Proceedings in Theoretical Computer Science, 2019 Boolean networks are conventionally used to represent and simulate gene regulatory networks. In the analysis of the dynamic of a Boolean network, the attractors are the objects of a special attention.
Tarek Khaled, Belaïd Benhamou
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On the Lyapunov Exponent of Monotone Boolean Networks †
Mathematics, 2020 Boolean networks are discrete dynamical systems comprised of coupled Boolean functions. An important parameter that characterizes such systems is the Lyapunov exponent, which measures the state stability of the system to small perturbations.
Ilya Shmulevich
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An attractor-based complexity measurement for Boolean recurrent neural networks. [PDF]
PLoS ONE, 2014 We provide a novel refined attractor-based complexity measurement for Boolean recurrent neural networks that represents an assessment of their computational power in terms of the significance of their attractor dynamics.
Jérémie Cabessa, Alessandro E P Villa
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Boolean network model predicts knockout mutant phenotypes of fission yeast. [PDF]
PLoS ONE, 2013 Boolean networks (ornetworks of switches) are extremely simple mathematical models of biochemical signaling networks. Under certain circumstances, Boolean networks, despite their simplicity, are capable of predicting dynamical activation patterns of gene
Maria I Davidich, Stefan Bornholdt
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Parallel One-Step Control of Parametrised Boolean Networks
Mathematics, 2021 Boolean network (BN) is a simple model widely used to study complex dynamic behaviour of biological systems. Nonetheless, it might be difficult to gather enough data to precisely capture the behavior of a biological system into a set of Boolean functions.
Luboš Brim +3 more
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