Boolean networks as modelling framework
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Florian eGreil
2012-08-01
Full Text Available In a network, the components of a given system are represented as nodes, the interactions are abstracted as links between the nodes. Boolean networks refer to a class of dynamics on networks, in fact it is the simplest possible dynamics where each node has a value 0 or 1. This allows to investigate extensively the dynamics both analytically and by numerical experiments. The present article focuses on the theoretical concept of relevant components and the immediate application in plant biology, references for more in-depths treatment of the mathematical details are also given.
Piecewise linear and Boolean models of chemical reaction networks.
Veliz-Cuba, Alan; Kumar, Ajit; Josić, Krešimir
2014-12-01
Models of biochemical networks are frequently complex and high-dimensional. Reduction methods that preserve important dynamical properties are therefore essential for their study. Interactions in biochemical networks are frequently modeled using Hill functions ([Formula: see text]). Reduced ODEs and Boolean approximations of such model networks have been studied extensively when the exponent [Formula: see text] is large. However, while the case of small constant [Formula: see text] appears in practice, it is not well understood. We provide a mathematical analysis of this limit and show that a reduction to a set of piecewise linear ODEs and Boolean networks can be mathematically justified. The piecewise linear systems have closed-form solutions that closely track those of the fully nonlinear model. The simpler, Boolean network can be used to study the qualitative behavior of the original system. We justify the reduction using geometric singular perturbation theory and compact convergence, and illustrate the results in network models of a toggle switch and an oscillator.
Approximating Attractors of Boolean Networks by Iterative CTL Model Checking.
Klarner, Hannes; Siebert, Heike
2015-01-01
This paper introduces the notion of approximating asynchronous attractors of Boolean networks by minimal trap spaces. We define three criteria for determining the quality of an approximation: "faithfulness" which requires that the oscillating variables of all attractors in a trap space correspond to their dimensions, "univocality" which requires that there is a unique attractor in each trap space, and "completeness" which requires that there are no attractors outside of a given set of trap spaces. Each is a reachability property for which we give equivalent model checking queries. Whereas faithfulness and univocality can be decided by model checking the corresponding subnetworks, the naive query for completeness must be evaluated on the full state space. Our main result is an alternative approach which is based on the iterative refinement of an initially poor approximation. The algorithm detects so-called autonomous sets in the interaction graph, variables that contain all their regulators, and considers their intersection and extension in order to perform model checking on the smallest possible state spaces. A benchmark, in which we apply the algorithm to 18 published Boolean networks, is given. In each case, the minimal trap spaces are faithful, univocal, and complete, which suggests that they are in general good approximations for the asymptotics of Boolean networks.
Additive functions in boolean models of gene regulatory network modules.
Darabos, Christian; Di Cunto, Ferdinando; Tomassini, Marco; Moore, Jason H; Provero, Paolo; Giacobini, Mario
2011-01-01
Gene-on-gene regulations are key components of every living organism. Dynamical abstract models of genetic regulatory networks help explain the genome's evolvability and robustness. These properties can be attributed to the structural topology of the graph formed by genes, as vertices, and regulatory interactions, as edges. Moreover, the actual gene interaction of each gene is believed to play a key role in the stability of the structure. With advances in biology, some effort was deployed to develop update functions in boolean models that include recent knowledge. We combine real-life gene interaction networks with novel update functions in a boolean model. We use two sub-networks of biological organisms, the yeast cell-cycle and the mouse embryonic stem cell, as topological support for our system. On these structures, we substitute the original random update functions by a novel threshold-based dynamic function in which the promoting and repressing effect of each interaction is considered. We use a third real-life regulatory network, along with its inferred boolean update functions to validate the proposed update function. Results of this validation hint to increased biological plausibility of the threshold-based function. To investigate the dynamical behavior of this new model, we visualized the phase transition between order and chaos into the critical regime using Derrida plots. We complement the qualitative nature of Derrida plots with an alternative measure, the criticality distance, that also allows to discriminate between regimes in a quantitative way. Simulation on both real-life genetic regulatory networks show that there exists a set of parameters that allows the systems to operate in the critical region. This new model includes experimentally derived biological information and recent discoveries, which makes it potentially useful to guide experimental research. The update function confers additional realism to the model, while reducing the complexity
Boolean networks with multiexpressions and parameters.
Zou, Yi Ming
2013-01-01
To model biological systems using networks, it is desirable to allow more than two levels of expression for the nodes and to allow the introduction of parameters. Various modeling and simulation methods addressing these needs using Boolean models, both synchronous and asynchronous, have been proposed in the literature. However, analytical study of these more general Boolean networks models is lagging. This paper aims to develop a concise theory for these different Boolean logic-based modeling methods. Boolean models for networks where each node can have more than two levels of expression and Boolean models with parameters are defined algebraically with examples provided. Certain classes of random asynchronous Boolean networks and deterministic moduli asynchronous Boolean networks are investigated in detail using the setting introduced in this paper. The derived theorems provide a clear picture for the attractor structures of these asynchronous Boolean networks.
Boolean network model predicts cell cycle sequence of fission yeast.
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Maria I Davidich
Full Text Available A Boolean network model of the cell-cycle regulatory network of fission yeast (Schizosaccharomyces Pombe is constructed solely on the basis of the known biochemical interaction topology. Simulating the model in the computer faithfully reproduces the known activity sequence of regulatory proteins along the cell cycle of the living cell. Contrary to existing differential equation models, no parameters enter the model except the structure of the regulatory circuitry. The dynamical properties of the model indicate that the biological dynamical sequence is robustly implemented in the regulatory network, with the biological stationary state G1 corresponding to the dominant attractor in state space, and with the biological regulatory sequence being a strongly attractive trajectory. Comparing the fission yeast cell-cycle model to a similar model of the corresponding network in S. cerevisiae, a remarkable difference in circuitry, as well as dynamics is observed. While the latter operates in a strongly damped mode, driven by external excitation, the S. pombe network represents an auto-excited system with external damping.
Learning restricted Boolean network model by time-series data.
Ouyang, Hongjia; Fang, Jie; Shen, Liangzhong; Dougherty, Edward R; Liu, Wenbin
2014-01-01
Restricted Boolean networks are simplified Boolean networks that are required for either negative or positive regulations between genes. Higa et al. (BMC Proc 5:S5, 2011) proposed a three-rule algorithm to infer a restricted Boolean network from time-series data. However, the algorithm suffers from a major drawback, namely, it is very sensitive to noise. In this paper, we systematically analyze the regulatory relationships between genes based on the state switch of the target gene and propose an algorithm with which restricted Boolean networks may be inferred from time-series data. We compare the proposed algorithm with the three-rule algorithm and the best-fit algorithm based on both synthetic networks and a well-studied budding yeast cell cycle network. The performance of the algorithms is evaluated by three distance metrics: the normalized-edge Hamming distance [Formula: see text], the normalized Hamming distance of state transition [Formula: see text], and the steady-state distribution distance μ (ssd). Results show that the proposed algorithm outperforms the others according to both [Formula: see text] and [Formula: see text], whereas its performance according to μ (ssd) is intermediate between best-fit and the three-rule algorithms. Thus, our new algorithm is more appropriate for inferring interactions between genes from time-series data.
Modeling integrated cellular machinery using hybrid Petri-Boolean networks.
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Natalie Berestovsky
Full Text Available The behavior and phenotypic changes of cells are governed by a cellular circuitry that represents a set of biochemical reactions. Based on biological functions, this circuitry is divided into three types of networks, each encoding for a major biological process: signal transduction, transcription regulation, and metabolism. This division has generally enabled taming computational complexity dealing with the entire system, allowed for using modeling techniques that are specific to each of the components, and achieved separation of the different time scales at which reactions in each of the three networks occur. Nonetheless, with this division comes loss of information and power needed to elucidate certain cellular phenomena. Within the cell, these three types of networks work in tandem, and each produces signals and/or substances that are used by the others to process information and operate normally. Therefore, computational techniques for modeling integrated cellular machinery are needed. In this work, we propose an integrated hybrid model (IHM that combines Petri nets and Boolean networks to model integrated cellular networks. Coupled with a stochastic simulation mechanism, the model simulates the dynamics of the integrated network, and can be perturbed to generate testable hypotheses. Our model is qualitative and is mostly built upon knowledge from the literature and requires fine-tuning of very few parameters. We validated our model on two systems: the transcriptional regulation of glucose metabolism in human cells, and cellular osmoregulation in S. cerevisiae. The model produced results that are in very good agreement with experimental data, and produces valid hypotheses. The abstract nature of our model and the ease of its construction makes it a very good candidate for modeling integrated networks from qualitative data. The results it produces can guide the practitioner to zoom into components and interconnections and investigate them
Dynamical modeling of the cholesterol regulatory pathway with Boolean networks
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Corcos Laurent
2008-11-01
Full Text Available Abstract Background Qualitative dynamics of small gene regulatory networks have been studied in quite some details both with synchronous and asynchronous analysis. However, both methods have their drawbacks: synchronous analysis leads to spurious attractors and asynchronous analysis lacks computational efficiency, which is a problem to simulate large networks. We addressed this question through the analysis of a major biosynthesis pathway. Indeed the cholesterol synthesis pathway plays a pivotal role in dislypidemia and, ultimately, in cancer through intermediates such as mevalonate, farnesyl pyrophosphate and geranyl geranyl pyrophosphate, but no dynamic model of this pathway has been proposed until now. Results We set up a computational framework to dynamically analyze large biological networks. This framework associates a classical and computationally efficient synchronous Boolean analysis with a newly introduced method based on Markov chains, which identifies spurious cycles among the results of the synchronous simulation. Based on this method, we present here the results of the analysis of the cholesterol biosynthesis pathway and its physiological regulation by the Sterol Response Element Binding Proteins (SREBPs, as well as the modeling of the action of statins, inhibitor drugs, on this pathway. The in silico experiments show the blockade of the cholesterol endogenous synthesis by statins and its regulation by SREPBs, in full agreement with the known biochemical features of the pathway. Conclusion We believe that the method described here to identify spurious cycles opens new routes to compute large and biologically relevant models, thanks to the computational efficiency of synchronous simulation. Furthermore, to the best of our knowledge, we present here the first dynamic systems biology model of the human cholesterol pathway and several of its key regulatory control elements, hoping it would provide a good basis to perform in silico
Mapping Complex Networks: Exploring Boolean Modeling of Signal Transduction Pathways
Bhardwaj, Gaurav; Wells, Christine P.; Albert, Reka; van Rossum, Damian B.; Patterson, Randen L
2009-01-01
In this study, we explored the utility of a descriptive and predictive bionetwork model for phospholipase C-coupled calcium signaling pathways, built with non-kinetic experimental information. Boolean models generated from these data yield oscillatory activity patterns for both the endoplasmic reticulum resident inositol-1,4,5-trisphosphate receptor (IP3R) and the plasma-membrane resident canonical transient receptor potential channel 3 (TRPC3). These results are specific as randomization of ...
Synchronization of Asynchronous Switched Boolean Network.
Zhang, Hao; Wang, Xingyuan; Lin, Xiaohui
2015-01-01
In this paper, the complete synchronizations for asynchronous switched Boolean network with free Boolean sequence controllers and close-loop controllers are studied. First, the basic asynchronous switched Boolean network model is provided. With the method of semi-tensor product, the Boolean dynamics is translated into linear representation. Second, necessary and sufficient conditions for ASBN synchronization with free Boolean sequence control and close-loop control are derived, respectively. Third, some illustrative examples are provided to show the efficiency of the proposed methods.
Relative stability of network states in Boolean network models of gene regulation in development.
Zhou, Joseph Xu; Samal, Areejit; d'Hérouël, Aymeric Fouquier; Price, Nathan D; Huang, Sui
2016-01-01
Progress in cell type reprogramming has revived the interest in Waddington's concept of the epigenetic landscape. Recently researchers developed the quasi-potential theory to represent the Waddington's landscape. The Quasi-potential U(x), derived from interactions in the gene regulatory network (GRN) of a cell, quantifies the relative stability of network states, which determine the effort required for state transitions in a multi-stable dynamical system. However, quasi-potential landscapes, originally developed for continuous systems, are not suitable for discrete-valued networks which are important tools to study complex systems. In this paper, we provide a framework to quantify the landscape for discrete Boolean networks (BNs). We apply our framework to study pancreas cell differentiation where an ensemble of BN models is considered based on the structure of a minimal GRN for pancreas development. We impose biologically motivated structural constraints (corresponding to specific type of Boolean functions) and dynamical constraints (corresponding to stable attractor states) to limit the space of BN models for pancreas development. In addition, we enforce a novel functional constraint corresponding to the relative ordering of attractor states in BN models to restrict the space of BN models to the biological relevant class. We find that BNs with canalyzing/sign-compatible Boolean functions best capture the dynamics of pancreas cell differentiation. This framework can also determine the genes' influence on cell state transitions, and thus can facilitate the rational design of cell reprogramming protocols.
From Boolean Network Model to Continuous Model Helps in Design of Functional Circuits
Bin Shao; Xiang Liu; Dongliang Zhang; Jiayi Wu; Qi Ouyang
2015-01-01
Computational circuit design with desired functions in a living cell is a challenging task in synthetic biology. To achieve this task, numerous methods that either focus on small scale networks or use evolutionary algorithms have been developed. Here, we propose a two-step approach to facilitate the design of functional circuits. In the first step, the search space of possible topologies for target functions is reduced by reverse engineering using a Boolean network model. In the second step, ...
Reverse engineering Boolean networks: from Bernoulli mixture models to rule based systems.
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Mehreen Saeed
Full Text Available A Boolean network is a graphical model for representing and analyzing the behavior of gene regulatory networks (GRN. In this context, the accurate and efficient reconstruction of a Boolean network is essential for understanding the gene regulation mechanism and the complex relations that exist therein. In this paper we introduce an elegant and efficient algorithm for the reverse engineering of Boolean networks from a time series of multivariate binary data corresponding to gene expression data. We call our method ReBMM, i.e., reverse engineering based on Bernoulli mixture models. The time complexity of most of the existing reverse engineering techniques is quite high and depends upon the indegree of a node in the network. Due to the high complexity of these methods, they can only be applied to sparsely connected networks of small sizes. ReBMM has a time complexity factor, which is independent of the indegree of a node and is quadratic in the number of nodes in the network, a big improvement over other techniques and yet there is little or no compromise in accuracy. We have tested ReBMM on a number of artificial datasets along with simulated data derived from a plant signaling network. We also used this method to reconstruct a network from real experimental observations of microarray data of the yeast cell cycle. Our method provides a natural framework for generating rules from a probabilistic model. It is simple, intuitive and illustrates excellent empirical results.
Boolean Networks with Multi-Expressions and Parameters.
Zou, Yi Ming
2013-07-01
To model biological systems using networks, it is desirable to allow more than two levels of expression for the nodes and to allow the introduction of parameters. Various modeling and simulation methods addressing these needs using Boolean models, both synchronous and asynchronous, have been proposed in the literature. However, analytical study of these more general Boolean networks models is lagging. This paper aims to develop a concise theory for these different Boolean logic based modeling methods. Boolean models for networks where each node can have more than two levels of expression and Boolean models with parameters are defined algebraically with examples provided. Certain classes of random asynchronous Boolean networks and deterministic moduli asynchronous Boolean networks are investigated in detail using the setting introduced in this paper. The derived theorems provide a clear picture for the attractor structures of these asynchronous Boolean networks.
Boolean networks with veto functions
Ebadi, Haleh; Klemm, Konstantin
2014-08-01
Boolean networks are discrete dynamical systems for modeling regulation and signaling in living cells. We investigate a particular class of Boolean functions with inhibiting inputs exerting a veto (forced zero) on the output. We give analytical expressions for the sensitivity of these functions and provide evidence for their role in natural systems. In an intracellular signal transduction network [Helikar et al., Proc. Natl. Acad. Sci. USA 105, 1913 (2008), 10.1073/pnas.0705088105], the functions with veto are over-represented by a factor exceeding the over-representation of threshold functions and canalyzing functions in the same system. In Boolean networks for control of the yeast cell cycle [Li et al., Proc. Natl. Acad. Sci. USA 101, 4781 (2004), 10.1073/pnas.0305937101; Davidich et al., PLoS ONE 3, e1672 (2008), 10.1371/journal.pone.0001672], no or minimal changes to the wiring diagrams are necessary to formulate their dynamics in terms of the veto functions introduced here.
Dahlhaus, Meike; Burkovski, Andre; Hertwig, Falk; Mussel, Christoph; Volland, Ruth; Fischer, Matthias; Debatin, Klaus-Michael; Kestler, Hans A; Beltinger, Christian
2016-02-01
Aurora Kinase A (AURKA) is often overexpressed in neuroblastoma (NB) with poor outcome. The causes of AURKA overexpression in NB are unknown. Here, we describe a gene regulatory network consisting of core regulators of AURKA protein expression and activation during mitosis to identify potential causes. This network was transformed to a dynamic Boolean model. Simulated activation of the serine/threonine protein kinase Greatwall (GWL, encoded by MASTL) that attenuates the pivotal AURKA inhibitor PP2A, predicted stabilization of AURKA. Consistent with this notion, gene set enrichment analysis showed enrichment of mitotic spindle assembly genes and MYCN target genes in NB with high GWL/MASTL expression. In line with the prediction of GWL/MASTL enhancing AURKA, elevated expression of GWL/MASTL was associated with NB risk factors and poor survival of patients. These results establish Boolean network modeling of oncogenic pathways in NB as a useful means for guided discovery in this enigmatic cancer.
Exploring phospholipase C-coupled Ca(2+) signalling networks using Boolean modelling.
Bhardwaj, G; Wells, C P; Albert, R; van Rossum, D B; Patterson, R L
2011-05-01
In this study, the authors explored the utility of a descriptive and predictive bionetwork model for phospholipase C-coupled calcium signalling pathways, built with non-kinetic experimental information. Boolean models generated from these data yield oscillatory activity patterns for both the endoplasmic reticulum resident inositol-1,4,5-trisphosphate receptor (IP(3)R) and the plasma-membrane resident canonical transient receptor potential channel 3 (TRPC3). These results are specific as randomisation of the Boolean operators ablates oscillatory pattern formation. Furthermore, knock-out simulations of the IP(3)R, TRPC3 and multiple other proteins recapitulate experimentally derived results. The potential of this approach can be observed by its ability to predict previously undescribed cellular phenotypes using in vitro experimental data. Indeed, our cellular analysis of the developmental and calcium-regulatory protein, DANGER1a, confirms the counter-intuitive predictions from our Boolean models in two highly relevant cellular models. Based on these results, the authors theorise that with sufficient legacy knowledge and/or computational biology predictions, Boolean networks can provide a robust method for predictive modelling of any biological system. [Includes supplementary material]. PMID:21639591
The role of certain Post classes in Boolean network models of genetic networks.
Shmulevich, Ilya; Lähdesmäki, Harri; Dougherty, Edward R; Astola, Jaakko; Zhang, Wei
2003-09-16
A topic of great interest and debate concerns the source of order and remarkable robustness observed in genetic regulatory networks. The study of the generic properties of Boolean networks has proven to be useful for gaining insight into such phenomena. The main focus, as regards ordered behavior in networks, has been on canalizing functions, internal homogeneity or bias, and network connectivity. Here we examine the role that certain classes of Boolean functions that are closed under composition play in the emergence of order in Boolean networks. The closure property implies that any gene at any number of steps in the future is guaranteed to be governed by a function from the same class. By means of Derrida curves on random Boolean networks and percolation simulations on square lattices, we demonstrate that networks constructed from functions belonging to these classes have a tendency toward ordered behavior. Thus they are not overly sensitive to initial conditions, and damage does not readily spread throughout the network. In addition, the considered classes are significantly larger than the class of canalizing functions as the connectivity increases. The functions in these classes exhibit the same kind of preference toward biased functions as do canalizing functions, meaning that functions from this class are likely to be biased. Finally, functions from this class have a natural way of ensuring robustness against noise and perturbations, thus representing plausible evolutionarily selected candidates for regulatory rules in genetic networks. PMID:12963822
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Wei Lu
Full Text Available In this paper, we consider the Minimum Reaction Insertion (MRI problem for finding the minimum number of additional reactions from a reference metabolic network to a host metabolic network so that a target compound becomes producible in the revised host metabolic network in a Boolean model. Although a similar problem for larger networks is solvable in a flux balance analysis (FBA-based model, the solution of the FBA-based model tends to include more reactions than that of the Boolean model. However, solving MRI using the Boolean model is computationally more expensive than using the FBA-based model since the Boolean model needs more integer variables. Therefore, in this study, to solve MRI for larger networks in the Boolean model, we have developed an efficient Integer Programming formalization method in which the number of integer variables is reduced by the notion of feedback vertex set and minimal valid assignment. As a result of computer experiments conducted using the data of metabolic networks of E. coli and reference networks downloaded from the Kyoto Encyclopedia of Genes and Genomes (KEGG database, we have found that the developed method can appropriately solve MRI in the Boolean model and is applicable to large scale-networks for which an exhaustive search does not work. We have also compared the developed method with the existing connectivity-based methods and FBA-based methods, and show the difference between the solutions of our method and the existing methods. A theoretical analysis of MRI is also conducted, and the NP-completeness of MRI is proved in the Boolean model. Our developed software is available at "http://sunflower.kuicr.kyoto-u.ac.jp/~rogi/minRect/minRect.html."
Lu, Wei; Tamura, Takeyuki; Song, Jiangning; Akutsu, Tatsuya
2014-01-01
In this paper, we consider the Minimum Reaction Insertion (MRI) problem for finding the minimum number of additional reactions from a reference metabolic network to a host metabolic network so that a target compound becomes producible in the revised host metabolic network in a Boolean model. Although a similar problem for larger networks is solvable in a flux balance analysis (FBA)-based model, the solution of the FBA-based model tends to include more reactions than that of the Boolean model. However, solving MRI using the Boolean model is computationally more expensive than using the FBA-based model since the Boolean model needs more integer variables. Therefore, in this study, to solve MRI for larger networks in the Boolean model, we have developed an efficient Integer Programming formalization method in which the number of integer variables is reduced by the notion of feedback vertex set and minimal valid assignment. As a result of computer experiments conducted using the data of metabolic networks of E. coli and reference networks downloaded from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, we have found that the developed method can appropriately solve MRI in the Boolean model and is applicable to large scale-networks for which an exhaustive search does not work. We have also compared the developed method with the existing connectivity-based methods and FBA-based methods, and show the difference between the solutions of our method and the existing methods. A theoretical analysis of MRI is also conducted, and the NP-completeness of MRI is proved in the Boolean model. Our developed software is available at "http://sunflower.kuicr.kyoto-u.ac.jp/~rogi/minRect/minRect.html."
Li, X Y; Yang, G W; Zheng, D S; Guo, W S; Hung, W N N
2015-01-01
Genetic regulatory networks are the key to understanding biochemical systems. One condition of the genetic regulatory network under different living environments can be modeled as a synchronous Boolean network. The attractors of these Boolean networks will help biologists to identify determinant and stable factors. Existing methods identify attractors based on a random initial state or the entire state simultaneously. They cannot identify the fixed length attractors directly. The complexity of including time increases exponentially with respect to the attractor number and length of attractors. This study used the bounded model checking to quickly locate fixed length attractors. Based on the SAT solver, we propose a new algorithm for efficiently computing the fixed length attractors, which is more suitable for large Boolean networks and numerous attractors' networks. After comparison using the tool BooleNet, empirical experiments involving biochemical systems demonstrated the feasibility and efficiency of our approach.
Dynamical modeling of the cholesterol regulatory pathway with Boolean networks
Corcos Laurent; Kervizic Gwenael
2008-01-01
Abstract Background Qualitative dynamics of small gene regulatory networks have been studied in quite some details both with synchronous and asynchronous analysis. However, both methods have their drawbacks: synchronous analysis leads to spurious attractors and asynchronous analysis lacks computational efficiency, which is a problem to simulate large networks. We addressed this question through the analysis of a major biosynthesis pathway. Indeed the cholesterol synthesis pathway plays a pivo...
Altered Micro-RNA Degradation Promotes Tumor Heterogeneity: A Result from Boolean Network Modeling.
Wu, Yunyi; Krueger, Gerhard R F; Wang, Guanyu
2016-02-01
Cancer heterogeneity may reflect differential dynamical outcomes of the regulatory network encompassing biomolecules at both transcriptional and post-transcriptional levels. In other words, differential gene-expression profiles may correspond to different stable steady states of a mathematical model for simulation of biomolecular networks. To test this hypothesis, we simplified a regulatory network that is important for soft-tissue sarcoma metastasis and heterogeneity, comprising of transcription factors, micro-RNAs, and signaling components of the NOTCH pathway. We then used a Boolean network model to simulate the dynamics of this network, and particularly investigated the consequences of differential miRNA degradation modes. We found that efficient miRNA degradation is crucial for sustaining a homogenous and healthy phenotype, while defective miRNA degradation may lead to multiple stable steady states and ultimately to carcinogenesis and heterogeneity.
Computing smallest intervention strategies for multiple metabolic networks in a boolean model.
Lu, Wei; Tamura, Takeyuki; Song, Jiangning; Akutsu, Tatsuya
2015-02-01
This article considers the problem whereby, given two metabolic networks N1 and N2, a set of source compounds, and a set of target compounds, we must find the minimum set of reactions whose removal (knockout) ensures that the target compounds are not producible in N1 but are producible in N2. Similar studies exist for the problem of finding the minimum knockout with the smallest side effect for a single network. However, if technologies of external perturbations are advanced in the near future, it may be important to develop methods of computing the minimum knockout for multiple networks (MKMN). Flux balance analysis (FBA) is efficient if a well-polished model is available. However, that is not always the case. Therefore, in this article, we study MKMN in Boolean models and an elementary mode (EM)-based model. Integer linear programming (ILP)-based methods are developed for these models, since MKMN is NP-complete for both the Boolean model and the EM-based model. Computer experiments are conducted with metabolic networks of clostridium perfringens SM101 and bifidobacterium longum DJO10A, respectively known as bad bacteria and good bacteria for the human intestine. The results show that larger networks are more likely to have MKMN solutions. However, solving for these larger networks takes a very long time, and often the computation cannot be completed. This is reasonable, because small networks do not have many alternative pathways, making it difficult to satisfy the MKMN condition, whereas in large networks the number of candidate solutions explodes. Our developed software minFvskO is available online.
The receptor mosaic hypothesis of the engram: possible relevance of Boolean network modeling.
Zoli, M; Guidolin, D; Fuxe, K; Agnati, L F
1996-09-01
In the past 15 years, several lines of evidence have shown that receptors for chemical signals can interact in domains of the plasma membrane and possibly form molecular circuits encoding logical operators. In this frame, the receptor mosaic hypothesis of the engram was advanced. According to this proposal, aggregates of different receptor species (mosaics) may form in neuronal membranes (typically synapses) and constitute a memory trace (engram) of its activity. In the present paper, we present an attempt to model the functioning of aggregates of interacting receptors in membrane domains by means of random Boolean networks. PMID:8968825
Partial stability and stabilisation of Boolean networks
Chen, Hong-Wei; Sun, Liang-Jie; Liu, Yang
2016-07-01
In this paper, we investigate the stability of Boolean networks and the stabilisation of Boolean control networks with respect to part of the system's states. First, an algebraic expression of the Boolean (control) network is derived by the semi-tensor product of matrices. Then, some necessary and sufficient conditions for partial stability of Boolean networks are given. Finally, the stabilisation of Boolean control networks by a free control sequence and a state-feedback control is investigated and the respective necessary and sufficient conditions are obtained. Examples are provided to illustrate the efficiency of the obtained results.
Effect of memory in non-Markovian Boolean networks
Ebadi, Haleh; Ausloos, Marcel; Jafari, GholamReza
2016-01-01
One successful model of interacting biological systems is the Boolean network. The dynamics of a Boolean network, controlled with Boolean functions, is usually considered to be a Markovian (memory-less) process. However, both self organizing features of biological phenomena and their intelligent nature should raise some doubt about ignoring the history of their time evolution. Here, we extend the Boolean network Markovian approach: we involve the effect of memory on the dynamics. This can be explored by modifying Boolean functions into non-Markovian functions, for example, by investigating the usual non-Markovian threshold function, - one of the most applied Boolean functions. By applying the non-Markovian threshold function on the dynamical process of a cell cycle network, we discover a power law memory with a more robust dynamics than the Markovian dynamics.
Modeling and controlling the two-phase dynamics of the p53 network: a Boolean network approach
Lin, Guo-Qiang; Ao, Bin; Chen, Jia-Wei; Wang, Wen-Xu; Di, Zeng-Ru
2014-12-01
Although much empirical evidence has demonstrated that p53 plays a key role in tumor suppression, the dynamics and function of the regulatory network centered on p53 have not yet been fully understood. Here, we develop a Boolean network model to reproduce the two-phase dynamics of the p53 network in response to DNA damage. In particular, we map the fates of cells into two types of Boolean attractors, and we find that the apoptosis attractor does not exist for minor DNA damage, reflecting that the cell is reparable. As the amount of DNA damage increases, the basin of the repair attractor shrinks, accompanied by the rising of the apoptosis attractor and the expansion of its basin, indicating that the cell becomes more irreparable with more DNA damage. For severe DNA damage, the repair attractor vanishes, and the apoptosis attractor dominates the state space, accounting for the exclusive fate of death. Based on the Boolean network model, we explore the significance of links, in terms of the sensitivity of the two-phase dynamics, to perturbing the weights of links and removing them. We find that the links are either critical or ordinary, rather than redundant. This implies that the p53 network is irreducible, but tolerant of small mutations at some ordinary links, and this can be interpreted with evolutionary theory. We further devised practical control schemes for steering the system into the apoptosis attractor in the presence of DNA damage by pinning the state of a single node or perturbing the weight of a single link. Our approach offers insights into understanding and controlling the p53 network, which is of paramount importance for medical treatment and genetic engineering.
French, J.; Burningham, H.
2011-12-01
A major challenge in coastal geomorphology is the prediction of morphological change at a meso-scale (10 to 100 km; 10 to 100 yr). This scale sits awkwardly between understanding of geomorphological processes at the micro-scale, and broader aspects of coastal evolution informed by the Holocene stratigraphic record. In this paper, we explore the potential of a new kind of qualitative mathematical model implemented at a system level. Qualitative models derive predictions from the structure of the system rather from the detailed physics of the underlying processes. Although systems thinking is well established in geomorphology methodologies for converting system diagrams into simulation tools have not been widely investigated. In a recent Defra-funded project in the UK, a Boolean network approach was piloted and applied to the simulation of generic aspects of estuary response to environmental and anthropogenic forcing. We build on this to present a generic approach to the construction of system diagrams for estuaries and adjacent open coasts and their conversion into a network graph. In a Boolean model, each node of this graph is assigned a binary value, the state of which is determined by a logical function that specifies the combined influence of other nodes to which it is connected. System evolution is simulated by specifying a set of initial conditions and repeatedly evaluating the logical functions until an equilibrium condition is reached (either a steady state or a cyclical sequence between two end states). In our enhanced Boolean scheme, changes in morphology are allowed to feed back into intrinsic process variables (e.g. estuary waves or tidal prism), although some processes are externally imposed (e.g. sea-level rise). Arbitrary time lags condition the response of morphology to a change in process, such that some landforms adjust more rapidly than others. We also present a simulator architecture based around a solver and externally specified model components
Albert, Réka; Thakar, Juilee
2014-01-01
The biomolecules inside or near cells form a complex interacting system. Cellular phenotypes and behaviors arise from the totality of interactions among the components of this system. A fruitful way of modeling interacting biomolecular systems is by network-based dynamic models that characterize each component by a state variable, and describe the change in the state variables due to the interactions in the system. Dynamic models can capture the stable state patterns of this interacting system and can connect them to different cell fates or behaviors. A Boolean or logic model characterizes each biomolecule by a binary state variable that relates the abundance of that molecule to a threshold abundance necessary for downstream processes. The regulation of this state variable is described in a parameter free manner, making Boolean modeling a practical choice for systems whose kinetic parameters have not been determined. Boolean models integrate the body of knowledge regarding the components and interactions of biomolecular systems, and capture the system's dynamic repertoire, for example the existence of multiple cell fates. These models were used for a variety of systems and led to important insights and predictions. Boolean models serve as an efficient exploratory model, a guide for follow-up experiments, and as a foundation for more quantitative models.
Synchronization of Boolean Networks with Different Update Schemes.
Zhang, Hao; Wang, Xingyuan; Lin, Xiaohui
2014-01-01
In this paper, the synchronizations of Boolean networks with different update schemes (synchronized Boolean networks and asynchronous Boolean networks) are investigated. All nodes in Boolean network are represented in terms of semi-tensor product. First, we give the concept of inner synchronization and observe that all nodes in a Boolean network are synchronized with each other. Second, we investigate the outer synchronization between a driving Boolean network and a corresponding response Boolean network. We provide not only the concept of traditional complete synchronization, but also the anti-synchronization and get the anti-synchronization in simulation. Third, we extend the outer synchronization to asynchronous Boolean network and get the complete synchronization between an asynchronous Boolean network and a response Boolean network. Consequently, theorems for synchronization of Boolean networks and asynchronous Boolean networks are derived. Examples are provided to show the correctness of our theorems.
Boolean networks with reliable dynamics
Peixoto, Tiago P
2009-01-01
We investigated the properties of Boolean networks that follow a given reliable trajectory in state space. A reliable trajectory is defined as a sequence of states which is independent of the order in which the nodes are updated. We explored numerically the topology, the update functions, and the state space structure of these networks, which we constructed using a minimum number of links and the simplest update functions. We found that the clustering coefficient is larger than in random networks, and that the probability distribution of three-node motifs is similar to that found in gene regulation networks. Among the update functions, only a subset of all possible functions occur, and they can be classified according to their probability. More homogeneous functions occur more often, leading to a dominance of canalyzing functions. Finally, we studied the entire state space of the networks. We observed that with increasing systems size, fixed points become more dominant, moving the networks close to the frozen...
Intervention in Context-Sensitive Probabilistic Boolean Networks Revisited
Directory of Open Access Journals (Sweden)
Babak Faryabi
2009-01-01
Full Text Available An approximate representation for the state space of a context-sensitive probabilistic Boolean network has previously been proposed and utilized to devise therapeutic intervention strategies. Whereas the full state of a context-sensitive probabilistic Boolean network is specified by an ordered pair composed of a network context and a gene-activity profile, this approximate representation collapses the state space onto the gene-activity profiles alone. This reduction yields an approximate transition probability matrix, absent of context, for the Markov chain associated with the context-sensitive probabilistic Boolean network. As with many approximation methods, a price must be paid for using a reduced model representation, namely, some loss of optimality relative to using the full state space. This paper examines the effects on intervention performance caused by the reduction with respect to various values of the model parameters. This task is performed using a new derivation for the transition probability matrix of the context-sensitive probabilistic Boolean network. This expression of transition probability distributions is in concert with the original definition of context-sensitive probabilistic Boolean network. The performance of optimal and approximate therapeutic strategies is compared for both synthetic networks and a real case study. It is observed that the approximate representation describes the dynamics of the context-sensitive probabilistic Boolean network through the instantaneously random probabilistic Boolean network with similar parameters.
Delay synchronization of temporal Boolean networks
Wei, Qiang; Xie, Cheng-jun; Liang, Yi; Niu, Yu-jun; Lin, Da
2016-01-01
This paper investigates the delay synchronization between two temporal Boolean networks base on semi-tensor product method, which improve complete synchronization. Necessary and sufficient conditions for delay synchronization are drawn base on algebraic expression of temporal Boolean networks. A example is presented to show the effectiveness of theoretical analysis.
A full bayesian approach for boolean genetic network inference.
Directory of Open Access Journals (Sweden)
Shengtong Han
Full Text Available Boolean networks are a simple but efficient model for describing gene regulatory systems. A number of algorithms have been proposed to infer Boolean networks. However, these methods do not take full consideration of the effects of noise and model uncertainty. In this paper, we propose a full Bayesian approach to infer Boolean genetic networks. Markov chain Monte Carlo algorithms are used to obtain the posterior samples of both the network structure and the related parameters. In addition to regular link addition and removal moves, which can guarantee the irreducibility of the Markov chain for traversing the whole network space, carefully constructed mixture proposals are used to improve the Markov chain Monte Carlo convergence. Both simulations and a real application on cell-cycle data show that our method is more powerful than existing methods for the inference of both the topology and logic relations of the Boolean network from observed data.
Energy and criticality in random Boolean networks
Energy Technology Data Exchange (ETDEWEB)
Andrecut, M. [Institute for Biocomplexity and Informatics, University of Calgary, 2500 University Drive NW, Calgary, Alberta, T2N 1N4 (Canada)], E-mail: mandrecu@ucalgary.ca; Kauffman, S.A. [Institute for Biocomplexity and Informatics, University of Calgary, 2500 University Drive NW, Calgary, Alberta, T2N 1N4 (Canada)
2008-06-30
The central issue of the research on the Random Boolean Networks (RBNs) model is the characterization of the critical transition between ordered and chaotic phases. Here, we discuss an approach based on the 'energy' associated with the unsatisfiability of the Boolean functions in the RBNs model, which provides an upper bound estimation for the energy used in computation. We show that in the ordered phase the RBNs are in a 'dissipative' regime, performing mostly 'downhill' moves on the 'energy' landscape. Also, we show that in the disordered phase the RBNs have to 'hillclimb' on the 'energy' landscape in order to perform computation. The analytical results, obtained using Derrida's approximation method, are in complete agreement with numerical simulations.
Enhancing Boolean networks with fuzzy operators and edge tuning
Poret, Arnaud; Monteiro Sousa, Claudio; Boissel, Jean-Pierre
2014-01-01
Quantitative modeling in systems biology can be difficult due to the scarcity of quantitative details about biological phenomenons, especially at the subcellular scale. An alternative to escape this difficulty is qualitative modeling since it requires few to no quantitative information. Among the qualitative modeling approaches, the Boolean network formalism is one of the most popular. However, Boolean models allow variables to be valued at only true or false, which can appear too simplistic ...
Symmetry in critical random Boolean network dynamics.
Hossein, Shabnam; Reichl, Matthew D; Bassler, Kevin E
2014-04-01
Using Boolean networks as prototypical examples, the role of symmetry in the dynamics of heterogeneous complex systems is explored. We show that symmetry of the dynamics, especially in critical states, is a controlling feature that can be used both to greatly simplify analysis and to characterize different types of dynamics. Symmetry in Boolean networks is found by determining the frequency at which the various Boolean output functions occur. There are classes of functions that consist of Boolean functions that behave similarly. These classes are orbits of the controlling symmetry group. We find that the symmetry that controls the critical random Boolean networks is expressed through the frequency by which output functions are utilized by nodes that remain active on dynamical attractors. This symmetry preserves canalization, a form of network robustness. We compare it to a different symmetry known to control the dynamics of an evolutionary process that allows Boolean networks to organize into a critical state. Our results demonstrate the usefulness and power of using the symmetry of the behavior of the nodes to characterize complex network dynamics, and introduce an alternative approach to the analysis of heterogeneous complex systems.
Model Checking of Boolean Process Models
Schneider, Christoph; Wehler, Joachim
2011-01-01
In the field of Business Process Management formal models for the control flow of business processes have been designed since more than 15 years. Which methods are best suited to verify the bulk of these models? The first step is to select a formal language which fixes the semantics of the models. We adopt the language of Boolean systems as reference language for Boolean process models. Boolean systems form a simple subclass of coloured Petri nets. Their characteristics are low tokens to mode...
Forced synchronization of autonomous dynamical Boolean networks
Energy Technology Data Exchange (ETDEWEB)
Rivera-Durón, R. R., E-mail: roberto.rivera@ipicyt.edu.mx; Campos-Cantón, E., E-mail: eric.campos@ipicyt.edu.mx [División de Matemáticas Aplicadas, Instituto Potosino de Investigación Científica y Tecnológica A. C., Camino a la Presa San José 2055, Col. Lomas 4 Sección, C.P. 78216, San Luis Potosí, S.L.P. (Mexico); Campos-Cantón, I. [Facultad de Ciencias, Universidad Autónoma de San Luis Potosí, Álvaro Obregón 64, C.P. 78000, San Luis Potosí, S.L.P. (Mexico); Gauthier, Daniel J. [Department of Physics and Center for Nonlinear and Complex Systems, Duke University, Box 90305, Durham, North Carolina 27708 (United States)
2015-08-15
We present the design of an autonomous time-delay Boolean network realized with readily available electronic components. Through simulations and experiments that account for the detailed nonlinear response of each circuit element, we demonstrate that a network with five Boolean nodes displays complex behavior. Furthermore, we show that the dynamics of two identical networks display near-instantaneous synchronization to a periodic state when forced by a common periodic Boolean signal. A theoretical analysis of the network reveals the conditions under which complex behavior is expected in an individual network and the occurrence of synchronization in the forced networks. This research will enable future experiments on autonomous time-delay networks using readily available electronic components with dynamics on a slow enough time-scale so that inexpensive data collection systems can faithfully record the dynamics.
Forced synchronization of autonomous dynamical Boolean networks.
Rivera-Durón, R R; Campos-Cantón, E; Campos-Cantón, I; Gauthier, Daniel J
2015-08-01
We present the design of an autonomous time-delay Boolean network realized with readily available electronic components. Through simulations and experiments that account for the detailed nonlinear response of each circuit element, we demonstrate that a network with five Boolean nodes displays complex behavior. Furthermore, we show that the dynamics of two identical networks display near-instantaneous synchronization to a periodic state when forced by a common periodic Boolean signal. A theoretical analysis of the network reveals the conditions under which complex behavior is expected in an individual network and the occurrence of synchronization in the forced networks. This research will enable future experiments on autonomous time-delay networks using readily available electronic components with dynamics on a slow enough time-scale so that inexpensive data collection systems can faithfully record the dynamics.
Mental Models of Boolean Concepts
Goodwin, Geoffrey P.; Johnson-Laird, P. N.
2011-01-01
Negation, conjunction, and disjunction are major building blocks in the formation of concepts. This article presents a new model-based theory of these Boolean components. It predicts that individuals simplify the models of instances of concepts. Evidence corroborates the theory and challenges alternative accounts, such as those based on minimal…
The Influence of Canalization on the Robustness of Boolean Networks
Kadelka, Claus; Laubenbacher, Reinhard
2016-01-01
Time- and state-discrete dynamical systems are frequently used to model molecular networks. This paper provides a collection of mathematical and computational tools for the study of robustness in Boolean network models. The focus is on networks governed by $k$-canalizing functions, a recently introduced class of Boolean functions that contains the well-studied class of nested canalizing functions. The activities and sensitivity of a function quantify the impact of input changes on the function output. This paper generalizes the latter concept to $c$-sensitivity and provides formulas for the activities and $c$-sensitivity of general $k$-canalizing functions as well as canalizing functions with more precisely defined structure. A popular measure for the robustness of a network, the Derrida value, can be expressed as a weighted sum of the $c$-sensitivities of the governing canalizing functions, and can also be calculated for a stochastic extension of Boolean networks. These findings provide a computationally eff...
An efficient approach of attractor calculation for large-scale Boolean gene regulatory networks.
He, Qinbin; Xia, Zhile; Lin, Bin
2016-11-01
Boolean network models provide an efficient way for studying gene regulatory networks. The main dynamics of a Boolean network is determined by its attractors. Attractor calculation plays a key role for analyzing Boolean gene regulatory networks. An approach of attractor calculation was proposed in this study, which improved the predecessor-based approach. Furthermore, the proposed approach combined with the identification of constant nodes and simplified Boolean networks to accelerate attractor calculation. The proposed algorithm is effective to calculate all attractors for large-scale Boolean gene regulatory networks. If the average degree of the network is not too large, the algorithm can get all attractors of a Boolean network with dozens or even hundreds of nodes.
Probabilistic Boolean Network Modelling and Analysis Framework for mRNA Translation.
Zhao, Yun-Bo; Krishnan, J
2016-01-01
mRNA translation is a complex process involving the progression of ribosomes on the mRNA, resulting in the synthesis of proteins, and is subject to multiple layers of regulation. This process has been modelled using different formalisms, both stochastic and deterministic. Recently, we introduced a Probabilistic Boolean modelling framework for mRNA translation, which possesses the advantage of tools for numerically exact computation of steady state probability distribution, without requiring simulation. Here, we extend this model to incorporate both random sequential and parallel update rules, and demonstrate its effectiveness in various settings, including its flexibility in accommodating additional static and dynamic biological complexities and its role in parameter sensitivity analysis. In these applications, the results from the model analysis match those of TASEP model simulations. Importantly, the proposed modelling framework maintains the stochastic aspects of mRNA translation and provides a way to exactly calculate probability distributions, providing additional tools of analysis in this context. Finally, the proposed modelling methodology provides an alternative approach to the understanding of the mRNA translation process, by bridging the gap between existing approaches, providing new analysis tools, and contributing to a more robust platform for modelling and understanding translation.
Supercriticality for Annealed Approximations of Boolean Networks
Mountford, Thomas
2010-01-01
We consider a model proposed by Derrida and Pomeau (1986) and recently studied by Chatterjee and Durrett (2009); it is defined as an approximation to S. Kauffman's boolean networks (1969). The model starts with the choice of a random directed graph on $n$ vertices; each node has $r$ input nodes pointing at it. A discrete time threshold contact process is then considered on this graph: at each instant, each site has probability $q$ of choosing to receive input; if it does, and if at least one of its inputs were occupied by a $1$ at the previous instant, then it is labeled with a $1$; in all other cases, it is labeled with a $0$. $r$ and $q$ are kept fixed and $n$ is taken to infinity. Improving a result of Chatterjee and Durrett, we show that if $qr > 1$, then the time of persistence of the dynamics is exponential in $n$.
An Evaluation of Methods for Inferring Boolean Networks from Time-Series Data.
Berestovsky, Natalie; Nakhleh, Luay
2013-01-01
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 bioinformatics. Boolean networks have been used extensively for modeling regulatory networks. In this model, the state of each gene can be either 'on' or 'off' and that next-state of a gene is updated, synchronously or asynchronously, according to a Boolean rule that is applied to the current-state of the entire system. Inferring a Boolean network from a set of experimental data entails two main steps: first, the experimental time-series data are discretized into Boolean trajectories, and then, a Boolean network is learned from these Boolean trajectories. In this paper, we consider three methods for data discretization, including a new one we propose, and three methods for learning Boolean networks, and study the performance of all possible nine combinations on four regulatory systems of varying dynamics complexities. We find that employing the right combination of methods for data discretization and network learning results in Boolean networks that capture the dynamics well and provide predictive power. Our findings are in contrast to a recent survey that placed Boolean networks on the low end of the "faithfulness to biological reality" and "ability to model dynamics" spectra. Further, contrary to the common argument in favor of Boolean networks, we find that a relatively large number of time points in the time-series data is required to learn good Boolean networks for certain data sets. Last but not least, while methods have been proposed for inferring Boolean networks, as discussed above, missing still are publicly available implementations thereof. Here, we make our implementation of the methods available publicly in open source at http://bioinfo.cs.rice.edu/.
Model Checking of Boolean Process Models
Schneider, Christoph
2011-01-01
In the field of Business Process Management formal models for the control flow of business processes have been designed since more than 15 years. Which methods are best suited to verify the bulk of these models? The first step is to select a formal language which fixes the semantics of the models. We adopt the language of Boolean systems as reference language for Boolean process models. Boolean systems form a simple subclass of coloured Petri nets. Their characteristics are low tokens to model explicitly states with a subsequent skipping of activations and arbitrary logical rules of type AND, XOR, OR etc. to model the split and join of the control flow. We apply model checking as a verification method for the safeness and liveness of Boolean systems. Model checking of Boolean systems uses the elementary theory of propositional logic, no modal operators are needed. Our verification builds on a finite complete prefix of a certain T-system attached to the Boolean system. It splits the processes of the Boolean sy...
Stability of biological networks as represented in Random Boolean Nets.
Energy Technology Data Exchange (ETDEWEB)
Slepoy, Alexander; Thompson, Marshall
2004-09-01
We explore stability of Random Boolean Networks as a model of biological interaction networks. We introduce surface-to-volume ratio as a measure of stability of the network. Surface is defined as the set of states within a basin of attraction that maps outside the basin by a bit-flip operation. Volume is defined as the total number of states in the basin. We report development of an object-oriented Boolean network analysis code (Attract) to investigate the structure of stable vs. unstable networks. We find two distinct types of stable networks. The first type is the nearly trivial stable network with a few basins of attraction. The second type contains many basins. We conclude that second type stable networks are extremely rare.
Estimation of delays in generalized asynchronous Boolean networks.
Das, Haimabati; Layek, Ritwik Kumar
2016-10-20
A new generalized asynchronous Boolean network (GABN) model has been proposed in this paper. This continuous-time discrete-state model captures the biological reality of cellular dynamics without compromising the computational efficiency of the Boolean framework. The GABN synthesis procedure is based on the prior knowledge of the logical structure of the regulatory network, and the experimental transcriptional parameters. The novelty of the proposed methodology lies in considering different delays associated with the activation and deactivation of a particular protein (especially the transcription factors). A few illustrative examples of some well-studied network motifs have been provided to explore the scope of using the GABN model for larger networks. The GABN model of the p53-signaling pathway in response to γ-irradiation has also been simulated in the current paper to provide an indirect validation of the proposed schema. PMID:27464825
Estimation of delays in generalized asynchronous Boolean networks.
Das, Haimabati; Layek, Ritwik Kumar
2016-10-20
A new generalized asynchronous Boolean network (GABN) model has been proposed in this paper. This continuous-time discrete-state model captures the biological reality of cellular dynamics without compromising the computational efficiency of the Boolean framework. The GABN synthesis procedure is based on the prior knowledge of the logical structure of the regulatory network, and the experimental transcriptional parameters. The novelty of the proposed methodology lies in considering different delays associated with the activation and deactivation of a particular protein (especially the transcription factors). A few illustrative examples of some well-studied network motifs have been provided to explore the scope of using the GABN model for larger networks. The GABN model of the p53-signaling pathway in response to γ-irradiation has also been simulated in the current paper to provide an indirect validation of the proposed schema.
Boolean network representation of contagion dynamics during a financial crisis
Caetano, Marco Antonio Leonel; Yoneyama, Takashi
2015-01-01
This work presents a network model for representation of the evolution of certain patterns of economic behavior. More specifically, after representing the agents as points in a space in which each dimension associated to a relevant economic variable, their relative "motions" that can be either stationary or discordant, are coded into a boolean network. Patterns with stationary averages indicate the maintenance of status quo, whereas discordant patterns represent aggregation of new agent into the cluster or departure from the former policies. The changing patterns can be embedded into a network representation, particularly using the concept of autocatalytic boolean networks. As a case study, the economic tendencies of the BRIC countries + Argentina were studied. Although Argentina is not included in the cluster formed by BRIC countries, it tends to follow the BRIC members because of strong commercial ties.
Controllability and observability of Boolean networks arising from biology.
Li, Rui; Yang, Meng; Chu, Tianguang
2015-02-01
Boolean networks are currently receiving considerable attention as a computational scheme for system level analysis and modeling of biological systems. Studying control-related problems in Boolean networks may reveal new insights into the intrinsic control in complex biological systems and enable us to develop strategies for manipulating biological systems using exogenous inputs. This paper considers controllability and observability of Boolean biological networks. We propose a new approach, which draws from the rich theory of symbolic computation, to solve the problems. Consequently, simple necessary and sufficient conditions for reachability, controllability, and observability are obtained, and algorithmic tests for controllability and observability which are based on the Gröbner basis method are presented. As practical applications, we apply the proposed approach to several different biological systems, namely, the mammalian cell-cycle network, the T-cell activation network, the large granular lymphocyte survival signaling network, and the Drosophila segment polarity network, gaining novel insights into the control and/or monitoring of the specific biological systems.
Controllability and observability of Boolean networks arising from biology
Li, Rui; Yang, Meng; Chu, Tianguang
2015-02-01
Boolean networks are currently receiving considerable attention as a computational scheme for system level analysis and modeling of biological systems. Studying control-related problems in Boolean networks may reveal new insights into the intrinsic control in complex biological systems and enable us to develop strategies for manipulating biological systems using exogenous inputs. This paper considers controllability and observability of Boolean biological networks. We propose a new approach, which draws from the rich theory of symbolic computation, to solve the problems. Consequently, simple necessary and sufficient conditions for reachability, controllability, and observability are obtained, and algorithmic tests for controllability and observability which are based on the Gröbner basis method are presented. As practical applications, we apply the proposed approach to several different biological systems, namely, the mammalian cell-cycle network, the T-cell activation network, the large granular lymphocyte survival signaling network, and the Drosophila segment polarity network, gaining novel insights into the control and/or monitoring of the specific biological systems.
Control of Large-Scale Boolean Networks via Network Aggregation.
Zhao, Yin; Ghosh, Bijoy K; Cheng, Daizhan
2016-07-01
A major challenge to solve problems in control of Boolean networks is that the computational cost increases exponentially when the number of nodes in the network increases. We consider the problem of controllability and stabilizability of Boolean control networks, address the increasing cost problem by partitioning the network graph into several subnetworks, and analyze the subnetworks separately. Easily verifiable necessary conditions for controllability and stabilizability are proposed for a general aggregation structure. For acyclic aggregation, we develop a sufficient condition for stabilizability. It dramatically reduces the computational complexity if the number of nodes in each block of the acyclic aggregation is small enough compared with the number of nodes in the entire Boolean network.
Inference of asynchronous Boolean network from biological pathways.
Das, Haimabati; Layek, Ritwik Kumar
2015-01-01
Gene regulation is a complex process with multiple levels of interactions. In order to describe this complex dynamical system with tractable parameterization, the choice of the dynamical system model is of paramount importance. The right abstraction of the modeling scheme can reduce the complexity in the inference and intervention design, both computationally and experimentally. This article proposes an asynchronous Boolean network framework to capture the transcriptional regulation as well as the protein-protein interactions in a genetic regulatory system. The inference of asynchronous Boolean network from biological pathways information and experimental evidence are explained using an algorithm. The suitability of this paradigm for the variability of several reaction rates is also discussed. This methodology and model selection open up new research challenges in understanding gene-protein interactive system in a coherent way and can be beneficial for designing effective therapeutic intervention strategy.
Densities of mixed volumes for Boolean models
Weil, Wolfgang
2001-01-01
In generalization of the well-known formulae for quermass densities of stationary and isotropic Boolean models, we prove corresponding results for densities of mixed volumes in the stationary situation and show how they can be used to determine the intensity of non-isotropic Boolean models Z in d-dimensional space for d = 2, 3, 4. We then consider non-stationary Boolean models and extend results of Fallert on quermass densities to densities of mixed volumes. In particular, we present explicit...
A more robust Boolean model describing inhibitor binding
Institute of Scientific and Technical Information of China (English)
Zhaoqian Steven XIE; Chao TANG
2008-01-01
From the first application of the Boolean model to the cell cycle regulation network of budding yeast, new regulative pathways have been discovered, par-ticularly in the G1/S transition circuit. This discovery called for finer modeling to study the essential biology, and the resulting outcomes are first introduced in the ar-ticle. A traditional Boolean network model set up for the new G1/S transition circuit shows that it cannot correctly simulate real biology unless the model parameters are fine tuned. The deficiency is caused by an overly coarse-grained description of the inhibitor binding process, which shall be overcome by a two-vector model proposed whose robustness is surveyed using random perturba-tions. Simulations show that the proposed two-vector model is much more robust in describing inhibitor binding processes within the Boolean framework.
ON REDUCED SCALAR EQUATIONS FOR SYNCHRONOUS BOOLEAN NETWORKS
Ali Muhammad Ali Rushdi; Adnan Ahmad Alsogati
2013-01-01
A total description of a synchronous Boolean network is typically achieved by a matrix recurrence relation. A simpler alternative is to use a scalar equation which is a possibly nonlinear equation that involves two or more instances of a single scalar variable and some Boolean operator(s). Further simplification is possible in terms of a linear reduced scalar equation which is the simplest two-term scalar equation that includes no Boolean operators and equates the value of a scalar variable a...
A SAT-based algorithm for finding attractors in synchronous Boolean networks.
Dubrova, Elena; Teslenko, Maxim
2011-01-01
This paper addresses the problem of finding attractors in synchronous Boolean networks. The existing Boolean decision diagram-based algorithms have limited capacity due to the excessive memory requirements of decision diagrams. The simulation-based algorithms can be applied to larger networks, however, they are incomplete. We present an algorithm, which uses a SAT-based bounded model checking to find all attractors in a Boolean network. The efficiency of the presented algorithm is evaluated by analyzing seven networks models of real biological processes, as well as 150,000 randomly generated Boolean networks of sizes between 100 and 7,000. The results show that our approach has a potential to handle an order of magnitude larger models than currently possible. PMID:21778527
Self-organized networks of competing boolean agents
Paczuski; Bassler; Corral
2000-04-01
A model of Boolean agents competing in a market is presented where each agent bases his action on information obtained from a small group of other agents. The agents play a competitive game that rewards those in the minority. After a long time interval, the poorest player's strategy is changed randomly, and the process is repeated. Eventually the network evolves to a stationary but intermittent state where random mutation of the worst strategy can change the behavior of the entire network, often causing a switch in the dynamics between attractors of vastly different lengths. PMID:11019043
The value of less connected agents in Boolean networks
Epstein, Daniel; Bazzan, Ana L. C.
2013-11-01
In multiagent systems, agents often face binary decisions where one seeks to take either the minority or the majority side. Examples are minority and congestion games in general, i.e., situations that require coordination among the agents in order to depict efficient decisions. In minority games such as the El Farol Bar Problem, previous works have shown that agents may reach appropriate levels of coordination, mostly by looking at the history of past decisions. Not many works consider any kind of structure of the social network, i.e., how agents are connected. Moreover, when structure is indeed considered, it assumes some kind of random network with a given, fixed connectivity degree. The present paper departs from the conventional approach in some ways. First, it considers more realistic network topologies, based on preferential attachments. This is especially useful in social networks. Second, the formalism of random Boolean networks is used to help agents to make decisions given their attachments (for example acquaintances). This is coupled with a reinforcement learning mechanism that allows agents to select strategies that are locally and globally efficient. Third, we use agent-based modeling and simulation, a microscopic approach, which allows us to draw conclusions about individuals and/or classes of individuals. Finally, for the sake of illustration we use two different scenarios, namely the El Farol Bar Problem and a binary route choice scenario. With this approach we target systems that adapt dynamically to changes in the environment, including other adaptive decision-makers. Our results using preferential attachments and random Boolean networks are threefold. First we show that an efficient equilibrium can be achieved, provided agents do experimentation. Second, microscopic analysis show that influential agents tend to consider few inputs in their Boolean functions. Third, we have also conducted measurements related to network clustering and centrality
An attractor-based complexity measurement for Boolean recurrent neural networks.
Directory of Open Access Journals (Sweden)
Jérémie Cabessa
Full Text Available 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. This complexity measurement is achieved by first proving a computational equivalence between Boolean recurrent neural networks and some specific class of ω-automata, and then translating the most refined classification of ω-automata to the Boolean neural network context. As a result, a hierarchical classification of Boolean neural networks based on their attractive dynamics is obtained, thus providing a novel refined attractor-based complexity measurement for Boolean recurrent neural networks. These results provide new theoretical insights to the computational and dynamical capabilities of neural networks according to their attractive potentialities. An application of our findings is illustrated by the analysis of the dynamics of a simplified model of the basal ganglia-thalamocortical network simulated by a Boolean recurrent neural network. This example shows the significance of measuring network complexity, and how our results bear new founding elements for the understanding of the complexity of real brain circuits.
An attractor-based complexity measurement for Boolean recurrent neural networks.
Cabessa, Jérémie; Villa, Alessandro E P
2014-01-01
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. This complexity measurement is achieved by first proving a computational equivalence between Boolean recurrent neural networks and some specific class of ω-automata, and then translating the most refined classification of ω-automata to the Boolean neural network context. As a result, a hierarchical classification of Boolean neural networks based on their attractive dynamics is obtained, thus providing a novel refined attractor-based complexity measurement for Boolean recurrent neural networks. These results provide new theoretical insights to the computational and dynamical capabilities of neural networks according to their attractive potentialities. An application of our findings is illustrated by the analysis of the dynamics of a simplified model of the basal ganglia-thalamocortical network simulated by a Boolean recurrent neural network. This example shows the significance of measuring network complexity, and how our results bear new founding elements for the understanding of the complexity of real brain circuits.
Propagation of external regulation and asynchronous dynamics in random Boolean networks
Mahmoudi, Hamed; Pagnani, Andrea; Weigt, Martin; Zecchina, Riccardo
2007-01-01
Boolean Networks and their dynamics are of great interest as abstract modeling schemes in various disciplines, ranging from biology to computer science. Whereas parallel update schemes have been studied extensively in past years, the level of understanding of asynchronous updates schemes is still very poor. In this paper we study the propagation of external information given by regulatory input variables into a random Boolean network. We compute both analytically and numerically the time evol...
Towards a theory of modelling with Boolean automata networks - I. Theorisation and observations
Noual, Mathilde
2011-01-01
Although models are built on the basis of some observations of reality, the concepts that derive theoretically from their definitions as well as from their characteristics and properties are not necessarily direct consequences of these initial observations. Indeed, many of them rather follow from chains of theoretical inferences that are only based on the precise model definitions and rely strongly, in addition, on some consequential working hypotheses. Thus, it is important to address the question of which features of a model effectively carry some modelling meaning and which only result from the task of formalising observations of reality into a mathematical language. In this article, we address this question with a theoretical point view that sets our discussion strictly between the two stages of the modelling process that require knowledge of real systems, that is, between the initial stage that chooses a global theoretical framework to build the model and the final stage that exploits its formal predicti...
EXACT SIMULATION OF A BOOLEAN MODEL
Directory of Open Access Journals (Sweden)
Christian Lantuéjoul
2013-06-01
Full Text Available A Boolean model is a union of independent objects (compact random subsets located at Poisson points. Two algorithms are proposed for simulating a Boolean model in a bounded domain. The first one applies only to stationary models. It generates the objects prior to their Poisson locations. Two examples illustrate its applicability. The second algorithm applies to stationary and non-stationary models. It generates the Poisson points prior to the objects. Its practical difficulties of implementation are discussed. Both algorithms are based on importance sampling techniques, and the generated objects are weighted.
Perturbation propagation in random and evolved Boolean networks
Energy Technology Data Exchange (ETDEWEB)
Fretter, Christoph [Instistut fuer Informatik, Martin-Luther-Universitaet Halle-Wittenberg, Von-Seckendorffplatz 1, 06120 Halle (Germany); Szejka, Agnes; Drossel, Barbara [Institut fuer Festkoerperphysik, Technische Universitaet Darmstadt, Hochschulstrasse 6, 64289 Darmstadt (Germany)], E-mail: Christoph.Fretter@informatik.uni-halle.de
2009-03-15
In this paper, we investigate the propagation of perturbations in Boolean networks by evaluating the Derrida plot and its modifications. We show that even small random Boolean networks agree well with the predictions of the annealed approximation, but nonrandom networks show a very different behaviour. We focus on networks that were evolved for high dynamical robustness. The most important conclusion is that the simple distinction between frozen, critical and chaotic networks is no longer useful, since such evolved networks can display the properties of all three types of networks. Furthermore, we evaluate a simplified empirical network and show how its specific state space properties are reflected in the modified Derrida plots.
Guo, Wensheng; Yang, Guowu; Wu, Wei; He, Lei; Sun, Mingyu
2014-01-01
In biological systems, the dynamic analysis method has gained increasing attention in the past decade. The Boolean network is the most common model of a genetic regulatory network. The interactions of activation and inhibition in the genetic regulatory network are modeled as a set of functions of the Boolean network, while the state transitions in the Boolean network reflect the dynamic property of a genetic regulatory network. A difficult problem for state transition analysis is the finding of attractors. In this paper, we modeled the genetic regulatory network as a Boolean network and proposed a solving algorithm to tackle the attractor finding problem. In the proposed algorithm, we partitioned the Boolean network into several blocks consisting of the strongly connected components according to their gradients, and defined the connection between blocks as decision node. Based on the solutions calculated on the decision nodes and using a satisfiability solving algorithm, we identified the attractors in the state transition graph of each block. The proposed algorithm is benchmarked on a variety of genetic regulatory networks. Compared with existing algorithms, it achieved similar performance on small test cases, and outperformed it on larger and more complex ones, which happens to be the trend of the modern genetic regulatory network. Furthermore, while the existing satisfiability-based algorithms cannot be parallelized due to their inherent algorithm design, the proposed algorithm exhibits a good scalability on parallel computing architectures.
Directory of Open Access Journals (Sweden)
Wensheng Guo
Full Text Available In biological systems, the dynamic analysis method has gained increasing attention in the past decade. The Boolean network is the most common model of a genetic regulatory network. The interactions of activation and inhibition in the genetic regulatory network are modeled as a set of functions of the Boolean network, while the state transitions in the Boolean network reflect the dynamic property of a genetic regulatory network. A difficult problem for state transition analysis is the finding of attractors. In this paper, we modeled the genetic regulatory network as a Boolean network and proposed a solving algorithm to tackle the attractor finding problem. In the proposed algorithm, we partitioned the Boolean network into several blocks consisting of the strongly connected components according to their gradients, and defined the connection between blocks as decision node. Based on the solutions calculated on the decision nodes and using a satisfiability solving algorithm, we identified the attractors in the state transition graph of each block. The proposed algorithm is benchmarked on a variety of genetic regulatory networks. Compared with existing algorithms, it achieved similar performance on small test cases, and outperformed it on larger and more complex ones, which happens to be the trend of the modern genetic regulatory network. Furthermore, while the existing satisfiability-based algorithms cannot be parallelized due to their inherent algorithm design, the proposed algorithm exhibits a good scalability on parallel computing architectures.
Control of random Boolean networks via average sensitivity of Boolean functions
Institute of Scientific and Technical Information of China (English)
Chen Shi-Jian; Hong Yi-Guang
2011-01-01
In this paper, we discuss how to transform the disordered phase into an ordered phase in random Boolean networks. To increase the effectiveness, a control scheme is proposed, which periodically freezes a fraction of the network based on the average sensitivity of Boolean functions of the nodes. Theoretical analysis is carried out to estimate the expected critical value of the fraction, and shows that the critical value is reduced using this scheme compared to that of randomly freezing a fraction of the nodes. Finally, the simulation is given for illustrating the effectiveness of the proposed method.
Stability depends on positive autoregulation in Boolean gene regulatory networks.
Directory of Open Access Journals (Sweden)
Ricardo Pinho
2014-11-01
Full Text Available Network motifs have been identified as building blocks of regulatory networks, including gene regulatory networks (GRNs. The most basic motif, autoregulation, has been associated with bistability (when positive and with homeostasis and robustness to noise (when negative, but its general importance in network behavior is poorly understood. Moreover, how specific autoregulatory motifs are selected during evolution and how this relates to robustness is largely unknown. Here, we used a class of GRN models, Boolean networks, to investigate the relationship between autoregulation and network stability and robustness under various conditions. We ran evolutionary simulation experiments for different models of selection, including mutation and recombination. Each generation simulated the development of a population of organisms modeled by GRNs. We found that stability and robustness positively correlate with autoregulation; in all investigated scenarios, stable networks had mostly positive autoregulation. Assuming biological networks correspond to stable networks, these results suggest that biological networks should often be dominated by positive autoregulatory loops. This seems to be the case for most studied eukaryotic transcription factor networks, including those in yeast, flies and mammals.
Arshad, Osama A; Venkatasubramani, Priyadharshini S; Datta, Aniruddha; Venkatraj, Jijayanagaram
2016-01-01
The uncontrolled cell proliferation that is characteristically associated with cancer is usually accompanied by alterations in the genome and cell metabolism. Indeed, the phenomenon of cancer cells metabolizing glucose using a less efficient anaerobic process even in the presence of normal oxygen levels, termed the Warburg effect, is currently considered to be one of the hallmarks of cancer. Diabetes, much like cancer, is defined by significant metabolic changes. Recent epidemiological studies have shown that diabetes patients treated with the antidiabetic drug Metformin have significantly lowered risk of cancer as compared to patients treated with other antidiabetic drugs. We utilize a Boolean logic model of the pathways commonly mutated in cancer to not only investigate the efficacy of Metformin for cancer therapeutic purposes but also demonstrate how Metformin in concert with other cancer drugs could provide better and less toxic clinical outcomes as compared to using cancer drugs alone.
On the robustness of NK-Kauffman networks against changes in their connections and Boolean functions
Zertuche, Federico
2009-04-01
NK-Kauffman networks LKN are a subset of the Boolean functions on N Boolean variables to themselves, ΛN={ξ :Z2N→Z2N}. To each NK-Kauffman network it is possible to assign a unique Boolean function on N variables through the function Ψ :LKN→ΛN. The probability PK that Ψ(f )=Ψ(f'), when f' is obtained through f by a change in one of its K-Boolean functions (bK:Z2K→Z2), and/or connections, is calculated. The leading term of the asymptotic expansion of PK, for N ≫1, turns out to depend on the probability to extract the tautology and contradiction Boolean functions, and in the average value of the distribution of probability of the Boolean functions, the other terms decay as O(1/N). In order to accomplish this, a classification of the Boolean functions in terms of what I have called their irreducible degree of connectivity is established. The mathematical findings are discussed in the biological context, where Ψ is used to model the genotype-phenotype map.
Evolution and Controllability of Cancer Networks: A Boolean Perspective.
Srihari, Sriganesh; Raman, Venkatesh; Leong, Hon Wai; Ragan, Mark A
2014-01-01
Cancer forms a robust system capable of maintaining stable functioning (cell sustenance and proliferation) despite perturbations. Cancer progresses as stages over time typically with increasing aggressiveness and worsening prognosis. Characterizing these stages and identifying the genes driving transitions between them is critical to understand cancer progression and to develop effective anti-cancer therapies. In this work, we propose a novel model for the `cancer system' as a Boolean state space in which a Boolean network, built from protein-interaction and gene-expression data from different stages of cancer, transits between Boolean satisfiability states by "editing" interactions and "flipping" genes. Edits reflect rewiring of the PPI network while flipping of genes reflect activation or silencing of genes between stages. We formulate a minimization problem min flip to identify these genes driving the transitions. The application of our model (called BoolSpace) on three case studies-pancreatic and breast tumours in human and post spinal-cord injury (SCI) in rats-reveals valuable insights into the phenomenon of cancer progression: (i) interactions involved in core cell-cycle and DNA-damage repair pathways are significantly rewired in tumours, indicating significant impact to key genome-stabilizing mechanisms; (ii) several of the genes flipped are serine/threonine kinases which act as biological switches, reflecting cellular switching mechanisms between stages; and (iii) different sets of genes are flipped during the initial and final stages indicating a pattern to tumour progression. Based on these results, we hypothesize that robustness of cancer partly stems from "passing of the baton" between genes at different stages-genes from different biological processes and/or cellular components are involved in different stages of tumour progression thereby allowing tumour cells to evade targeted therapy, and therefore an effective therapy should target a "cover set" of
IMS Algorithm for Learning Representations in Boolean Neural Networks
Biswas, Nripendra N; Murthy, TVMK; Chandrasekhar, M.
1991-01-01
A new algorithm for learning representations in Boolean neural networks, where the inputs and outputs are binary bits, is presented. The algorithm has become feasible because of a newly discovered theorem which states that any non-linearly separable Boolean function can be expressed as a convergent series of linearly separable functions connected by the logical OR (+) and the logical INHIBIT (-) operators. The formation of the series is carried out by many important properties exhibited by th...
Polynomial-Time Algorithm for Controllability Test of a Class of Boolean Biological Networks
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Koichi Kobayashi
2010-01-01
Full Text Available In recent years, Boolean-network-model-based 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 polynomial-time 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 large-scale 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.
Grieb, Melanie; Burkovski, Andre; Sträng, J Eric; Kraus, Johann M; Groß, Alexander; Palm, Günther; Kühl, Michael; Kestler, Hans A
2015-01-01
Gene interactions in cells can be represented by gene regulatory networks. A Boolean network models gene interactions according to rules where gene expression is represented by binary values (on / off or {1, 0}). In reality, however, the gene's state can have multiple values due to biological properties. Furthermore, the noisy nature of the experimental design results in uncertainty about a state of the gene. Here we present a new Boolean network paradigm to allow intermediate values on the interval [0, 1]. As in the Boolean network, fixed points or attractors of such a model correspond to biological phenotypes or states. We use our new extension of the Boolean network paradigm to model gene expression in first and second heart field lineages which are cardiac progenitor cell populations involved in early vertebrate heart development. By this we are able to predict additional biological phenotypes that the Boolean model alone is not able to identify without utilizing additional biological knowledge. The additional phenotypes predicted by the model were confirmed by published biological experiments. Furthermore, the new method predicts gene expression propensities for modelled but yet to be analyzed genes.
An optimal control approach to probabilistic Boolean networks
Liu, Qiuli
2012-12-01
External control of some genes in a genetic regulatory network is useful for avoiding undesirable states associated with some diseases. For this purpose, a number of stochastic optimal control approaches have been proposed. Probabilistic Boolean networks (PBNs) as powerful tools for modeling gene regulatory systems have attracted considerable attention in systems biology. In this paper, we deal with a problem of optimal intervention in a PBN with the help of the theory of discrete time Markov decision process. Specifically, we first formulate a control model for a PBN as a first passage model for discrete time Markov decision processes and then find, using a value iteration algorithm, optimal effective treatments with the minimal expected first passage time over the space of all possible treatments. In order to demonstrate the feasibility of our approach, an example is also displayed.
Cho Kwang-Hyun; Choi Sun; Kwon Yung-Keun
2007-01-01
Abstract Background A number of studies on biological networks have been carried out to unravel the topological characteristics that can explain the functional importance of network nodes. For instance, connectivity, clustering coefficient, and shortest path length were previously proposed for this purpose. However, there is still a pressing need to investigate another topological measure that can better describe the functional importance of network nodes. In this respect, we considered a fee...
ILP/SMT-Based Method for Design of Boolean Networks Based on Singleton Attractors.
Kobayashi, Koichi; Hiraishi, Kunihiko
2014-01-01
Attractors in gene regulatory networks represent cell types or states of cells. In system biology and synthetic biology, it is important to generate gene regulatory networks with desired attractors. In this paper, we focus on a singleton attractor, which is also called a fixed point. Using a Boolean network (BN) model, we consider the problem of finding Boolean functions such that the system has desired singleton attractors and has no undesired singleton attractors. To solve this problem, we propose a matrix-based representation of BNs. Using this representation, the problem of finding Boolean functions can be rewritten as an Integer Linear Programming (ILP) problem and a Satisfiability Modulo Theories (SMT) problem. Furthermore, the effectiveness of the proposed method is shown by a numerical example on a WNT5A network, which is related to melanoma. The proposed method provides us a basic method for design of gene regulatory networks.
Controllability of Boolean networks via input controls under Harvey's update scheme
Luo, Chao; Zhang, Xiaolin; Shao, Rui; Zheng, YuanJie
2016-02-01
In this article, the controllability of Boolean networks via input controls under Harvey's update scheme is investigated. First, the model of Boolean control networks under Harvey's stochastic update is proposed, by means of semi-tensor product approach, which is converted into discrete-time linear representation. And, a general formula of control-depending network transition matrix is provided. Second, based on discrete-time dynamics, controllability of the proposed model is analytically discussed by revealing the necessary and sufficient conditions of the reachable sets, respectively, for three kinds of controls, i.e., free Boolean control sequence, input control networks, and close-loop control. Examples are showed to demonstrate the effectiveness and feasibility of the proposed scheme.
Boolean Models of Biological Processes Explain Cascade-Like Behavior.
Chen, Hao; Wang, Guanyu; Simha, Rahul; Du, Chenghang; Zeng, Chen
2016-01-01
Biological networks play a key role in determining biological function and therefore, an understanding of their structure and dynamics is of central interest in systems biology. In Boolean models of such networks, the status of each molecule is either "on" or "off" and along with the molecules interact with each other, their individual status changes from "on" to "off" or vice-versa and the system of molecules in the network collectively go through a sequence of changes in state. This sequence of changes is termed a biological process. In this paper, we examine the common perception that events in biomolecular networks occur sequentially, in a cascade-like manner, and ask whether this is likely to be an inherent property. In further investigations of the budding and fission yeast cell-cycle, we identify two generic dynamical rules. A Boolean system that complies with these rules will automatically have a certain robustness. By considering the biological requirements in robustness and designability, we show that those Boolean dynamical systems, compared to an arbitrary dynamical system, statistically present the characteristics of cascadeness and sequentiality, as observed in the budding and fission yeast cell- cycle. These results suggest that cascade-like behavior might be an intrinsic property of biological processes.
Boolean Models of Biological Processes Explain Cascade-Like Behavior.
Chen, Hao; Wang, Guanyu; Simha, Rahul; Du, Chenghang; Zeng, Chen
2016-01-01
Biological networks play a key role in determining biological function and therefore, an understanding of their structure and dynamics is of central interest in systems biology. In Boolean models of such networks, the status of each molecule is either "on" or "off" and along with the molecules interact with each other, their individual status changes from "on" to "off" or vice-versa and the system of molecules in the network collectively go through a sequence of changes in state. This sequence of changes is termed a biological process. In this paper, we examine the common perception that events in biomolecular networks occur sequentially, in a cascade-like manner, and ask whether this is likely to be an inherent property. In further investigations of the budding and fission yeast cell-cycle, we identify two generic dynamical rules. A Boolean system that complies with these rules will automatically have a certain robustness. By considering the biological requirements in robustness and designability, we show that those Boolean dynamical systems, compared to an arbitrary dynamical system, statistically present the characteristics of cascadeness and sequentiality, as observed in the budding and fission yeast cell- cycle. These results suggest that cascade-like behavior might be an intrinsic property of biological processes. PMID:26821940
Poret, Arnaud; Boissel, Jean-Pierre
2014-12-01
Target identification aims at identifying biomolecules whose function should be therapeutically altered to cure the considered pathology. An algorithm for in silico target identification using Boolean network attractors is proposed. It assumes that attractors correspond to phenotypes produced by the modeled biological network. It identifies target combinations which allow disturbed networks to avoid attractors associated with pathological phenotypes. The algorithm is tested on a Boolean model of the mammalian cell cycle and its applications are illustrated on a Boolean model of Fanconi anemia. Results show that the algorithm returns target combinations able to remove attractors associated with pathological phenotypes and then succeeds in performing the proposed in silico target identification. However, as with any in silico evidence, there is a bridge to cross between theory and practice. Nevertheless, it is expected that the algorithm is of interest for target identification.
Tracking perturbations in Boolean networks with spectral methods.
Kesseli, Juha; Rämö, Pauli; Yli-Harja, Olli
2005-08-01
In this paper we present a method for predicting the spread of perturbations in Boolean networks. The method is applicable to networks that have no regular topology. The prediction of perturbations can be performed easily by using a presented result which enables the efficient computation of the required iterative formulas. This result is based on abstract Fourier transform of the functions in the network. In this paper the method is applied to show the spread of perturbations in networks containing a distribution of functions found from biological data. The advances in the study of the spread of perturbations can directly be applied to enable ways of quantifying chaos in Boolean networks. Derrida plots over an arbitrary number of time steps can be computed and thus distributions of functions compared with each other with respect to the amount of order they create in random networks. PMID:16196674
Binary higher order neural networks for realizing Boolean functions.
Zhang, Chao; Yang, Jie; Wu, Wei
2011-05-01
In order to more efficiently realize Boolean functions by using neural networks, we propose a binary product-unit neural network (BPUNN) and a binary π-ς neural network (BPSNN). The network weights can be determined by one-step training. It is shown that the addition " σ," the multiplication " π," and two kinds of special weighting operations in BPUNN and BPSNN can implement the logical operators " ∨," " ∧," and " ¬" on Boolean algebra 〈Z(2),∨,∧,¬,0,1〉 (Z(2)={0,1}), respectively. The proposed two neural networks enjoy the following advantages over the existing networks: 1) for a complete truth table of N variables with both truth and false assignments, the corresponding Boolean function can be realized by accordingly choosing a BPUNN or a BPSNN such that at most 2(N-1) hidden nodes are needed, while O(2(N)), precisely 2(N) or at most 2(N), hidden nodes are needed by existing networks; 2) a new network BPUPS based on a collaboration of BPUNN and BPSNN can be defined to deal with incomplete truth tables, while the existing networks can only deal with complete truth tables; and 3) the values of the weights are all simply -1 or 1, while the weights of all the existing networks are real numbers. Supporting numerical experiments are provided as well. Finally, we present the risk bounds of BPUNN, BPSNN, and BPUPS, and then analyze their probably approximately correct learnability. PMID:21427020
ON REDUCED SCALAR EQUATIONS FOR SYNCHRONOUS BOOLEAN NETWORKS
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Ali Muhammad Ali Rushdi
2013-01-01
Full Text Available A total description of a synchronous Boolean network is typically achieved by a matrix recurrence relation. A simpler alternative is to use a scalar equation which is a possibly nonlinear equation that involves two or more instances of a single scalar variable and some Boolean operator(s. Further simplification is possible in terms of a linear reduced scalar equation which is the simplest two-term scalar equation that includes no Boolean operators and equates the value of a scalar variable at a latter instance t2 to its value at an earlier instance t1. This equation remains valid when the times t1 and t2 are both augmented by any integral multiple of the underlying time period. In other words, there are infinitely many versions of a reduced scalar equation, any of which is useful for deducing information about the cyclic behavior of the network. However, to obtain correct information about the transient behavior of the network, one must find the true reduced scalar equation for which instances t1 and t2 are minimal. This study investigates the nature, derivation and utilization of reduced scalar equations. It relies on Boolean-algebraic manipulations for the derivation of such equations and suggests that this derivation can be facilitated by seeking certain orthogonality relations among certain successive (albeit not necessarily consecutive instances of the same scalar variable. We demonstrate, contrary to previously published assumptions or assertions, that there is typically no common reduced scalar equation for all the scalar variables. Each variable usually satisfies its own distinct reduced scalar equation. We also demonstrate that the derivation of a reduced scalar equation is achieved not only by proving it but also by disproving an immediately preceding version of it when such a version might exist. We also demonstrate that, despite the useful insight supplied by the reduced scalar equations, they do not provide a total solution like the
Feedback Controller Design for the Synchronization of Boolean Control Networks.
Liu, Yang; Sun, Liangjie; Lu, Jianquan; Liang, Jinling
2016-09-01
This brief investigates the partial and complete synchronization of two Boolean control networks (BCNs). Necessary and sufficient conditions for partial and complete synchronization are established by the algebraic representations of logical dynamics. An algorithm is obtained to construct the feedback controller that guarantees the synchronization of master and slave BCNs. Two biological examples are provided to illustrate the effectiveness of the obtained results.
Optimal Computation of Symmetric Boolean Functions in Collocated Networks
Kowshik, Hemant
2011-01-01
We consider collocated wireless sensor networks, where each node has a Boolean measurement and the goal is to compute a given Boolean function of these measurements. We first consider the worst case setting and study optimal block computation strategies for computing symmetric Boolean functions. We study three classes of functions: threshold functions, delta functions and interval functions. We provide exactly optimal strategies for the first two classes, and a scaling law order-optimal strategy with optimal preconstant for interval functions. We also extend the results to the case of integer measurements and certain integer-valued functions. We use lower bounds from communication complexity theory, and provide an achievable scheme using information theoretic tools. Next, we consider the case where nodes measurements are random and drawn from independent Bernoulli distributions. We address the problem of optimal function computation so as to minimize the expected total number of bits that are transmitted. In ...
Robust Boolean Operation for Sculptured Models
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
To enhance the ability of current modeling system, an uniformed representation is designed to represent wire-frame, solid, surface models. We present an algorithm for Boolean operation between the models under this representation. Accuracy, efficiency and robustness are the main consideration. The geometric information is represented with trimmed parametric patches and trimmed parametric splines. The topological information is represented with an extended half-edge data structure. In the process of intersection calculation, hierarchy intersection method is applied for unified classification. Tracing the intersection curve to overcome degenerate cases that occur frequently in practice. The algorithm has been implemented as the modeling kernel of a feature based modeling system named GS-CAD98, which was developed on Windows/NT platform.
Boolean Queries and Term Dependencies in Probabilistic Retrieval Models.
Croft, W. Bruce
1986-01-01
Proposes approach to integrating Boolean and statistical systems where Boolean queries are interpreted as a means of specifying term dependencies in relevant set of documents. Highlights include series of retrieval experiments designed to test retrieval strategy based on term dependence model and relation of results to other work. (18 references)…
Directory of Open Access Journals (Sweden)
Frolova A. O.
2012-06-01
Full Text Available Reverse engineering of gene regulatory networks is an intensively studied topic in Systems Biology as it reconstructs regulatory interactions between all genes in the genome in the most complete form. The extreme computational complexity of this problem and lack of thorough reviews on reconstruction methods of gene regulatory network is a significant obstacle to further development of this area. In this article the two most common methods for modeling gene regulatory networks are surveyed: Boolean and Bayesian networks. The mathematical description of each method is given, as well as several algorithmic approaches to modeling gene networks using these methods; the complexity of algorithms and the problems that arise during its implementation are also noted.
Pulse-transmission Oscillators: Autonomous Boolean Models and the Yeast Cell Cycle
Sevim, Volkan; Gong, Xinwei; Socolar, Joshua
2010-03-01
Models of oscillatory gene expression typically involve a constitutively expressed or positively autoregulated gene which is repressed by a negative feedback loop. In Boolean representations of such systems, which include the repressilator and relaxation oscillators, dynamical stability stems from the impossibility of satisfying all of the Boolean rules at once. We consider a different class of networks, in which oscillations are due to the transmission of a pulse of gene activation around a ring. Using autonomous Boolean modeling methods, we show how the circulating pulse can be stabilized by decoration of the ring with certain feedback and feed-forward motifs. We then discuss the relation of these models to ODE models of transcriptional networks, emphasizing the role of explicit time delays. Finally, we show that a network recently proposed as a generator of cell cycle oscillations in yeast contains the motifs required to support stable transmission oscillations.
Stabilizing Motifs in Autonomous Boolean Networks and the Yeast Cell Cycle Oscillator
Sevim, Volkan; Gong, Xinwei; Socolar, Joshua
2009-03-01
Synchronously updated Boolean networks are widely used to model gene regulation. Some properties of these model networks are known to be artifacts of the clocking in the update scheme. Autonomous updating is a less artificial scheme that allows one to introduce small timing perturbations and study stability of the attractors. We argue that the stabilization of a limit cycle in an autonomous Boolean network requires a combination of motifs such as feed-forward loops and auto-repressive links that can correct small fluctuations in the timing of switching events. A recently published model of the transcriptional cell-cycle oscillator in yeast contains the motifs necessary for stability under autonomous updating [1]. [1] D. A. Orlando, et al. Nature (London), 4530 (7197):0 944--947, 2008.
Evolution of a designless nanoparticle network into reconfigurable Boolean logic
Bose, S. K.; Lawrence, C. P.; Liu, Z.; Makarenko, K. S.; van Damme, R. M. J.; Broersma, H. J.; van der Wiel, W. G.
2015-12-01
Natural computers exploit the emergent properties and massive parallelism of interconnected networks of locally active components. Evolution has resulted in systems that compute quickly and that use energy efficiently, utilizing whatever physical properties are exploitable. Man-made computers, on the other hand, are based on circuits of functional units that follow given design rules. Hence, potentially exploitable physical processes, such as capacitive crosstalk, to solve a problem are left out. Until now, designless nanoscale networks of inanimate matter that exhibit robust computational functionality had not been realized. Here we artificially evolve the electrical properties of a disordered nanomaterials system (by optimizing the values of control voltages using a genetic algorithm) to perform computational tasks reconfigurably. We exploit the rich behaviour that emerges from interconnected metal nanoparticles, which act as strongly nonlinear single-electron transistors, and find that this nanoscale architecture can be configured in situ into any Boolean logic gate. This universal, reconfigurable gate would require about ten transistors in a conventional circuit. Our system meets the criteria for the physical realization of (cellular) neural networks: universality (arbitrary Boolean functions), compactness, robustness and evolvability, which implies scalability to perform more advanced tasks. Our evolutionary approach works around device-to-device variations and the accompanying uncertainties in performance. Moreover, it bears a great potential for more energy-efficient computation, and for solving problems that are very hard to tackle in conventional architectures.
Harmonic Analysis of Boolean Networks: Determinative Power and Perturbations
Heckel, Reinhard; Bossert, Martin
2011-01-01
Consider a large Boolean network with a feed forward structure. Given a probability distribution for the inputs, can one find-possibly small-collections of input nodes that determine the states of most other nodes in the network? To identify these nodes, a notion that quantifies the determinative power of an input over states in the network is needed. We argue that the mutual information (MI) between a subset of the inputs X = {X_1, ..., X_n} of node i and the function f_i(X)$ associated with node i quantifies the determinative power of this subset of inputs over node i. To study the relation of determinative power to sensitivity to perturbations, we relate the MI to measures of perturbations, such as the influence of a variable, in terms of inequalities. The result shows that, maybe surprisingly, an input that has large influence does not necessarily have large determinative power. The main tool for the analysis is Fourier analysis of Boolean functions. Whether a function is sensitive to perturbations or not...
Evolution of a designless nanoparticle network into reconfigurable Boolean logic.
Bose, S K; Lawrence, C P; Liu, Z; Makarenko, K S; van Damme, R M J; Broersma, H J; van der Wiel, W G
2015-12-01
Natural computers exploit the emergent properties and massive parallelism of interconnected networks of locally active components. Evolution has resulted in systems that compute quickly and that use energy efficiently, utilizing whatever physical properties are exploitable. Man-made computers, on the other hand, are based on circuits of functional units that follow given design rules. Hence, potentially exploitable physical processes, such as capacitive crosstalk, to solve a problem are left out. Until now, designless nanoscale networks of inanimate matter that exhibit robust computational functionality had not been realized. Here we artificially evolve the electrical properties of a disordered nanomaterials system (by optimizing the values of control voltages using a genetic algorithm) to perform computational tasks reconfigurably. We exploit the rich behaviour that emerges from interconnected metal nanoparticles, which act as strongly nonlinear single-electron transistors, and find that this nanoscale architecture can be configured in situ into any Boolean logic gate. This universal, reconfigurable gate would require about ten transistors in a conventional circuit. Our system meets the criteria for the physical realization of (cellular) neural networks: universality (arbitrary Boolean functions), compactness, robustness and evolvability, which implies scalability to perform more advanced tasks. Our evolutionary approach works around device-to-device variations and the accompanying uncertainties in performance. Moreover, it bears a great potential for more energy-efficient computation, and for solving problems that are very hard to tackle in conventional architectures. PMID:26389658
Synchronization Analysis of Master-Slave Probabilistic Boolean Networks.
Lu, Jianquan; Zhong, Jie; Li, Lulu; Ho, Daniel W C; Cao, Jinde
2015-01-01
In this paper, we analyze the synchronization problem of master-slave probabilistic Boolean networks (PBNs). The master Boolean network (BN) is a deterministic BN, while the slave BN is determined by a series of possible logical functions with certain probability at each discrete time point. In this paper, we firstly define the synchronization of master-slave PBNs with probability one, and then we investigate synchronization with probability one. By resorting to new approach called semi-tensor product (STP), the master-slave PBNs are expressed in equivalent algebraic forms. Based on the algebraic form, some necessary and sufficient criteria are derived to guarantee synchronization with probability one. Further, we study the synchronization of master-slave PBNs in probability. Synchronization in probability implies that for any initial states, the master BN can be synchronized by the slave BN with certain probability, while synchronization with probability one implies that master BN can be synchronized by the slave BN with probability one. Based on the equivalent algebraic form, some efficient conditions are derived to guarantee synchronization in probability. Finally, several numerical examples are presented to show the effectiveness of the main results.
A Boolean Approach to Airline Business Model Innovation
DEFF Research Database (Denmark)
Hvass, Kristian Anders
Research in business model innovation has identified its significance in creating a sustainable competitive advantage for a firm, yet there are few empirical studies identifying which combination of business model activities lead to success and therefore deserve innovative attention. This study...... analyzes the business models of North America low-cost carriers from 2001 to 2010 using a Boolean minimization algorithm to identify which combinations of business model activities lead to operational profitability. The research aim is threefold: complement airline literature in the realm of business model...... innovation, introduce Boolean minimization methods to the field, and propose alternative business model activities to North American carriers striving for positive operating results....
Detecting small attractors of large Boolean networks by function-reduction-based strategy.
Zheng, Qiben; Shen, Liangzhong; Shang, Xuequn; Liu, Wenbin
2016-04-01
Boolean networks (BNs) are widely used to model gene regulatory networks and to design therapeutic intervention strategies to affect the long-term behaviour of systems. A central aim of Boolean-network analysis is to find attractors that correspond to various cellular states, such as cell types or the stage of cell differentiation. This problem is NP-hard and various algorithms have been used to tackle it with considerable success. The idea is that a singleton attractor corresponds to n consistent subsequences in the truth table. To find these subsequences, the authors gradually reduce the entire truth table of Boolean functions by extending a partial gene activity profile (GAP). Not only does this process delete inconsistent subsequences in truth tables, it also directly determines values for some nodes not extended, which means it can abandon the partial GAPs that cannot lead to an attractor as early as possible. The results of simulation show that the proposed algorithm can detect small attractors with length p = 4 in BNs of up to 200 nodes with average indegree K = 2.
Stötzel, Claudia; Röblitz, Susanna; Siebert, Heike
2015-01-01
In this paper, we present a systematic transition scheme for a large class of ordinary differential equations (ODEs) into Boolean networks. Our transition scheme can be applied to any system of ODEs whose right hand sides can be written as sums and products of monotone functions. It performs an Euler-like step which uses the signs of the right hand sides to obtain the Boolean update functions for every variable of the corresponding discrete model. The discrete model can, on one hand, be considered as another representation of the biological system or, alternatively, it can be used to further the analysis of the original ODE model. Since the generic transformation method does not guarantee any property conservation, a subsequent validation step is required. Depending on the purpose of the model this step can be based on experimental data or ODE simulations and characteristics. Analysis of the resulting Boolean model, both on its own and in comparison with the ODE model, then allows to investigate system properties not accessible in a purely continuous setting. The method is exemplarily applied to a previously published model of the bovine estrous cycle, which leads to new insights regarding the regulation among the components, and also indicates strongly that the system is tailored to generate stable oscillations.
Directory of Open Access Journals (Sweden)
Claudia Stötzel
Full Text Available In this paper, we present a systematic transition scheme for a large class of ordinary differential equations (ODEs into Boolean networks. Our transition scheme can be applied to any system of ODEs whose right hand sides can be written as sums and products of monotone functions. It performs an Euler-like step which uses the signs of the right hand sides to obtain the Boolean update functions for every variable of the corresponding discrete model. The discrete model can, on one hand, be considered as another representation of the biological system or, alternatively, it can be used to further the analysis of the original ODE model. Since the generic transformation method does not guarantee any property conservation, a subsequent validation step is required. Depending on the purpose of the model this step can be based on experimental data or ODE simulations and characteristics. Analysis of the resulting Boolean model, both on its own and in comparison with the ODE model, then allows to investigate system properties not accessible in a purely continuous setting. The method is exemplarily applied to a previously published model of the bovine estrous cycle, which leads to new insights regarding the regulation among the components, and also indicates strongly that the system is tailored to generate stable oscillations.
Coevolution of information processing and topology in hierarchical adaptive random Boolean networks
Górski, Piotr J.; Czaplicka, Agnieszka; Hołyst, Janusz A.
2016-02-01
Random Boolean Networks (RBNs) are frequently used for modeling complex systems driven by information processing, e.g. for gene regulatory networks (GRNs). Here we propose a hierarchical adaptive random Boolean Network (HARBN) as a system consisting of distinct adaptive RBNs (ARBNs) - subnetworks - connected by a set of permanent interlinks. We investigate mean node information, mean edge information as well as mean node degree. Information measures and internal subnetworks topology of HARBN coevolve and reach steady-states that are specific for a given network structure. The main natural feature of ARBNs, i.e. their adaptability, is preserved in HARBNs and they evolve towards critical configurations which is documented by power law distributions of network attractor lengths. The mean information processed by a single node or a single link increases with the number of interlinks added to the system. The mean length of network attractors and the mean steady-state connectivity possess minima for certain specific values of the quotient between the density of interlinks and the density of all links in networks. It means that the modular network displays extremal values of its observables when subnetworks are connected with a density a few times lower than a mean density of all links.
Discrete interference modeling via boolean algebra.
Beckhoff, Gerhard
2011-01-01
Two types of boolean functions are considered, the locus function of n variables, and the interval function of ν = n - 1 variables. A 1-1 mapping is given that takes elements (cells) of the interval function to antidual pairs of elements in the locus function, and vice versa. A set of ν binary codewords representing the intervals are defined and used to generate the codewords of all genomic regions. Next a diallelic three-point system is reviewed in the light of boolean functions, which leads to redefining complete interference by a logic function. Together with the upper bound of noninterference already defined by a boolean function, it confines the region of interference. Extensions of these two functions to any finite number of ν are straightforward, but have been also made in terms of variables taken from the inclusion-exclusion principle (expressing "at least" and "exactly equal to" a decimal integer). Two coefficients of coincidence for systems with more than three loci are defined and discussed, one using the average of several individual coefficients and the other taking as coefficient a real number between zero and one. Finally, by way of a malfunction of the mod-2 addition, it is shown that a four-point system may produce two different functions, one of which exhibiting loss of a class of odd recombinants.
Sampled-Data State Feedback Stabilization of Boolean Control Networks.
Liu, Yang; Cao, Jinde; Sun, Liangjie; Lu, Jianquan
2016-04-01
In this letter, we investigate the sampled-data state feedback control (SDSFC) problem of Boolean control networks (BCNs). Some necessary and sufficient conditions are obtained for the global stabilization of BCNs by SDSFC. Different from conventional state feedback controls, new phenomena observed the study of SDSFC. Based on the controllability matrix, we derive some necessary and sufficient conditions under which the trajectories of BCNs can be stabilized to a fixed point by piecewise constant control (PCC). It is proved that the global stabilization of BCNs under SDSFC is equivalent to that by PCC. Moreover, algorithms are given to construct the sampled-data state feedback controllers. Numerical examples are given to illustrate the efficiency of the obtained results.
Feedback control design for the complete synchronisation of two coupled Boolean networks
Li, Fangfei
2016-09-01
In the literatures, to design state feedback controllers to make the response Boolean network synchronise with the drive Boolean network is rarely considered. Motivated by this, feedback control design for the complete synchronisation of two coupled Boolean networks is investigated in this paper. A necessary condition for the existence of a state feedback controller achieving the complete synchronisation is established first. Then, based on the necessary condition, the feedback control law is proposed. Finally, an example is worked out to illustrate the proposed design procedure.
"Antelope": a hybrid-logic model checker for branching-time Boolean GRN analysis
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Arellano Gustavo
2011-12-01
Full Text Available Abstract Background In Thomas' formalism for modeling gene regulatory networks (GRNs, branching time, where a state can have more than one possible future, plays a prominent role. By representing a certain degree of unpredictability, branching time can model several important phenomena, such as (a asynchrony, (b incompletely specified behavior, and (c interaction with the environment. Introducing more than one possible future for a state, however, creates a difficulty for ordinary simulators, because infinitely many paths may appear, limiting ordinary simulators to statistical conclusions. Model checkers for branching time, by contrast, are able to prove properties in the presence of infinitely many paths. Results We have developed Antelope ("Analysis of Networks through TEmporal-LOgic sPEcifications", http://turing.iimas.unam.mx:8080/AntelopeWEB/, a model checker for analyzing and constructing Boolean GRNs. Currently, software systems for Boolean GRNs use branching time almost exclusively for asynchrony. Antelope, by contrast, also uses branching time for incompletely specified behavior and environment interaction. We show the usefulness of modeling these two phenomena in the development of a Boolean GRN of the Arabidopsis thaliana root stem cell niche. There are two obstacles to a direct approach when applying model checking to Boolean GRN analysis. First, ordinary model checkers normally only verify whether or not a given set of model states has a given property. In comparison, a model checker for Boolean GRNs is preferable if it reports the set of states having a desired property. Second, for efficiency, the expressiveness of many model checkers is limited, resulting in the inability to express some interesting properties of Boolean GRNs. Antelope tries to overcome these two drawbacks: Apart from reporting the set of all states having a given property, our model checker can express, at the expense of efficiency, some properties that ordinary
Super-transient scaling in time-delay autonomous Boolean network motifs
D'Huys, Otti; Lohmann, Johannes; Haynes, Nicholas D.; Gauthier, Daniel J.
2016-09-01
Autonomous Boolean networks are commonly used to model the dynamics of gene regulatory networks and allow for the prediction of stable dynamical attractors. However, most models do not account for time delays along the network links and noise, which are crucial features of real biological systems. Concentrating on two paradigmatic motifs, the toggle switch and the repressilator, we develop an experimental testbed that explicitly includes both inter-node time delays and noise using digital logic elements on field-programmable gate arrays. We observe transients that last millions to billions of characteristic time scales and scale exponentially with the amount of time delays between nodes, a phenomenon known as super-transient scaling. We develop a hybrid model that includes time delays along network links and allows for stochastic variation in the delays. Using this model, we explain the observed super-transient scaling of both motifs and recreate the experimentally measured transient distributions.
Characterizing short-term stability for Boolean networks over any distribution of transfer functions
Seshadhri, C.; Smith, Andrew M.; Vorobeychik, Yevgeniy; Mayo, Jackson R.; Armstrong, Robert C.
2016-07-01
We present a characterization of short-term stability of Kauffman's N K (random) Boolean networks under arbitrary distributions of transfer functions. Given such a Boolean network where each transfer function is drawn from the same distribution, we present a formula that determines whether short-term chaos (damage spreading) will happen. Our main technical tool which enables the formal proof of this formula is the Fourier analysis of Boolean functions, which describes such functions as multilinear polynomials over the inputs. Numerical simulations on mixtures of threshold functions and nested canalyzing functions demonstrate the formula's correctness.
Optimal computation of symmetric Boolean functions in Tree networks
Kowshik, Hemant
2010-01-01
In this paper, we address the scenario where nodes with sensor data are connected in a tree network, and every node wants to compute a given symmetric Boolean function of the sensor data. We first consider the problem of computing a function of two nodes with integer measurements. We allow for block computation to enhance data fusion efficiency, and determine the minimum worst-case total number of bits to be exchanged to perform the desired computation. We establish lower bounds using fooling sets, and provide a novel scheme which attains the lower bounds, using information theoretic tools. For a class of functions called sum-threshold functions, this scheme is shown to be optimal. We then turn to tree networks and derive a lower bound for the number of bits exchanged on each link by viewing it as a two node problem. We show that the protocol of recursive innetwork aggregation achieves this lower bound in the case of sumthreshold functions. Thus we have provided a communication and in-network computation stra...
SAT-based Distributed Reactive Control Protocol Synthesis for Boolean Networks
Sahin, Yunus Emre; Ozay, Necmiye
2016-01-01
This paper considers the synthesis of distributed reactive control protocols for a Boolean network in a distributed manner. We start with a directed acyclic graph representing a network of Boolean subsystems and a global contract, given as an assumption-guarantee pair. Assumption captures the environment behavior, and guarantee is the requirements to be satisfied by the system. Local assumption-guarantee contracts, together with local control protocols ensuring these local contracts, are comp...
Feedback control and output feedback control for the stabilisation of switched Boolean networks
Li, Fangfei; Yu, Zhaoxu
2016-02-01
This paper presents the feedback control and output feedback control for the stabilisation of switched Boolean network. A necessary condition for the existence of a state feedback controller for the stabilisation of switched Boolean networks under arbitrary switching signal is derived first, and constructive procedures for feedback control and output feedback control design are provided. An example is introduced to show the effectiveness of this paper.
A Boolean Approach to Airline Business Model Innovation
Hvass, Kristian
2012-01-01
Research in business model innovation has identified its significance in creating a sustainable competitive advantage for a firm, yet there are few empirical studies identifying which combination of business model activities lead to success and therefore deserve innovative attention. This study analyzes the business models of North America low-cost carriers from 2001 to 2010 using a Boolean minimization algorithm to identify which combinations of business model activities le...
Damage spreading in spatial and small-world random boolean networks
Energy Technology Data Exchange (ETDEWEB)
Lu, Qiming [Los Alamos National Laboratory; Teuscher, Christof [Los Alamos National Laboratory
2008-01-01
Random Boolean Networks (RBNs) are often used as generic models for certain dynamics of complex systems, ranging from social networks, neural networks, to gene or protein interaction networks. Traditionally, RBNs are interconnected randomly and without considering any spatial arrangement of the links and nodes. However, most real-world networks are spatially extended and arranged with regular, small-world, or other non-random connections. Here we explore the RBN network topology between extreme local connections, random small-world, and random networks, and study the damage spreading with small perturbations. We find that spatially local connections change the scaling of the relevant component at very low connectivities ({bar K} << 1) and that the critical connectivity of stability K{sub s} changes compared to random networks. At higher {bar K}, this scaling remains unchanged. We also show that the relevant component of spatially local networks scales with a power-law as the system size N increases, but with a different exponent for local and small-world networks. The scaling behaviors are obtained by finite-size scaling. We further investigate the wiring cost of the networks. From an engineering perspective, our new findings provide the key trade-offs between damage spreading (robustness), the network wiring cost, and the network's communication characteristics.
Damage Spreading in Spatial and Small-world Random Boolean Networks
Energy Technology Data Exchange (ETDEWEB)
Lu, Qiming [Fermilab; Teuscher, Christof [Portland State U.
2014-02-18
The study of the response of complex dynamical social, biological, or technological networks to external perturbations has numerous applications. Random Boolean Networks (RBNs) are commonly used a simple generic model for certain dynamics of complex systems. Traditionally, RBNs are interconnected randomly and without considering any spatial extension and arrangement of the links and nodes. However, most real-world networks are spatially extended and arranged with regular, power-law, small-world, or other non-random connections. Here we explore the RBN network topology between extreme local connections, random small-world, and pure random networks, and study the damage spreading with small perturbations. We find that spatially local connections change the scaling of the relevant component at very low connectivities ($\\bar{K} \\ll 1$) and that the critical connectivity of stability $K_s$ changes compared to random networks. At higher $\\bar{K}$, this scaling remains unchanged. We also show that the relevant component of spatially local networks scales with a power-law as the system size N increases, but with a different exponent for local and small-world networks. The scaling behaviors are obtained by finite-size scaling. We further investigate the wiring cost of the networks. From an engineering perspective, our new findings provide the key design trade-offs between damage spreading (robustness), the network's wiring cost, and the network's communication characteristics.
Decisional Processes with Boolean Neural Network: the Emergence of Mental Schemes
Barnabei, Graziano; Conversano, Ciro; Lensi, Elena
2010-01-01
Human decisional processes result from the employment of selected quantities of relevant information, generally synthesized from environmental incoming data and stored memories. Their main goal is the production of an appropriate and adaptive response to a cognitive or behavioral task. Different strategies of response production can be adopted, among which haphazard trials, formation of mental schemes and heuristics. In this paper, we propose a model of Boolean neural network that incorporates these strategies by recurring to global optimization strategies during the learning session. The model characterizes as well the passage from an unstructured/chaotic attractor neural network typical of data-driven processes to a faster one, forward-only and representative of schema-driven processes. Moreover, a simplified version of the Iowa Gambling Task (IGT) is introduced in order to test the model. Our results match with experimental data and point out some relevant knowledge coming from psychological domain.
MILES FORMULAE FOR BOOLEAN MODELS OBSERVED ON LATTICES
Directory of Open Access Journals (Sweden)
Joachim Ohser
2011-05-01
Full Text Available The densities of the intrinsic volumes – in 3D the volume density, surface density, the density of the integral of the mean curvature and the density of the Euler number – are a very useful collection of geometric characteristics of random sets. Combining integral and digital geometry we develop a method for efficient and simultaneous calculation of the intrinsic volumes of random sets observed in binary images in arbitrary dimensions. We consider isotropic and reflection invariant Boolean models sampled on homogeneous lattices and compute the expectations of the estimators of the intrinsic volumes. It turns out that the estimator for the surface density is proved to be asymptotically unbiased and thusmultigrid convergent for Boolean models with convex grains. The asymptotic bias of the estimators for the densities of the integral of the mean curvature and of the Euler number is assessed for Boolean models of balls of random diameters. Miles formulae with corresponding correction terms are derived for the 3D case.
Local digital estimators of intrinsic volumes for Boolean models and in the design based setting
DEFF Research Database (Denmark)
Svane, Anne Marie
In order to estimate the specific intrinsic volumes of a planar Boolean model from a binary image, we consider local digital algorithms based on weigted sums of 2×2 configuration counts. For Boolean models with balls as grains, explicit formulas for the bias of such algorithms are derived...... for the bias obtained for Boolean models are applied to existing algorithms in order to compare their accuracy....
Chaos Control in Random Boolean Networks by Reducing Mean Damage Percolation Rate
Institute of Scientific and Technical Information of China (English)
JIANG Nan; CHEN Shi-Jian
2011-01-01
Chaos control in random Boolean networks is implemented by freezing part of the network to drive it from chaotic to ordered phase. However, controlled nodes are only viewed as passive blocks to prevent perturbation spread. We propose a new control method in which controlled nodes can exert an active impact on the network.Controlled nodes and frozen values are deliberately selected according to the information of connection and Boolean functions. Simulation results showy that the number of nodes needed to achieve control is largely reduced compared to the previous method. Theoretical analysis is also given to estimate the least fraction of nodes needed to achieve control.%Chaos control in random Boolean networks is implemented by freezing part of the network to drive it from chaotic to ordered phase.However, controlled nodes are only viewed as passive blocks to prevent perturbation spread.We propose a new control method in which controlled nodes can exert an active impact on the network.Controlled nodes and frozen values are deliberately selected according to the information of connection and Boolean functions.Simulation results show that the number of nodes needed to achieve control is largely reduced compared to the previous method.Theoretical analysis is also given to estimate the least fraction of nodes needed to achieve control
Boolean Variables in Economic Models Solved by Linear Programming
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Lixandroiu D.
2014-12-01
Full Text Available The article analyses the use of logical variables in economic models solved by linear programming. Focus is given to the presentation of the way logical constraints are obtained and of the definition rules based on predicate logic. Emphasis is also put on the possibility to use logical variables in constructing a linear objective function on intervals. Such functions are encountered when costs or unitary receipts are different on disjunct intervals of production volumes achieved or sold. Other uses of Boolean variables are connected to constraint systems with conditions and the case of a variable which takes values from a finite set of integers.
Lahoz-Beltra, R; Hameroff, S R; Dayhoff, J E
1993-01-01
Adaptive behaviors and dynamic activities within living cells are organized by the cytoskeleton: intracellular networks of interconnected protein polymers which include microtubules (MTs), actin, intermediate filaments, microtubule associated proteins (MAPs) and other protein structures. Cooperative interactions among cytoskeletal protein subunit conformational states have been used to model signal transmission and information processing. In the present work we present a theoretical model for molecular computing in which Boolean logic is implemented in parallel networks of individual MTs interconnected by MAPs. Conformational signals propagate on MTs as in data buses and in the model MAPs are considered as Boolean operators, either as bit-lines (like MTs) where a signal can be transported unchanged between MTs ('BUS-MAP'), or as bit-lines where a Boolean operation is performed in one of the two MAP-MT attachments ('LOGIC-MAP'). Three logic MAPs have been defined ('NOT-MAP, 'AND-MAP', 'XOR-MAP') and used to demonstrate addition, subtraction and other arithmetic operations. Although our choice of Boolean logic is arbitrary, the simulations demonstrate symbolic manipulation in a connectionist system and suggest that MT-MAP networks can perform computation in living cells and are candidates for future molecular computing devices. PMID:8318677
Influence of a Structure on System's Dynamics on Example of Boolean Networks
Kirillova, O.
1999-01-01
In this work we study the Boolean Networks of different geometric shape and lattice organization. It was revealed that no only a spatial shape but also type of lattice are very important for definition of the structure-dynamics relation. The regular structures do not give a critical regime in the investigated cases. Hierarchy together with the irregular structure reveals characteristic features of criticality.
Analysis and control of Boolean networks a semi-tensor product approach
Cheng, Daizhan; Li, Zhiqiang
2010-01-01
This book presents a new approach to the investigation of Boolean control networks, using the semi-tensor product (STP), which can express a logical function as a conventional discrete-time linear system. This makes it possible to analyze basic control problems.
Direct relations between morphology and transport in Boolean models.
Scholz, Christian; Wirner, Frank; Klatt, Michael A; Hirneise, Daniel; Schröder-Turk, Gerd E; Mecke, Klaus; Bechinger, Clemens
2015-10-01
We study the relation of permeability and morphology for porous structures composed of randomly placed overlapping circular or elliptical grains, so-called Boolean models. Microfluidic experiments and lattice Boltzmann simulations allow us to evaluate a power-law relation between the Euler characteristic of the conducting phase and its permeability. Moreover, this relation is so far only directly applicable to structures composed of overlapping grains where the grain density is known a priori. We develop a generalization to arbitrary structures modeled by Boolean models and characterized by Minkowski functionals. This generalization works well for the permeability of the void phase in systems with overlapping grains, but systematic deviations are found if the grain phase is transporting the fluid. In the latter case our analysis reveals a significant dependence on the spatial discretization of the porous structure, in particular the occurrence of single isolated pixels. To link the results to percolation theory we performed Monte Carlo simulations of the Euler characteristic of the open cluster, which reveals different regimes of applicability for our permeability-morphology relations close to and far away from the percolation threshold.
Direct relations between morphology and transport in Boolean models
Scholz, Christian; Wirner, Frank; Klatt, Michael A.; Hirneise, Daniel; Schröder-Turk, Gerd E.; Mecke, Klaus; Bechinger, Clemens
2015-10-01
We study the relation of permeability and morphology for porous structures composed of randomly placed overlapping circular or elliptical grains, so-called Boolean models. Microfluidic experiments and lattice Boltzmann simulations allow us to evaluate a power-law relation between the Euler characteristic of the conducting phase and its permeability. Moreover, this relation is so far only directly applicable to structures composed of overlapping grains where the grain density is known a priori. We develop a generalization to arbitrary structures modeled by Boolean models and characterized by Minkowski functionals. This generalization works well for the permeability of the void phase in systems with overlapping grains, but systematic deviations are found if the grain phase is transporting the fluid. In the latter case our analysis reveals a significant dependence on the spatial discretization of the porous structure, in particular the occurrence of single isolated pixels. To link the results to percolation theory we performed Monte Carlo simulations of the Euler characteristic of the open cluster, which reveals different regimes of applicability for our permeability-morphology relations close to and far away from the percolation threshold.
Combinatorics of Boolean automata circuits dynamics
Demongeot, Jacques; Noual, Mathilde; Sené, Sylvain
2012-01-01
International audience In line with fields of theoretical computer science and biology that study Boolean automata networks to model regulation networks, we present some results concerning the dynamics of networks whose underlying structures are oriented cycles, that is, Boolean automata circuits. In the context of biological regulation, former studies have highlighted the importance of circuits on the asymptotic dynamical behaviour of the biological networks that contain them. Our work fo...
Cecil, Alexander; Gentschev, Ivaylo; Adelfinger, Marion; Nolte, Ingo; Dandekar, Thomas; Szalay, Aladar A
2014-01-01
Virotherapy on the basis of oncolytic vaccinia virus (VACV) strains is a novel approach for cancer therapy. In this study we describe for the first time the use of dynamic boolean modeling for tumor growth prediction of vaccinia virus GLV-1h68-injected canine tumors including canine mammary adenoma (ZMTH3), canine mammary carcinoma (MTH52c), canine prostate carcinoma (CT1258), and canine soft tissue sarcoma (STSA-1). Additionally, the STSA-1 xenografted mice were injected with either LIVP 1.1.1 or LIVP 5.1.1 vaccinia virus strains. Antigen profiling data of the four different vaccinia virus-injected canine tumors were obtained, analyzed and used to calculate differences in the tumor growth signaling network by type and tumor type. Our model combines networks for apoptosis, MAPK, p53, WNT, Hedgehog, TK cell, Interferon, and Interleukin signaling networks. The in silico findings conform with in vivo findings of tumor growth. Boolean modeling describes tumor growth and remission semi-quantitatively with a good fit to the data obtained for all cancer type variants. At the same time it monitors all signaling activities as a basis for treatment planning according to antigen levels. Mitigation and elimination of VACV- susceptible tumor types as well as effects on the non-susceptible type CT1258 are predicted correctly. Thus the combination of Antigen profiling and semi-quantitative modeling optimizes the therapy already before its start.
A Boolean delay equation model of ENSO variability
Saunders, Amira; Ghil, Michael
2001-12-01
Boolean delay equations (BDEs) provide a mathematical framework to formulate and analyze conceptual models of complex multi-component systems. This framework is used here to construct a simple conceptual model for the El-Niño/Southern Oscillation (ENSO) phenomenon. ENSO involves the coupling of atmospheric and oceanic processes that are far from being completely understood. Our BDE model uses Boolean variables to represent key atmospheric and oceanic quantities and equations that involve logical operators to describe their evolution. Two distinct time-delay parameters, one for the local atmosphere-ocean coupling effects and the other for oceanic wave propagation, are introduced. Over a range of physically relevant delay values, this truly minimal model captures two essential features of ENSO’s interannual variability - its regularity and its tendency to phase-lock to the annual cycle. Oscillations with average cycle length that is an integer multiple of the seasonal cycle are prevalent and range from 2 to 7 years. Transition zones - where the average period lengths are noninteger rational multiples of the forcing period - exhibit Devil’s staircases, a signature of the quasi-periodic (QP) route to chaos. Our BDE model thus validates results from previous studies of the interaction of the seasonal cycle with ENSO’s “delayed oscillator”. It gives therewith support to the view that the observed irregularity results predominantly from low-order chaotic processes rather than from stochastic weather noise. Moreover, in the transition zone between the two integer periodicities of 2 and 3 years, a heretofore unsuspected, self-similar “fractal sunburst” pattern emerges in phase-parameter space. This pattern provides a distinct and more complex scenario than the QP route to chaos found in earlier, more detailed ENSO models. Period selection in this 2-3-year transitional region seems to play a key role in ENSO’s irregularity, as well as in the appearance of
Detecting a Singleton Attractor in a Boolean Network Utilizing SAT Algorithms
Tamura, Takeyuki; Akutsu, Tatsuya
The Boolean network (BN) is a mathematical model of genetic networks. It is known that detecting a singleton attractor, which is also called a fixed point, is NP-hard even for AND/OR BNs (i.e., BNs consisting of AND/OR nodes), where singleton attractors correspond to steady states. Though a naive algorithm can detect a singleton attractor for an AND/OR BN in O(n 2n) time, no O((2-ε)n) (ε > 0) time algorithm was known even for an AND/OR BN with non-restricted indegree, where n is the number of nodes in a BN. In this paper, we present an O(1.787n) time algorithm for detecting a singleton attractor of a given AND/OR BN, along with related results. We also show that detection of a singleton attractor in a BN with maximum indegree two is NP-hard and can be polynomially reduced to a satisfiability problem.
Boolean Modeling of Cellular and Molecular Pathways Involved in Influenza Infection.
Anderson, Christopher S; DeDiego, Marta L; Topham, David J; Thakar, Juilee
2016-01-01
Systems virology integrates host-directed approaches with molecular profiling to understand viral pathogenesis. Self-contained statistical approaches that combine expression profiles of genes with the available databases defining the genes involved in the pathways (gene-sets) have allowed characterization of predictive gene-signatures associated with outcome of the influenza virus (IV) infection. However, such enrichment techniques do not take into account interactions among pathways that are responsible for the IV infection pathogenesis. We investigate dendritic cell response to seasonal H1N1 influenza A/New Caledonia/20/1999 (NC) infection and infer the Boolean logic rules underlying the interaction network of ligand induced signaling pathways and transcription factors. The model reveals several novel regulatory modes and provides insights into mechanism of cross talk between NFκB and IRF mediated signaling. Additionally, the logic rule underlying the regulation of IL2 pathway that was predicted by the Boolean model was experimentally validated. Thus, the model developed in this paper integrates pathway analysis tools with the dynamic modeling approaches to reveal the regulation between signaling pathways and transcription factors using genome-wide transcriptional profiles measured upon influenza infection.
Boolean Modeling of Cellular and Molecular Pathways Involved in Influenza Infection
Anderson, Christopher S.; DeDiego, Marta L.; Topham, David J.; Thakar, Juilee
2016-01-01
Systems virology integrates host-directed approaches with molecular profiling to understand viral pathogenesis. Self-contained statistical approaches that combine expression profiles of genes with the available databases defining the genes involved in the pathways (gene-sets) have allowed characterization of predictive gene-signatures associated with outcome of the influenza virus (IV) infection. However, such enrichment techniques do not take into account interactions among pathways that are responsible for the IV infection pathogenesis. We investigate dendritic cell response to seasonal H1N1 influenza A/New Caledonia/20/1999 (NC) infection and infer the Boolean logic rules underlying the interaction network of ligand induced signaling pathways and transcription factors. The model reveals several novel regulatory modes and provides insights into mechanism of cross talk between NFκB and IRF mediated signaling. Additionally, the logic rule underlying the regulation of IL2 pathway that was predicted by the Boolean model was experimentally validated. Thus, the model developed in this paper integrates pathway analysis tools with the dynamic modeling approaches to reveal the regulation between signaling pathways and transcription factors using genome-wide transcriptional profiles measured upon influenza infection. PMID:26981147
Boolean Modeling of Cellular and Molecular Pathways Involved in Influenza Infection
Directory of Open Access Journals (Sweden)
Christopher S. Anderson
2016-01-01
Full Text Available Systems virology integrates host-directed approaches with molecular profiling to understand viral pathogenesis. Self-contained statistical approaches that combine expression profiles of genes with the available databases defining the genes involved in the pathways (gene-sets have allowed characterization of predictive gene-signatures associated with outcome of the influenza virus (IV infection. However, such enrichment techniques do not take into account interactions among pathways that are responsible for the IV infection pathogenesis. We investigate dendritic cell response to seasonal H1N1 influenza A/New Caledonia/20/1999 (NC infection and infer the Boolean logic rules underlying the interaction network of ligand induced signaling pathways and transcription factors. The model reveals several novel regulatory modes and provides insights into mechanism of cross talk between NFκB and IRF mediated signaling. Additionally, the logic rule underlying the regulation of IL2 pathway that was predicted by the Boolean model was experimentally validated. Thus, the model developed in this paper integrates pathway analysis tools with the dynamic modeling approaches to reveal the regulation between signaling pathways and transcription factors using genome-wide transcriptional profiles measured upon influenza infection.
Coletti, Cristian F.; Miranda, Daniel; Mussini, Filipe
2016-02-01
In this work we study the Poisson Boolean model of percolation in locally compact Polish metric spaces and we prove the invariance of subcritical and supercritical phases under mm-quasi-isometries. More precisely, we prove that if a metric space M is mm-quasi-isometric to another metric space N and the Poisson Boolean model in M exhibits any of the following: (a) a subcritical phase; (b) a supercritical phase; or (c) a phase transition, then respectively so does the Poisson Boolean model of percolation in N. Then we use these results in order to understand the phase transition phenomenon in a large family of metric spaces. Indeed, we study the Poisson Boolean model of percolation in the context of Riemannian manifolds, in a large family of nilpotent Lie groups and in Cayley graphs. Also, we prove the existence of a subcritical phase in Gromov spaces with bounded growth at some scale.
Random Boolean Networks and Attractors of their Intersecting Circuits
Demongeot, Jacques; Elena, Adrien; Noual, Mathilde; Sené, Sylvain
2011-01-01
International audience The multi-scale strategy in studying biological regulatory networks analysis is based on two level of analysis. The first level is structural and consists in examining the architecture of the interaction graph underlying the network and the second level is functional and analyse the regulatory properties of the network. We apply this dual approach to the "immunetworks" involved in the control of the immune system. As a result, we show that the small number of attract...
HSP70 mediates survival in apoptotic cells-Boolean network prediction and experimental validation.
Vasaikar, Suhas V; Ghosh, Sourish; Narain, Priyam; Basu, Anirban; Gomes, James
2015-01-01
Neuronal stress or injury results in the activation of proteins, which regulate the balance between survival and apoptosis. However, the complex mechanism of cell signaling involving cell death and survival, activated in response to cellular stress is not yet completely understood. To bring more clarity about these mechanisms, a Boolean network was constructed that represented the apoptotic pathway in neuronal cells. FasL and neurotrophic growth factor (NGF) were considered as inputs in the absence and presence of heat shock proteins known to shift the balance toward survival by rescuing pro-apoptotic cells. The probabilities of survival, DNA repair and apoptosis as cellular fates, in the presence of either the growth factor or FasL, revealed a survival bias encoded in the network. Boolean predictions tested by measuring the mRNA level of caspase-3, caspase-8, and BAX in neuronal Neuro2a (N2a) cell line with NGF and FasL as external input, showed positive correlation with the observed experimental results for survival and apoptotic states. It was observed that HSP70 contributed more toward rescuing cells from apoptosis in comparison to HSP27, HSP40, and HSP90. Overexpression of HSP70 in N2a transfected cells showed reversal of cellular fate from FasL-induced apoptosis to survival. Further, the pro-survival role of the proteins BCL2, IAP, cFLIP, and NFκB determined by vertex perturbation analysis was experimentally validated through protein inhibition experiments using EM20-25, Embelin and Wedelolactone, which resulted in 1.27-, 1.26-, and 1.46-fold increase in apoptosis of N2a cells. The existence of a one-to-one correspondence between cellular fates and attractor states shows that Boolean networks may be employed with confidence in qualitative analytical studies of biological networks.
Evolution of a designless nanoparticle network into reconfigurable Boolean logic
Bose, S.K.; Lawrence, C.P.; Liu, Z.; Makarenko, K.S.; Damme, van R.M.J.; Broersma, H.J.; Wiel, van der W.G.
2015-01-01
Natural computers exploit the emergent properties and massive parallelism of interconnected networks of locally active components. Evolution has resulted in systems that compute quickly and that use energy efficiently, utilizing whatever physical properties are exploitable. Man-made computers, on th
Variances as order parameter and complexity measure for random Boolean networks
Energy Technology Data Exchange (ETDEWEB)
Luque, Bartolo [Departamento de Matematica Aplicada y EstadIstica, Escuela Superior de Ingenieros Aeronauticos, Universidad Politecnica de Madrid, Plaza Cardenal Cisneros 3, Madrid 28040 (Spain); Ballesteros, Fernando J [Observatori Astronomic, Universitat de Valencia, Ed. Instituts d' Investigacio, Pol. La Coma s/n, E-46980 Paterna, Valencia (Spain); Fernandez, Manuel [Departamento de Matematica Aplicada y EstadIstica, Escuela Superior de Ingenieros Aeronauticos, Universidad Politecnica de Madrid, Plaza Cardenal Cisneros 3, Madrid 28040 (Spain)
2005-02-04
Several order parameters have been considered to predict and characterize the transition between ordered and disordered phases in random Boolean networks, such as the Hamming distance between replicas or the stable core, which have been successfully used. In this work, we propose a natural and clear new order parameter: the temporal variance. We compute its value analytically and compare it with the results of numerical experiments. Finally, we propose a complexity measure based on the compromise between temporal and spatial variances. This new order parameter and its related complexity measure can be easily applied to other complex systems.
Lovrics, Anna
2014-11-14
We have assembled a network of cell-fate determining transcription factors that play a key role in the specification of the ventral neuronal subtypes of the spinal cord on the basis of published transcriptional interactions. Asynchronous Boolean modelling of the network was used to compare simulation results with reported experimental observations. Such comparison highlighted the need to include additional regulatory connections in order to obtain the fixed point attractors of the model associated with the five known progenitor cell types located in the ventral spinal cord. The revised gene regulatory network reproduced previously observed cell state switches between progenitor cells observed in knock-out animal models or in experiments where the transcription factors were overexpressed. Furthermore the network predicted the inhibition of Irx3 by Nkx2.2 and this prediction was tested experimentally. Our results provide evidence for the existence of an as yet undescribed inhibitory connection which could potentially have significance beyond the ventral spinal cord. The work presented in this paper demonstrates the strength of Boolean modelling for identifying gene regulatory networks.
Trinh, Hung-Cuong; Kwon, Yung-Keun
2015-11-01
Efficiently identifying functionally important genes in order to understand the minimal requirements of normal cellular development is challenging. To this end, a variety of structural measures have been proposed and their effectiveness has been investigated in recent literature; however, few studies have shown the effectiveness of dynamics-based measures. This led us to investigate a dynamic measure to identify functionally important genes, and the effectiveness of which was verified through application on two large-scale human signaling networks. We specifically consider Boolean sensitivity-based dynamics against an update-rule perturbation (BSU) as a dynamic measure. Through investigations on two large-scale human signaling networks, we found that genes with relatively high BSU values show slower evolutionary rate and higher proportions of essential genes and drug targets than other genes. Gene-ontology analysis showed clear differences between the former and latter groups of genes. Furthermore, we compare the identification accuracies of essential genes and drug targets via BSU and five well-known structural measures. Although BSU did not always show the best performance, it effectively identified the putative set of genes, which is significantly different from the results obtained via the structural measures. Most interestingly, BSU showed the highest synergy effect in identifying the functionally important genes in conjunction with other measures. Our results imply that Boolean-sensitive dynamics can be used as a measure to effectively identify functionally important genes in signaling networks.
高阶布尔网络的结构%Structure of higher order Boolean networks*
Institute of Scientific and Technical Information of China (English)
李志强; 赵寅; 程代展
2011-01-01
The higher order Boolean (control) network is introduced and its topological structure is studied.Using semi-tensor product of matrices,its dynamics is converted into two algebraic forms,which are standard discrete-time dynamic systems.The one-to-one correspondence of the network dynamics and its first algebraic form is proved,and certain topological structures,including fixed points,cycles,and transient time,of higher order Boolean (control) networks are revealed.The relationship between the original system and its second algebraic form is also studied.%介绍高阶布尔（控制）网络,并研究了其拓扑结构.以矩阵的半张量积作为工具,把高阶布尔网络的动态过程转化为2种标准离散事件动态系统的代数形式.证明了高阶布尔网络和第1代数形式的一一对应关系,并由此得到其拓扑结构（不动点、极限圈以及暂态期等）.还研究了高阶布尔网络系统与它第2代数形式的关系.
On the number of attractors of Boolean automata circuits
Demongeot, Jacques; Noual, Mathilde; Sené, Sylvain
2009-01-01
In line with fields of theoretical computer science and biology that study Boolean automata networks often seen as models of regulation networks, we present some results concerning the dynamics of networks whose underlying interaction graphs are circuits, that is Boolean automata circuits. In the context of biological regulation, former studies have highlighted the importance of circuits on the asymptotic dynamical behaviour of the biological networks that contain them. Our work focuses on th...
Directory of Open Access Journals (Sweden)
Tomáš Mrkvička
2011-03-01
Full Text Available Methods for testing the Boolean model assumption from binary images are briefly reviewed. Two hundred binary images of mammary cancer tissue and 200 images of mastopathic tissue were tested individually on the Boolean model assumption. In a previous paper, it had been found that a Monte Carlo method based on the approximation of the envelopes by a multi-normal distribution with the normalized intrinsic volume densities of parallel sets as a summary statistics had the highest power for this purpose. Hence, this method was used here as its first application to real biomedical data. It was found that mastopathic tissue deviates from the Boolean model significantly more strongly than mammary cancer tissue does.
On a Boolean-valued Model of the Strict Implication System(Continuous)
Institute of Scientific and Technical Information of China (English)
LI Na; LIU Hua-ke
2004-01-01
The reference [4] proved the consistency of S 1 and S 2 among Lewis'five strict implicationsystems in the modal logic by using the method of the Boolean-valued model. But, in this method, the consistency of S 3 , S 4 and S 5 in Lewis'five strictimplication systems is not decided. This paper makes use of the properties : (1) the equivalence of the modal systems S 3 andP 3 , S 4 and P 4 ; (2) the modal systems P 3 and P 4 all contained the modal axiom T(□p) ; (3) the modal axiom T is correspondence to the reflexiveproperty in VB . Hence, the paper proves: (a) |A S 31|=1 ; (b) |A S 41|=1 ;(c) |A S 51|=1 in the model (where B is a complete Boolean algebra, R is reflexive property in VB ). Therefore, the paper finallyproves that the Boolean-valued model VB of the ZFC axiomsystem in set theory is also a Boolean-valued model of Lewis'the strict implication system S 3 , S 4 and S 5 .
Application of fuzzy logic to Boolean models for digital soil assessment
Gruijter, de J.J.; Walvoort, D.J.J.; Bragato, G.
2011-01-01
Boolean models based on expert knowledge are often used to classify soils into a limited number of classes of a difficult-to-measure soil attribute. Although the primary data used for these classifications contain information on whether the soil is a typical class member or a boundary case between t
Stratification and enumeration of Boolean functions by canalizing depth
He, Qijun; Macauley, Matthew
2016-01-01
Boolean network models have gained popularity in computational systems biology over the last dozen years. Many of these networks use canalizing Boolean functions, which has led to increased interest in the study of these functions. The canalizing depth of a function describes how many canalizing variables can be recursively "picked off", until a non-canalizing function remains. In this paper, we show how every Boolean function has a unique algebraic form involving extended monomial layers and a well-defined core polynomial. This generalizes recent work on the algebraic structure of nested canalizing functions, and it yields a stratification of all Boolean functions by their canalizing depth. As a result, we obtain closed formulas for the number of n-variable Boolean functions with depth k, which simultaneously generalizes enumeration formulas for canalizing, and nested canalizing functions.
Stratification and enumeration of Boolean functions by canalizing depth
He, Qijun
2015-01-01
Boolean network models have gained popularity in computational systems biology over the last dozen years. Many of these networks use canalizing Boolean functions, which has led to increased interest in the study of these functions. The canalizing depth of a function describes how many canalizing variables can be recursively picked off, until a non-canalizing function remains. In this paper, we show how every Boolean function has a unique algebraic form involving extended monomial layers and a well-defined core polynomial. This generalizes recent work on the algebraic structure of nested canalizing functions, and it yields a stratification of all Boolean functions by their canalizing depth. As a result, we obtain closed formulas for the number of n-variable Boolean functions with depth k, which simultaneously generalizes enumeration formulas for canalizing, and nested canalizing functions.
Directory of Open Access Journals (Sweden)
Yih-Lon Lin
2013-01-01
Full Text Available If the given Boolean function is linearly separable, a robust uncoupled cellular neural network can be designed as a maximal margin classifier. On the other hand, if the given Boolean function is linearly separable but has a small geometric margin or it is not linearly separable, a popular approach is to find a sequence of robust uncoupled cellular neural networks implementing the given Boolean function. In the past research works using this approach, the control template parameters and thresholds are restricted to assume only a given finite set of integers, and this is certainly unnecessary for the template design. In this study, we try to remove this restriction. Minterm- and maxterm-based decomposition algorithms utilizing the soft margin and maximal margin support vector classifiers are proposed to design a sequence of robust templates implementing an arbitrary Boolean function. Several illustrative examples are simulated to demonstrate the efficiency of the proposed method by comparing our results with those produced by other decomposition methods with restricted weights.
Hu, Mingxiao; Shen, Liangzhong; Zan, Xiangzhen; Shang, Xuequn; Liu, Wenbin
2016-01-01
Boolean networks are widely used to model gene regulatory networks and to design therapeutic intervention strategies to affect the long-term behavior of systems. In this paper, we investigate the less-studied one-bit perturbation, which falls under the category of structural intervention. Previous works focused on finding the optimal one-bit perturbation to maximally alter the steady-state distribution (SSD) of undesirable states through matrix perturbation theory. However, the application of the SSD is limited to Boolean networks with about ten genes. In 2007, Xiao et al. proposed to search the optimal one-bit perturbation by altering the sizes of the basin of attractions (BOAs). However, their algorithm requires close observation of the state-transition diagram. In this paper, we propose an algorithm that efficiently determines the BOA size after a perturbation. Our idea is that, if we construct the basin of states for all states, then the size of the BOA of perturbed networks can be obtained just by updating the paths of the states whose transitions have been affected. Results from both synthetic and real biological networks show that the proposed algorithm performs better than the exhaustive SSD-based algorithm and can be applied to networks with about 25 genes.
Hu, Mingxiao; Shen, Liangzhong; Zan, Xiangzhen; Shang, Xuequn; Liu, Wenbin
2016-01-01
Boolean networks are widely used to model gene regulatory networks and to design therapeutic intervention strategies to affect the long-term behavior of systems. In this paper, we investigate the less-studied one-bit perturbation, which falls under the category of structural intervention. Previous works focused on finding the optimal one-bit perturbation to maximally alter the steady-state distribution (SSD) of undesirable states through matrix perturbation theory. However, the application of the SSD is limited to Boolean networks with about ten genes. In 2007, Xiao et al. proposed to search the optimal one-bit perturbation by altering the sizes of the basin of attractions (BOAs). However, their algorithm requires close observation of the state-transition diagram. In this paper, we propose an algorithm that efficiently determines the BOA size after a perturbation. Our idea is that, if we construct the basin of states for all states, then the size of the BOA of perturbed networks can be obtained just by updating the paths of the states whose transitions have been affected. Results from both synthetic and real biological networks show that the proposed algorithm performs better than the exhaustive SSD-based algorithm and can be applied to networks with about 25 genes. PMID:27196530
Boolean modeling of neural systems with point-process inputs and outputs.
Marmarelis, Vasilis Z; Zanos, Theodoros P; Courellis, Spiros H; Berger, Theodore W
2006-01-01
This paper presents a novel modeling approach for neural systems with point-process inputs and outputs (binary time-series of 0's and 1's) that utilizes Boolean operators of modulo-2 multiplication and addition, corresponding to the logical AND and OR operations respectively. The form of the employed mathematical model is akin to a "Boolean-Volterra" model that contains the product terms of all relevant input lags in a hierarchical order, where terms of order higher than first represent nonlinear interactions among the various lagged values of each input point-process or among lagged values of various inputs (if multiple inputs exist) as they reflect on the output. The coefficients of this Boolean model are also binary variables that indicate the presence or absence of the respective term in each specific model/system. Simulations are used to explore the properties of such models and the feasibility of accurate estimation of such models from short data-records in the presence of noise (i.e. spurious spikes). The results demonstrate the feasibility of obtaining reliable estimates of such models, even in the presence of considerable noise in the input and/or output, thus making the proposed approach an attractive candidate for modeling neural systems in a practical context. PMID:17946091
Modeling boolean decision rules applied to multiple-observer decision strategies.
Maguire, W
1996-01-01
A model that derives multiple-observer decision strategy ROC curves for boolean decision rules applied to binary decisions of two or three observers is presented. It is assumed that covert decision variables consistent with ROC models of observer performance underlie decisions and that readers' decision criteria are in a fixed relationship. The specific parameters of individual ROC curves and the correlational structure that describes interobserver agreement have dramatic effects upon the relative benefits to be derived from different boolean strategies. A common strategy employed in clinical practice, in which the overall decision is positive if any observer makes a positive decision, is most effective when the readers are of similar ability, when they adopt similar decision criteria, when interreader agreement is greater for negative than for positive cases, and when the individual ROC slope is observer decision strategies can be evaluated using the model equations. A bootstrap method for testing model-associated hypotheses is described. PMID:8717599
Computational complexity of Boolean functions
Korshunov, Aleksei D.
2012-02-01
Boolean functions are among the fundamental objects of discrete mathematics, especially in those of its subdisciplines which fall under mathematical logic and mathematical cybernetics. The language of Boolean functions is convenient for describing the operation of many discrete systems such as contact networks, Boolean circuits, branching programs, and some others. An important parameter of discrete systems of this kind is their complexity. This characteristic has been actively investigated starting from Shannon's works. There is a large body of scientific literature presenting many fundamental results. The purpose of this survey is to give an account of the main results over the last sixty years related to the complexity of computation (realization) of Boolean functions by contact networks, Boolean circuits, and Boolean circuits without branching. Bibliography: 165 titles.
PARAMETER ESTIMATION IN NON-HOMOGENEOUS BOOLEAN MODELS: AN APPLICATION TO PLANT DEFENSE RESPONSE
Directory of Open Access Journals (Sweden)
Maria Angeles Gallego
2014-11-01
Full Text Available Many medical and biological problems require to extract information from microscopical images. Boolean models have been extensively used to analyze binary images of random clumps in many scientific fields. In this paper, a particular type of Boolean model with an underlying non-stationary point process is considered. The intensity of the underlying point process is formulated as a fixed function of the distance to a region of interest. A method to estimate the parameters of this Boolean model is introduced, and its performance is checked in two different settings. Firstly, a comparative study with other existent methods is done using simulated data. Secondly, the method is applied to analyze the longleaf data set, which is a very popular data set in the context of point processes included in the R package spatstat. Obtained results show that the new method provides as accurate estimates as those obtained with more complex methods developed for the general case. Finally, to illustrate the application of this model and this method, a particular type of phytopathological images are analyzed. These images show callose depositions in leaves of Arabidopsis plants. The analysis of callose depositions, is very popular in the phytopathological literature to quantify activity of plant immunity.
Zhan, Qiqin; Chen, Xiaojun
2016-01-01
This paper proposes an interactive method of model clipping for computer-assisted surgical planning. The model is separated by a data filter that is defined by the implicit function of the clipping path. Being interactive to surgeons, the clipping path that is composed of the plane widgets can be manually repositioned along the desirable presurgical path, which means that surgeons can produce any accurate shape of the clipped model. The implicit function is acquired through a recursive algorithm based on the Boolean combinations (including Boolean union and Boolean intersection) of a series of plane widgets' implicit functions. The algorithm is evaluated as highly efficient because the best time performance of the algorithm is linear, which applies to most of the cases in the computer-assisted surgical planning. Based on the above stated algorithm, a user-friendly module named SmartModelClip is developed on the basis of Slicer platform and VTK. A number of arbitrary clipping paths have been tested. Experimental results of presurgical planning for three types of Le Fort fractures and for tumor removal demonstrate the high reliability and efficiency of our recursive algorithm and robustness of the module.
Techniques for solving Boolean equation systems
Keinänen, Misa
2006-01-01
Boolean equation systems are ordered sequences of Boolean equations decorated with least and greatest fixpoint operators. Boolean equation systems provide a useful framework for formal verification because various specification and verification problems, for instance, μ-calculus model checking can be represented as the problem of solving Boolean equation systems. The general problem of solving a Boolean equation system is a computationally hard task, and no polynomial time solution technique ...
Dependency in Cooperative Boolean Games
Sauro, Luigi; van der Torre, Leon; Villata, Serena
2009-01-01
Cooperative boolean games are coalitional games with both goals and costs associated to actions, and dependence networks for boolean games are a kind of social networks representing how the actions of other agents have an influence on the achievement of an agent’s goal. In this paper, we introduce two new types of dependence networks, called the abstract dependence network and the refined dependence network. Moreover, we show that the notion of stability is complete with respect to the soluti...
Determining a singleton attractor of a boolean network with nested canalyzing functions.
Akutsu, Tatsuya; Melkman, Avraham A; Tamura, Takeyuki; Yamamoto, Masaki
2011-10-01
In this article, we study the problem of finding a singleton attractor for several biologically important subclasses of Boolean networks (BNs). The problem of finding a singleton attractor in a BN is known to be NP-hard in general. For BNs consisting of n nested canalyzing functions, we present an O(1.799(n)) time algorithm. The core part of this development is an O(min(2(k/2) · 2(m/2), 2(k)) · poly(k, m)) time algorithm for the satisfiability problem for m nested canalyzing functions over k variables. For BNs consisting of chain functions, a subclass of nested canalyzing functions, we present an O(1.619(n)) time algorithm and show that the problem remains NP-hard, even though the satisfiability problem for m chain functions over k variables is solvable in polynomial time. Finally, we present an o(2(n)) time algorithm for bounded degree BNs consisting of canalyzing functions.
Offermann, Barbara; Knauer, Steffen; Singh, Amit; Fernández-Cachón, María L; Klose, Martin; Kowar, Silke; Busch, Hauke; Boerries, Melanie
2016-01-01
The nerve growth factor NGF has been shown to cause cell fate decisions toward either differentiation or proliferation depending on the relative activity of downstream pERK, pAKT, or pJNK signaling. However, how these protein signals are translated into and fed back from transcriptional activity to complete cellular differentiation over a time span of hours to days is still an open question. Comparing the time-resolved transcriptome response of NGF- or EGF-stimulated PC12 cells over 24 h in combination with protein and phenotype data we inferred a dynamic Boolean model capturing the temporal sequence of protein signaling, transcriptional response and subsequent autocrine feedback. Network topology was optimized by fitting the model to time-resolved transcriptome data under MEK, PI3K, or JNK inhibition. The integrated model confirmed the parallel use of MAPK/ERK, PI3K/AKT, and JNK/JUN for PC12 cell differentiation. Redundancy of cell signaling is demonstrated from the inhibition of the different MAPK pathways. As suggested in silico and confirmed in vitro, differentiation was substantially suppressed under JNK inhibition, yet delayed only under MEK/ERK inhibition. Most importantly, we found that positive transcriptional feedback induces bistability in the cell fate switch. De novo gene expression was necessary to activate autocrine feedback that caused Urokinase-Type Plasminogen Activator (uPA) Receptor signaling to perpetuate the MAPK activity, finally resulting in the expression of late, differentiation related genes. Thus, the cellular decision toward differentiation depends on the establishment of a transcriptome-induced positive feedback between protein signaling and gene expression thereby constituting a robust control between proliferation and differentiation.
Institute of Scientific and Technical Information of China (English)
闵应骅; 李忠诚; 赵著行
1997-01-01
Boolean algebra successfully describes the logical behavior of a digital circuit, and has been widely used in electronic circuit design and test With the development of high speed VLSIs it is a drawback for Boolean algebra to be unable to describe circuit timing behavior. Therefore a Boolean process is defined as a family of Boolean van ables relevant to the time parameter t. A real-valued sample of a Boolean process is a waveform. Waveform functions can be manipulated formally by using mathematical tools. The distance, difference and limit of a waveform polynomial are defined, and a sufficient and necessary condition of the limit existence is presented. Based on this, the concept of sensitization is redefined precisely to demonstrate the potential and wide application possibility The new definition is very different from the traditional one, and has an impact on determining the sensitizable paths with maximum or minimum length, and false paths, and then designing and testing high performance circuits
On the Number of Attractors of Positive and Negative Boolean Automata Circuits.
Demongeot, Jacques; Noual, Mathilde; Sené, Sylvain
2010-01-01
International audience In line with fields of theoretical computer science and biology that study Boolean automata networks often seen as models of regulation networks, we present some results concerning the dynamics of networks whose underlying interaction graphs are circuits, that is, Boolean automata circuits. In the context of biological regulation, former studies have highlighted the importance of circuits on the asymptotic dynamical behaviour of the biological networks that contain t...
Parameter Learning of Boolean Bayesian Networks%布尔型贝叶斯网络参数学习
Institute of Scientific and Technical Information of China (English)
吴永广; 周兴旺
2015-01-01
布尔型贝叶斯网络是一类由布尔型变量组成的网络，它能够以线性多变量函数描述，使计算和处理上灵活高效。通过运用连接树算法对络进行分块化处理的方法，可以提高算法的效率，然后以传统的最大似然估计方法对布尔型网络的参数进行学习。服从同一分布律的贝叶斯网络参数学习算法发展比较成熟，这类以狄利克雷或者高斯分布为基础的算法在应用领域中难以发挥其应有的价值。相比之下，基于布尔型贝叶斯网络下的参数学习更贴近于应用，在人工智能和数据挖掘等领域有很好的发展前景。%Boolean Bayesian network is a class of Bayesian networks which are made up of Boolean varia-bles. The method to describe the network with a multi-linear function is flexible and efficient to compute and process variables. By introducing Junction Tree algorithm,the network can be divided into blocks which can make it easy to calculate. Then the traditional maximum likelihood estimation method was used for learning Boolean networks. Parameter learning algorithm following the same distribution is more ma-ture,but this kind of algorithm based on Dirichlet or Gaussian distribution is difficult to play its proper val-ue in practice. In contrast,parameter learning based on Boolean networks gets close to applications. It has good prospects for development in areas such as artificial intelligence and data mining.
Directory of Open Access Journals (Sweden)
Panuwat Trairatphisan
Full Text Available Signal transduction networks are increasingly studied with mathematical modelling approaches while each of them is suited for a particular problem. For the contextualisation and analysis of signalling networks with steady-state protein data, we identified probabilistic Boolean network (PBN as a promising framework which could capture quantitative changes of molecular changes at steady-state with a minimal parameterisation.In our case study, we successfully applied the PBN approach to model and analyse the deregulated Platelet-Derived Growth Factor (PDGF signalling pathway in Gastrointestinal Stromal Tumour (GIST. We experimentally determined a rich and accurate dataset of steady-state profiles of selected downstream kinases of PDGF-receptor-alpha mutants in combination with inhibitor treatments. Applying the tool optPBN, we fitted a literature-derived candidate network model to the training dataset consisting of single perturbation conditions. Model analysis suggested several important crosstalk interactions. The validity of these predictions was further investigated experimentally pointing to relevant ongoing crosstalk from PI3K to MAPK signalling in tumour cells. The refined model was evaluated with a validation dataset comprising multiple perturbation conditions. The model thereby showed excellent performance allowing to quantitatively predict the combinatorial responses from the individual treatment results in this cancer setting. The established optPBN pipeline is also widely applicable to gain a better understanding of other signalling networks at steady-state in a context-specific fashion.
Research on Modeling of Genetic Networks Based on Information Measurement
Institute of Scientific and Technical Information of China (English)
ZHANG Guo-wei; SHAO Shi-huang; ZHANG Ying; LI Hai-ying
2006-01-01
As the basis of network of biology organism, the genetic network is concerned by many researchers.Current modeling methods to genetic network, especially the Boolean networks modeling method are analyzed. For modeling the genetic network, the information theory is proposed to mining the relations between elements in network. Through calculating the values of information entropy and mutual entropy in a case, the effectiveness of the method is verified.
A solution to the surface intersection problem. [Boolean functions in geometric modeling
Timer, H. G.
1977-01-01
An application-independent geometric model within a data base framework should support the use of Boolean operators which allow the user to construct a complex model by appropriately combining a series of simple models. The use of these operators leads to the concept of implicitly and explicitly defined surfaces. With an explicitly defined model, the surface area may be computed by simply summing the surface areas of the bounding surfaces. For an implicitly defined model, the surface area computation must deal with active and inactive regions. Because the surface intersection problem involves four unknowns and its solution is a space curve, the parametric coordinates of each surface must be determined as a function of the arc length. Various subproblems involved in the general intersection problem are discussed, and the mathematical basis for their solution is presented along with a program written in FORTRAN IV for implementation on the IBM 370 TSO system.
ON THE SPECIFIC AREA OF INHOMOGENEOUS BOOLEAN MODELS. EXISTENCE RESULTS AND APPLICATIONS
Directory of Open Access Journals (Sweden)
Elena Villa
2011-05-01
Full Text Available The problem of the evaluation of the so-called specific area of a random closed set, in connection with its mean boundary measure, is mentioned in the classical book by Matheron on random closed sets (Matheron, 1975, p. 50; it is still an open problem, in general. We offer here an overview of some recent results concerning the existence of the specific area of inhomogeneous Boolean models, unifying results from geometric measure theory and from stochastic geometry. A discussion of possible applications to image analysis concerning the estimation of the mean surface density of random closed sets, and, in particular, to material science concerning birth-and-growth processes, is also provided.
Boolean Differential Operators
Catumba, Jorge; Diaz, Rafael
2012-01-01
We consider four combinatorial interpretations for the algebra of Boolean differential operators. We show that each interpretation yields an explicit matrix representation for Boolean differential operators.
Marmarelis, Vasilis Z; Zanos, Theodoros P; Berger, Theodore W
2009-08-01
This paper presents a new modeling approach for neural systems with point-process (spike) inputs and outputs that utilizes Boolean operators (i.e. modulo 2 multiplication and addition that correspond to the logical AND and OR operations respectively, as well as the AND_NOT logical operation representing inhibitory effects). The form of the employed mathematical models is akin to a "Boolean-Volterra" model that contains the product terms of all relevant input lags in a hierarchical order, where terms of order higher than first represent nonlinear interactions among the various lagged values of each input point-process or among lagged values of various inputs (if multiple inputs exist) as they reflect on the output. The coefficients of this Boolean-Volterra model are also binary variables that indicate the presence or absence of the respective term in each specific model/system. Simulations are used to explore the properties of such models and the feasibility of their accurate estimation from short data-records in the presence of noise (i.e. spurious spikes). The results demonstrate the feasibility of obtaining reliable estimates of such models, with excitatory and inhibitory terms, in the presence of considerable noise (spurious spikes) in the outputs and/or the inputs in a computationally efficient manner. A pilot application of this approach to an actual neural system is presented in the companion paper (Part II). PMID:19517238
Boolean operations of STL models based on edge-facet intersection
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
For the data processing of the Rapid Prototyping Manufacturing, Boolean operation can offer a versatile tool for editing or modifying the STL model, adding the artificial construction, and creating the complex assistant support structure to meet the special technical requests. The topological structure of STL models was built firstly in order to obtain the neighborhood relationship among the triangular facets. The intersection test between every edge of one solid and every facet of another solid Was taken to get the intersection points. According to the matching relationship of the triangle index recorded in the data structure of the intersection points, the intersection segments array and the intersection loop were traced out. Each intersected triangle was subdivided by the Constrained Delaunay Triangulations. The intersected surfaces were divided into several surface patches along the intersection loops. The inclusion prediction between the surface patch and the other solid was taken by testing whether the candidate point Was inside or outside the solid region of the slice. Detecting the loops for determination of the valid intersection lines greatly increases the efficiency and the reliability of the process.
Memory-Based Boolean Game and Self-Organized Phenomena on Networks
Institute of Scientific and Technical Information of China (English)
HUANG Zi-Gang; WU Zhi-Xi; GUAN Jian-Yue; WANG Ying-Hai
2006-01-01
@@ We study a memory-based Boolean game (MBBG) taking place on a regular ring, wherein each agent acts according to its local optimal states of the last M time steps recorded in memory, and the agents in the minority are rewarded. One free parameter p between 0 and 1 is introduced to denote the strength of the agent willing to make a decision according to its memory. It is found that giving proper willing strength p, the MBBG system can spontaneously evolve to a state of performance better than the random game; while for larger p, the herd behaviour emerges to reduce the system profit. By analysing the dependence of dynamics of the system on the memory capacity M, we find that a higher memory capacity favours the emergence of the better performance state, and effectively restrains the herd behaviour, thus increases the system profit. Considering the high cost of long-time memory, the enhancement of memory capacity for restraining the herd behaviour is also discussed,and M = 5 is suggested to be a good choice.
Boolean gates on actin filaments
Siccardi, Stefano; Tuszynski, Jack A.; Adamatzky, Andrew
2016-01-01
Actin is a globular protein which forms long polar filaments in the eukaryotic cytoskeleton. Actin networks play a key role in cell mechanics and cell motility. They have also been implicated in information transmission and processing, memory and learning in neuronal cells. The actin filaments have been shown to support propagation of voltage pulses. Here we apply a coupled nonlinear transmission line model of actin filaments to study interactions between voltage pulses. To represent digital information we assign a logical TRUTH value to the presence of a voltage pulse in a given location of the actin filament, and FALSE to the pulse's absence, so that information flows along the filament with pulse transmission. When two pulses, representing Boolean values of input variables, interact, then they can facilitate or inhibit further propagation of each other. We explore this phenomenon to construct Boolean logical gates and a one-bit half-adder with interacting voltage pulses. We discuss implications of these findings on cellular process and technological applications.
Montanaro, Ashley; Osborne, Tobias J.
2008-01-01
In this paper we introduce the study of quantum boolean functions, which are unitary operators f whose square is the identity: f^2 = I. We describe several generalisations of well-known results in the theory of boolean functions, including quantum property testing; a quantum version of the Goldreich-Levin algorithm for finding the large Fourier coefficients of boolean functions; and two quantum versions of a theorem of Friedgut, Kalai and Naor on the Fourier spectra of boolean functions. In o...
布尔表达式的化简与并行排序网络验证%Boolean expression simplification and parallel sort network validation
Institute of Scientific and Technical Information of China (English)
王德才; 徐建国; 吴哲辉; 罗永亮; 王传民
2009-01-01
To design an effective tool that can be used to verify the correctness of a parallel sorting network, a Boolean expression sim-plification algorithm based on the [0,1] theory and Boolean function of the characteristics and the nature is put forward, based on this algorithm a validation tool is designed. The characteristics and the nature of [0,1] theory and Boolean function are discussed and the natures that are helpful to simplify of the operation are pointed out. The tool can be used for the design of parallel sorting networks based on the parameters of the network graphics, and it can automatically generate the Boolean expressions and simplify it. The tool's output will be helpful to analyze the network, and it can also be used to design and optimize the sort network. Finally, the validity of the tool is demonstrated by the application.%为设计出能够验证并行排序网络正确性的有效工具,根据[0,1]原理和布尔函数的特点和性质,提出一种布尔表达式的化简算法,并根据此算法设计出验证工具.对[0,1]原理和布尔函数的特点和性质进行了讨论,指出有利于化简操作的性质.设计出的工具能够根据并行排序网络的参数显示网络图形、自动生成布尔表达式并实现化简验证,工具的输出有利于对排序网络的分析,也可以用于辅助排序网络的设计和优化.实验结果表明了该工具的有效性.
Free Boolean Topological Groups
Directory of Open Access Journals (Sweden)
Ol’ga Sipacheva
2015-11-01
Full Text Available Known and new results on free Boolean topological groups are collected. An account of the properties that these groups share with free or free Abelian topological groups and properties specific to free Boolean groups is given. Special emphasis is placed on the application of set-theoretic methods to the study of Boolean topological groups.
Boolean reasoning the logic of boolean equations
Brown, Frank Markham
2012-01-01
A systematic treatment of Boolean reasoning, this concise, newly revised edition combines the works of early logicians with recent investigations, including previously unpublished research results. Brown begins with an overview of elementary mathematical concepts and outlines the theory of Boolean algebras. Two concluding chapters deal with applications. 1990 edition.
Controllability and observability of Boolean control networks%布尔控制网络的能控性与能观性
Institute of Scientific and Technical Information of China (English)
李志强; 宋金利
2013-01-01
Using the semi-tensor product,we convert the Boolean control network to its algebraic form.From the structure matrix of Boolean control network,the controllability and observability of the Boolean control network are discussed.A novel necessary and sufficient condition for controllability,which improves the recent results,is given.The new controllability condition eliminates the redundant computation of controllability matrix.The highest power of matrix is reduced from 2m+n to 2 n.Also,a sufficient condition for observability is obtained,which can be computed easily.A numerical example is presented to show the applicability of our controllability and observability condition.%利用矩阵的半张量积,布尔控制网络被转化为离散时间系统.本文从离散时间系统的结构矩阵出发,讨论了逻辑控制系统的能控能观性条件,得到了一个新的能控性条件.新的条件简化了原有能控性矩阵的计算复杂性,矩阵的最高阶数由原来的2m+n降到了2n.另外,还得到了检验布尔控制网络能观性的条件.与原有条件相比,新的条件更容易计算检验.最后,给出一个实例,检验给出的能控能观性判断条件的正确性.
Profiling of genetic switches using boolean implications in expression data.
Çakır, Mehmet Volkan; Binder, Hans; Wirth, Henry
2014-01-01
Correlation analysis assuming coexpression of the genes is a widely used method for gene expression analysis in molecular biology. Yet growing extent, quality and dimensionality of the molecular biological data permits emerging, more sophisticated approaches like Boolean implications. We present an approach which is a combination of the SOM (self organizing maps) machine learning method and Boolean implication analysis to identify relations between genes, metagenes and similarly behaving metagene groups (spots). Our method provides a way to assign Boolean states to genes/metagenes/spots and offers a functional view over significantly variant elements of gene expression data on these three different levels. While being able to cover relations between weakly correlated entities Boolean implication method also decomposes these relations into six implication classes. Our method allows one to validate or identify potential relationships between genes and functional modules of interest and to assess their switching behaviour. Furthermore the output of the method renders it possible to construct and study the network of genes. By providing logical implications as updating rules for the network it can also serve to aid modelling approaches.
Solomon, Alan D
2012-01-01
REA's Essentials provide quick and easy access to critical information in a variety of different fields, ranging from the most basic to the most advanced. As its name implies, these concise, comprehensive study guides summarize the essentials of the field covered. Essentials are helpful when preparing for exams, doing homework and will remain a lasting reference source for students, teachers, and professionals. Boolean Algebra includes set theory, sentential calculus, fundamental ideas of Boolean algebras, lattices, rings and Boolean algebras, the structure of a Boolean algebra, and Boolean
"Antelope": a hybrid-logic model checker for branching-time Boolean GRN analysis
Arellano Gustavo; Argil Julián; Azpeitia Eugenio; Benítez Mariana; Carrillo Miguel; Góngora Pedro; Rosenblueth David A.; Alvarez-Buylla Elena R
2011-01-01
Abstract Background In Thomas' formalism for modeling gene regulatory networks (GRNs), branching time, where a state can have more than one possible future, plays a prominent role. By representing a certain degree of unpredictability, branching time can model several important phenomena, such as (a) asynchrony, (b) incompletely specified behavior, and (c) interaction with the environment. Introducing more than one possible future for a state, however, creates a difficulty for ordinary simulat...
Tucker, Jerry H.; Tapia, Moiez A.; Bennett, A. Wayne
1988-01-01
The concept of Boolean integration is developed, and different Boolean integral operators are introduced. Given the changes in a desired function in terms of the changes in its arguments, the ways of 'integrating' (i.e. realizing) such a function, if it exists, are presented. The necessary and sufficient conditions for integrating, in different senses, the expression specifying the changes are obtained. Boolean calculus has applications in the design of logic circuits and in fault analysis.
DEFF Research Database (Denmark)
Andersen, Henrik Reif; Hulgaard, Henrik
1997-01-01
This paper presents a new data structure called Boolean Expression Diagrams (BEDs) for representing and manipulating Boolean functions. BEDs are a generalization of Binary Decision Diagrams (BDDs) which can represent any Boolean circuit in linear space and still maintain many of the desirable pro...... standard BDD techniques this problem is infeasible. BEDs are useful in applications where the end-result as a reduced ordered BDD is small, for example for tautology checking...
Directory of Open Access Journals (Sweden)
Mikko Niilo-Rämä
2014-06-01
Full Text Available A novel estimator for estimating the mean length of fibres is proposed for censored data observed in square shaped windows. Instead of observing the fibre lengths, we observe the ratio between the intensity estimates of minus-sampling and plus-sampling. It is well-known that both intensity estimators are biased. In the current work, we derive the ratio of these biases as a function of the mean length assuming a Boolean line segment model with exponentially distributed lengths and uniformly distributed directions. Having the observed ratio of the intensity estimators, the inverse of the derived function is suggested as a new estimator for the mean length. For this estimator, an approximation of its variance is derived. The accuracies of the approximations are evaluated by means of simulation experiments. The novel method is compared to other methods and applied to real-world industrial data from nanocellulose crystalline.
Image Restoration Research Based on Boolean Cloud Model Algorithm%基于布尔云模型算法的图像修复研究
Institute of Scientific and Technical Information of China (English)
王宝红; 郭水旺; 季钢
2013-01-01
Aiming at the deficiencies of the existing image restoration algorithm,Boolean cloud model algorithm is used.First the cloud model is constructed,cloud entropy to determine cloud Boolean relations,clouds appear,Boolean logic to calculate each cloud droplet collection of mutual information entropy,entropy of different results to determine value.Followed by the input and Boolean cloud state function determines the cloud model decision by input Boolean function can produce new clouds again,optimize cloud states choose different cloud entropy dynamic changes.Finally,the algorithm processes.simulation results show operator to connect natural repair image,smoothness,to maintain the overall continuous,and PSNR value.%针对现有图像修复算法的不足,采用布尔云模型算法.首先构造云模型,利用云熵确定云布尔关系.不同的云团值出现时,布尔逻辑计算每个云滴集合的互信息熵.通过比较熵的不同来确定结果值；接着在受输入和布尔函数决定后产生云态,云模型在受输入和布尔函数决定后,可以再次产生新的云团.对云态进行选择优化,其不同的云熵动态变化,最后给出了算法流程.仿真结果显示算法对修复图像连接自然,有光滑性,保持了整体连续,并且PSNR值较大.
Analysis and Control of Boolean Networks:A Semi-tensor Product Approach%布尔网络的分析与控制-矩阵半张量积方法
Institute of Scientific and Technical Information of China (English)
程代展; 齐洪胜; 赵寅
2011-01-01
布尔网络是描述基因调控网络的一个有力工具.由于系统生物学的发展,布尔网络的分析与控制成为生物学与系统控制学科的交叉热点.本文综述作者用其原创的矩阵半张量积方法在布尔网络的分析与控制中得到的一系列结果.内容包括:布尔网络的拓扑结构,布尔控制网络的能控、能观性与实现,布尔网络的稳定性和布尔控制网络的镇定,布尔控制网络的干扰解耦,布尔(控制)网络的辨识,以及布尔网络的最优控制等.%Boolean network is a powerful tool for describing gene regulatory network. With the development of the systems biology, the analysis and control of Boolean networks become a hot topic for multidisciplinary research. This paper surveys some recent results obtained in the analysis and control of Boolean networks using semi-tensor product of matrices. The contents of this paper include the topological structure of Boolean networks, the controllability and observability, realization, stability and stabilization, disturbance decoupling, identification, and optimal control of Boolean (control) networks.
广义Boolean-like环%Generalized Boolean-like Rings
Institute of Scientific and Technical Information of China (English)
秦蕊
2013-01-01
广义Boolean-like环是Boolean-like环的一个推广,文章主要介绍了广义Boolean-like环的构建,从而列举了若干广义Boolean-like环的相关例子及基本性质.并且,考虑了广义Boolean-like环的部分扩张,如上三角矩阵环.
An autocatalytic network model for stock markets
Caetano, Marco Antonio Leonel; Yoneyama, Takashi
2015-02-01
The stock prices of companies with businesses that are closely related within a specific sector of economy might exhibit movement patterns and correlations in their dynamics. The idea in this work is to use the concept of autocatalytic network to model such correlations and patterns in the trends exhibited by the expected returns. The trends are expressed in terms of positive or negative returns within each fixed time interval. The time series derived from these trends is then used to represent the movement patterns by a probabilistic boolean network with transitions modeled as an autocatalytic network. The proposed method might be of value in short term forecasting and identification of dependencies. The method is illustrated with a case study based on four stocks of companies in the field of natural resource and technology.
Leont'ev, V. K.
2015-11-01
A pseudo-Boolean function is an arbitrary mapping of the set of binary n-tuples to the real line. Such functions are a natural generalization of classical Boolean functions and find numerous applications in various applied studies. Specifically, the Fourier transform of a Boolean function is a pseudo-Boolean function. A number of facts associated with pseudo-Boolean polynomials are presented, and their applications to well-known discrete optimization problems are described.
Boolean differential equations
Steinbach, Bernd
2013-01-01
The Boolean Differential Calculus (BDC) is a very powerful theory that extends the structure of a Boolean Algebra significantly. Based on a small number of definitions, many theorems have been proven. The available operations have been efficiently implemented in several software packages. There is a very wide field of applications. While a Boolean Algebra is focused on values of logic functions, the BDC allows the evaluation of changes of function values. Such changes can be explored for pairs of function values as well as for whole subspaces. Due to the same basic data structures, the BDC can
Collaborative networks: Reference modeling
L.M. Camarinha-Matos; H. Afsarmanesh
2008-01-01
Collaborative Networks: Reference Modeling works to establish a theoretical foundation for Collaborative Networks. Particular emphasis is put on modeling multiple facets of collaborative networks and establishing a comprehensive modeling framework that captures and structures diverse perspectives of
DEFF Research Database (Denmark)
Andersen, Henrik Reif; Hulgaard, Henrik
2002-01-01
Boolean function. Using BEDs, this verification problem is solved efficiently, while using standard BDD techniques this problem is infeasible. Generally, BEDs are useful in applications, for example tautology checking, where the end-result as a reduced ordered BDD is small. Moreover, using operators...
Inadmissible Class of Boolean Functions under Stuck-at Faults
Das, Debesh K.; Chowdhury, Debabani; Bhattacharya, Bhargab B; Sasao, Tsutomu
2013-01-01
Many underlying structural and functional factors that determine the fault behavior of a combinational network, are not yet fully understood. In this paper, we show that there exists a large class of Boolean functions, called root functions, which can never appear as faulty response in irredundant two-level circuits even when any arbitrary multiple stuck-at faults are injected. Conversely, we show that any other Boolean function can appear as a faulty response from an irredundant realization ...
Institute of Scientific and Technical Information of China (English)
Antonio AIZPURU; Antonio GUTI(E)RREZ-D(A)VILA
2004-01-01
In this paper we will study some families and subalgebras ( ) of ( )(N) that let us characterize the unconditional convergence of series through the weak convergence of subseries ∑i∈A xi, A ∈ ( ).As a consequence, we obtain a new version of the Orlicz-Pettis theorem, for Banach spaces. We also study some relationships between algebraic properties of Boolean algebras and topological properties of the corresponding Stone spaces.
Geometric Operators on Boolean Functions
Frisvad, Jeppe Revall; Falster, Peter
2007-01-01
In truth-functional propositional logic, any propositional formula represents a Boolean function (according to some valuation of the formula). We describe operators based on Decartes' concept of constructing coordinate systems, for translation of a propositional formula to the image of a Boolean function. With this image of a Boolean function corresponding to a propositional formula, we prove that the orthogonal projection operator leads to a theorem describing all rules of inference in propo...
基于布尔语义的Gentzen推导模型%Gentzen Deduction Model Based on Boolean Logic Semantics
Institute of Scientific and Technical Information of China (English)
陈博; 眭跃飞
2015-01-01
Deduction systems are important arts of searching technology. This paper gives a new correspondence between the propositional logic and Boolean algebra, where an inequation is corresponding to a Gentzen sequent, so that the inequation is true in every Boolean algebra if and only if the Gentzen sequent is provable. In information retrieval, the information inference can effectively turn into the operation on poset. Precisely, the logical language for the propositional logic contains operators Ø'Ù'Ú;the terms instead of formulas are defined (a|Øt|t1 Ù t2|t1 Ú t2 , where a is an element) and used to represent elements in Boolean algebra. This paper defines an assignment v using Boolean algebra as its domain, and assigns the terms to be the element in Boolean algebra. The sequence ΓÞΔ is satisfied if tv £tv. Finally, this paper gives a Gentzen system to prove the soundness and completeness theorem.%布尔模型是信息检索系统的一种基础模型。给出了命题逻辑和布尔代数间的一种新的对应关系，其中布尔代数中的不等式对应Gentzen系统中的矢列式，使得当一个不等式在任意布尔代数中为真，当且仅当它所对应的矢列式是可证的。并且使得在信息检索中，针对信息的推理可以有效地转为偏序集上的运算。讨论的命题逻辑语言的运算符为Ø、Ù、Ú；并且定义了项（a|Øt|t1Ù t2|t1Ú t2'其中a是一个元素）来替代原先的公式和表示布尔代数中的元素。此外，定义了以布尔代数为论域的赋值v，将命题逻辑中的项赋值为布尔代数中的元素，并且如果tv £t v ，则矢列式ΓÞ D为真。最后给出了Gentzen系统下的可靠性和完备性定理的证明。tÎΓtÎΔ
硬件加速的渐进式多边形模型布尔运算%GPU-Accelerated Progressive Boolean Operations on Polygonal Models
Institute of Scientific and Technical Information of China (English)
赵汉理; 孟庆如; 金小刚; 黄辉; 王明
2015-01-01
多边形模型的布尔运算中包含复杂的求交计算以及多边形重建过程，精度控制和处理效率是其中的关键。为了降低布尔运算复杂度，提出一种适合硬件加速的基于渐进式布尔运算的多层次细节网格模型生成方法。该方法采用分层深度图像来近似表示多边形实体的封闭边界，将多边形的求交计算简化为坐标轴平行的采样点的实体内外部判断；为了免去各层次细节模型的重复采样过程，渐进式地将边界采样点归并到低分辨率下的立方体中；运用特征保持的多边形重建算法将相同立方体内的边界采样点转换成多边形顶点，根据邻接关系生成网格模型。上述算法使用支持图形硬件加速的CUDA编程并行实现。实验结果表明了算法的可行性。%Boolean operations on polygonal models involve the complex intersection calculations and po-lygonal reconstruction, where the precision control and processing efficiency are two key problems. To re-duce the Boolean operation complexity, this paper proposes a progressive and GPU accelerated Boolean op-eration approach to generate levels-of-detail polygonal models. Layered depth images are employed to ap-proximate the enclosed boundaries of polygons and the intersection calculations are performed as the in/out classification of axis-aligned sampling points. To avoid the additional sampling process for levels-of-detail models, the boundary points are progressively merged into low-resolution cubes. The feature-preserving dual contouring algorithm is adopted to convert boundary points into a mesh model. The proposed algorithm can be implementation in parallel on GPU with the hardware-supported CUDA. Finally, experimental results show the feasibility of the proposed approach.
J-Boolean like环%J-Boolean Like Ring
Institute of Scientific and Technical Information of China (English)
秦蕊
2013-01-01
本文首先引进了Boolean-like环的一类新的扩张J-Boolean like环,即对任意环R中元素a,b都有(a-a2)(b-b2)∈J(R),这里J(R)为环R的Jacobson根,则环R称为J-Boolean like环.证明了两个定理分别为(1)设D是一个环,C是D的一个子环,R[D,C]是一个J-Boolean like环(=)(a)C,D是J-Boolean like环,(b)J2(C)(∈)J(D).(2)如果B/J(B)是Boolean环,并且B[i]={a+bi| i2=ui+η,a,b,u,η∈B},那么B[i]是J-Booleanlike环当且仅当uη∈J(B).
Approximate Reasoning with Fuzzy Booleans
Broek, van den P.M.; Noppen, J.A.R.
2004-01-01
This paper introduces, in analogy to the concept of fuzzy numbers, the concept of fuzzy booleans, and examines approximate reasoning with the compositional rule of inference using fuzzy booleans. It is shown that each set of fuzzy rules is equivalent to a set of fuzzy rules with singleton crisp ante
Symmetry in Boolean Satisfiability
Directory of Open Access Journals (Sweden)
Fadi A. Aloul
2010-06-01
Full Text Available This paper reviews recent approaches on how to accelerate Boolean Satisfiability (SAT search by exploiting symmetries in the problem space. SAT search algorithms traverse an exponentially large search space looking for an assignment that satisfies a set of constraints. The presence of symmetries in the search space induces equivalence classes on the set of truth assignments. The goal is to use symmetries to avoid traversing all assignments by constraining the search to visit a few representative assignments in each equivalence class. This can lead to a significant reduction in search runtime without affecting the completeness of the search.
Boolean Orthogonalizing Combination Methods
Directory of Open Access Journals (Sweden)
Yavuz Can
2015-05-01
Full Text Available In this paper a new logical operation method called “ presented. It is used to calculate the difference, but also the complement of a function as well as the EXOR and EXNOR of two minterms respectively two ternary respectively two ternary-vector logical operation method called “orthogonal OR advantages of both methods are their results, which are already available form that has an essential advantage for continuing calculations. Since it applies, an orthogonal disjunctive normal form is equal to orthogonal antivalence normal form, subsequent Boolean differential calculus will be simplified.
语义特征建模系统中布尔算法的研究%Research on Boolean Operation in Semantic Feature Modeling System
Institute of Scientific and Technical Information of China (English)
金瑛浩; 孙立镌
2012-01-01
To improve the efficiency of boolean operations in semantic feature modeling system,a semantic representation based method was proposed. It represents feature models with semantic representation, manages feature elements by cellular model,improves detect efficiency of features interaction and builds new the feature entity by splitting inter-sectant cells and semantic faces. This method can not only build the boolean entity rapidly and exactly, but also avoid errors such as holes and losing geometry faces. Experiments on computer show that this new method is more adaptable and practicable.%为了提高语义特征建模系统中布尔操作的运行效率,提出了一种基于语义表示法的布尔操作算法.该算法用语义表示法表示特征模型,用细胞元模型组织和管理特征元素,用语义面替代几何面来提高特征的交互检测效率,通过细胞分裂和语义面分解来生成新实体.该算法不仅可以快速准确地生成布尔实体,还可以避免几何面的丢失及“孔洞”等错误的发生.实验证明,该算法具有广泛的使用前景和实用价值.
A unified biological modeling and simulation system for analyzing biological reaction networks
Yu, Seok Jong; Tung, Thai Quang; Park, Junho; Lim, Jongtae; Yoo, Jaesoo
2013-12-01
In order to understand the biological response in a cell, a researcher has to create a biological network and design an experiment to prove it. Although biological knowledge has been accumulated, we still don't have enough biological models to explain complex biological phenomena. If a new biological network is to be created, integrated modeling software supporting various biological models is required. In this research, we design and implement a unified biological modeling and simulation system, called ezBioNet, for analyzing biological reaction networks. ezBioNet designs kinetic and Boolean network models and simulates the biological networks using a server-side simulation system with Object Oriented Parallel Accelerator Library framework. The main advantage of ezBioNet is that a user can create a biological network by using unified modeling canvas of kinetic and Boolean models and perform massive simulations, including Ordinary Differential Equation analyses, sensitivity analyses, parameter estimates and Boolean network analysis. ezBioNet integrates useful biological databases, including the BioModels database, by connecting European Bioinformatics Institute servers through Web services Application Programming Interfaces. In addition, we employ Eclipse Rich Client Platform, which is a powerful modularity framework to allow various functional expansions. ezBioNet is intended to be an easy-to-use modeling tool and a simulation system for understanding the control mechanism by monitoring the change of each component in a biological network. The simulation result can be managed and visualized on ezBioNet, which is available free of charge at http://ezbionet.sourceforge.net or http://ezbionet.cbnu.ac.kr.
Geometric Operators on Boolean Functions
DEFF Research Database (Denmark)
Frisvad, Jeppe Revall; Falster, Peter
In truth-functional propositional logic, any propositional formula represents a Boolean function (according to some valuation of the formula). We describe operators based on Decartes' concept of constructing coordinate systems, for translation of a propositional formula to the image of a Boolean...... function. With this image of a Boolean function corresponding to a propositional formula, we prove that the orthogonal projection operator leads to a theorem describing all rules of inference in propositional reasoning. In other words, we can capture all kinds of inference in propositional logic by means...... of a few geometric operators working on the images of Boolean functions. The operators we describe, arise from the niche area of array-based logic and have previously been tightly bound to an array-based representation of Boolean functions. We redefine the operators in an abstract form to make them...
Cryptographic Boolean functions and applications
Cusick, Thomas W
2009-01-01
Boolean functions are the building blocks of symmetric cryptographic systems. Symmetrical cryptographic algorithms are fundamental tools in the design of all types of digital security systems (i.e. communications, financial and e-commerce).Cryptographic Boolean Functions and Applications is a concise reference that shows how Boolean functions are used in cryptography. Currently, practitioners who need to apply Boolean functions in the design of cryptographic algorithms and protocols need to patch together needed information from a variety of resources (books, journal articles and other sources). This book compiles the key essential information in one easy to use, step-by-step reference. Beginning with the basics of the necessary theory the book goes on to examine more technical topics, some of which are at the frontier of current research.-Serves as a complete resource for the successful design or implementation of cryptographic algorithms or protocols using Boolean functions -Provides engineers and scient...
Boolean Equi-propagation for Optimized SAT Encoding
Metodi, Amit; Lagoon, Vitaly; Stuckey, Peter J
2011-01-01
We present an approach to propagation based solving, Boolean equi-propagation, where constraints are modelled as propagators of information about equalities between Boolean literals. Propagation based solving applies this information as a form of partial evaluation resulting in optimized SAT encodings. We demonstrate for a variety of benchmarks that our approach results in smaller CNF encodings and leads to speed-ups in solving times.
On Kolmogorov's superpositions and Boolean functions
Energy Technology Data Exchange (ETDEWEB)
Beiu, V.
1998-12-31
The paper overviews results dealing with the approximation capabilities of neural networks, as well as bounds on the size of threshold gate circuits. Based on an explicit numerical (i.e., constructive) algorithm for Kolmogorov's superpositions they will show that for obtaining minimum size neutral networks for implementing any Boolean function, the activation function of the neurons is the identity function. Because classical AND-OR implementations, as well as threshold gate implementations require exponential size (in the worst case), it will follow that size-optimal solutions for implementing arbitrary Boolean functions require analog circuitry. Conclusions and several comments on the required precision are ending the paper.
Inference in hybrid Bayesian networks
DEFF Research Database (Denmark)
Lanseth, Helge; Nielsen, Thomas Dyhre; Rumí, Rafael;
2009-01-01
Since the 1980s, Bayesian Networks (BNs) have become increasingly popular for building statistical models of complex systems. This is particularly true for boolean systems, where BNs often prove to be a more efficient modelling framework than traditional reliability-techniques (like fault trees...... decade's research on inference in hybrid Bayesian networks. The discussions are linked to an example model for estimating human reliability....
A Note on the Inversion Complexity of Boolean Functions in Boolean Formulas
Morizumi, Hiroki
2008-01-01
In this note, we consider the minimum number of NOT operators in a Boolean formula representing a Boolean function. In circuit complexity theory, the minimum number of NOT gates in a Boolean circuit computing a Boolean function $f$ is called the inversion complexity of $f$. In 1958, Markov determined the inversion complexity of every Boolean function and particularly proved that $\\lceil \\log_2(n+1) \\rceil$ NOT gates are sufficient to compute any Boolean function on $n$ variables. As far as we...
基于布尔感知模型的边界线多重覆盖算法%Boundary Line Algorithm Multiple Coverage Based on Boolean Perception Model
Institute of Scientific and Technical Information of China (English)
薛兴亮; 孙荣凯; 高玉章
2013-01-01
无线传感器网络监视预警系统的区域边界具有特殊重要性，针对节点的布尔感知模型，根据节点感知圆盘的相互关系，可将整条边界线划分为不可再分割的可数个最小曲线段，利用改进的贪婪式算法研究了集中式多重覆盖算法和分布式多重覆盖算法，通过仿真实验，验证了多重覆盖带来的高检测概率以及高覆盖概率。%As the special importance of boundary area in wireless sensor network monitoring and warn-ing system, for the perception of the node boolean perception model, the entire boundary line can be divided into countable smallest curve segments based on the relationship between the node sens-ing disc. Then the centralized multiple coverage and the distributed multiple coverage were studied by using an improved greedy algorithm. At last, the high probability of detection and high coverage was verified probability in multiple coverage by means of simulation results.
Modeling worldwide highway networks
Villas Boas, Paulino R.; Rodrigues, Francisco A.; da F. Costa, Luciano
2009-12-01
This Letter addresses the problem of modeling the highway systems of different countries by using complex networks formalism. More specifically, we compare two traditional geographical models with a modified geometrical network model where paths, rather than edges, are incorporated at each step between the origin and the destination vertices. Optimal configurations of parameters are obtained for each model and used for the comparison. The highway networks of Australia, Brazil, India, and Romania are considered and shown to be properly modeled by the modified geographical model.
DEFF Research Database (Denmark)
Andersen, Kasper Winther
Three main topics are presented in this thesis. The first and largest topic concerns network modelling of functional Magnetic Resonance Imaging (fMRI) and Diffusion Weighted Imaging (DWI). In particular nonparametric Bayesian methods are used to model brain networks derived from resting state f...... for their ability to reproduce node clustering and predict unseen data. Comparing the models on whole brain networks, BCD and IRM showed better reproducibility and predictability than IDM, suggesting that resting state networks exhibit community structure. This also points to the importance of using models, which...... allow for complex interactions between all pairs of clusters. In addition, it is demonstrated how the IRM can be used for segmenting brain structures into functionally coherent clusters. A new nonparametric Bayesian network model is presented. The model builds upon the IRM and can be used to infer...
Modeling Epidemic Network Failures
DEFF Research Database (Denmark)
Ruepp, Sarah Renée; Fagertun, Anna Manolova
2013-01-01
This paper presents the implementation of a failure propagation model for transport networks when multiple failures occur resulting in an epidemic. We model the Susceptible Infected Disabled (SID) epidemic model and validate it by comparing it to analytical solutions. Furthermore, we evaluate...... the SID model’s behavior and impact on the network performance, as well as the severity of the infection spreading. The simulations are carried out in OPNET Modeler. The model provides an important input to epidemic connection recovery mechanisms, and can due to its flexibility and versatility be used...... to evaluate multiple epidemic scenarios in various network types....
Artificial neural network modelling
Samarasinghe, Sandhya
2016-01-01
This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling. .
A Boolean Network Model of Nuclear Receptor Mediated Cell Cycle Progression
Nuclear receptors (NRs) are ligand-activated transcription factors that regulate a broad range of cellular processes. Hormones, lipids and xenobiotics have been shown to activate NRs with a range of consequences on development, metabolism, oxidative stress, apoptosis, and prolif...
Modeling Nuclear Receptor-Mediated Activity and Hepatotoxicity with Boolean Networks
Predicting the human health risk of chronic exposure to environmental contaminants remains an open problem. Chronic exposure to a wide array of chemicals – e.g., conazoles, perfluourinated chemicals and phthalates – has been associated with a range of hepatic lesions in rodents t...
A Boolean Network Model of Nuclear Receptor Mediated Cell Cycle Progression (S)
Nuclear receptors (NRs) are ligand-activated transcription factors that regulate a broad range of cellular processes. Hormones, lipids and xenobiotics have been shown to activate NRs with a range of consequences on development, metabolism, oxidative stress, apoptosis, and prolif...
Boolean Searches--A Life Skill.
Ala, Judy; Cerabona, Kathy
1992-01-01
Discusses the importance of Boolean searching as a skill that students will need in the future. Methods for teaching Boolean searching are described, and the value of truncation as an online searching aid is considered. (MES)
Generalized join-hemimorphisms on Boolean algebras
Sergio Celani
2003-01-01
We introduce the notions of generalized join-hemimorphism and generalized Boolean relation as an extension of the notions of join-hemimorphism and Boolean relation, respectively. We prove a duality between these two notions. We will also define a generalization of the notion of Boolean algebra with operators by considering a finite family of Boolean algebras endowed with a generalized join-hemimorphism. Finally, we define suitable notions of subalgebra, congruences, Boole...
Boolean Operations on Conic Polygons
Institute of Scientific and Technical Information of China (English)
Yong-Xi Gong; Yu Liu; Lun Wu; Yu-Bo Xie
2009-01-01
An algorithm for Boolean operations on conic polygons is proposed. Conic polygons are polygons consisting of conic segments or bounded conics with directions. Preliminaries of Boolean operations on general polygons are presented. In our algorithm, the intersection points and the topological relationships between two conic polygons are computed. Boundaries are obtained by tracking path and selecting uncrossed boundaries following rule tables to build resulting conic polygons.We define a set of rules for the intersection, union, and subtraction operations on conic polygons. The algorithm considers degeneration cases such as homology, complement, interior, and exterior. The algorithm is also evaluated and implemented.
Ordered Boolean List (OBL): reducing the footprint for evaluating Boolean expressions.
Rossignac, Jaroslaw Jarek
2011-09-01
An Expanded Boolean Expression (EBE) does not contain any XOR or EQUAL operators. The occurrence of each variable is a different literal. We provide a linear time algorithm that converts an EBE of n literals into a logically equivalent Ordered Boolean List (OBL) and show how to use the OBL to evaluate the EBE in n steps and O(log log n) space, if the values of the literals are each read once in the order prescribed by the OBL. (An evaluation workspace of 5 bits suffices for all EBEs of up to six billion literals.) The primary application is the SIMD architecture, where the same EBE is evaluated in parallel for different input vectors when rendering solid models on the GPU directly from their Constructive Solid Geometry (CSG) representation. We compare OBL to the Reduced Ordered Binary Decision Diagram (ROBDD) and suggest possible applications of OBL to logic verification and to circuit design. PMID:21737862
Odefy -- From discrete to continuous models
Directory of Open Access Journals (Sweden)
Wittmann Dominik M
2010-05-01
Full Text Available Abstract Background Phenomenological information about regulatory interactions is frequently available and can be readily converted to Boolean models. Fully quantitative models, on the other hand, provide detailed insights into the precise dynamics of the underlying system. In order to connect discrete and continuous modeling approaches, methods for the conversion of Boolean systems into systems of ordinary differential equations have been developed recently. As biological interaction networks have steadily grown in size and complexity, a fully automated framework for the conversion process is desirable. Results We present Odefy, a MATLAB- and Octave-compatible toolbox for the automated transformation of Boolean models into systems of ordinary differential equations. Models can be created from sets of Boolean equations or graph representations of Boolean networks. Alternatively, the user can import Boolean models from the CellNetAnalyzer toolbox, GINSim and the PBN toolbox. The Boolean models are transformed to systems of ordinary differential equations by multivariate polynomial interpolation and optional application of sigmoidal Hill functions. Our toolbox contains basic simulation and visualization functionalities for both, the Boolean as well as the continuous models. For further analyses, models can be exported to SQUAD, GNA, MATLAB script files, the SB toolbox, SBML and R script files. Odefy contains a user-friendly graphical user interface for convenient access to the simulation and exporting functionalities. We illustrate the validity of our transformation approach as well as the usage and benefit of the Odefy toolbox for two biological systems: a mutual inhibitory switch known from stem cell differentiation and a regulatory network giving rise to a specific spatial expression pattern at the mid-hindbrain boundary. Conclusions Odefy provides an easy-to-use toolbox for the automatic conversion of Boolean models to systems of ordinary
Method of Boolean operation based on 3D grid model%三维网格模型的布尔运算方法
Institute of Scientific and Technical Information of China (English)
陈学工; 杨兰; 黄伟; 季兴
2011-01-01
提出了一种基于三维网格模型的布尔运算方法.首先通过基于方向包围盒(OBB)层次包围盒树的碰撞检测算法,得到实体的相交三角形对;接下来求出两相交三角形之间的交线,建立与三角形的交线拓扑关系;通过分类处理三种交线类型来对相交三角形进行区域划分,得到一系列多边形,并对多边形进行三角剖分形成结果区域;最后根据体的包含关系构建关系邻接表,判断多边形区域的相对于其他实体的内外关系并通过网格模型的拓扑关系,定位表面三角网格区域;同时根据交、并、差等布尔操作,对结果区域进行取舍,得到最终结果.实验结果表明相交部分的岩性与实体的岩性相吻合,验证了该算法的正确性以及可行性.%A kind of Boolean operational method based on a three-dimensional grid model was proposed. Firstly, through collision detection algorithm based on hierarchical bounding box tree of Oriented Bounding Box (ORB), the intersecting triangles could be got. Through the intersection test of the triangles, the intersecting lines could be obtained and the intersecting lines topology relations with the triangles could be established. Secondly, a regional division for the intersecting triangles was made through processing the three types of intersecting lines, so as to get a series of polygons, and carry out Delaunay triangulations for polygon to get the result area. Lastly, relation adjacency list was constructed based on solid containing relations, the polygon's internal relation and external relation with other entities were judged, and the triangles were located according to the mesh model topology relations. Simultaneously, according to such Boolean operations as the intersection, union, and differences, according to the grid model topology relations were judged, the position of the triangles were judged and then the final results could be obtained. Experimental results show that this
Construction of optimized Boolean functions
Institute of Scientific and Technical Information of China (English)
CHEN Wei; YANG Yi-xian; NIU Xin-xin
2006-01-01
Considering connections of characteristics,this paper is aimed at the construction of optimized Boolean functions.A new method based on the Bent function,discrete Walsh spectrum and characteristics matrices are presented by concatenating,breaking,and revising output sequences conditionally.This new construction can be used to construct different kinds of functions satisfying different design criteria.
Modular Decomposition of Boolean Functions
J.C. Bioch (Cor)
2002-01-01
textabstractModular decomposition is a thoroughly investigated topic in many areas such as switching theory, reliability theory, game theory and graph theory. Most appli- cations can be formulated in the framework of Boolean functions. In this paper we give a uni_ed treatment of modular decompositio
Evolutionary Design of Boolean Functions
Institute of Scientific and Technical Information of China (English)
WANG Zhang-yi; ZHANG Huan-guo; QIN Zhong-ping; MENG Qing-shu
2005-01-01
We use evolutionary computing to synthesize Boolean functions randomly. By using specific crossover and mutation operator in evolving process and modifying search space and fitness function, we get some high non-linearity functions which have other good cryptography characteristics such as autocorrelation etc. Comparing to other heuristic search techniques, evolutionary computing approach is more effective because of global search strategy and implicit parallelism.
Construction of cell type-specific logic models of signaling networks using CellNOpt.
Morris, Melody K; Melas, Ioannis; Saez-Rodriguez, Julio
2013-01-01
Mathematical models are useful tools for understanding protein signaling networks because they provide an integrated view of pharmacological and toxicological processes at the molecular level. Here we describe an approach previously introduced based on logic modeling to generate cell-specific, mechanistic and predictive models of signal transduction. Models are derived from a network encoding prior knowledge that is trained to signaling data, and can be either binary (based on Boolean logic) or quantitative (using a recently developed formalism, constrained fuzzy logic). The approach is implemented in the freely available tool CellNetOptimizer (CellNOpt). We explain the process CellNOpt uses to train a prior knowledge network to data and illustrate its application with a toy example as well as a realistic case describing signaling networks in the HepG2 liver cancer cell line.
Exploiting Surroundedness for Saliency Detection: A Boolean Map Approach.
Zhang, Jianming; Sclaroff, Stan
2016-05-01
We demonstrate the usefulness of surroundedness for eye fixation prediction by proposing a Boolean Map based Saliency model (BMS). In our formulation, an image is characterized by a set of binary images, which are generated by randomly thresholding the image's feature maps in a whitened feature space. Based on a Gestalt principle of figure-ground segregation, BMS computes a saliency map by discovering surrounded regions via topological analysis of Boolean maps. Furthermore, we draw a connection between BMS and the Minimum Barrier Distance to provide insight into why and how BMS can properly captures the surroundedness cue via Boolean maps. The strength of BMS is verified by its simplicity, efficiency and superior performance compared with 10 state-of-the-art methods on seven eye tracking benchmark datasets.
Network Reliability Algorithm Based on Pathset Matrix and Boolean Operation%基于路集矩阵与布尔运算的网络可靠度算法
Institute of Scientific and Technical Information of China (English)
高会生; 展敬宇; 王博颖; 李潇睿
2012-01-01
This paper analyzes the network reliability algorithm based on pathset matrix, and there exists a serious combination explosion problem in this algorithm. Aiming at this problem, it proposes a network reliability algorithm based on pathset matrix and boolean operation. The concept of bit vector is introduced. In addition, the pre-process of special pathsets and count of all-one bit vectors are also implied. Experimental results show that it not only increases the memory utilization, reduce the redundancy but also relieve the combination explosion problem in some degree.%分析基于路集矩阵与布尔运算的网络可靠度算法,指出其存在组合爆炸问题.为此,提出一种改进算法,引入位矢量以减少内存需求,对特殊路集进行预处理并统计全1位矢量.实验结果表明,改进算法可提高内存利用率、减少冗余运算,能在一定程度上缓解组合爆炸问题.
Laomettachit, Teeraphan; Chen, Katherine C.; Baumann, William T.
2016-01-01
To understand the molecular mechanisms that regulate cell cycle progression in eukaryotes, a variety of mathematical modeling approaches have been employed, ranging from Boolean networks and differential equations to stochastic simulations. Each approach has its own characteristic strengths and weaknesses. In this paper, we propose a “standard component” modeling strategy that combines advantageous features of Boolean networks, differential equations and stochastic simulations in a framework that acknowledges the typical sorts of reactions found in protein regulatory networks. Applying this strategy to a comprehensive mechanism of the budding yeast cell cycle, we illustrate the potential value of standard component modeling. The deterministic version of our model reproduces the phenotypic properties of wild-type cells and of 125 mutant strains. The stochastic version of our model reproduces the cell-to-cell variability of wild-type cells and the partial viability of the CLB2-dbΔ clb5Δ mutant strain. Our simulations show that mathematical modeling with “standard components” can capture in quantitative detail many essential properties of cell cycle control in budding yeast. PMID:27187804
Cost-Optimal Execution of Trees of Boolean Operators with Shared Streams
Casanova, Henri; Lim, Lipyeow; Robert, Yves; Vivien, Frédéric; Zaidouni, Dounia
2013-01-01
The processing of queries expressed as trees of boolean operators applied to predicates on sensor data streams has several applications in mobile computing. Sensor data must be retrieved from the sensors to a query processing device, such as a smartphone, over one or more network interfaces. Retrieving a data item incurs a cost, e.g., an energy expense that depletes the smartphone's battery. Since the query tree contains boolean operators, part of the tree can be shortcircuited depending on t...
A Network-of-Networks Model for Electrical Infrastructure Networks
Halappanavar, Mahantesh; Hogan, Emilie; Duncan, Daniel; Zhenyu,; Huang,; Hines, Paul D H
2015-01-01
Modeling power transmission networks is an important area of research with applications such as vulnerability analysis, study of cascading failures, and location of measurement devices. Graph-theoretic approaches have been widely used to solve these problems, but are subject to several limitations. One of the limitations is the ability to model a heterogeneous system in a consistent manner using the standard graph-theoretic formulation. In this paper, we propose a {\\em network-of-networks} approach for modeling power transmission networks in order to explicitly incorporate heterogeneity in the model. This model distinguishes between different components of the network that operate at different voltage ratings, and also captures the intra and inter-network connectivity patterns. By building the graph in this fashion we present a novel, and fundamentally different, perspective of power transmission networks. Consequently, this novel approach will have a significant impact on the graph-theoretic modeling of powe...
Inverse fracture network modelling
International Nuclear Information System (INIS)
The basic problem in analyzing flow and transport in fractured rock is that the flow may be largely governed by a poorly connected network of fractures. Flow in such a system cannot be modeled with traditional modelling techniques. Fracture network models also have a limitation, in that they are based on geological data on fracture geometry even though it is known that only a small portion of fractures observed is hydraulically active. This paper discusses a new technique developed for treating the problem as well as presents a modelling example carried out to apply it. The approach is developed in Lawrence Berkeley Laboratory and it treats the fracture zone as an 'equivalent discontinuum'. The discontinuous nature of the problem is represented through flow on a partially filled lattice. An equivalent discontinuum model is constructed by adding and removing conductive elements through a statistical inverse technique called 'simulated annealing'. The fracture network model is 'annealed' until the modified systems behaves like the observed. The further development of the approach continues at LBL and in a joint LBL/VTT collaboration project the possibilities to apply the technique in Finnish conditions are investigated
Boolean Factor Congruences and Property (*)
Terraf, Pedro Sánchez
2008-01-01
A variety V has Boolean factor congruences (BFC) if the set of factor congruences of every algebra in V is a distributive sublattice of its congruence lattice; this property holds in rings with unit and in every variety which has a semilattice operation. BFC has a prominent role in the study of uniqueness of direct product representations of algebras, since it is a strengthening of the refinement property. We provide an explicit Mal'cev condition for BFC. With the aid of this condition, it is shown that BFC is equivalent to a variant of the definability property (*), an open problem in R. Willard's work ("Varieties Having Boolean Factor Congruences," J. Algebra, 132 (1990)).
Quantum algorithms for testing Boolean functions
Directory of Open Access Journals (Sweden)
Erika Andersson
2010-06-01
Full Text Available We discuss quantum algorithms, based on the Bernstein-Vazirani algorithm, for finding which variables a Boolean function depends on. There are 2^n possible linear Boolean functions of n variables; given a linear Boolean function, the Bernstein-Vazirani quantum algorithm can deterministically identify which one of these Boolean functions we are given using just one single function query. The same quantum algorithm can also be used to learn which input variables other types of Boolean functions depend on, with a success probability that depends on the form of the Boolean function that is tested, but does not depend on the total number of input variables. We also outline a procedure to futher amplify the success probability, based on another quantum algorithm, the Grover search.
Duality methods in networks, computer science models, and disordered condensed matter systems
Mitchell, Joseph Dan
In this thesis, I explore lattice independent duality and systems to which it can be applied. I first demonstrate classical duality on models in an external field, including the Ising, Potts, and x -- y models, showing in particular how this modifies duality to be lattice independent and applicable to networks. I then present a novel application of duality on the boolean satsifiability problem, one of the most important problems in computational complexity, through mapping to a low temperature Ising model. This establishes the equivalence between boolean satisfiability and a problem of enumerating the positive solutions to a Diophantine system of equations. I continue by combining duality with a prominent tool for models on networks, belief propagation, deriving a new message passing procedure, dual belief propagation. In the final part of my thesis, I shift to propose and examine a semiclassical model, the two-component Coulomb glass model, which can explain the giant magnetoresistance peak present in disordered films near a superconductor-insulator transition as the effect of competition between single particle and localized pair transport. I numerically analyze the density of states and transport properties of this model.
Boolean Reasoning with Graphs of Partitions
Goossens, Daniel
2010-01-01
version longue du papier court "A Dynamic Boolean Knowledge Base" accepté à ICTAI 2010. This paper presents an implemented architecture for easy learning, reorganizing and navigation into a Boolean knowledge base. As the base grows with new definitions and constraints, it is normalized by the closure of a completion operator. This normalization allows arbitrary formats for Boolean expressions. It ensures basic reasoning abilities and spontaneously organizes intermingled taxonomies of conce...
Limitations of Lower-Bound Methods for the Wire Complexity of Boolean Operators
Drucker, Andrew
2012-01-01
We study the circuit complexity of Boolean operators, i.e., collections of Boolean functions defined over a common input. Our focus is the well-studied model in which arbitrary Boolean functions are allowed as gates, and in which a circuit's complexity is measured by its depth and number of wires. We show sharp limitations of several existing lower-bound methods for this model. First, we study an information-theoretic lower-bound method due to Cherukhin, that yields bounds of form $\\Omega_d(n...
Polynomial threshold functions and Boolean threshold circuits
DEFF Research Database (Denmark)
Hansen, Kristoffer Arnsfelt; Podolskii, Vladimir V.
2013-01-01
We study the complexity of computing Boolean functions on general Boolean domains by polynomial threshold functions (PTFs). A typical example of a general Boolean domain is 12n . We are mainly interested in the length (the number of monomials) of PTFs, with their degree and weight being...... of secondary interest. We show that PTFs on general Boolean domains are tightly connected to depth two threshold circuits. Our main results in regard to this connection are: PTFs of polynomial length and polynomial degree compute exactly the functions computed by THRMAJ circuits. An exponential length lower...
Progress in Applications of Boolean Functions
Sasao, Tsutomu
2010-01-01
This book brings together five topics on the application of Boolean functions. They are 1. Equivalence classes of Boolean functions: The number of n-variable functions is large, even for values as small as n = 6, and there has been much research on classifying functions. There are many classifications, each with their own distinct merit. 2. Boolean functions for cryptography: The process of encrypting/decrypting plain text messages often depends on Boolean functions with specific properties. For example, highly nonlinear functions are valued because they are less susceptible to linear attacks.
Coevolutionary modeling in network formation
Al-Shyoukh, Ibrahim
2014-12-03
Network coevolution, the process of network topology evolution in feedback with dynamical processes over the network nodes, is a common feature of many engineered and natural networks. In such settings, the change in network topology occurs at a comparable time scale to nodal dynamics. Coevolutionary modeling offers the possibility to better understand how and why network structures emerge. For example, social networks can exhibit a variety of structures, ranging from almost uniform to scale-free degree distributions. While current models of network formation can reproduce these structures, coevolutionary modeling can offer a better understanding of the underlying dynamics. This paper presents an overview of recent work on coevolutionary models of network formation, with an emphasis on the following three settings: (i) dynamic flow of benefits and costs, (ii) transient link establishment costs, and (iii) latent preferential attachment.
Goldberg, S R; Evans, T S
2014-01-01
The distribution of the number of academic publications as a function of citation count for a given year is remarkably similar from year to year. We measure this similarity as a width of the distribution and find it to be approximately constant from year to year. We show that simple citation models fail to capture this behaviour. We then provide a simple three parameter citation network model using a mixture of local and global search processes which can reproduce the correct distribution over time. We use the citation network of papers from the hep-th section of arXiv to test our model. For this data, around 20% of citations use global information to reference recently published papers, while the remaining 80% are found using local searches. We note that this is consistent with other studies though our motivation is very different from previous work. Finally, we also find that the fluctuations in the size of an academic publication's bibliography is important for the model. This is not addressed in most mode...
Boolean Search: Current State and Perspectives.
Frants, Valery I.; Shapiro, Jacob; Taksa, Isak; Voiskunskii, Vladimir G.
1999-01-01
Discusses the use of Boolean logic in information-retrieval systems and analyzes existing criticisms of operational systems. Considers users' ability to use and understand Boolean operators, ranking, the quality of query formulations, and negative effects of criticism; and concludes that criticism is directed at the methodology employed in…
Boolean integral calculus for digital systems
Tucker, J. H.; Tapia, M. A.; Bennett, A. W.
1985-01-01
The concept of Boolean integration is introduced and developed. When the changes in a desired function are specified in terms of changes in its arguments, then ways of 'integrating' (i.e., realizing) the function, if it exists, are presented. Boolean integral calculus has applications in design of logic circuits.
Version Spaces and Generalized Monotone Boolean Functions
J.C. Bioch (Cor); T. Ibaraki
2002-01-01
textabstractWe consider generalized monotone functions f: X --> {0,1} defined for an arbitrary binary relation <= on X by the property x <= y implies f(x) <= f(y). These include the standard monotone (or positive) Boolean functions, regular Boolean functions and other interesting functions as speci
A Network Synthesis Model for Generating Protein Interaction Network Families
Sayed Mohammad Ebrahim Sahraeian; Byung-Jun Yoon
2012-01-01
In this work, we introduce a novel network synthesis model that can generate families of evolutionarily related synthetic protein-protein interaction (PPI) networks. Given an ancestral network, the proposed model generates the network family according to a hypothetical phylogenetic tree, where the descendant networks are obtained through duplication and divergence of their ancestors, followed by network growth using network evolution models. We demonstrate that this network synthesis model ca...
Difference equation for tracking perturbations in systems of Boolean nested canalyzing functions.
Dimitrova, Elena S; Yordanov, Oleg I; Matache, Mihaela T
2015-06-01
This paper studies the spread of perturbations through networks composed of Boolean functions with special canalyzing properties. Canalyzing functions have the property that at least for one value of one of the inputs the output is fixed, irrespective of the values of the other inputs. In this paper the focus is on partially nested canalyzing functions, in which multiple, but not all inputs have this property in a cascading fashion. They naturally describe many relationships in real networks. For example, in a gene regulatory network, the statement "if gene A is expressed, then gene B is not expressed regardless of the states of other genes" implies that A is canalyzing. On the other hand, the additional statement "if gene A is not expressed, and gene C is expressed, then gene B is automatically expressed; otherwise gene B's state is determined by some other type of rule" implies that gene B is expressed by a partially nested canalyzing function with more than two variables, but with two canalyzing variables. In this paper a difference equation model of the probability that a network node's value is affected by an initial perturbation over time is developed, analyzed, and validated numerically. It is shown that the effect of a perturbation decreases towards zero over time if the Boolean functions are canalyzing in sufficiently many variables. The maximum dynamical impact of a perturbation is shown to be comparable to the average impact for a wide range of values of the average sensitivity of the network. Percolation limits are also explored; these are parameter values which generate a transition of the expected perturbation effect to zero as other parameters are varied, so that the initial perturbation does not scale up with the parameters once the percolation limits are reached.
A neighbourhood evolving network model
International Nuclear Information System (INIS)
Many social, technological, biological and economical systems are best described by evolved network models. In this short Letter, we propose and study a new evolving network model. The model is based on the new concept of neighbourhood connectivity, which exists in many physical complex networks. The statistical properties and dynamics of the proposed model is analytically studied and compared with those of Barabasi-Albert scale-free model. Numerical simulations indicate that this network model yields a transition between power-law and exponential scaling, while the Barabasi-Albert scale-free model is only one of its special (limiting) cases. Particularly, this model can be used to enhance the evolving mechanism of complex networks in the real world, such as some social networks development
Multipath Detection Using Boolean Satisfiability Techniques
Directory of Open Access Journals (Sweden)
Fadi A. Aloul
2011-01-01
Full Text Available A new technique for multipath detection in wideband mobile radio systems is presented. The proposed scheme is based on an intelligent search algorithm using Boolean Satisfiability (SAT techniques to search through the uncertainty region of the multipath delays. The SAT-based scheme utilizes the known structure of the transmitted wideband signal, for example, pseudo-random (PN code, to effectively search through the entire space by eliminating subspaces that do not contain a possible solution. The paper presents a framework for modeling the multipath detection problem as a SAT application. It also provides simulation results that demonstrate the effectiveness of the proposed scheme in detecting the multipath components in frequency-selective Rayleigh fading channels.
Mining and modeling character networks
Bonato, Anthony; Elenberg, Ethan R; Gleich, David F; Hou, Yangyang
2016-01-01
We investigate social networks of characters found in cultural works such as novels and films. These character networks exhibit many of the properties of complex networks such as skewed degree distribution and community structure, but may be of relatively small order with a high multiplicity of edges. Building on recent work of beveridge, we consider graph extraction, visualization, and network statistics for three novels: Twilight by Stephanie Meyer, Steven King's The Stand, and J.K. Rowling's Harry Potter and the Goblet of Fire. Coupling with 800 character networks from films found in the http://moviegalaxies.com/ database, we compare the data sets to simulations from various stochastic complex networks models including random graphs with given expected degrees (also known as the Chung-Lu model), the configuration model, and the preferential attachment model. Using machine learning techniques based on motif (or small subgraph) counts, we determine that the Chung-Lu model best fits character networks and we ...
Analysis of Boolean Functions based on Interaction Graphs and their influence in System Biology
Das, Jayanta Kumar; Rout, Ranjeet Kumar; Choudhury, Pabitra Pal
2014-01-01
Interaction graphs provide an important qualitative modeling approach for System Biology. This paper presents a novel approach for construction of interaction graph with the help of Boolean function decomposition. Each decomposition part (Consisting of 2-bits) of the Boolean functions has some important significance. In the dynamics of a biological system, each variable or node is nothing but gene or protein. Their regulation has been explored in terms of interaction graphs which are generate...
Mining TCGA data using Boolean implications.
Sinha, Subarna; Tsang, Emily K; Zeng, Haoyang; Meister, Michela; Dill, David L
2014-01-01
Boolean implications (if-then rules) provide a conceptually simple, uniform and highly scalable way to find associations between pairs of random variables. In this paper, we propose to use Boolean implications to find relationships between variables of different data types (mutation, copy number alteration, DNA methylation and gene expression) from the glioblastoma (GBM) and ovarian serous cystadenoma (OV) data sets from The Cancer Genome Atlas (TCGA). We find hundreds of thousands of Boolean implications from these data sets. A direct comparison of the relationships found by Boolean implications and those found by commonly used methods for mining associations show that existing methods would miss relationships found by Boolean implications. Furthermore, many relationships exposed by Boolean implications reflect important aspects of cancer biology. Examples of our findings include cis relationships between copy number alteration, DNA methylation and expression of genes, a new hierarchy of mutations and recurrent copy number alterations, loss-of-heterozygosity of well-known tumor suppressors, and the hypermethylation phenotype associated with IDH1 mutations in GBM. The Boolean implication results used in the paper can be accessed at http://crookneck.stanford.edu/microarray/TCGANetworks/.
Mining TCGA data using Boolean implications.
Directory of Open Access Journals (Sweden)
Subarna Sinha
Full Text Available Boolean implications (if-then rules provide a conceptually simple, uniform and highly scalable way to find associations between pairs of random variables. In this paper, we propose to use Boolean implications to find relationships between variables of different data types (mutation, copy number alteration, DNA methylation and gene expression from the glioblastoma (GBM and ovarian serous cystadenoma (OV data sets from The Cancer Genome Atlas (TCGA. We find hundreds of thousands of Boolean implications from these data sets. A direct comparison of the relationships found by Boolean implications and those found by commonly used methods for mining associations show that existing methods would miss relationships found by Boolean implications. Furthermore, many relationships exposed by Boolean implications reflect important aspects of cancer biology. Examples of our findings include cis relationships between copy number alteration, DNA methylation and expression of genes, a new hierarchy of mutations and recurrent copy number alterations, loss-of-heterozygosity of well-known tumor suppressors, and the hypermethylation phenotype associated with IDH1 mutations in GBM. The Boolean implication results used in the paper can be accessed at http://crookneck.stanford.edu/microarray/TCGANetworks/.
Modeling Dynamics of Information Networks
Rosvall, Martin; Sneppen, Kim
2003-01-01
We propose an information-based model for network dynamics in which imperfect information leads to networks where the different vertices have widely different number of edges to other vertices, and where the topology has hierarchical features. The possibility to observe scale free networks is linked to a minimally connected system where hubs remain dynamic.
Directory of Open Access Journals (Sweden)
G.C. Rao
2012-11-01
Full Text Available A C- algebra is the algebraic form of the 3-valued conditional logic, which was introduced by F. Guzman and C. C. Squier in 1990. In this paper, some equivalent conditions for a C- algebra to become a boolean algebra in terms of congruences are given. It is proved that the set of all central elements B(A is isomorphic to the Boolean algebra of all C-algebras Sa, where a B(A. It is also proved that B(A is isomorphic to the Boolean algebra of all C-algebras Aa, where a B(A.
Generalizing Boolean Satisfiability II: Theory
Dixon, H E; Luks, E M; Parkes, A J; 10.1613/jair.1555
2011-01-01
This is the second of three planned papers describing ZAP, a satisfiability engine that substantially generalizes existing tools while retaining the performance characteristics of modern high performance solvers. The fundamental idea underlying ZAP is that many problems passed to such engines contain rich internal structure that is obscured by the Boolean representation used; our goal is to define a representation in which this structure is apparent and can easily be exploited to improve computational performance. This paper presents the theoretical basis for the ideas underlying ZAP, arguing that existing ideas in this area exploit a single, recurring structure in that multiple database axioms can be obtained by operating on a single axiom using a subgroup of the group of permutations on the literals in the problem. We argue that the group structure precisely captures the general structure at which earlier approaches hinted, and give numerous examples of its use. We go on to extend the Davis-Putnam-Logemann-...
Local Correction of Boolean Functions
Alon, Noga
2011-01-01
A Boolean function f over n variables is said to be q-locally correctable if, given a black-box access to a function g which is "close" to an isomorphism f_sigma of f, we can compute f_sigma(x) for any x in Z_2^n with good probability using q queries to g. We observe that any k-junta, that is, any function which depends only on k of its input variables, is O(2^k)-locally correctable. Moreover, we show that there are examples where this is essentially best possible, and locally correcting some k-juntas requires a number of queries which is exponential in k. These examples, however, are far from being typical, and indeed we prove that for almost every k-junta, O(k log k) queries suffice.
In silico design and in vivo implementation of yeast gene Boolean gates.
Marchisio, Mario A
2014-01-01
In our previous computational work, we showed that gene digital circuits can be automatically designed in an electronic fashion. This demands, first, a conversion of the truth table into Boolean formulas with the Karnaugh map method and, then, the translation of the Boolean formulas into circuit schemes organized into layers of Boolean gates and Pools of signal carriers. In our framework, gene digital circuits that take up to three different input signals (chemicals) arise from the composition of three kinds of basic Boolean gates, namely YES, NOT, and AND. Here we present a library of YES, NOT, and AND gates realized via plasmidic DNA integration into the yeast genome. Boolean behavior is reproduced via the transcriptional control of a synthetic bipartite promoter that contains sequences of the yeast VPH1 and minimal CYC1 promoters together with operator binding sites for bacterial (i.e. orthogonal) repressor proteins. Moreover, model-driven considerations permitted us to pinpoint a strategy for re-designing gates when a better digital performance is required. Our library of well-characterized Boolean gates is the basis for the assembly of more complex gene digital circuits. As a proof of concepts, we engineered two 2-input OR gates, designed by our software, by combining YES and NOT gates present in our library.
Complex Networks in Psychological Models
Wedemann, R. S.; Carvalho, L. S. A. V. D.; Donangelo, R.
We develop schematic, self-organizing, neural-network models to describe mechanisms associated with mental processes, by a neurocomputational substrate. These models are examples of real world complex networks with interesting general topological structures. Considering dopaminergic signal-to-noise neuronal modulation in the central nervous system, we propose neural network models to explain development of cortical map structure and dynamics of memory access, and unify different mental processes into a single neurocomputational substrate. Based on our neural network models, neurotic behavior may be understood as an associative memory process in the brain, and the linguistic, symbolic associative process involved in psychoanalytic working-through can be mapped onto a corresponding process of reconfiguration of the neural network. The models are illustrated through computer simulations, where we varied dopaminergic modulation and observed the self-organizing emergent patterns at the resulting semantic map, interpreting them as different manifestations of mental functioning, from psychotic through to normal and neurotic behavior, and creativity.
Internet Network Resource Information Model
Institute of Scientific and Technical Information of China (English)
陈传峰; 李增智; 唐亚哲; 刘康平
2002-01-01
The foundation of any network management systens is a database that con-tains information about the network resources relevant to the management tasks. A networkinformation model is an abstraction of network resources, including both managed resources andmanaging resources. In the SNMP-based management framework, management information isdefined almost exclusively from a "device" viewpoint, namely, managing a network is equiva-lent to managing a collection of individual nodes. Aiming at making use of recent advances indistributed computing and in object-oriented analysis and design, the Internet management ar-chitecture can also be based on the Open Distributed Processing Reference Model (RM-ODP).The purpose of this article is to provide an Internet Network Resource Information Model.First, a layered management information architecture will be discussed. Then the Internetnetwork resource information model is presented. The information model is specified usingObject-Z.
Boolean Logic with Fault Tolerant Coding
Alagoz, B. Baykant
2009-01-01
Error detectable and error correctable coding in Hamming space was researched to discover possible fault tolerant coding constellations, which can implement Boolean logic with fault tolerant property. Basic logic operators of the Boolean algebra were developed to apply fault tolerant coding in the logic circuits. It was shown that application of three-bit fault tolerant codes have provided the digital system skill of auto-recovery without need for designing additional-fault tolerance mechanisms.
Developing Personal Network Business Models
DEFF Research Database (Denmark)
Saugstrup, Dan; Henten, Anders
2006-01-01
The aim of the paper is to examine the issue of business modeling in relation to personal networks, PNs. The paper builds on research performed on business models in the EU 1ST MAGNET1 project (My personal Adaptive Global NET). The paper presents the Personal Network concept and briefly reports...... on the 'state of the art' in the field of business modeling. Furthermore, the paper suggests three generic business models for PNs: a service oriented model, a self-organized model, and a combination model. Finally, examples of relevant services and applications in relation to three different cases...... are presented and analyzed in light of business modeling of PN....
Telecommunications network modelling, planning and design
Evans, Sharon
2003-01-01
Telecommunication Network Modelling, Planning and Design addresses sophisticated modelling techniques from the perspective of the communications industry and covers some of the major issues facing telecommunications network engineers and managers today. Topics covered include network planning for transmission systems, modelling of SDH transport network structures and telecommunications network design and performance modelling, as well as network costs and ROI modelling and QoS in 3G networks.
Neural network modeling of emotion
Levine, Daniel S.
2007-03-01
This article reviews the history and development of computational neural network modeling of cognitive and behavioral processes that involve emotion. The exposition starts with models of classical conditioning dating from the early 1970s. Then it proceeds toward models of interactions between emotion and attention. Then models of emotional influences on decision making are reviewed, including some speculative (not and not yet simulated) models of the evolution of decision rules. Through the late 1980s, the neural networks developed to model emotional processes were mainly embodiments of significant functional principles motivated by psychological data. In the last two decades, network models of these processes have become much more detailed in their incorporation of known physiological properties of specific brain regions, while preserving many of the psychological principles from the earlier models. Most network models of emotional processes so far have dealt with positive and negative emotion in general, rather than specific emotions such as fear, joy, sadness, and anger. But a later section of this article reviews a few models relevant to specific emotions: one family of models of auditory fear conditioning in rats, and one model of induced pleasure enhancing creativity in humans. Then models of emotional disorders are reviewed. The article concludes with philosophical statements about the essential contributions of emotion to intelligent behavior and the importance of quantitative theories and models to the interdisciplinary enterprise of understanding the interactions of emotion, cognition, and behavior.
Boolean integration. [applied to switching network synthesis
Tucker, J. H.; Tapia, M. A.; Bennett, A. W.
1976-01-01
This paper presents the necessary and sufficient conditions for a given differential expression to be compatibly integrable and it presents the necessary and sufficient conditions for a given expression to be exactly integrable. Methods are given for integrating a differential expression when it is exactly integrable and when it is compatibly integrable. The physical interpretation is given of the integral of order k, of a differential expression, and it is shown that any differential expression of the proper form is integrable by parts.
Simplified models of biological networks.
Sneppen, Kim; Krishna, Sandeep; Semsey, Szabolcs
2010-01-01
The function of living cells is controlled by complex regulatory networks that are built of a wide diversity of interacting molecular components. The sheer size and intricacy of molecular networks of even the simplest organisms are obstacles toward understanding network functionality. This review discusses the achievements and promise of a bottom-up approach that uses well-characterized subnetworks as model systems for understanding larger networks. It highlights the interplay between the structure, logic, and function of various types of small regulatory circuits. The bottom-up approach advocates understanding regulatory networks as a collection of entangled motifs. We therefore emphasize the potential of negative and positive feedback, as well as their combinations, to generate robust homeostasis, epigenetics, and oscillations. PMID:20192769
Duality theories for Boolean algebras with operators
Givant, Steven
2014-01-01
In this new text, Steven Givant—the author of several acclaimed books, including works co-authored with Paul Halmos and Alfred Tarski—develops three theories of duality for Boolean algebras with operators. Givant addresses the two most recognized dualities (one algebraic and the other topological) and introduces a third duality, best understood as a hybrid of the first two. This text will be of interest to graduate students and researchers in the fields of mathematics, computer science, logic, and philosophy who are interested in exploring special or general classes of Boolean algebras with operators. Readers should be familiar with the basic arithmetic and theory of Boolean algebras, as well as the fundamentals of point-set topology.
A finite alternation result for reversible boolean circuits
Selinger, Peter
2016-01-01
We say that a reversible boolean function on n bits has alternation depth d if it can be written as the sequential composition of d reversible boolean functions, each of which acts only on the top n-1 bits or on the bottom n-1 bits. We show that every reversible boolean function of n >= 4 bits has alternation depth 9.
Advances in theoretical models of network science
Institute of Scientific and Technical Information of China (English)
FANG Jin-qing; BI Qiao; LI Yong
2007-01-01
In this review article, we will summarize the main advances in network science investigated by the CIAE Group of Complex Network in this field. Several theoretical models of network science were proposed and their topological and dynamical properties are reviewed and compared with the other models. Our models mainly include a harmonious unifying hybrid preferential model, a large unifying hybrid network model, a quantum interference network, a hexagonal nanowire network, and a small-world network with the same degree. The models above reveal some new phenomena and findings, which are useful for deeply understanding and investigating complex networks and their applications.
Analysis of affinely equivalent Boolean functions
Institute of Scientific and Technical Information of China (English)
MENG QingShu; ZHANG HuanGuo; YANG Min; WANG ZhangYi
2007-01-01
By some basic transforms and invariant theory, we give two results: 1) an algorithm,which can be used to judge if two Boolean functions are affinely equivalent and to obtain the equivalence relationship if they are equivalent. This is useful in studying Boolean functions and in engineering. For example, we classify all 8-variable homogeneous bent functions of degree 3 into two classes; 2) Reed-Muller codes R(4,6)/R(1,6), R(3,7)/R(1,7) are classified efficiently.
Coherent spaces, Boolean rings and quantum gates
Vourdas, A.
2016-10-01
Coherent spaces spanned by a finite number of coherent states, are introduced. Their coherence properties are studied, using the Dirac contour representation. It is shown that the corresponding projectors resolve the identity, and that they transform into projectors of the same type, under displacement transformations, and also under time evolution. The set of these spaces, with the logical OR and AND operations is a distributive lattice, and with the logical XOR and AND operations is a Boolean ring (Stone's formalism). Applications of this Boolean ring into classical CNOT gates with n-ary variables, and also quantum CNOT gates with coherent states, are discussed.
Boolean differentiation and integration using Karnaugh maps
Tucker, J. H.; Tapia, M. A.; Bennett, A. W.
1977-01-01
Algorithms are presented for differentiation and integration of Boolean functions by means of Karnaugh maps. The algorithms are considered simple when the number of variables is six or less; in this case Boolean differentiation and integration is said to be as easy as the Karnaugh map method of simplifying switching functions. It is suggested that the algorithms would be useful in the analysis of faults in combinational systems and in the synthesis of asynchronous sequential systems which utilize edge-sensitive flip-flops.
Algorithms for Boolean Function Query Properties
Aaronson, Scott
2001-01-01
We present new algorithms to compute fundamental properties of a Boolean function given in truth-table form. Specifically, we give an O(N^2.322 log N) algorithm for block sensitivity, an O(N^1.585 log N) algorithm for `tree decomposition,' and an O(N) algorithm for `quasisymmetry.' These algorithms are based on new insights into the structure of Boolean functions that may be of independent interest. We also give a subexponential-time algorithm for the space-bounded quantum query complexity of...
Current approaches to gene regulatory network modelling
Directory of Open Access Journals (Sweden)
Brazma Alvis
2007-09-01
Full Text Available Abstract Many different approaches have been developed to model and simulate gene regulatory networks. We proposed the following categories for gene regulatory network models: network parts lists, network topology models, network control logic models, and dynamic models. Here we will describe some examples for each of these categories. We will study the topology of gene regulatory networks in yeast in more detail, comparing a direct network derived from transcription factor binding data and an indirect network derived from genome-wide expression data in mutants. Regarding the network dynamics we briefly describe discrete and continuous approaches to network modelling, then describe a hybrid model called Finite State Linear Model and demonstrate that some simple network dynamics can be simulated in this model.
Saez-Rodriguez, Julio; Alexopoulos, Leonidas G; Zhang, Mingsheng; Morris, Melody K; Lauffenburger, Douglas A; Sorger, Peter K
2011-08-15
Substantial effort in recent years has been devoted to constructing and analyzing large-scale gene and protein networks on the basis of "omic" data and literature mining. These interaction graphs provide valuable insight into the topologies of complex biological networks but are rarely context specific and cannot be used to predict the responses of cell signaling proteins to specific ligands or drugs. Conversely, traditional approaches to analyzing cell signaling are narrow in scope and cannot easily make use of network-level data. Here, we combine network analysis and functional experimentation by using a hybrid approach in which graphs are converted into simple mathematical models that can be trained against biochemical data. Specifically, we created Boolean logic models of immediate-early signaling in liver cells by training a literature-based prior knowledge network against biochemical data obtained from primary human hepatocytes and 4 hepatocellular carcinoma cell lines exposed to combinations of cytokines and small-molecule kinase inhibitors. Distinct families of models were recovered for each cell type, and these families clustered topologically into normal and diseased sets.
A Multilayer Model of Computer Networks
Shchurov, Andrey A.
2015-01-01
The fundamental concept of applying the system methodology to network analysis declares that network architecture should take into account services and applications which this network provides and supports. This work introduces a formal model of computer networks on the basis of the hierarchical multilayer networks. In turn, individual layers are represented as multiplex networks. The concept of layered networks provides conditions of top-down consistency of the model. Next, we determined the...
Target-Centric Network Modeling
DEFF Research Database (Denmark)
Mitchell, Dr. William L.; Clark, Dr. Robert M.
In Target-Centric Network Modeling: Case Studies in Analyzing Complex Intelligence Issues, authors Robert Clark and William Mitchell take an entirely new approach to teaching intelligence analysis. Unlike any other book on the market, it offers case study scenarios using actual intelligence...... reporting formats, along with a tested process that facilitates the production of a wide range of analytical products for civilian, military, and hybrid intelligence environments. Readers will learn how to perform the specific actions of problem definition modeling, target network modeling......, and collaborative sharing in the process of creating a high-quality, actionable intelligence product. The case studies reflect the complexity of twenty-first century intelligence issues by dealing with multi-layered target networks that cut across political, economic, social, technological, and military issues...
Hydraulic Modeling: Pipe Network Analysis
Datwyler, Trevor T.
2012-01-01
Water modeling is becoming an increasingly important part of hydraulic engineering. One application of hydraulic modeling is pipe network analysis. Using programmed algorithms to repeatedly solve continuity and energy equations, computer software can greatly reduce the amount of time required to analyze a closed conduit system. Such hydraulic models can become a valuable tool for cities to maintain their water systems and plan for future growth. The Utah Division of Drinking Water regulations...
Institute of Scientific and Technical Information of China (English)
刘卫锋
2013-01-01
将软集理论应用到布尔代数中，提出了软布尔代数、软布尔子代数、软布尔代数的软理想、软理想布尔代数等概念，研究了它们的相关性质，并初步讨论了软布尔代数与几类布尔代数的模糊子代数的关系。%The soft set theory is applied to the Boolean algebra.The concepts of soft Boolean algebra, soft Boolean sub-algebra, soft ideal of soft Boolean algebra and idealistic soft Boolean algebra are presented and some related algebraic properties are discussed.The relations between soft Boolean algebra and several kinds of fuzzy subalgebras of Boolean algebra are preliminarily investigated.
A complexity theory based on Boolean algebra
DEFF Research Database (Denmark)
Skyum, Sven; Valiant, Leslie
1985-01-01
A projection of a Boolean function is a function obtained by substituting for each of its variables a variable, the negation of a variable, or a constant. Reducibilities among computational problems under this relation of projection are considered. It is shown that much of what is of everyday rel...
Demonstrating Boolean Logic Using Simple Electrical Circuits
McElhaney, Kevin W.
2004-01-01
While exploring the subject of geometric proofs, boolean logic operators AND and OR can be used to allow students to visualize their true-or-false patterns. An activity in the form of constructing electrical circuits is illustrated to explain the concept.
A Boolean Map Theory of Visual Attention
Huang, Liqiang; Pashler, Harold
2007-01-01
A theory is presented that attempts to answer two questions. What visual contents can an observer consciously access at one moment? Answer: only one feature value (e.g., green) per dimension, but those feature values can be associated (as a group) with multiple spatially precise locations (comprising a single labeled Boolean map). How can an…
Some Aspects of Boolean Valued Analysis
Kusraev, A. G.; Kutateladze, S. S.
2015-01-01
This is a survey of some recent applications of Boolean valued analysis to operator theory and harmonic analysis. Under consideration are pseudoembedding operators, the noncommutative Wickstead problem, the Radon-Nikodym Theorem for JB-algebras, and the Bochner Theorem for lattice-valued positive definite mappings on locally compact groups.
Thermal Network Modelling Handbook
1972-01-01
Thermal mathematical modelling is discussed in detail. A three-fold purpose was established: (1) to acquaint the new user with the terminology and concepts used in thermal mathematical modelling, (2) to present the more experienced and occasional user with quick formulas and methods for solving everyday problems, coupled with study cases which lend insight into the relationships that exist among the various solution techniques and parameters, and (3) to begin to catalog in an orderly fashion the common formulas which may be applied to automated conversational language techniques.
Meghabghab, George
2001-01-01
Discusses the evaluation of search engines and uses neural networks in stochastic simulation of the number of rejected Web pages per search query. Topics include the iterative radial basis functions (RBF) neural network; precision; response time; coverage; Boolean logic; regression models; crawling algorithms; and implications for search engine…
CNEM: Cluster Based Network Evolution Model
Directory of Open Access Journals (Sweden)
Sarwat Nizamani
2015-01-01
Full Text Available This paper presents a network evolution model, which is based on the clustering approach. The proposed approach depicts the network evolution, which demonstrates the network formation from individual nodes to fully evolved network. An agglomerative hierarchical clustering method is applied for the evolution of network. In the paper, we present three case studies which show the evolution of the networks from the scratch. These case studies include: terrorist network of 9/11 incidents, terrorist network of WMD (Weapons Mass Destruction plot against France and a network of tweets discussing a topic. The network of 9/11 is also used for evaluation, using other social network analysis methods which show that the clusters created using the proposed model of network evolution are of good quality, thus the proposed method can be used by law enforcement agencies in order to further investigate the criminal networks
Quantum walks outside of boolean domain as a gate for one, two, or three qubits
Cavin, Thomas; Solenov, Dmitry
Quantum computing needs entangling quantum gates to perform computation and error correction. We will discuss a novel way to implement quantum gates, such as CNOT, using quantum walks that are directed through a network of states outside of the boolean domain. In such implementations it is important to investigate walks on networks of different connectivities. Specifically, we will discuss solutions to non-symmetric linear chain networks and demonstrate how solutions to more complex networks that have branching, such as cubes, can be expressed in terms of linear chain solutions. We then show examples of implementing single qubit and two-qubit entangling gates.
Research on the model of home networking
Yun, Xiang; Feng, Xiancheng
2007-11-01
It is the research hotspot of current broadband network to combine voice service, data service and broadband audio-video service by IP protocol to transport various real time and mutual services to terminal users (home). Home Networking is a new kind of network and application technology which can provide various services. Home networking is called as Digital Home Network. It means that PC, home entertainment equipment, home appliances, Home wirings, security, illumination system were communicated with each other by some composing network technology, constitute a networking internal home, and connect with WAN by home gateway. It is a new network technology and application technology, and can provide many kinds of services inside home or between homes. Currently, home networking can be divided into three kinds: Information equipment, Home appliances, Communication equipment. Equipment inside home networking can exchange information with outer networking by home gateway, this information communication is bidirectional, user can get information and service which provided by public networking by using home networking internal equipment through home gateway connecting public network, meantime, also can get information and resource to control the internal equipment which provided by home networking internal equipment. Based on the general network model of home networking, there are four functional entities inside home networking: HA, HB, HC, and HD. (1) HA (Home Access) - home networking connects function entity; (2) HB (Home Bridge) Home networking bridge connects function entity; (3) HC (Home Client) - Home networking client function entity; (4) HD (Home Device) - decoder function entity. There are many physical ways to implement four function entities. Based on theses four functional entities, there are reference model of physical layer, reference model of link layer, reference model of IP layer and application reference model of high layer. In the future home network
Mathematical Modelling Plant Signalling Networks
Muraro, D.
2013-01-01
During the last two decades, molecular genetic studies and the completion of the sequencing of the Arabidopsis thaliana genome have increased knowledge of hormonal regulation in plants. These signal transduction pathways act in concert through gene regulatory and signalling networks whose main components have begun to be elucidated. Our understanding of the resulting cellular processes is hindered by the complex, and sometimes counter-intuitive, dynamics of the networks, which may be interconnected through feedback controls and cross-regulation. Mathematical modelling provides a valuable tool to investigate such dynamics and to perform in silico experiments that may not be easily carried out in a laboratory. In this article, we firstly review general methods for modelling gene and signalling networks and their application in plants. We then describe specific models of hormonal perception and cross-talk in plants. This mathematical analysis of sub-cellular molecular mechanisms paves the way for more comprehensive modelling studies of hormonal transport and signalling in a multi-scale setting. © EDP Sciences, 2013.
Directory of Open Access Journals (Sweden)
Jensen Paul A
2011-09-01
Full Text Available Abstract Background Several methods have been developed for analyzing genome-scale models of metabolism and transcriptional regulation. Many of these methods, such as Flux Balance Analysis, use constrained optimization to predict relationships between metabolic flux and the genes that encode and regulate enzyme activity. Recently, mixed integer programming has been used to encode these gene-protein-reaction (GPR relationships into a single optimization problem, but these techniques are often of limited generality and lack a tool for automating the conversion of rules to a coupled regulatory/metabolic model. Results We present TIGER, a Toolbox for Integrating Genome-scale Metabolism, Expression, and Regulation. TIGER converts a series of generalized, Boolean or multilevel rules into a set of mixed integer inequalities. The package also includes implementations of existing algorithms to integrate high-throughput expression data with genome-scale models of metabolism and transcriptional regulation. We demonstrate how TIGER automates the coupling of a genome-scale metabolic model with GPR logic and models of transcriptional regulation, thereby serving as a platform for algorithm development and large-scale metabolic analysis. Additionally, we demonstrate how TIGER's algorithms can be used to identify inconsistencies and improve existing models of transcriptional regulation with examples from the reconstructed transcriptional regulatory network of Saccharomyces cerevisiae. Conclusion The TIGER package provides a consistent platform for algorithm development and extending existing genome-scale metabolic models with regulatory networks and high-throughput data.
Generalization performance of regularized neural network models
DEFF Research Database (Denmark)
Larsen, Jan; Hansen, Lars Kai
1994-01-01
Architecture optimization is a fundamental problem of neural network modeling. The optimal architecture is defined as the one which minimizes the generalization error. This paper addresses estimation of the generalization performance of regularized, complete neural network models. Regularization...
Probabilistic logic modeling of network reliability for hybrid network architectures
Energy Technology Data Exchange (ETDEWEB)
Wyss, G.D.; Schriner, H.K.; Gaylor, T.R.
1996-10-01
Sandia National Laboratories has found that the reliability and failure modes of current-generation network technologies can be effectively modeled using fault tree-based probabilistic logic modeling (PLM) techniques. We have developed fault tree models that include various hierarchical networking technologies and classes of components interconnected in a wide variety of typical and atypical configurations. In this paper we discuss the types of results that can be obtained from PLMs and why these results are of great practical value to network designers and analysts. After providing some mathematical background, we describe the `plug-and-play` fault tree analysis methodology that we have developed for modeling connectivity and the provision of network services in several current- generation network architectures. Finally, we demonstrate the flexibility of the method by modeling the reliability of a hybrid example network that contains several interconnected ethernet, FDDI, and token ring segments. 11 refs., 3 figs., 1 tab.
Boolean representations of simplicial complexes and matroids
Rhodes, John
2015-01-01
This self-contained monograph explores a new theory centered around boolean representations of simplicial complexes leading to a new class of complexes featuring matroids as central to the theory. The book illustrates these new tools to study the classical theory of matroids as well as their important geometric connections. Moreover, many geometric and topological features of the theory of matroids find their counterparts in this extended context. Graduate students and researchers working in the areas of combinatorics, geometry, topology, algebra and lattice theory will find this monograph appealing due to the wide range of new problems raised by the theory. Combinatorialists will find this extension of the theory of matroids useful as it opens new lines of research within and beyond matroids. The geometric features and geometric/topological applications will appeal to geometers. Topologists who desire to perform algebraic topology computations will appreciate the algorithmic potential of boolean represent...
Fast Vertical Mining Using Boolean Algebra
Directory of Open Access Journals (Sweden)
Hosny M. Ibrahim
2015-01-01
Full Text Available The vertical association rules mining algorithm is an efficient mining method, which makes use of support sets of frequent itemsets to calculate the support of candidate itemsets. It overcomes the disadvantage of scanning database many times like Apriori algorithm. In vertical mining, frequent itemsets can be represented as a set of bit vectors in memory, which enables for fast computation. The sizes of bit vectors for itemsets are the main space expense of the algorithm that restricts its expansibility. Therefore, in this paper, a proposed algorithm that compresses the bit vectors of frequent itemsets will be presented. The new bit vector schema presented here depends on Boolean algebra rules to compute the intersection of two compressed bit vectors without making any costly decompression operation. The experimental results show that the proposed algorithm, Vertical Boolean Mining (VBM algorithm is better than both Apriori algorithm and the classical vertical association rule mining algorithm in the mining time and the memory usage.
Efficient Analog Circuits for Boolean Satisfiability
Yin, Xunzhao; Sedighi, Behnam; Varga, Melinda; Ercsey-Ravasz, Maria; Toroczkai, Zoltan; Hu, Xiaobo Sharon
2016-01-01
Efficient solutions to NP-complete problems would significantly benefit both science and industry. However, such problems are intractable on digital computers based on the von Neumann architecture, thus creating the need for alternative solutions to tackle such problems. Recently, a deterministic, continuous-time dynamical system (CTDS) was proposed (Nature Physics, 7(12), 966 (2011)) to solve a representative NP-complete problem, Boolean Satisfiability (SAT). This solver shows polynomial ana...
An evolving network model with community structure
International Nuclear Information System (INIS)
Many social and biological networks consist of communities-groups of nodes within which connections are dense, but between which connections are sparser. Recently, there has been considerable interest in designing algorithms for detecting community structures in real-world complex networks. In this paper, we propose an evolving network model which exhibits community structure. The network model is based on the inner-community preferential attachment and inter-community preferential attachment mechanisms. The degree distributions of this network model are analysed based on a mean-field method. Theoretical results and numerical simulations indicate that this network model has community structure and scale-free properties
Brand Marketing Model on Social Networks
Directory of Open Access Journals (Sweden)
Jolita Jezukevičiūtė
2014-04-01
Full Text Available The paper analyzes the brand and its marketing solutions onsocial networks. This analysis led to the creation of improvedbrand marketing model on social networks, which will contributeto the rapid and cheap organization brand recognition, increasecompetitive advantage and enhance consumer loyalty. Therefore,the brand and a variety of social networks are becoming a hotresearch area for brand marketing model on social networks.The world‘s most successful brand marketing models exploratoryanalysis of a single case study revealed a brand marketingsocial networking tools that affect consumers the most. Basedon information analysis and methodological studies, develop abrand marketing model on social networks.
Qualitative networks: a symbolic approach to analyze biological signaling networks
Directory of Open Access Journals (Sweden)
Henzinger Thomas A
2007-01-01
Full Text Available Abstract Background A central goal of Systems Biology is to model and analyze biological signaling pathways that interact with one another to form complex networks. Here we introduce Qualitative networks, an extension of Boolean networks. With this framework, we use formal verification methods to check whether a model is consistent with the laboratory experimental observations on which it is based. If the model does not conform to the data, we suggest a revised model and the new hypotheses are tested in-silico. Results We consider networks in which elements range over a small finite domain allowing more flexibility than Boolean values, and add target functions that allow to model a rich set of behaviors. We propose a symbolic algorithm for analyzing the steady state of these networks, allowing us to scale up to a system consisting of 144 elements and state spaces of approximately 1086 states. We illustrate the usefulness of this approach through a model of the interaction between the Notch and the Wnt signaling pathways in mammalian skin, and its extensive analysis. Conclusion We introduce an approach for constructing computational models of biological systems that extends the framework of Boolean networks and uses formal verification methods for the analysis of the model. This approach can scale to multicellular models of complex pathways, and is therefore a useful tool for the analysis of complex biological systems. The hypotheses formulated during in-silico testing suggest new avenues to explore experimentally. Hence, this approach has the potential to efficiently complement experimental studies in biology.
Boolean Logic Optimization in Majority-Inverter Graphs
Amarù, Luca; Gaillardon, Pierre-Emmanuel; De Micheli, Giovanni
2015-01-01
We present a Boolean logic optimization framework based on Majority-Inverter Graph (MIG). An MIG is a directed acyclic graph consisting of three-input majority nodes and regular/complemented edges. Current MIG optimization is supported by a consistent algebraic framework. However, when algebraic methods cannot improve a result quality, stronger Boolean methods are needed to attain further optimization. For this purpose, we propose MIG Boolean methods exploiting the error masking property of m...
Using Boolean Constraint Propagation for Sub-clause Deduction
Darras, Sylvain; Dequen, Gilles; Devendeville, Laure; Mazure, Bertrand; Ostrowski, Richard; Sais, Lahkdar
2005-01-01
Boolean Constraint Propagation (BCP) is recognized as one of the most use- ful technique for efficient satisfiability checking. In this paper a new extension of the scope of boolean constraint propagation is proposed. It makes an original use of BCP to achieve further reduction of boolean formulas. Considering the impli- cation graph generated by the constraint propagation process as a resolution tree, sub-clauses from the original formula can be deduced. Then, we show how such extension can ...
Modeling the Dynamics of Compromised Networks
Energy Technology Data Exchange (ETDEWEB)
Soper, B; Merl, D M
2011-09-12
Accurate predictive models of compromised networks would contribute greatly to improving the effectiveness and efficiency of the detection and control of network attacks. Compartmental epidemiological models have been applied to modeling attack vectors such as viruses and worms. We extend the application of these models to capture a wider class of dynamics applicable to cyber security. By making basic assumptions regarding network topology we use multi-group epidemiological models and reaction rate kinetics to model the stochastic evolution of a compromised network. The Gillespie Algorithm is used to run simulations under a worst case scenario in which the intruder follows the basic connection rates of network traffic as a method of obfuscation.
Combinational Logic-Level Verification using Boolean Expression Diagrams
DEFF Research Database (Denmark)
Hulgaard, Henrik; Williams, Poul Frederick; Andersen, Henrik Reif
1997-01-01
Boolean Expression Diagrams (BEDs) is a new data structure for representing and manipulating Boolean functions. BEDs are a generalization of Binary Decision Diagrams (BDDs) that are capable of representing any Boolean circuit in linear space and still maintain many of the desirable properties...... of BDDs. This paper demonstrates that BEDs are well suited for solving the combinational logic-level verification problem which is, given two combinational circuits, to determine whether they implement the same Boolean functions. Based on all combinational circuits in the ISCAS 85 and LGSynth 91...
A Generalization of J-Boolean Like Rings%J-Boolean like 环的扩张
Institute of Scientific and Technical Information of China (English)
秦蕊
2014-01-01
对 J-Boolean like环进行了扩张，并且将 J-Boolean like环与广义矩阵环和Morita Context环联系起来，进而探索了部分环为 J-Boolean like环时应具备的条件，且给出若干相关例子。%The paper mainly explored the generalization of J-Boolean like rings ,and connected J-Boolean like rings with generalized matrix rings and Morita Context rings ,then studied the conditions when a part of other rings became J-Boolean like rings ,and listed some examples .
Information Network Model Query Processing
Song, Xiaopu
Information Networking Model (INM) [31] is a novel database model for real world objects and relationships management. It naturally and directly supports various kinds of static and dynamic relationships between objects. In INM, objects are networked through various natural and complex relationships. INM Query Language (INM-QL) [30] is designed to explore such information network, retrieve information about schema, instance, their attributes, relationships, and context-dependent information, and process query results in the user specified form. INM database management system has been implemented using Berkeley DB, and it supports INM-QL. This thesis is mainly focused on the implementation of the subsystem that is able to effectively and efficiently process INM-QL. The subsystem provides a lexical and syntactical analyzer of INM-QL, and it is able to choose appropriate evaluation strategies and index mechanism to process queries in INM-QL without the user's intervention. It also uses intermediate result structure to hold intermediate query result and other helping structures to reduce complexity of query processing.
Multilayer weighted social network model
Murase, Yohsuke; Török, János; Jo, Hang-Hyun; Kaski, Kimmo; Kertész, János
2014-11-01
Recent empirical studies using large-scale data sets have validated the Granovetter hypothesis on the structure of the society in that there are strongly wired communities connected by weak ties. However, as interaction between individuals takes place in diverse contexts, these communities turn out to be overlapping. This implies that the society has a multilayered structure, where the layers represent the different contexts. To model this structure we begin with a single-layer weighted social network (WSN) model showing the Granovetterian structure. We find that when merging such WSN models, a sufficient amount of interlayer correlation is needed to maintain the relationship between topology and link weights, while these correlations destroy the enhancement in the community overlap due to multiple layers. To resolve this, we devise a geographic multilayer WSN model, where the indirect interlayer correlations due to the geographic constraints of individuals enhance the overlaps between the communities and, at the same time, the Granovetterian structure is preserved.
Brand Marketing Model on Social Networks
Jolita Jezukevičiūtė; Vida Davidavičienė
2014-01-01
The paper analyzes the brand and its marketing solutions onsocial networks. This analysis led to the creation of improvedbrand marketing model on social networks, which will contributeto the rapid and cheap organization brand recognition, increasecompetitive advantage and enhance consumer loyalty. Therefore,the brand and a variety of social networks are becoming a hotresearch area for brand marketing model on social networks.The world‘s most successful brand marketing models exploratoryanalys...
Boolean and brain-inspired computing using spin-transfer torque devices
Fan, Deliang
Several completely new approaches (such as spintronic, carbon nanotube, graphene, TFETs, etc.) to information processing and data storage technologies are emerging to address the time frame beyond current Complementary Metal-Oxide-Semiconductor (CMOS) roadmap. The high speed magnetization switching of a nano-magnet due to current induced spin-transfer torque (STT) have been demonstrated in recent experiments. Such STT devices can be explored in compact, low power memory and logic design. In order to truly leverage STT devices based computing, researchers require a re-think of circuit, architecture, and computing model, since the STT devices are unlikely to be drop-in replacements for CMOS. The potential of STT devices based computing will be best realized by considering new computing models that are inherently suited to the characteristics of STT devices, and new applications that are enabled by their unique capabilities, thereby attaining performance that CMOS cannot achieve. The goal of this research is to conduct synergistic exploration in architecture, circuit and device levels for Boolean and brain-inspired computing using nanoscale STT devices. Specifically, we first show that the non-volatile STT devices can be used in designing configurable Boolean logic blocks. We propose a spin-memristor threshold logic (SMTL) gate design, where memristive cross-bar array is used to perform current mode summation of binary inputs and the low power current mode spintronic threshold device carries out the energy efficient threshold operation. Next, for brain-inspired computing, we have exploited different spin-transfer torque device structures that can implement the hard-limiting and soft-limiting artificial neuron transfer functions respectively. We apply such STT based neuron (or 'spin-neuron') in various neural network architectures, such as hierarchical temporal memory and feed-forward neural network, for performing "human-like" cognitive computing, which show more than
Network Bandwidth Utilization Forecast Model on High Bandwidth Network
Energy Technology Data Exchange (ETDEWEB)
Yoo, Wucherl; Sim, Alex
2014-07-07
With the increasing number of geographically distributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency of resource utilization and scheduling data movements on high-bandwidth network to accommodate ever increasing data volume for large-scale scientific data applications. Univariate model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology, our forecast model reduces computation time by 83.2percent. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.
Complete Boolean Satisfiability Solving Algorithms Based on Local Search
Institute of Scientific and Technical Information of China (English)
Wen-Sheng Guo; Guo-Wu Yang; William N.N.Hung; Xiaoyu Song
2013-01-01
Boolean satisfiability (SAT) is a well-known problem in computer science,artificial intelligence,and operations research.This paper focuses on the satisfiability problem of Model RB structure that is similar to graph coloring problems and others.We propose a translation method and three effective complete SAT solving algorithms based on the characterization of Model RB structure.We translate clauses into a graph with exclusive sets and relative sets.In order to reduce search depth,we determine search order using vertex weights and clique in the graph.The results show that our algorithms are much more effective than the best SAT solvers in numerous Model RB benchmarks,especially in those large benchmark instances.
Distributed Combat System of Systems Network Modeling
Directory of Open Access Journals (Sweden)
Yanbo Qi
2013-08-01
Full Text Available How to generate the topology model of Distributed combat System of Systems network is an important issue in combat analysis. A combat network construction algorithm was proposed to solve the problem. The improved hierarchy network evolving method was used to construct the command and control network, and the combat network generation algorithm was developed by the growth and local priority connections of new node joining into the command network. And then the analytical expression of the degree distribution of the network model was deduced via the mean-field theory method. Finally, the network model was analyzed according to the topology statistical parameters. The analyzing results show that under the same command span, though the command network topology doesn’t change when command level was increased, but the topology performance of the combat network is improved. This is in line with actual combat network; the comparison of degree distribution of analytical results and simulation results indicated that the degree distribution of network model we proposed follows a power law distribution, with exponential value depending on the initial number of command and control network and the number of nodes connected to the rest of the network , verifying the validity of the algorithm model.
Analysis by fracture network modelling
International Nuclear Information System (INIS)
This report describes the Fracture Network Modelling and Performance Assessment Support performed by Golder Associates Inc. during the Heisei-11 (1999-2000) fiscal year. The primary objective of the Golder Associates work scope during HY-11 was to provide theoretical and review support to the JNC HY-12 Performance assessment effort. In addition, Golder Associates provided technical support to JNC for the Aespoe Project. Major efforts for performance assessment support included analysis of PAWorks pathways and software documentation, verification, and performance assessment visualization. Support for the Aespoe project including 'Task 4' predictive modelling of sorbing tracer transport in TRUE-1 rock block, and integrated hydrogeological and geochemical modelling of Aespoe island for 'Task 5'. Technical information about Golder Associates HY-11 support to JNC is provided in the appendices to this report. (author)
Modelling delay propagation within an airport network
Pyrgiotis, N.; Malone, K.M.; Odoni, A.
2013-01-01
We describe an analytical queuing and network decomposition model developed to study the complex phenomenon of the propagation of delays within a large network of major airports. The Approximate Network Delays (AND) model computes the delays due to local congestion at individual airports and capture
An acoustical model based monitoring network
Wessels, P.W.; Basten, T.G.H.; Eerden, F.J.M. van der
2010-01-01
In this paper the approach for an acoustical model based monitoring network is demonstrated. This network is capable of reconstructing a noise map, based on the combination of measured sound levels and an acoustic model of the area. By pre-calculating the sound attenuation within the network the noi
Eight challenges for network epidemic models
Directory of Open Access Journals (Sweden)
Lorenzo Pellis
2015-03-01
Full Text Available Networks offer a fertile framework for studying the spread of infection in human and animal populations. However, owing to the inherent high-dimensionality of networks themselves, modelling transmission through networks is mathematically and computationally challenging. Even the simplest network epidemic models present unanswered questions. Attempts to improve the practical usefulness of network models by including realistic features of contact networks and of host–pathogen biology (e.g. waning immunity have made some progress, but robust analytical results remain scarce. A more general theory is needed to understand the impact of network structure on the dynamics and control of infection. Here we identify a set of challenges that provide scope for active research in the field of network epidemic models.
An evolutionary model of social networks
Ludwig, M.; Abell, P.
2007-07-01
Social networks in communities, markets, and societies self-organise through the interactions of many individuals. In this paper we use a well-known mechanism of social interactions — the balance of sentiment in triadic relations — to describe the development of social networks. Our model contrasts with many existing network models, in that people not only establish but also break up relations whilst the network evolves. The procedure generates several interesting network features such as a variety of degree distributions and degree correlations. The resulting network converges under certain conditions to a steady critical state where temporal disruptions in triangles follow a power-law distribution.
Computer-aided design of modular protein devices: Boolean AND gene activation
Salis, H.; Kaznessis, Y. N.
2006-12-01
Many potentially useful synthetic gene networks require the expression of an engineered gene if and only if two different DNA-binding proteins exist in sufficient concentration. While some natural and engineered systems activate gene expression according to a logical AND-like behavior, they often utilize allosteric or cooperative protein-protein interactions, rendering their components unsuitable for a toolbox of modular parts for use in multiple applications. Here, we develop a quantitative model to demonstrate that a small system of interacting fusion proteins, called a protein device, can activate an engineered gene according to the Boolean AND behavior while using only modular protein domains and DNA sites. The fusion proteins are created from transactivating, DNA-binding, non-DNA binding, and protein-protein interaction domains along with the corresponding peptide ligands. Using a combined kinetic and thermodynamic model, we identify the characteristics of the molecular components and their rates of constitutive production that maximize the fidelity of AND behavior. These AND protein devices facilitate the creation of complex genetic programs and may be used to create gene therapies, biosensors and other biomedical and biotechnological applications that turn on gene expression only when multiple DNA-binding proteins are simultaneously present.
Modeling Diagnostic Assessments with Bayesian Networks
Almond, Russell G.; DiBello, Louis V.; Moulder, Brad; Zapata-Rivera, Juan-Diego
2007-01-01
This paper defines Bayesian network models and examines their applications to IRT-based cognitive diagnostic modeling. These models are especially suited to building inference engines designed to be synchronous with the finer grained student models that arise in skills diagnostic assessment. Aspects of the theory and use of Bayesian network models…
Graph Annotations in Modeling Complex Network Topologies
Dimitropoulos, Xenofontas; Vahdat, Amin; Riley, George
2007-01-01
The coarsest approximation of the structure of a complex network, such as the Internet, is a simple undirected unweighted graph. This approximation, however, loses too much detail. In reality, objects represented by vertices and edges in such a graph possess some non-trivial internal structure that varies across and differentiates among distinct types of links or nodes. In this work, we abstract such additional information as network annotations. We introduce a network topology modeling framework that treats annotations as an extended correlation profile of a network. Assuming we have this profile measured for a given network, we present an algorithm to rescale it in order to construct networks of varying size that still reproduce the original measured annotation profile. Using this methodology, we accurately capture the network properties essential for realistic simulations of network applications and protocols, or any other simulations involving complex network topologies, including modeling and simulation ...
Constructions of vector output Boolean functions with high generalized nonlinearity
Institute of Scientific and Technical Information of China (English)
KE Pin-hui; ZHANG Sheng-yuan
2008-01-01
Carlet et al. recently introduced generalized nonlinearity to measure the ability to resist the improved correlation attack of a vector output Boolean function. This article presents a construction of vector output Boolean functions with high generalized nonlinearity using the sample space. The relation between the resilient order and generalized nonlinearity is also discussed.
Towards Truly Boolean Arrays in Data-Parallel Array Processing
C. Grelck; H. Luyat
2013-01-01
We investigate several dense bit-wise implementations of Boolean arrays in the context of the functional data-parallel array programming language SAC. A particular problem arises in compiler or directive based parallelisation as the scheduling of loops over Boolean arrays is unaware of the restricte
Reasoning formalism in Boolean operator fuzzy logic
Institute of Scientific and Technical Information of China (English)
邓安生; 刘叙华
1995-01-01
Based on the newly introduced concepts of true-level and false-level, the formal structure of reasoning in Boolean operator fuzzy logic is presented. As a generalization of the theory of epistemic process in open logic, a formalism is also proposed to describe human reasoning with uncertain, inconsistent and insufficient knowledge, which can characterize the knowledge increment and revision, as well as the epistemic evolution. The formalism provides an explanation to the dynamic properties of human reasoning, i. e. continuous revision and combination of beliefs.
The Boolean algebra and central Galois algebras
Directory of Open Access Journals (Sweden)
George Szeto
2001-01-01
Full Text Available Let B be a Galois algebra with Galois group G, Jg={b∈B∣bx=g(xb for all x∈B} for g∈G, and BJg=Beg for a central idempotent eg. Then a relation is given between the set of elements in the Boolean algebra (Ba,≤ generated by {0,eg∣g∈G} and a set of subgroups of G, and a central Galois algebra Be with a Galois subgroup of G is characterized for an e∈Ba.
Towards boolean operations with thermal photons
Ben-Abdallah, Philippe
2016-01-01
The Boolean algebra is the natural theoretical framework for a classical information treatment. The basic logical operations are usually performed using logic gates. In this Letter we demonstrate that NOT, OR and AND gates can be realized exploiting the near-field radiative interaction in N-body systems with phase change materials. With the recent development of a photon thermal transistor and thermal memory, this result paves the way for a full information treatment and smart solutions for active thermal management at nanoscale with photons.
An approach to 3D NURBS modeling of complex fault network considering its historic tectonics
Institute of Scientific and Technical Information of China (English)
ZHONG Denghua; LIU Jie; LI Mingchao
2006-01-01
Fault disposal is a research area that presents difficulties in 3D geological modeling and visualization. In this paper, we propose an integrated approach to reconstructing a complex fault network (CFN). Based on the non-uniform rational B-spline (NURBS)techniques, fault surface was constructed, reflecting the regulation of its spatial tendency, and correlative surfaces were enclosed to form a fault body model. Based on these models and considering their historic tectonics, a method was put forward to settle the 3D modeling problem when the intersection of two faults in CFN induced the change of their relative positions. First, according to the relationships of intersection obtained from geological interpretation, we introduced the topological sort to determine the order of fault body construction and rebuilt fault bodies in terms of the order; then, with the disposal method of two intersectant faults in 3D modeling and applying the Boolean operation, we investigated the characteristic of faults at the intersectant part. An example of its application in hydropower engineering project was proposed. Its results show that this modeling approach can increase the computing efficiency while less computer memory is required, and it can also factually and objectively reproduce the CFN in the engineering region, which establishes a theoretical basis for 3D modeling and analysis of complex engineering geology.
How to model wireless mesh networks topology
International Nuclear Information System (INIS)
The specification of network connectivity model or topology is the beginning of design and analysis in Computer Network researches. Wireless Mesh Networks is an autonomic network that is dynamically self-organised, self-configured while the mesh nodes establish automatic connectivity with the adjacent nodes in the relay network of wireless backbone routers. Researches in Wireless Mesh Networks range from node deployment to internetworking issues with sensor, Internet and cellular networks. These researches require modelling of relationships and interactions among nodes including technical characteristics of the links while satisfying the architectural requirements of the physical network. However, the existing topology generators model geographic topologies which constitute different architectures, thus may not be suitable in Wireless Mesh Networks scenarios. The existing methods of topology generation are explored, analysed and parameters for their characterisation are identified. Furthermore, an algorithm for the design of Wireless Mesh Networks topology based on square grid model is proposed in this paper. The performance of the topology generated is also evaluated. This research is particularly important in the generation of a close-to-real topology for ensuring relevance of design to the intended network and validity of results obtained in Wireless Mesh Networks researches
Model Of Neural Network With Creative Dynamics
Zak, Michail; Barhen, Jacob
1993-01-01
Paper presents analysis of mathematical model of one-neuron/one-synapse neural network featuring coupled activation and learning dynamics and parametrical periodic excitation. Demonstrates self-programming, partly random behavior of suitable designed neural network; believed to be related to spontaneity and creativity of biological neural networks.
Magneto-electric network models in electromagnetism
Demenko, A.; Sykulski, J. K.
2006-01-01
Purpose – The aim of this paper is to develop network models of an electromagnetic field containing both eddy and displacement currents. The proposed network models provide good physical insight, help understanding of complicated electromagnetic phenomena and aid explanation of methods of analysis of electromagnetic systems. Design/methodology/approach – The models consist of magnetic and electric networks coupled via sources. The analogy between the finite element method and the loop and nod...
CIMS Network Protocol and Its Net Models
Institute of Scientific and Technical Information of China (English)
罗军舟; 顾冠群
1997-01-01
Computer communication network architectures for cims are based on the OSI Reference Model.In this paper,CIMS network protocol model is set up on the basis of the corresqonding service model.Then the authors present a formal specification of transport protocols by using an extended Predicate/Transition net system that is briefly introduced in the third part.Finally,the general methods for the Petri nets based formal specification of CIMS network protocols are outlined.
Agent-based modeling and network dynamics
Namatame, Akira
2016-01-01
The book integrates agent-based modeling and network science. It is divided into three parts, namely, foundations, primary dynamics on and of social networks, and applications. The book begins with the network origin of agent-based models, known as cellular automata, and introduce a number of classic models, such as Schelling’s segregation model and Axelrod’s spatial game. The essence of the foundation part is the network-based agent-based models in which agents follow network-based decision rules. Under the influence of the substantial progress in network science in late 1990s, these models have been extended from using lattices into using small-world networks, scale-free networks, etc. The book also shows that the modern network science mainly driven by game-theorists and sociophysicists has inspired agent-based social scientists to develop alternative formation algorithms, known as agent-based social networks. The book reviews a number of pioneering and representative models in this family. Upon the gi...
Nandi, Anjan K; Sumana, Annagiri; Bhattacharya, Kunal
2014-12-01
Social insects provide an excellent platform to investigate flow of information in regulatory systems since their successful social organization is essentially achieved by effective information transfer through complex connectivity patterns among the colony members. Network representation of such behavioural interactions offers a powerful tool for structural as well as dynamical analysis of the underlying regulatory systems. In this paper, we focus on the dominance interaction networks in the tropical social wasp Ropalidia marginata-a species where behavioural observations indicate that such interactions are principally responsible for the transfer of information between individuals about their colony needs, resulting in a regulation of their own activities. Our research reveals that the dominance networks of R. marginata are structurally similar to a class of naturally evolved information processing networks, a fact confirmed also by the predominance of a specific substructure-the 'feed-forward loop'-a key functional component in many other information transfer networks. The dynamical analysis through Boolean modelling confirms that the networks are sufficiently stable under small fluctuations and yet capable of more efficient information transfer compared to their randomized counterparts. Our results suggest the involvement of a common structural design principle in different biological regulatory systems and a possible similarity with respect to the effect of selection on the organization levels of such systems. The findings are also consistent with the hypothesis that dominance behaviour has been shaped by natural selection to co-opt the information transfer process in such social insect species, in addition to its primal function of mediation of reproductive competition in the colony. PMID:25320069
Edge exchangeable models for network data
Crane, Harry
2016-01-01
Exchangeable models for vertex labeled graphs cannot replicate the large sample behaviors of sparsity and power law degree distributions observed in many network datasets. Out of this mathematical impossibility emerges the question of how network data can be modeled in a way that reflects known empirical behaviors and respects basic statistical principles. We address this question by observing that edges, not vertices, act as the statistical units in most network datasets, making a theory of edge labeled networks more natural for most applications. Within this context we introduce the new invariance principle of {\\em edge exchangeability}, which unlike its vertex exchangeable counterpart can produce networks with sparse and/or power law structure. We characterize the class of all edge exchangeable network models and identify a particular two parameter family of models with suitable theoretical properties for statistical inference. We discuss issues of estimation from edge exchangeable models and compare our a...
Neutral Mutations and Punctuated Equilibrium in Evolving Genetic Networks
Bornholdt, Stefan; Sneppen, Kim
1997-01-01
Boolean networks may be viewed as idealizations of biological genetic networks, where each node is represented by an on-off switch which is a function of the binary output from some other nodes. We evolve connectivity in a single Boolean network, and demonstrate how the sole requirement of sequential matching of attractors may open for an evolution that exhibits punctuated equilibrium.
Modeling Network Evolution Using Graph Motifs
Conway, Drew
2011-01-01
Network structures are extremely important to the study of political science. Much of the data in its subfields are naturally represented as networks. This includes trade, diplomatic and conflict relationships. The social structure of several organization is also of interest to many researchers, such as the affiliations of legislators or the relationships among terrorist. A key aspect of studying social networks is understanding the evolutionary dynamics and the mechanism by which these structures grow and change over time. While current methods are well suited to describe static features of networks, they are less capable of specifying models of change and simulating network evolution. In the following paper I present a new method for modeling network growth and evolution. This method relies on graph motifs to generate simulated network data with particular structural characteristic. This technique departs notably from current methods both in form and function. Rather than a closed-form model, or stochastic ...
Modeling data throughput on communication networks
Energy Technology Data Exchange (ETDEWEB)
Eldridge, J.M.
1993-11-01
New challenges in high performance computing and communications are driving the need for fast, geographically distributed networks. Applications such as modeling physical phenomena, interactive visualization, large data set transfers, and distributed supercomputing require high performance networking [St89][Ra92][Ca92]. One measure of a communication network`s performance is the time it takes to complete a task -- such as transferring a data file or displaying a graphics image on a remote monitor. Throughput, defined as the ratio of the number of useful data bits transmitted per the time required to transmit those bits, is a useful gauge of how well a communication system meets this performance measure. This paper develops and describes an analytical model of throughput. The model is a tool network designers can use to predict network throughput. It also provides insight into those parts of the network that act as a performance bottleneck.
Queuing theory models for computer networks
Galant, David C.
1989-01-01
A set of simple queuing theory models which can model the average response of a network of computers to a given traffic load has been implemented using a spreadsheet. The impact of variations in traffic patterns and intensities, channel capacities, and message protocols can be assessed using them because of the lack of fine detail in the network traffic rates, traffic patterns, and the hardware used to implement the networks. A sample use of the models applied to a realistic problem is included in appendix A. Appendix B provides a glossary of terms used in this paper. This Ames Research Center computer communication network is an evolving network of local area networks (LANs) connected via gateways and high-speed backbone communication channels. Intelligent planning of expansion and improvement requires understanding the behavior of the individual LANs as well as the collection of networks as a whole.
Boolean Algebra. Geometry Module for Use in a Mathematics Laboratory Setting.
Brotherton, Sheila; And Others
This module is recommended as an honors unit to follow a unit on logic. There are four basic parts: (1) What is a Boolean Algebra; (2) Using Boolean Algebra to Prove Theorems; (3) Using Boolean Algebra to Simplify Logical Statements; and (4) Circuit Problems with Logic and Boolean Algebra. Of these, sections 1, 2, and 3 are primarily written…
A language for real time simulation of processes with boolean inputs and outputs
Fernández Camacho, Eduardo; García Franquelo, Leopoldo; Lozano, J.
1983-01-01
This paper deals with the problem of real time simulation of processes with boolean inputs and outputs. A language for this purpose and the programs that processes it is presented. The language allows the description of processes with simultaneous evolutions as a timed petri net type of description is used. Random failures can also be Introduced in the behaviour of the model. The language allows the control of a semlgraphic CRT in order to facilitate the task of following the model behaviour.
Network class superposition analyses.
Pearson, Carl A B; Zeng, Chen; Simha, Rahul
2013-01-01
Networks are often used to understand a whole system by modeling the interactions among its pieces. Examples include biomolecules in a cell interacting to provide some primary function, or species in an environment forming a stable community. However, these interactions are often unknown; instead, the pieces' dynamic states are known, and network structure must be inferred. Because observed function may be explained by many different networks (e.g., ≈ 10(30) for the yeast cell cycle process), considering dynamics beyond this primary function means picking a single network or suitable sample: measuring over all networks exhibiting the primary function is computationally infeasible. We circumvent that obstacle by calculating the network class ensemble. We represent the ensemble by a stochastic matrix T, which is a transition-by-transition superposition of the system dynamics for each member of the class. We present concrete results for T derived from boolean time series dynamics on networks obeying the Strong Inhibition rule, by applying T to several traditional questions about network dynamics. We show that the distribution of the number of point attractors can be accurately estimated with T. We show how to generate Derrida plots based on T. We show that T-based Shannon entropy outperforms other methods at selecting experiments to further narrow the network structure. We also outline an experimental test of predictions based on T. We motivate all of these results in terms of a popular molecular biology boolean network model for the yeast cell cycle, but the methods and analyses we introduce are general. We conclude with open questions for T, for example, application to other models, computational considerations when scaling up to larger systems, and other potential analyses. PMID:23565141
A Conceptual Model of Learning Networks
Koper, Rob
In the TENCompetence project a set of UML models (Booch et al. 1999) have been developed to specify the core concepts for Learning Networks Services that support professional competence development. The three most important, high-level models are (a) the use case model, (b) the conceptual model, and (c) the domain model. The first model identifies the primary use cases we need in order to support professional competence development. The second model describes the concept of competence and competence development from a theoretical point of view. What is a competence? How does it relate to the cognitive system of an actor? How are competences developed? The third model is a UML Domain Model that defines, among other things, the components of a Learning Network, defines the concepts and relationships between the concepts in a Learning Network and provides a starting point for the design of the overall architecture for Learning Network Services, including the data model.
Random graph models for dynamic networks
Zhang, Xiao; Newman, M E J
2016-01-01
We propose generalizations of a number of standard network models, including the classic random graph, the configuration model, and the stochastic block model, to the case of time-varying networks. We assume that the presence and absence of edges are governed by continuous-time Markov processes with rate parameters that can depend on properties of the nodes. In addition to computing equilibrium properties of these models, we demonstrate their use in data analysis and statistical inference, giving efficient algorithms for fitting them to observed network data. This allows us, for instance, to estimate the time constants of network evolution or infer community structure from temporal network data using cues embedded both in the probabilities over time that node pairs are connected by edges and in the characteristic dynamics of edge appearance and disappearance. We illustrate our methods with a selection of applications, both to computer-generated test networks and real-world examples.
Exponential-family Random Network Models
Fellows, I; Handcock, MS
2012-01-01
Random graphs, where the connections between nodes are considered random variables, have wide applicability in the social sciences. Exponential-family Random Graph Models (ERGM) have shown themselves to be a useful class of models for representing com- plex social phenomena. We generalize ERGM by also modeling nodal attributes as random variates, thus creating a random model of the full network, which we call Exponential-family Random Network Models (ERNM). We demonstrate how this framework a...
Modelling the structure of complex networks
DEFF Research Database (Denmark)
Herlau, Tue
networks has been independently studied as mathematical objects in their own right. As such, there has been both an increased demand for statistical methods for complex networks as well as a quickly growing mathematical literature on the subject. In this dissertation we explore aspects of modelling complex....... The next chapters will treat some of the various symmetries, representer theorems and probabilistic structures often deployed in the modelling complex networks, the construction of sampling methods and various network models. The introductory chapters will serve to provide context for the included written...
Quantifying and Analyzing the Network Basis of Genetic Complexity
Thompson, Ethan G.; Galitski, Timothy
2012-01-01
Genotype-to-phenotype maps exhibit complexity. This genetic complexity is mentioned frequently in the literature, but a consistent and quantitative definition is lacking. Here, we derive such a definition and investigate its consequences for model genetic systems. The definition equates genetic complexity with a surplus of genotypic diversity over phenotypic diversity. Applying this definition to ensembles of Boolean network models, we found that the in-degree distribution and the number of p...
Monochromaticity in Neutral Evolutionary Network Models
Halu, Arda; Bianconi, Ginestra
2012-01-01
Recent studies on epistatic networks of model organisms have unveiled a certain type of modular property called monochromaticity in which the networks are clusterable into functional modules that interact with each other through the same type of epistasis. Here we propose and study three epistatic network models that are inspired by the Duplication-Divergence mechanism to gain insight into the evolutionary basis of monochromaticity and to test if it can be explained as the outcome of a neutra...
Nonconsensus opinion model on directed networks
Qu, B.; Li, Q.; Havlin, S.; Stanley, E.; Wang, H.
2014-01-01
Dynamic social opinion models have been widely studied on undirected networks, and most of them are based on spin interaction models that produce a consensus. In reality, however, many networks such as Twitter and the World Wide Web are directed and are composed of both unidirectional and bidirectio
Equivalence Checking of Combinational Circuits using Boolean Expression Diagrams
DEFF Research Database (Denmark)
Hulgaard, Henrik; Williams, Poul Frederick; Andersen, Henrik Reif
1999-01-01
The combinational logic-level equivalence problem is to determine whether two given combinational circuits implement the same Boolean function. This problem arises in a number of CAD applications, for example when checking the correctness of incremental design changes (performed either manually...... or by a design automation tool).This paper introduces a data structure called Boolean Expression Diagrams (BEDs) and two algorithms for transforming a BED into a Reduced Ordered Binary Decision Diagram (OBDD). BEDs are capable of representing any Boolean circuit in linear space and can exploit structural...
Performance modeling of network data services
Energy Technology Data Exchange (ETDEWEB)
Haynes, R.A.; Pierson, L.G.
1997-01-01
Networks at major computational organizations are becoming increasingly complex. The introduction of large massively parallel computers and supercomputers with gigabyte memories are requiring greater and greater bandwidth for network data transfers to widely dispersed clients. For networks to provide adequate data transfer services to high performance computers and remote users connected to them, the networking components must be optimized from a combination of internal and external performance criteria. This paper describes research done at Sandia National Laboratories to model network data services and to visualize the flow of data from source to sink when using the data services.
Unified Hybrid Network Theoretical Model Trilogy
Institute of Scientific and Technical Information of China (English)
无
2011-01-01
The first of the unified hybrid network theoretical model trilogy (UHNTF) is the harmonious unification hybrid preferential model (HUHPM), seen in the inner loop of Fig. 1, the unified hybrid ratio is defined.
Network models in economics and finance
Pardalos, Panos; Rassias, Themistocles
2014-01-01
Using network models to investigate the interconnectivity in modern economic systems allows researchers to better understand and explain some economic phenomena. This volume presents contributions by known experts and active researchers in economic and financial network modeling. Readers are provided with an understanding of the latest advances in network analysis as applied to economics, finance, corporate governance, and investments. Moreover, recent advances in market network analysis that focus on influential techniques for market graph analysis are also examined. Young researchers will find this volume particularly useful in facilitating their introduction to this new and fascinating field. Professionals in economics, financial management, various technologies, and network analysis, will find the network models presented in this book beneficial in analyzing the interconnectivity in modern economic systems.
布尔代数的软商布尔代数%Soft quotient Boolean algebra of Boolean algebra
Institute of Scientific and Technical Information of China (English)
刘卫锋
2015-01-01
The concepts of soft congruence relation,soft quotient algebra and soft quotient Boolean algebra of Boolean algebra are defined,and it is proved that soft congruence relation and soft ideal of Boolean algebra can be determined by each other.Then soft quotient Boolean algebra of Boolean algebra is obtained from soft proper ideal of Boolean algebra. Finally,the nature of preserving soft congruence relation of soft homomorphism of Boolean algebras is proved.%定义了布尔代数的软合同关系、软商代数和软商布尔代数等概念，证明了布尔代数的软合同关系与软理想相互确定，进而由布尔代数的软真理想得到布尔代数的软商布尔代数。最后，证明了布尔代数的软同态具有保软合同性。
Strategic games on a hierarchical network model
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
Among complex network models, the hierarchical network model is the one most close to such real networks as world trade web, metabolic network, WWW, actor network, and so on. It has not only the property of power-law degree distribution, but growth based on growth and preferential attachment, showing the scale-free degree distribution property. In this paper, we study the evolution of cooperation on a hierarchical network model, adopting the prisoner's dilemma (PD) game and snowdrift game (SG) as metaphors of the interplay between connected nodes. BA model provides a unifying framework for the emergence of cooperation. But interestingly, we found that on hierarchical model, there is no sign of cooperation for PD game, while the frequency of cooperation decreases as the common benefit decreases for SG. By comparing the scaling clustering coefficient properties of the hierarchical network model with that of BA model, we found that the former amplifies the effect of hubs. Considering different performances of PD game and SG on complex network, we also found that common benefit leads to cooperation in the evolution. Thus our study may shed light on the emergence of cooperation in both natural and social environments.
Representing Boolean Functions by Decision Trees
Chikalov, Igor
2011-01-01
A Boolean or discrete function can be represented by a decision tree. A compact form of decision tree named binary decision diagram or branching program is widely known in logic design [2, 40]. This representation is equivalent to other forms, and in some cases it is more compact than values table or even the formula [44]. Representing a function in the form of decision tree allows applying graph algorithms for various transformations [10]. Decision trees and branching programs are used for effective hardware [15] and software [5] implementation of functions. For the implementation to be effective, the function representation should have minimal time and space complexity. The average depth of decision tree characterizes the expected computing time, and the number of nodes in branching program characterizes the number of functional elements required for implementation. Often these two criteria are incompatible, i.e. there is no solution that is optimal on both time and space complexity. © Springer-Verlag Berlin Heidelberg 2011.
Polynomial threshold functions and Boolean threshold circuits
DEFF Research Database (Denmark)
Hansen, Kristoffer Arnsfelt; Podolskii, Vladimir V.
2013-01-01
secondary interest. We show that PTFs on general Boolean domains are tightly connected to depth two threshold circuits. Our main results in regard to this connection are: PTFs of polynomial length and polynomial degree compute exactly the functions computed by THRMAJ circuits. An exponential length lower...... bound for PTFs that holds regardless of degree, thereby extending known lower bounds for THRMAJ circuits. We generalize two-party unbounded error communication complexity to the multi-party number-on-the-forehead setting, and show that communication lower bounds for 3-player protocols would yield size...... lower bounds for THRTHR circuits. We obtain several other results about PTFs. These include relationships between weight and degree of PTFs, and a degree lower bound for PTFs of constant length. We also consider a variant of PTFs over the max-plus algebra. We show that they are connected to PTFs over...
Towards reproducible descriptions of neuronal network models.
Directory of Open Access Journals (Sweden)
Eilen Nordlie
2009-08-01
Full Text Available Progress in science depends on the effective exchange of ideas among scientists. New ideas can be assessed and criticized in a meaningful manner only if they are formulated precisely. This applies to simulation studies as well as to experiments and theories. But after more than 50 years of neuronal network simulations, we still lack a clear and common understanding of the role of computational models in neuroscience as well as established practices for describing network models in publications. This hinders the critical evaluation of network models as well as their re-use. We analyze here 14 research papers proposing neuronal network models of different complexity and find widely varying approaches to model descriptions, with regard to both the means of description and the ordering and placement of material. We further observe great variation in the graphical representation of networks and the notation used in equations. Based on our observations, we propose a good model description practice, composed of guidelines for the organization of publications, a checklist for model descriptions, templates for tables presenting model structure, and guidelines for diagrams of networks. The main purpose of this good practice is to trigger a debate about the communication of neuronal network models in a manner comprehensible to humans, as opposed to machine-readable model description languages. We believe that the good model description practice proposed here, together with a number of other recent initiatives on data-, model-, and software-sharing, may lead to a deeper and more fruitful exchange of ideas among computational neuroscientists in years to come. We further hope that work on standardized ways of describing--and thinking about--complex neuronal networks will lead the scientific community to a clearer understanding of high-level concepts in network dynamics, and will thus lead to deeper insights into the function of the brain.
Closure Properties of Classes of Spatio-Temporal Objects for Boolean Set Operations
Haesevoets, Sofie; Kuijpers, Bart
2000-01-01
We study a model for spatio-temporal objects, introduced by Chomicki and Revesz, in which spatio-temporal data is specified by a spatial reference object together with a geometric transformation that determines the movement of the reference object in time. We give complete results concerning closure under Boolean set operators for the different classes of spatio-temporal objects introduced by these authors (In particular, we also answer a conjecture by Chomicki and Revesz negatively). Since o...
Characterization and Modeling of Network Traffic
DEFF Research Database (Denmark)
Shawky, Ahmed; Bergheim, Hans; Ragnarsson, Olafur;
2011-01-01
This paper attempts to characterize and model backbone network traffic, using a small number of statistics. In order to reduce cost and processing power associated with traffic analysis. The parameters affecting the behaviour of network traffic are investigated and the choice is that inter......-arrival time, IP addresses, port numbers and transport protocol are the only necessary parameters to model network traffic behaviour. In order to recreate this behaviour, a complex model is needed which is able to recreate traffic behaviour based on a set of statistics calculated from the parameters values....... The model investigates the traffic generation mechanisms, and grouping traffic into flows and applications....
Complex networks analysis in socioeconomic models
Varela, Luis M; Ausloos, Marcel; Carrete, Jesus
2014-01-01
This chapter aims at reviewing complex networks models and methods that were either developed for or applied to socioeconomic issues, and pertinent to the theme of New Economic Geography. After an introduction to the foundations of the field of complex networks, the present summary adds insights on the statistical mechanical approach, and on the most relevant computational aspects for the treatment of these systems. As the most frequently used model for interacting agent-based systems, a brief description of the statistical mechanics of the classical Ising model on regular lattices, together with recent extensions of the same model on small-world Watts-Strogatz and scale-free Albert-Barabasi complex networks is included. Other sections of the chapter are devoted to applications of complex networks to economics, finance, spreading of innovations, and regional trade and developments. The chapter also reviews results involving applications of complex networks to other relevant socioeconomic issues, including res...
A transition calculus for Boolean functions. [logic circuit analysis
Tucker, J. H.; Bennett, A. W.
1974-01-01
A transition calculus is presented for analyzing the effect of input changes on the output of logic circuits. The method is closely related to the Boolean difference, but it is more powerful. Both differentiation and integration are considered.
Construction and enumeration of Boolean functions with maximum algebraic immunity
Institute of Scientific and Technical Information of China (English)
ZHANG WenYing; WU ChuanKun; LIU XiangZhong
2009-01-01
Algebraic immunity is a new cryptographic criterion proposed against algebraic attacks. In order to resist algebraic attacks, Boolean functions used in many stream ciphers should possess high algebraic immunity. This paper presents two main results to find balanced Boolean functions with maximum algebraic immunity. Through swapping the values of two bits, and then generalizing the result to swap some pairs of bits of the symmetric Boolean function constructed by Dalai, a new class of Boolean functions with maximum algebraic immunity are constructed. Enumeration of such functions is also given. For a given function p(x) with deg(p(x)) < [n/2], we give a method to construct functions in the form p(x)+q(x) which achieve the maximum algebraic immunity, where every term with nonzero coefficient in the ANF of q(x) has degree no less than [n/2].
Boolean Burritos: How the Faculty Ate Up Keyword Searching.
York, Sherry
1999-01-01
Describes an activity that librarians can use to acquaint teachers with keyword searching and Boolean operators to more successfully use the library's online catalog. Uses food ingredients to represent various possible combinations. (LRW)
Approximate Counting for Complex-Weighted Boolean Constraint Satisfaction Problems
Yamakami, Tomoyuki
2010-01-01
Constraint satisfaction problems (or CSPs) have been extensively studied in AI, database theory, graph theory, etc. From an approximation viewpoint, it has been important to approximate the total number of assignments that satisfy all given Boolean constraints. There is a trichotomy theorem for such approximate counting for (non-weighted) Boolean CSPs; namely, all such counting problems are neatly classified into three categories under polynomial-time approximation-preserving reductions [Dyer, Goldberg, and Jerrum, 2010]. We extend this result to approximate counting for complex-weighted Boolean CSPs, provided that all arity-1 constraints are freely available to use. This makes a significant progress in the quest for the approximation classification of all counting Boolean CSPs in the most general form. To deal with complex weights, we employ proof techniques along the line of solving Holant problems [Valiant, 2002, 2008]. Our result also gives an approximation version of the dichotomy theorem of the complexi...
Automatic Ranked Output from Boolean Searches in SIRE
Noreault, Terry; And Others
1977-01-01
This study examined the effectiveness using an automatic algorithm to rank the results of Boolean searches of an inverted file design document retrieval system. Relevant documents were ranked significantly higher than nonrelevant documents on output lists. (Author/KP)
Implementing network constraints in the EMPS model
Energy Technology Data Exchange (ETDEWEB)
Helseth, Arild; Warland, Geir; Mo, Birger; Fosso, Olav B.
2010-02-15
This report concerns the coupling of detailed market and network models for long-term hydro-thermal scheduling. Currently, the EPF model (Samlast) is the only tool available for this task for actors in the Nordic market. A new prototype for solving the coupled market and network problem has been developed. The prototype is based on the EMPS model (Samkjoeringsmodellen). Results from the market model are distributed to a detailed network model, where a DC load flow detects if there are overloads on monitored lines or intersections. In case of overloads, network constraints are generated and added to the market problem. Theoretical and implementation details for the new prototype are elaborated in this report. The performance of the prototype is tested against the EPF model on a 20-area Nordic dataset. (Author)
Modelling and control of road traffic networks
Haut, Bertrand
2007-01-01
Road traffic networks offer a particularly challenging research subject to the control community. The traffic congestion around big cities is constantly increasing and is now becoming a major problem. However, the dynamics of a road network exhibit some complex behaviours such as nonlinearities, delays and saturation effects that prevent the use of some classical control algorithms. This thesis presents different models and control algorithms used for road traffic networks. The dynamics ar...
Delivery Time Reliability Model of Logistics Network
Liusan Wu; Qingmei Tan; Yuehui Zhang
2013-01-01
Natural disasters like earthquake and flood will surely destroy the existing traffic network, usually accompanied by delivery delay or even network collapse. A logistics-network-related delivery time reliability model defined by a shortest-time entropy is proposed as a means to estimate the actual delivery time reliability. The less the entropy is, the stronger the delivery time reliability remains, and vice versa. The shortest delivery time is computed separately based on two different assum...
Modelling of virtual production networks
Directory of Open Access Journals (Sweden)
2011-03-01
Full Text Available Nowadays many companies, especially small and medium-sized enterprises (SMEs, specialize in a limited field of production. It requires forming virtual production networks of cooperating enterprises to manufacture better, faster and cheaper. Apart from that, some production orders cannot be realized, because there is not a company of sufficient production potential. In this case the virtual production networks of cooperating companies can realize these production orders. These networks have larger production capacity and many different resources. Therefore it can realize many more production orders together than each of them separately. Such organization allows for executing high quality product. The maintenance costs of production capacity and used resources are not so high. In this paper a methodology of rapid prototyping of virtual production networks is proposed. It allows to execute production orders on time considered existing logistic constraints.
Introducing Synchronisation in Deterministic Network Models
DEFF Research Database (Denmark)
Schiøler, Henrik; Jessen, Jan Jakob; Nielsen, Jens Frederik D.;
2006-01-01
The paper addresses performance analysis for distributed real time systems through deterministic network modelling. Its main contribution is the introduction and analysis of models for synchronisation between tasks and/or network elements. Typical patterns of synchronisation are presented leading....... The suggested models are intended for incorporation into an existing analysis tool a.k.a. CyNC based on the MATLAB/SimuLink framework for graphical system analysis and design....
Integrating Boolean and Mathematical Solving: Foundations, Basic Algorithms and Requirements
Audemard, Gilles; Bertoli, Piergiorgio; Cimatti, Alessandro; Kornilowicz, Artur; Sebastiani, Roberto
2002-01-01
In the last years we have witnessed an impressive advance in the efficiency of boolean solving techniques, which has brought large previously intractable problems at the reach of state-of-the-art solvers. Unfortunately, simple boolean expressions are not expressive enough for representing many real-world problems, which require handling also integer or real values and operators. On the other hand, mathematical solvers, like computer-algebra systems or constraint solvers, cannot handle efficie...
Homophyly/Kinship Model: Naturally Evolving Networks
Li, Angsheng; Li, Jiankou; Pan, Yicheng; Yin, Xianchen; Yong, Xi
2015-10-01
It has been a challenge to understand the formation and roles of social groups or natural communities in the evolution of species, societies and real world networks. Here, we propose the hypothesis that homophyly/kinship is the intrinsic mechanism of natural communities, introduce the notion of the affinity exponent and propose the homophyly/kinship model of networks. We demonstrate that the networks of our model satisfy a number of topological, probabilistic and combinatorial properties and, in particular, that the robustness and stability of natural communities increase as the affinity exponent increases and that the reciprocity of the networks in our model decreases as the affinity exponent increases. We show that both homophyly/kinship and reciprocity are essential to the emergence of cooperation in evolutionary games and that the homophyly/kinship and reciprocity determined by the appropriate affinity exponent guarantee the emergence of cooperation in evolutionary games, verifying Darwin’s proposal that kinship and reciprocity are the means of individual fitness. We propose the new principle of structure entropy minimisation for detecting natural communities of networks and verify the functional module property and characteristic properties by a healthy tissue cell network, a citation network, some metabolic networks and a protein interaction network.
Dynamic regimes of random fuzzy logic networks
Wittmann, Dominik M.; Theis, Fabian J.
2011-01-01
Random multistate networks, generalizations of the Boolean Kauffman networks, are generic models for complex systems of interacting agents. Depending on their mean connectivity, these networks exhibit ordered as well as chaotic behavior with a critical boundary separating both regimes. Typically, the nodes of these networks are assigned single discrete states. Here, we describe nodes by fuzzy numbers, i.e. vectors of degree-of-membership (DOM) functions specifying the degree to which the nodes are in each of their discrete states. This allows our models to deal with imprecision and uncertainties. Compatible update rules are constructed by expressing the update rules of the multistate network in terms of Boolean operators and generalizing them to fuzzy logic (FL) operators. The standard choice for these generalizations is the Gödel FL, where AND and OR are replaced by the minimum and maximum of two DOMs, respectively. In mean-field approximations we are able to analytically describe the percolation and asymptotic distribution of DOMs in random Gödel FL networks. This allows us to characterize the different dynamic regimes of random multistate networks in terms of FL. In a low-dimensional example, we provide explicit computations and validate our mean-field results by showing that they agree well with network simulations.
Evaluating Network Models: A Likelihood Analysis
Wang, Wen-Qiang; Zhou, Tao
2011-01-01
Many models are put forward to mimic the evolution of real networked systems. A well-accepted way to judge the validity is to compare the modeling results with real networks subject to several structural features. Even for a specific real network, we cannot fairly evaluate the goodness of different models since there are too many structural features while there is no criterion to select and assign weights on them. Motivated by the studies on link prediction algorithms, we propose a unified method to evaluate the network models via the comparison of the likelihoods of the currently observed network driven by different models, with an assumption that the higher the likelihood is, the better the model is. We test our method on the real Internet at the Autonomous System (AS) level, and the results suggest that the Generalized Linear Preferential (GLP) model outperforms the Tel Aviv Network Generator (Tang), while both two models are better than the Barab\\'asi-Albert (BA) and Erd\\"os-R\\'enyi (ER) models. Our metho...
Modelling and designing electric energy networks
International Nuclear Information System (INIS)
The author gives an overview of his research works in the field of electric network modelling. After a brief overview of technological evolutions from the telegraph to the all-electric fly-by-wire aircraft, he reports and describes various works dealing with a simplified modelling of electric systems and with fractal simulation. Then, he outlines the challenges for the design of electric networks, proposes a design process, gives an overview of various design models, methods and tools, and reports an application in the design of electric networks for future jumbo jets
Stochastic discrete model of karstic networks
Jaquet, O.; Siegel, P.; Klubertanz, G.; Benabderrhamane, H.
Karst aquifers are characterised by an extreme spatial heterogeneity that strongly influences their hydraulic behaviour and the transport of pollutants. These aquifers are particularly vulnerable to contamination because of their highly permeable networks of conduits. A stochastic model is proposed for the simulation of the geometry of karstic networks at a regional scale. The model integrates the relevant physical processes governing the formation of karstic networks. The discrete simulation of karstic networks is performed with a modified lattice-gas cellular automaton for a representative description of the karstic aquifer geometry. Consequently, more reliable modelling results can be obtained for the management and the protection of karst aquifers. The stochastic model was applied jointly with groundwater modelling techniques to a regional karst aquifer in France for the purpose of resolving surface pollution issues.
Modeling GMPLS and Optical MPLS Networks
DEFF Research Database (Denmark)
Christiansen, Henrik Lehrmann; Wessing, Henrik
2003-01-01
. The MPLS concept is attractive because it can work as a unifying control structure. covering all technologies. This paper describes how a novel scheme for optical MPLS and circuit switched GMPLS based networks can incorporated in such multi-domain, MPLS-based scenarios and how it could be modeled. Network...
Simple models of human brain functional networks.
Vértes, Petra E; Alexander-Bloch, Aaron F; Gogtay, Nitin; Giedd, Jay N; Rapoport, Judith L; Bullmore, Edward T
2012-04-10
Human brain functional networks are embedded in anatomical space and have topological properties--small-worldness, modularity, fat-tailed degree distributions--that are comparable to many other complex networks. Although a sophisticated set of measures is available to describe the topology of brain networks, the selection pressures that drive their formation remain largely unknown. Here we consider generative models for the probability of a functional connection (an edge) between two cortical regions (nodes) separated by some Euclidean distance in anatomical space. In particular, we propose a model in which the embedded topology of brain networks emerges from two competing factors: a distance penalty based on the cost of maintaining long-range connections; and a topological term that favors links between regions sharing similar input. We show that, together, these two biologically plausible factors are sufficient to capture an impressive range of topological properties of functional brain networks. Model parameters estimated in one set of functional MRI (fMRI) data on normal volunteers provided a good fit to networks estimated in a second independent sample of fMRI data. Furthermore, slightly detuned model parameters also generated a reasonable simulation of the abnormal properties of brain functional networks in people with schizophrenia. We therefore anticipate that many aspects of brain network organization, in health and disease, may be parsimoniously explained by an economical clustering rule for the probability of functional connectivity between different brain areas.
Network Design Models for Container Shipping
DEFF Research Database (Denmark)
Reinhardt, Line Blander; Kallehauge, Brian; Nielsen, Anders Nørrelund;
This paper presents a study of the network design problem in container shipping. The paper combines the network design and fleet assignment problem into a mixed integer linear programming model minimizing the overall cost. The major contributions of this paper is that the time of a vessel route...
Queueing models for mobile ad hoc networks
Haan, de Roland
2009-01-01
This thesis presents models for the performance analysis of a recent communication paradigm: mobile ad hoc networking. The objective of mobile ad hoc networking is to provide wireless connectivity between stations in a highly dynamic environment. These dynamics are driven by the mobility of stations
Cyber threat model for tactical radio networks
Kurdziel, Michael T.
2014-05-01
The shift to a full information-centric paradigm in the battlefield has allowed ConOps to be developed that are only possible using modern network communications systems. Securing these Tactical Networks without impacting their capabilities has been a challenge. Tactical networks with fixed infrastructure have similar vulnerabilities to their commercial counterparts (although they need to be secure against adversaries with greater capabilities, resources and motivation). However, networks with mobile infrastructure components and Mobile Ad hoc Networks (MANets) have additional unique vulnerabilities that must be considered. It is useful to examine Tactical Network based ConOps and use them to construct a threat model and baseline cyber security requirements for Tactical Networks with fixed infrastructure, mobile infrastructure and/or ad hoc modes of operation. This paper will present an introduction to threat model assessment. A definition and detailed discussion of a Tactical Network threat model is also presented. Finally, the model is used to derive baseline requirements that can be used to design or evaluate a cyber security solution that can be scaled and adapted to the needs of specific deployments.
Model-based control of networked systems
Garcia, Eloy; Montestruque, Luis A
2014-01-01
This monograph introduces a class of networked control systems (NCS) called model-based networked control systems (MB-NCS) and presents various architectures and control strategies designed to improve the performance of NCS. The overall performance of NCS considers the appropriate use of network resources, particularly network bandwidth, in conjunction with the desired response of the system being controlled. The book begins with a detailed description of the basic MB-NCS architecture that provides stability conditions in terms of state feedback updates . It also covers typical problems in NCS such as network delays, network scheduling, and data quantization, as well as more general control problems such as output feedback control, nonlinear systems stabilization, and tracking control. Key features and topics include: Time-triggered and event-triggered feedback updates Stabilization of uncertain systems subject to time delays, quantization, and extended absence of feedback Optimal control analysis and ...
Modeling trust context in networks
Adali, Sibel
2013-01-01
We make complex decisions every day, requiring trust in many different entities for different reasons. These decisions are not made by combining many isolated trust evaluations. Many interlocking factors play a role, each dynamically impacting the others.? In this brief, 'trust context' is defined as the system level description of how the trust evaluation process unfolds.Networks today are part of almost all human activity, supporting and shaping it. Applications increasingly incorporate new interdependencies and new trust contexts. Social networks connect people and organizations throughout
Modeling Network Traffic in Wavelet Domain
Directory of Open Access Journals (Sweden)
Sheng Ma
2004-12-01
Full Text Available This work discovers that although network traffic has the complicated short- and long-range temporal dependence, the corresponding wavelet coefficients are no longer long-range dependent. Therefore, a "short-range" dependent process can be used to model network traffic in the wavelet domain. Both independent and Markov models are investigated. Theoretical analysis shows that the independent wavelet model is sufficiently accurate in terms of the buffer overflow probability for Fractional Gaussian Noise traffic. Any model, which captures additional correlations in the wavelet domain, only improves the performance marginally. The independent wavelet model is then used as a unified approach to model network traffic including VBR MPEG video and Ethernet data. The computational complexity is O(N for developing such wavelet models and generating synthesized traffic of length N, which is among the lowest attained.
Graphical Model Theory for Wireless Sensor Networks
Energy Technology Data Exchange (ETDEWEB)
Davis, William B.
2002-12-08
Information processing in sensor networks, with many small processors, demands a theory of computation that allows the minimization of processing effort, and the distribution of this effort throughout the network. Graphical model theory provides a probabilistic theory of computation that explicitly addresses complexity and decentralization for optimizing network computation. The junction tree algorithm, for decentralized inference on graphical probability models, can be instantiated in a variety of applications useful for wireless sensor networks, including: sensor validation and fusion; data compression and channel coding; expert systems, with decentralized data structures, and efficient local queries; pattern classification, and machine learning. Graphical models for these applications are sketched, and a model of dynamic sensor validation and fusion is presented in more depth, to illustrate the junction tree algorithm.
Monochromaticity in neutral evolutionary network models.
Halu, Arda; Bianconi, Ginestra
2012-12-01
Recent studies on epistatic networks of model organisms have unveiled a certain type of modular property called monochromaticity in which the networks are clustered into functional modules that interact with each other through the same type of epistasis. Here, we propose and study three epistatic network models that are inspired by the duplication-divergence mechanism to gain insight into the evolutionary basis of monochromaticity and to test if it can be explained as the outcome of a neutral evolutionary hypothesis. We show that the epistatic networks formed by these stochastic evolutionary models have monochromaticity conflict distributions that are centered close to zero and are statistically significantly different from their randomized counterparts. In particular, the last model we propose yields a strictly monochromatic solution. Our results agree with the monochromaticity findings in real organisms and point toward the possible role of a neutral mechanism in the evolution of this phenomenon. PMID:23367998
Graphical Model Theory for Wireless Sensor Networks
International Nuclear Information System (INIS)
Information processing in sensor networks, with many small processors, demands a theory of computation that allows the minimization of processing effort, and the distribution of this effort throughout the network. Graphical model theory provides a probabilistic theory of computation that explicitly addresses complexity and decentralization for optimizing network computation. The junction tree algorithm, for decentralized inference on graphical probability models, can be instantiated in a variety of applications useful for wireless sensor networks, including: sensor validation and fusion; data compression and channel coding; expert systems, with decentralized data structures, and efficient local queries; pattern classification, and machine learning. Graphical models for these applications are sketched, and a model of dynamic sensor validation and fusion is presented in more depth, to illustrate the junction tree algorithm
Modelling subtle growth of linguistic networks
Kulig, Andrzej; Kwapien, Jaroslaw; Oswiecimka, Pawel
2014-01-01
We investigate properties of evolving linguistic networks defined by the word-adjacency relation. Such networks belong to the category of networks with accelerated growth but their shortest path length appears to reveal the network size dependence of different functional form than the ones known so far. We thus compare the networks created from literary texts with their artificial substitutes based on different variants of the Dorogovtsev-Mendes model and observe that none of them is able to properly simulate the novel asymptotics of the shortest path length. Then, we identify grammar induced local chain-like linear growth as a missing element in this model and extend it by incorporating such effects. It is in this way that a satisfactory agreement with the empirical result is obtained.
Survey of propagation Model in wireless Network
Hemant Kumar Sharma; Sanjeev Sharma; Krishna Kumar Pandey
2011-01-01
To implementation of mobile ad hoc network wave propagation models are necessary to determine propagation characteristic through a medium. Wireless mobile ad hoc networks are self creating and self organizing entity. Propagation study provides an estimation of signal characteristics. Accurate prediction of radio propagation behaviour for MANET is becoming a difficult task. This paper presents investigation of propagation model. Radio wave propagation mechanisms are absorption, reflection, ref...
IP Network Management Model Based on NGOSS
Institute of Scientific and Technical Information of China (English)
ZHANG Jin-yu; LI Hong-hui; LIU Feng
2004-01-01
This paper addresses a management model for IP network based on Next Generation Operation Support System (NGOSS). It makes the network management on the base of all the operation actions of ISP, It provides QoS to user service through the whole path by providing end-to-end Service Level Agreements (SLA) management through whole path. Based on web and coordination technology, this paper gives an implement architecture of this model.
Energy-oriented models for WDM networks
Ricciardi, Sergio; Careglio, Davide; Palmieri, Francesco; Fiore, Ugo; Santos Boada, Germán; Solé Pareta, Josep
2010-01-01
A realistic energy-oriented model is necessary to formally characterize the energy consumption and the consequent carbon footprint of actual and future high-capacity WDM networks. The energy model describes the energy consumption of the various network elements (NE) and predicts their energy consumption behavior under different traffic loads and for the diverse traffic types, including all optical and electronic traffic, O/E/O conversions, 3R regenerations, add/drop multiplexing, etc. Besi...
Directory of Open Access Journals (Sweden)
Yanguang Zhu
2011-06-01
Full Text Available A military decision maker is typically confronted by the task of determining optimal course of action under some constraints in complex uncertain situation. Thus, a new class of Combinational Constraint Optimization Problem (CCOP is formalized, that is utilized to solve this complex Operation Optimization Problem. The object function of CCOP is modeled by Influence net, and the constraints of CCOP relate to resource and collaboration. These constraints are expressed by Pseudo-Boolean and Boolean constraints. Thus CCOP holds a complex mathematical configuration, which is expressed as a 0 1 integer optimization problem with compositional constraints and unobvious optimal object function. A novel method of Genetic Algorithm (GA combination of Boolean Constraint Programming (BCP is proposed to solve CCOP. The constraints of CCOP can be easily reduced and transformed into Disjunctive Normal Form (DNF by BCP. The DNF representation then can be used to drive GA so as to solve CCOP. Finally, a numerical experiment is given to demonstrate the effectiveness of above method.
Unlimited multistability and Boolean logic in microbial signalling.
Kothamachu, Varun B; Feliu, Elisenda; Cardelli, Luca; Soyer, Orkun S
2015-07-01
The ability to map environmental signals onto distinct internal physiological states or programmes is critical for single-celled microbes. A crucial systems dynamics feature underpinning such ability is multistability. While unlimited multistability is known to arise from multi-site phosphorylation seen in the signalling networks of eukaryotic cells, a similarly universal mechanism has not been identified in microbial signalling systems. These systems are generally known as two-component systems comprising histidine kinase (HK) receptors and response regulator proteins engaging in phosphotransfer reactions. We develop a mathematical framework for analysing microbial systems with multi-domain HK receptors known as hybrid and unorthodox HKs. We show that these systems embed a simple core network that exhibits multistability, thereby unveiling a novel biochemical mechanism for multistability. We further prove that sharing of downstream components allows a system with n multi-domain hybrid HKs to attain 3n steady states. We find that such systems, when sensing distinct signals, can readily implement Boolean logic functions on these signals. Using two experimentally studied examples of two-component systems implementing hybrid HKs, we show that bistability and implementation of logic functions are possible under biologically feasible reaction rates. Furthermore, we show that all sequenced microbial genomes contain significant numbers of hybrid and unorthodox HKs, and some genomes have a larger fraction of these proteins compared with regular HKs. Microbial cells are thus theoretically unbounded in mapping distinct environmental signals onto distinct physiological states and perform complex computations on them. These findings facilitate the understanding of natural two-component systems and allow their engineering through synthetic biology.
Metanetworks of artificially evolved regulatory networks
Danacı, Burçin
2014-01-01
We study metanetworks arising in genotype and phenotype spaces, in the context of a model population of Boolean graphs evolved under selection for short dynamical attractors. We define the adjacency matrix of a graph as its genotype, which gets mutated in the course of evolution, while its phenotype is its set of dynamical attractors. Metanetworks in the genotype and phenotype spaces are formed, respectively, by genetic proximity and by phenotypic similarity, the latter weighted by the sizes of the basins of attraction of the shared attractors. We find that populations of evolved networks form giant clusters in genotype space, have Poissonian degree distributions but exhibit hierarchically organized $k$-core decompositions, while random populations of Boolean graphs are typically so far removed from each other genetically that they cannot form a metanetwork. In phenotype space, the metanetworks of evolved populations are super robust both under the elimination of weak connections and random removal of nodes. ...
Model-Based Clustering of Large Networks
Vu, Duy Quang; Schweinberger, Michael
2012-01-01
We describe a network clustering framework, based on finite mixture models, that can be applied to discrete-valued networks with hundreds of thousands of nodes and billions of edge variables. Relative to other recent model-based clustering work for networks, we introduce a more flexible modeling framework, improve the variational-approximation estimation algorithm, discuss and implement standard error estimation via a parametric bootstrap approach, and apply these methods to much larger datasets than those seen elsewhere in the literature. The more flexible modeling framework is achieved through introducing novel parameterizations of the model, giving varying degrees of parsimony, using exponential family models whose structure may be exploited in various theoretical and algorithmic ways. The algorithms, which we show how to adapt to the more complicated optimization requirements introduced by the constraints imposed by the novel parameterizations we propose, are based on variational generalized EM algorithms...
A survey of statistical network models
Goldenberg, Anna; Fienberg, Stephen E; Airoldi, Edoardo M
2009-01-01
Networks are ubiquitous in science and have become a focal point for discussion in everyday life. Formal statistical models for the analysis of network data have emerged as a major topic of interest in diverse areas of study, and most of these involve a form of graphical representation. Probability models on graphs date back to 1959. Along with empirical studies in social psychology and sociology from the 1960s, these early works generated an active network community and a substantial literature in the 1970s. This effort moved into the statistical literature in the late 1970s and 1980s, and the past decade has seen a burgeoning network literature in statistical physics and computer science. The growth of the World Wide Web and the emergence of online networking communities such as Facebook, MySpace, and LinkedIn, and a host of more specialized professional network communities has intensified interest in the study of networks and network data. Our goal in this review is to provide the reader with an entry poin...
Efficient Analog Circuits for Boolean Satisfiability
Yin, Xunzhao; Varga, Melinda; Ercsey-Ravasz, Maria; Toroczkai, Zoltan; Hu, Xiaobo Sharon
2016-01-01
Efficient solutions to NP-complete problems would significantly benefit both science and industry. However, such problems are intractable on digital computers based on the von Neumann architecture, thus creating the need for alternative solutions to tackle such problems. Recently, a deterministic, continuous-time dynamical system (CTDS) was proposed (Nature Physics, 7(12), 966 (2011)) to solve a representative NP-complete problem, Boolean Satisfiability (SAT). This solver shows polynomial analog time-complexity on even the hardest benchmark $k$-SAT ($k \\geq 3$) formulas, but at an energy cost through exponentially driven auxiliary variables. With some modifications to the CTDS equations, here we present a novel analog hardware SAT solver, AC-SAT, implementing the CTDS. AC-SAT is intended to be used as a co-processor, and with its modular design can be readily extended to different problem sizes. The circuit is designed and simulated based on a 32nm CMOS technology. SPICE simulation results show speedup factor...
Designing Network-based Business Model Ontology
DEFF Research Database (Denmark)
Hashemi Nekoo, Ali Reza; Ashourizadeh, Shayegheh; Zarei, Behrouz
2015-01-01
Survival on dynamic environment is not achieved without a map. Scanning and monitoring of the market show business models as a fruitful tool. But scholars believe that old-fashioned business models are dead; as they are not included the effect of internet and network in themselves. This paper...... such as shared-mental model and trust. However, it mostly covers previous business model elements. To confirm the applicability of this ontology, it has been implemented in business angel network and showed how it works....
Network Modeling and Simulation A Practical Perspective
Guizani, Mohsen; Khan, Bilal
2010-01-01
Network Modeling and Simulation is a practical guide to using modeling and simulation to solve real-life problems. The authors give a comprehensive exposition of the core concepts in modeling and simulation, and then systematically address the many practical considerations faced by developers in modeling complex large-scale systems. The authors provide examples from computer and telecommunication networks and use these to illustrate the process of mapping generic simulation concepts to domain-specific problems in different industries and disciplines. Key features: Provides the tools and strate
Modeling, Optimization & Control of Hydraulic Networks
DEFF Research Database (Denmark)
Tahavori, Maryamsadat
2014-01-01
. The nonlinear network model is derived based on the circuit theory. A suitable projection is used to reduce the state vector and to express the model in standard state-space form. Then, the controllability of nonlinear nonaffine hydraulic networks is studied. The Lie algebra-based controllability matrix is used...... to solve nonlinear optimal control problems. In the water supply system model, the hydraulic resistance of the valve is estimated by real data and it is considered to be a disturbance. The disturbance in our system is updated every 24 hours based on the amount of water usage by consumers every day. Model...
Performance modeling, stochastic networks, and statistical multiplexing
Mazumdar, Ravi R
2013-01-01
This monograph presents a concise mathematical approach for modeling and analyzing the performance of communication networks with the aim of introducing an appropriate mathematical framework for modeling and analysis as well as understanding the phenomenon of statistical multiplexing. The models, techniques, and results presented form the core of traffic engineering methods used to design, control and allocate resources in communication networks.The novelty of the monograph is the fresh approach and insights provided by a sample-path methodology for queueing models that highlights the importan
MODEL FOR NETWORKED BUSINESS: Case study of Application Service Provider's network
Pesonen, Tero
2011-01-01
MODEL FOR NETWORKED BUSINESS Case study of Application Service Provider's network The aim of the research was to create a network business model to optimise benefits for a business network in the area of software industry. The main research questions were: ? What kind of network business models can be found? ? What are the value creation mechanisms as well as advantages and disadvantages of different models? ? How to use former frameworks to develop a network business mode...
Modeling Emergence in Neuroprotective Regulatory Networks
Energy Technology Data Exchange (ETDEWEB)
Sanfilippo, Antonio P.; Haack, Jereme N.; McDermott, Jason E.; Stevens, S.L.; Stenzel-Poore, Mary
2013-01-05
The use of predictive modeling in the analysis of gene expression data can greatly accelerate the pace of scientific discovery in biomedical research by enabling in silico experimentation to test disease triggers and potential drug therapies. Techniques that focus on modeling emergence, such as agent-based modeling and multi-agent simulations, are of particular interest as they support the discovery of pathways that may have never been observed in the past. Thus far, these techniques have been primarily applied at the multi-cellular level, or have focused on signaling and metabolic networks. We present an approach where emergence modeling is extended to regulatory networks and demonstrate its application to the discovery of neuroprotective pathways. An initial evaluation of the approach indicates that emergence modeling provides novel insights for the analysis of regulatory networks that can advance the discovery of acute treatments for stroke and other diseases.
Optimal transportation networks models and theory
Bernot, Marc; Morel, Jean-Michel
2009-01-01
The transportation problem can be formalized as the problem of finding the optimal way to transport a given measure into another with the same mass. In contrast to the Monge-Kantorovitch problem, recent approaches model the branched structure of such supply networks as minima of an energy functional whose essential feature is to favour wide roads. Such a branched structure is observable in ground transportation networks, in draining and irrigation systems, in electrical power supply systems and in natural counterparts such as blood vessels or the branches of trees. These lectures provide mathematical proof of several existence, structure and regularity properties empirically observed in transportation networks. The link with previous discrete physical models of irrigation and erosion models in geomorphology and with discrete telecommunication and transportation models is discussed. It will be mathematically proven that the majority fit in the simple model sketched in this volume.
Modeling of urban traffic networks with lattice Boltzmann model
Meng, Jian-ping; Qian, Yue-hong; Dai, Shi-qiang
2008-02-01
It is of great importance to uncover the characteristics of traffic networks. However, there have been few researches concerning kinetics models for urban traffic networks. In this work, a lattice Boltzmann model (LBM) for urban traffic networks is proposed by incorporating the ideas of the Biham-Middleton-Levine (BML) model into the LBM for road traffic. In the present model, situations at intersections with the red and green traffic signals are treated as a kind of boundary conditions varying with time. Thus, the urban traffic network could be described in the mesoscopic level. By performing numerical simulations under the periodic boundary conditions, the behavior of average velocity is investigated in detail. The numerical results agree quite well with those given by the Chowdhury-Schadschneider (ChSch) model (Chowdhury D. and Schadschneider A., Phys. Rev. E, 59 (1999) R1311). Furthermore, the statistical noise is reduced in this discrete kinetics model, thus, the present model has considerably high computational efficiency.
Enhanced Gravity Model of trade: reconciling macroeconomic and network models
Almog, Assaf; Garlaschelli, Diego
2015-01-01
The bilateral trade relations between world countries form a complex network, the International Trade Network (ITN), which is involved in an increasing number of worldwide economic processes, including globalization, integration, industrial production, and the propagation of shocks and instabilities. Characterizing the ITN via a simple yet accurate model is an open problem. The classical Gravity Model of trade successfully reproduces the volume of trade between two connected countries using known macroeconomic properties such as GDP and geographic distance. However, it generates a network with an unrealistically homogeneous topology, thus failing to reproduce the highly heterogeneous structure of the real ITN. On the other hand, network models successfully reproduce the complex topology of the ITN, but provide no information about trade volumes. Therefore macroeconomic and network models of trade suffer from complementary limitations but are still largely incompatible. Here, we make an important step forward ...
Efficient Algorithms for Membership in Boolean Hierarchies of Regular Languages
Glasser, Christian; Selivanov, Victor
2008-01-01
The purpose of this paper is to provide efficient algorithms that decide membership for classes of several Boolean hierarchies for which efficiency (or even decidability) were previously not known. We develop new forbidden-chain characterizations for the single levels of these hierarchies and obtain the following results: - The classes of the Boolean hierarchy over level $\\Sigma_1$ of the dot-depth hierarchy are decidable in $NL$ (previously only the decidability was known). The same remains true if predicates mod $d$ for fixed $d$ are allowed. - If modular predicates for arbitrary $d$ are allowed, then the classes of the Boolean hierarchy over level $\\Sigma_1$ are decidable. - For the restricted case of a two-letter alphabet, the classes of the Boolean hierarchy over level $\\Sigma_2$ of the Straubing-Th\\'erien hierarchy are decidable in $NL$. This is the first decidability result for this hierarchy. - The membership problems for all mentioned Boolean-hierarchy classes are logspace many-one hard for $NL$. - T...
Second moment method for a family of boolean CSP
Boufkhad, Yacine
2011-01-01
The estimation of phase transitions in random boolean Constraint Satisfaction Problems (CSP) is based on two fundamental tools: the first and second moment methods. While the first moment method on the number of solutions permits to compute upper bounds on any boolean CSP, the second moment method used for computing lower bounds proves to be more tricky and in most cases gives only the trivial lower bound 0. In this paper, we define a subclass of boolean CSP covering the monotone versions of many known NP-Complete boolean CSPs. We give a method for computing non trivial lower bounds for any member of this subclass. This is achieved thanks to an application of the second moment method to some selected solutions called characteristic solutions that depend on the boolean CSP considered. We apply, as an example, this method to establish that the threshold r_{k} of monotone 1-in-k-SAT is \\log k/k\\leq r_{k}\\leq\\log^{2}k/k
An evolving network model with modular growth
Institute of Scientific and Technical Information of China (English)
Zou Zhi-Yun; Liu Peng; Lei Li; Gao Jian-Zhi
2012-01-01
In this paper,we propose an evolving network model growing fast in units of module,according to the analysis of the evolution characteristics in real complex networks.Each module is a small-world network containing several interconnected nodes and the nodes between the modules are linked by preferential attachment on degree of nodes.We study the modularity measure of the proposed model,which can be adjusted by changing the ratio of the number of innermodule edges and the number of inter-module edges.In view of the mean-field theory,we develop an analytical function of the degree distribution,which is verified by a numerical example and indicates that the degree distribution shows characteristics of the small-world network and the scale-free network distinctly at different segments.The clustering coefficient and the average path length of the network are simulated numerically,indicating that the network shows the small-world property and is affected little by the randomness of the new module.
Modelling complex networks by random hierarchical graphs
Directory of Open Access Journals (Sweden)
M.Wróbel
2008-06-01
Full Text Available Numerous complex networks contain special patterns, called network motifs. These are specific subgraphs, which occur oftener than in randomized networks of Erdős-Rényi type. We choose one of them, the triangle, and build a family of random hierarchical graphs, being Sierpiński gasket-based graphs with random "decorations". We calculate the important characteristics of these graphs - average degree, average shortest path length, small-world graph family characteristics. They depend on probability of decorations. We analyze the Ising model on our graphs and describe its critical properties using a renormalization-group technique.
A Network Model of Credit Risk Contagion
Directory of Open Access Journals (Sweden)
Ting-Qiang Chen
2012-01-01
Full Text Available A network model of credit risk contagion is presented, in which the effect of behaviors of credit risk holders and the financial market regulators and the network structure are considered. By introducing the stochastic dominance theory, we discussed, respectively, the effect mechanisms of the degree of individual relationship, individual attitude to credit risk contagion, the individual ability to resist credit risk contagion, the monitoring strength of the financial market regulators, and the network structure on credit risk contagion. Then some derived and proofed propositions were verified through numerical simulations.
Grid architecture model of network centric warfare
Institute of Scientific and Technical Information of China (English)
Yan Tihua; Wang Baoshu
2006-01-01
NCW(network centric warfare) is an information warfare concentrating on network. A global network-centric warfare architecture with OGSA grid technology is put forward, which is a four levels system including the user level, the application level, the grid middleware layer and the resource level. In grid middleware layer, based on virtual hosting environment, a BEPL4WS grid service composition method is introduced. In addition, the NCW grid service model is built with the help of Eclipse-SDK-3.0.1 and Bpws4j.
Spatial Models and Networks of Living Systems
DEFF Research Database (Denmark)
Juul, Jeppe Søgaard
variables of the system. However, this approach disregards any spatial structure of the system, which may potentially change the behaviour drastically. An alternative approach is to construct a cellular automaton with nearest neighbour interactions, or even to model the system as a complex network....... Such systems are known to be stabilized by spatial structure. Finally, I analyse data from a large mobile phone network and show that people who are topologically close in the network have similar communication patterns. This main part of the thesis is based on six different articles, which I have co...
Modeling Computations in a Semantic Network
Rodriguez, Marko A
2007-01-01
Semantic network research has seen a resurgence from its early history in the cognitive sciences with the inception of the Semantic Web initiative. The Semantic Web effort has brought forth an array of technologies that support the encoding, storage, and querying of the semantic network data structure at the world stage. Currently, the popular conception of the Semantic Web is that of a data modeling medium where real and conceptual entities are related in semantically meaningful ways. However, new models have emerged that explicitly encode procedural information within the semantic network substrate. With these new technologies, the Semantic Web has evolved from a data modeling medium to a computational medium. This article provides a classification of existing computational modeling efforts and the requirements of supporting technologies that will aid in the further growth of this burgeoning domain.
Dynamic Modeling of the Electric Transportation Network
Scir`e, A; Eguiluz, V M; Scir\\`{e}, Alessandro; Tuval, Id\\'an
2005-01-01
We introduce a model for the dynamic self-organization of the electric grid. The model is characterized by a conserved magnitude, energy, that can travel following the links of the network to satisfy nodes' load. The load fluctuates in time causing local overloads that drive the dynamic evolution of the network topology. Our model displays a transition from a fully connected network to a configuration with a non-trivial topology and where global failures are suppressed. The most efficient topology is characterized by an exponential degree distribution, in agreement with the topology of the real electric grid. The model intrinsically presents self-induced break-down events, which can be thought as representative of real black-outs.
Keystone Business Models for Network Security Processors
Arthur Low; Steven Muegge
2013-01-01
Network security processors are critical components of high-performance systems built for cybersecurity. Development of a network security processor requires multi-domain experience in semiconductors and complex software security applications, and multiple iterations of both software and hardware implementations. Limited by the business models in use today, such an arduous task can be undertaken only by large incumbent companies and government organizations. Neither the “fabless semiconductor...
Decomposed Implicit Models of Piecewise - Linear Networks
Directory of Open Access Journals (Sweden)
J. Brzobohaty
1992-05-01
Full Text Available The general matrix form of the implicit description of a piecewise-linear (PWL network and the symbolic block diagram of the corresponding circuit model are proposed. Their decomposed forms enable us to determine quite separately the existence of the individual breakpoints of the resultant PWL characteristic and their coordinates using independent network parameters. For the two-diode and three-diode cases all the attainable types of the PWL characteristic are introduced.
Stochastic modeling and analysis of telecoms networks
Decreusefond, Laurent
2012-01-01
This book addresses the stochastic modeling of telecommunication networks, introducing the main mathematical tools for that purpose, such as Markov processes, real and spatial point processes and stochastic recursions, and presenting a wide list of results on stability, performances and comparison of systems.The authors propose a comprehensive mathematical construction of the foundations of stochastic network theory: Markov chains, continuous time Markov chains are extensively studied using an original martingale-based approach. A complete presentation of stochastic recursions from an
Non-nequilibrium model on Apollonian networks
Lima, F W S; Araújo, Ascânio D
2012-01-01
We investigate the Majority-Vote Model with two states ($-1,+1$) and a noise $q$ on Apollonian networks. The main result found here is the presence of the phase transition as a function of the noise parameter $q$. We also studies de effect of redirecting a fraction $p$ of the links of the network. By means of Monte Carlo simulations, we obtained the exponent ratio $\\gamma/\
Evaluation of EOR Processes Using Network Models
DEFF Research Database (Denmark)
Larsen, Jens Kjell; Krogsbøll, Anette
1998-01-01
The report consists of the following parts: 1) Studies of wetting properties of model fluids and fluid mixtures aimed at an optimal selection of candidates for micromodel experiments. 2) Experimental studies of multiphase transport properties using physical models of porous networks (micromodels)...
Modelling cooperative agents in infrastructure networks
Ligtvoet, A.; Chappin, E.J.L.; Stikkelman, R.M.
2010-01-01
This paper describes the translation of concepts of cooperation into an agent-based model of an industrial network. It first addresses the concept of cooperation and how this could be captured as heuristical rules within agents. Then it describes tests using these heuristics in an abstract model of
Empirical generalization assessment of neural network models
DEFF Research Database (Denmark)
Larsen, Jan; Hansen, Lars Kai
1995-01-01
This paper addresses the assessment of generalization performance of neural network models by use of empirical techniques. We suggest to use the cross-validation scheme combined with a resampling technique to obtain an estimate of the generalization performance distribution of a specific model...
Nonconsensus opinion model on directed networks.
Qu, Bo; Li, Qian; Havlin, Shlomo; Stanley, H Eugene; Wang, Huijuan
2014-11-01
Dynamic social opinion models have been widely studied on undirected networks, and most of them are based on spin interaction models that produce a consensus. In reality, however, many networks such as Twitter and the World Wide Web are directed and are composed of both unidirectional and bidirectional links. Moreover, from choosing a coffee brand to deciding who to vote for in an election, two or more competing opinions often coexist. In response to this ubiquity of directed networks and the coexistence of two or more opinions in decision-making situations, we study a nonconsensus opinion model introduced by Shao et al. [Phys. Rev. Lett. 103, 018701 (2009)PRLTAO0031-900710.1103/PhysRevLett.103.018701] on directed networks. We define directionality ξ as the percentage of unidirectional links in a network, and we use the linear correlation coefficient ρ between the in-degree and out-degree of a node to quantify the relation between the in-degree and out-degree. We introduce two degree-preserving rewiring approaches which allow us to construct directed networks that can have a broad range of possible combinations of directionality ξ and linear correlation coefficient ρ and to study how ξ and ρ impact opinion competitions. We find that, as the directionality ξ or the in-degree and out-degree correlation ρ increases, the majority opinion becomes more dominant and the minority opinion's ability to survive is lowered. PMID:25493838
Nonconsensus opinion model on directed networks
Qu, Bo; Li, Qian; Havlin, Shlomo; Stanley, H. Eugene; Wang, Huijuan
2014-11-01
Dynamic social opinion models have been widely studied on undirected networks, and most of them are based on spin interaction models that produce a consensus. In reality, however, many networks such as Twitter and the World Wide Web are directed and are composed of both unidirectional and bidirectional links. Moreover, from choosing a coffee brand to deciding who to vote for in an election, two or more competing opinions often coexist. In response to this ubiquity of directed networks and the coexistence of two or more opinions in decision-making situations, we study a nonconsensus opinion model introduced by Shao et al. [Phys. Rev. Lett. 103, 018701 (2009), 10.1103/PhysRevLett.103.018701] on directed networks. We define directionality ξ as the percentage of unidirectional links in a network, and we use the linear correlation coefficient ρ between the in-degree and out-degree of a node to quantify the relation between the in-degree and out-degree. We introduce two degree-preserving rewiring approaches which allow us to construct directed networks that can have a broad range of possible combinations of directionality ξ and linear correlation coefficient ρ and to study how ξ and ρ impact opinion competitions. We find that, as the directionality ξ or the in-degree and out-degree correlation ρ increases, the majority opinion becomes more dominant and the minority opinion's ability to survive is lowered.
Dual modeling of political opinion networks
Wang, R.; A. Wang, Q.
2011-09-01
We present the result of a dual modeling of opinion networks. The model complements the agent-based opinion models by attaching to the social agent (voters) network a political opinion (party) network having its own intrinsic mechanisms of evolution. These two subnetworks form a global network, which can be either isolated from, or dependent on, the external influence. Basically, the evolution of the agent network includes link adding and deleting, with the opinion changes influenced by social validation, the political climate, the attractivity of the parties, and the interaction between them. The opinion network is initially composed of numerous nodes representing opinions or parties that are located on a one dimensional axis according to their political positions. The mechanism of evolution includes union, splitting, change of position, and attractivity, taking into account the pairwise node interaction decaying with node distance in power law. The global evolution ends in a stable distribution of the social agents over a quasistable and fluctuating stationary number of remaining parties. Empirical study on the lifetime distribution of numerous parties and vote results is carried out to verify numerical results.
Delay and Disruption Tolerant Networking MACHETE Model
Segui, John S.; Jennings, Esther H.; Gao, Jay L.
2011-01-01
To verify satisfaction of communication requirements imposed by unique missions, as early as 2000, the Communications Networking Group at the Jet Propulsion Laboratory (JPL) saw the need for an environment to support interplanetary communication protocol design, validation, and characterization. JPL's Multi-mission Advanced Communications Hybrid Environment for Test and Evaluation (MACHETE), described in Simulator of Space Communication Networks (NPO-41373) NASA Tech Briefs, Vol. 29, No. 8 (August 2005), p. 44, combines various commercial, non-commercial, and in-house custom tools for simulation and performance analysis of space networks. The MACHETE environment supports orbital analysis, link budget analysis, communications network simulations, and hardware-in-the-loop testing. As NASA is expanding its Space Communications and Navigation (SCaN) capabilities to support planned and future missions, building infrastructure to maintain services and developing enabling technologies, an important and broader role is seen for MACHETE in design-phase evaluation of future SCaN architectures. To support evaluation of the developing Delay Tolerant Networking (DTN) field and its applicability for space networks, JPL developed MACHETE models for DTN Bundle Protocol (BP) and Licklider/Long-haul Transmission Protocol (LTP). DTN is an Internet Research Task Force (IRTF) architecture providing communication in and/or through highly stressed networking environments such as space exploration and battlefield networks. Stressed networking environments include those with intermittent (predictable and unknown) connectivity, large and/or variable delays, and high bit error rates. To provide its services over existing domain specific protocols, the DTN protocols reside at the application layer of the TCP/IP stack, forming a store-and-forward overlay network. The key capabilities of the Bundle Protocol include custody-based reliability, the ability to cope with intermittent connectivity
Periodic pattern detection in sparse boolean sequences
Directory of Open Access Journals (Sweden)
Hérisson Joan
2010-09-01
Full Text Available Abstract Background The specific position of functionally related genes along the DNA has been shown to reflect the interplay between chromosome structure and genetic regulation. By investigating the statistical properties of the distances separating such genes, several studies have highlighted various periodic trends. In many cases, however, groups built up from co-functional or co-regulated genes are small and contain wrong information (data contamination so that the statistics is poorly exploitable. In addition, gene positions are not expected to satisfy a perfectly ordered pattern along the DNA. Within this scope, we present an algorithm that aims to highlight periodic patterns in sparse boolean sequences, i.e. sequences of the type 010011011010... where the ratio of the number of 1's (denoting here the transcription start of a gene to 0's is small. Results The algorithm is particularly robust with respect to strong signal distortions such as the addition of 1's at arbitrary positions (contaminated data, the deletion of existing 1's in the sequence (missing data and the presence of disorder in the position of the 1's (noise. This robustness property stems from an appropriate exploitation of the remarkable alignment properties of periodic points in solenoidal coordinates. Conclusions The efficiency of the algorithm is demonstrated in situations where standard Fourier-based spectral methods are poorly adapted. We also show how the proposed framework allows to identify the 1's that participate in the periodic trends, i.e. how the framework allows to allocate a positional score to genes, in the same spirit of the sequence score. The software is available for public use at http://www.issb.genopole.fr/MEGA/Softwares/iSSB_SolenoidalApplication.zip.
Constant-Overhead Secure Computation of Boolean Circuits using Preprocessing
DEFF Research Database (Denmark)
Damgård, Ivan Bjerre; Zakarias, Sarah Nouhad Haddad
We present a protocol for securely computing a Boolean circuit $C$ in presence of a dishonest and malicious majority. The protocol is unconditionally secure, assuming access to a preprocessing functionality that is not given the inputs to compute on. For a large number of players the work done...... by each player is the same as the work needed to compute the circuit in the clear, up to a constant factor. Our protocol is the first to obtain these properties for Boolean circuits. On the technical side, we develop new homomorphic authentication schemes based on asymptotically good codes...... with an additional multiplication property. We also show a new algorithm for verifying the product of Boolean matrices in quadratic time with exponentially small error probability, where previous methods would only give a constant error....
Boolean Logic: An Aid for Searching Computer Databases in Special Education and Rehabilitation.
Summers, Edward G.
1989-01-01
The article discusses using Boolean logic as a tool for searching computerized information retrieval systems in special education and rehabilitation technology. It includes discussion of the Boolean search operators AND, OR, and NOT; Venn diagrams; and disambiguating parentheses. Six suggestions are offered for development of good Boolean logic…
Cellular automata modelling of biomolecular networks dynamics.
Bonchev, D; Thomas, S; Apte, A; Kier, L B
2010-01-01
The modelling of biological systems dynamics is traditionally performed by ordinary differential equations (ODEs). When dealing with intracellular networks of genes, proteins and metabolites, however, this approach is hindered by network complexity and the lack of experimental kinetic parameters. This opened the field for other modelling techniques, such as cellular automata (CA) and agent-based modelling (ABM). This article reviews this emerging field of studies on network dynamics in molecular biology. The basics of the CA technique are discussed along with an extensive list of related software and websites. The application of CA to networks of biochemical reactions is exemplified in detail by the case studies of the mitogen-activated protein kinase (MAPK) signalling pathway, the FAS-ligand (FASL)-induced and Bcl-2-related apoptosis. The potential of the CA method to model basic pathways patterns, to identify ways to control pathway dynamics and to help in generating strategies to fight with cancer is demonstrated. The different line of CA applications presented includes the search for the best-performing network motifs, an analysis of importance for effective intracellular signalling and pathway cross-talk. PMID:20373215
An integrated network model of psychotic symptoms.
Looijestijn, Jasper; Blom, Jan Dirk; Aleman, André; Hoek, Hans W; Goekoop, Rutger
2015-12-01
The full body of research on the nature of psychosis and its determinants indicates that a considerable number of factors are relevant to the development of hallucinations, delusions, and other positive symptoms, ranging from neurodevelopmental parameters and altered connectivity of brain regions to impaired cognitive functioning and social factors. We aimed to integrate these factors in a single mathematical model based on network theory. At the microscopic level this model explains positive symptoms of psychosis in terms of experiential equivalents of robust, high-frequency attractor states of neural networks. At the mesoscopic level it explains them in relation to global brain states, and at the macroscopic level in relation to social-network structures and dynamics. Due to the scale-free nature of biological networks, all three levels are governed by the same general laws, thereby allowing for an integrated model of biological, psychological, and social phenomena involved in the mediation of positive symptoms of psychosis. This integrated network model of psychotic symptoms (INMOPS) is described together with various possibilities for application in clinical practice. PMID:26432501
An integrated network model of psychotic symptoms.
Looijestijn, Jasper; Blom, Jan Dirk; Aleman, André; Hoek, Hans W; Goekoop, Rutger
2015-12-01
The full body of research on the nature of psychosis and its determinants indicates that a considerable number of factors are relevant to the development of hallucinations, delusions, and other positive symptoms, ranging from neurodevelopmental parameters and altered connectivity of brain regions to impaired cognitive functioning and social factors. We aimed to integrate these factors in a single mathematical model based on network theory. At the microscopic level this model explains positive symptoms of psychosis in terms of experiential equivalents of robust, high-frequency attractor states of neural networks. At the mesoscopic level it explains them in relation to global brain states, and at the macroscopic level in relation to social-network structures and dynamics. Due to the scale-free nature of biological networks, all three levels are governed by the same general laws, thereby allowing for an integrated model of biological, psychological, and social phenomena involved in the mediation of positive symptoms of psychosis. This integrated network model of psychotic symptoms (INMOPS) is described together with various possibilities for application in clinical practice.
Modelling Users` Trust in Online Social Networks
Directory of Open Access Journals (Sweden)
Iacob Cătoiu
2014-02-01
Full Text Available Previous studies (McKnight, Lankton and Tripp, 2011; Liao, Lui and Chen, 2011 have shown the crucial role of trust when choosing to disclose sensitive information online. This is the case of online social networks users, who must disclose a certain amount of personal data in order to gain access to these online services. Taking into account privacy calculus model and the risk/benefit ratio, we propose a model of users’ trust in online social networks with four variables. We have adapted metrics for the purpose of our study and we have assessed their reliability and validity. We use a Partial Least Squares (PLS based structural equation modelling analysis, which validated all our initial assumptions, indicating that our three predictors (privacy concerns, perceived benefits and perceived risks explain 48% of the variation of users’ trust in online social networks, the resulting variable of our study. We also discuss the implications and further research opportunities of our study.
The Kuramoto model in complex networks
Rodrigues, Francisco A; Ji, Peng; Kurths, Jürgen
2016-01-01
Synchronization of an ensemble of oscillators is an emergent phenomenon present in several complex systems, ranging from social and physical to biological and technological systems. The most successful approach to describe how coherent behavior emerges in these complex systems is given by the paradigmatic Kuramoto model. This model has been traditionally studied in complete graphs. However, besides being intrinsically dynamical, complex systems present very heterogeneous structure, which can be represented as complex networks. This report is dedicated to review main contributions in the field of synchronization in networks of Kuramoto oscillators. In particular, we provide an overview of the impact of network patterns on the local and global dynamics of coupled phase oscillators. We cover many relevant topics, which encompass a description of the most used analytical approaches and the analysis of several numerical results. Furthermore, we discuss recent developments on variations of the Kuramoto model in net...
Features and heterogeneities in growing network models
Ferretti, Luca; Cortelezzi, Michele; Yang, Bin; Marmorini, Giacomo; Bianconi, Ginestra
2012-06-01
Many complex networks from the World Wide Web to biological networks grow taking into account the heterogeneous features of the nodes. The feature of a node might be a discrete quantity such as a classification of a URL document such as personal page, thematic website, news, blog, search engine, social network, etc., or the classification of a gene in a functional module. Moreover the feature of a node can be a continuous variable such as the position of a node in the embedding space. In order to account for these properties, in this paper we provide a generalization of growing network models with preferential attachment that includes the effect of heterogeneous features of the nodes. The main effect of heterogeneity is the emergence of an “effective fitness” for each class of nodes, determining the rate at which nodes acquire new links. The degree distribution exhibits a multiscaling behavior analogous to the the fitness model. This property is robust with respect to variations in the model, as long as links are assigned through effective preferential attachment. Beyond the degree distribution, in this paper we give a full characterization of the other relevant properties of the model. We evaluate the clustering coefficient and show that it disappears for large network size, a property shared with the Barabási-Albert model. Negative degree correlations are also present in this class of models, along with nontrivial mixing patterns among features. We therefore conclude that both small clustering coefficients and disassortative mixing are outcomes of the preferential attachment mechanism in general growing networks.
Extracting protein regulatory networks with graphical models.
Grzegorczyk, Marco
2007-09-01
During the last decade the development of high-throughput biotechnologies has resulted in the production of exponentially expanding quantities of biological data, such as genomic and proteomic expression data. One fundamental problem in systems biology is to learn the architecture of biochemical pathways and regulatory networks in an inferential way from such postgenomic data. Along with the increasing amount of available data, a lot of novel statistical methods have been developed and proposed in the literature. This article gives a non-mathematical overview of three widely used reverse engineering methods, namely relevance networks, graphical Gaussian models, and Bayesian networks, whereby the focus is on their relative merits and shortcomings. In addition the reverse engineering results of these graphical methods on cytometric protein data from the RAF-signalling network are cross-compared via AUROC scatter plots. PMID:17893851
String networks with junctions in competition models
Avelino, P P; Losano, L; Menezes, J; de Oliveira, B F
2016-01-01
In this work we give specific examples of competition models, with six and eight species, whose three-dimensional dynamics naturally leads to the formation of string networks with junctions, associated with regions that have a high concentration of enemy species. We study the two- and three-dimensional evolution of such networks, both using stochastic network and mean field theory simulations. If the predation, reproduction and mobility probabilities do not vary in space and time, we find that the networks attain scaling regimes with a characteristic length roughly proportional to $t^{1/2}$, where $t$ is the physical time, thus showing that the presence of junctions, on its own, does not have a significant impact on their scaling properties.
Enciso, Jennifer; Mayani, Hector; Mendoza, Luis; Pelayo, Rosana
2016-01-01
Lineage fate decisions of hematopoietic cells depend on intrinsic factors and extrinsic signals provided by the bone marrow microenvironment, where they reside. Abnormalities in composition and function of hematopoietic niches have been proposed as key contributors of acute lymphoblastic leukemia (ALL) progression. Our previous experimental findings strongly suggest that pro-inflammatory cues contribute to mesenchymal niche abnormalities that result in maintenance of ALL precursor cells at the expense of normal hematopoiesis. Here, we propose a molecular regulatory network interconnecting the major communication pathways between hematopoietic stem and progenitor cells (HSPCs) and mesenchymal stromal cells (MSCs) within the BM. Dynamical analysis of the network as a Boolean model reveals two stationary states that can be interpreted as the intercellular contact status. Furthermore, simulations describe the molecular patterns observed during experimental proliferation and activation. Importantly, our model predicts instability in the CXCR4/CXCL12 and VLA4/VCAM1 interactions following microenvironmental perturbation due by temporal signaling from Toll like receptors (TLRs) ligation. Therefore, aberrant expression of NF-κB induced by intrinsic or extrinsic factors may contribute to create a tumor microenvironment where a negative feedback loop inhibiting CXCR4/CXCL12 and VLA4/VCAM1 cellular communication axes allows for the maintenance of malignant cells. PMID:27594840
Enciso, Jennifer; Mayani, Hector; Mendoza, Luis; Pelayo, Rosana
2016-01-01
Lineage fate decisions of hematopoietic cells depend on intrinsic factors and extrinsic signals provided by the bone marrow microenvironment, where they reside. Abnormalities in composition and function of hematopoietic niches have been proposed as key contributors of acute lymphoblastic leukemia (ALL) progression. Our previous experimental findings strongly suggest that pro-inflammatory cues contribute to mesenchymal niche abnormalities that result in maintenance of ALL precursor cells at the expense of normal hematopoiesis. Here, we propose a molecular regulatory network interconnecting the major communication pathways between hematopoietic stem and progenitor cells (HSPCs) and mesenchymal stromal cells (MSCs) within the BM. Dynamical analysis of the network as a Boolean model reveals two stationary states that can be interpreted as the intercellular contact status. Furthermore, simulations describe the molecular patterns observed during experimental proliferation and activation. Importantly, our model predicts instability in the CXCR4/CXCL12 and VLA4/VCAM1 interactions following microenvironmental perturbation due by temporal signaling from Toll like receptors (TLRs) ligation. Therefore, aberrant expression of NF-κB induced by intrinsic or extrinsic factors may contribute to create a tumor microenvironment where a negative feedback loop inhibiting CXCR4/CXCL12 and VLA4/VCAM1 cellular communication axes allows for the maintenance of malignant cells.
Directory of Open Access Journals (Sweden)
Jennifer Enciso
2016-08-01
Full Text Available Lineage fate decisions of hematopoietic cells depend on intrinsic factors and extrinsic signals provided by the bone marrow microenvironment, where they reside. Abnormalities in composition and function of hematopoietic niches have been proposed as key contributors of acute lymphoblastic leukemia (ALL progression. Our previous experimental findings strongly suggest that pro-inflammatory cues contribute to mesenchymal niche abnormalities that result in maintenance of ALL precursor cells at the expense of normal hematopoiesis. Here, we propose a molecular regulatory network interconnecting the major communication pathways between hematopoietic stem and progenitor cells (HSPCs and mesenchymal stromal cells (MSCs within the bone marrow. Dynamical analysis of the network as a Boolean model reveals two stationary states that can be interpreted as the intercellular contact status. Furthermore, simulations describe the molecular patterns observed during experimental proliferation and activation. Importantly, our model predicts instability in the CXCR4/CXCL12 and VLA4/VCAM1 interactions following microenvironmental perturbation due by temporal signaling from Toll like receptors (TLRs ligation. Therefore, aberrant expression of NF-κB induced by intrinsic or extrinsic factors may contribute to create a tumor microenvironment where a negative feedback loop inhibiting CXCR4/CXCL12 and VLA4/VCAM1 cellular communication axes allows for the maintenance of malignant cells.
Unsupervised model compression for multilayer bootstrap networks
ZHANG, XIAO-LEI
2015-01-01
Recently, multilayer bootstrap network (MBN) has demonstrated promising performance in unsupervised dimensionality reduction. It can learn compact representations in standard data sets, i.e. MNIST and RCV1. However, as a bootstrap method, the prediction complexity of MBN is high. In this paper, we propose an unsupervised model compression framework for this general problem of unsupervised bootstrap methods. The framework compresses a large unsupervised bootstrap model into a small model by ta...
A Model for Telestrok Network Evaluation
DEFF Research Database (Denmark)
Storm, Anna; Günzel, Franziska; Theiss, Stephan
2011-01-01
was developed from the third-party payer perspective. In principle, it enables telestroke networks to conduct cost-effectiveness studies, because the majority of the required data can be extracted from health insurance companies’ databases and the telestroke network itself. The model presents a basis...... analysis lacking, current telestroke reimbursement by third-party payers is limited to special contracts and not included in the regular billing system. Based on a systematic literature review and expert interviews with health care economists, third-party payers and neurologists, a Markov model...
A quantum speedup in machine learning: finding an N-bit Boolean function for a classification
International Nuclear Information System (INIS)
We compare quantum and classical machines designed for learning an N-bit Boolean function in order to address how a quantum system improves the machine learning behavior. The machines of the two types consist of the same number of operations and control parameters, but only the quantum machines utilize the quantum coherence naturally induced by unitary operators. We show that quantum superposition enables quantum learning that is faster than classical learning by expanding the approximate solution regions, i.e., the acceptable regions. This is also demonstrated by means of numerical simulations with a standard feedback model, namely random search, and a practical model, namely differential evolution. (paper)
A quantum speedup in machine learning: finding an N-bit Boolean function for a classification
Yoo, Seokwon; Bang, Jeongho; Lee, Changhyoup; Lee, Jinhyoung
2014-10-01
We compare quantum and classical machines designed for learning an N-bit Boolean function in order to address how a quantum system improves the machine learning behavior. The machines of the two types consist of the same number of operations and control parameters, but only the quantum machines utilize the quantum coherence naturally induced by unitary operators. We show that quantum superposition enables quantum learning that is faster than classical learning by expanding the approximate solution regions, i.e., the acceptable regions. This is also demonstrated by means of numerical simulations with a standard feedback model, namely random search, and a practical model, namely differential evolution.
Experimental Comparison of Schemes for Interpreting Boolean Queries
Lee, Whay C.; Edward A Fox
1988-01-01
The standard interpretation of the logical operators in a Boolean retrieval system is in general too strict. A standard Boolean query rarely comes close to retrieving all and only those documents which are relevant to the user. An AND query is often too narrow and an OR query is often too broad. The choice of the AND results in retrieving on the left end of a typical average recall-precision graph, while the choice of the OR results in retrieving on the right end, implying a tradeoff between ...
Some Properties of Inclusions of Multisets and Contractive Boolean Operators
Hyvernat, Pierre
2011-01-01
10 pages, including appendix Consider the following curious puzzle: call an n-tuple X=(X_1, ..., X_n) of sets smaller than another n-tuple Y if it has fewer //unordered sections//. We show that equivalence classes for this preorder are very easy to describe and characterize the preorder in terms of the simpler pointwise inclusion and the existence of a special increasing boolean operator f:B^n -> B^n. We also show that contrary to increasing boolean operators, the relevant operators are no...
Mobility Models for Next Generation Wireless Networks Ad Hoc, Vehicular and Mesh Networks
Santi, Paolo
2012-01-01
Mobility Models for Next Generation Wireless Networks: Ad Hoc, Vehicular and Mesh Networks provides the reader with an overview of mobility modelling, encompassing both theoretical and practical aspects related to the challenging mobility modelling task. It also: Provides up-to-date coverage of mobility models for next generation wireless networksOffers an in-depth discussion of the most representative mobility models for major next generation wireless network application scenarios, including WLAN/mesh networks, vehicular networks, wireless sensor networks, and
Security Modeling on the Supply Chain Networks
Directory of Open Access Journals (Sweden)
Marn-Ling Shing
2007-10-01
Full Text Available In order to keep the price down, a purchaser sends out the request for quotation to a group of suppliers in a supply chain network. The purchaser will then choose a supplier with the best combination of price and quality. A potential supplier will try to collect the related information about other suppliers so he/she can offer the best bid to the purchaser. Therefore, confidentiality becomes an important consideration for the design of a supply chain network. Chen et al. have proposed the application of the Bell-LaPadula model in the design of a secured supply chain network. In the Bell-LaPadula model, a subject can be in one of different security clearances and an object can be in one of various security classifications. All the possible combinations of (Security Clearance, Classification pair in the Bell-LaPadula model can be thought as different states in the Markov Chain model. This paper extends the work done by Chen et al., provides more details on the Markov Chain model and illustrates how to use it to monitor the security state transition in the supply chain network.
An evolving model of online bipartite networks
Zhang, Chu-Xu; Zhang, Zi-Ke; Liu, Chuang
2013-12-01
Understanding the structure and evolution of online bipartite networks is a significant task since they play a crucial role in various e-commerce services nowadays. Recently, various attempts have been tried to propose different models, resulting in either power-law or exponential degree distributions. However, many empirical results show that the user degree distribution actually follows a shifted power-law distribution, the so-called Mandelbrot’s law, which cannot be fully described by previous models. In this paper, we propose an evolving model, considering two different user behaviors: random and preferential attachment. Extensive empirical results on two real bipartite networks, Delicious and CiteULike, show that the theoretical model can well characterize the structure of real networks for both user and object degree distributions. In addition, we introduce a structural parameter p, to demonstrate that the hybrid user behavior leads to the shifted power-law degree distribution, and the region of power-law tail will increase with the increment of p. The proposed model might shed some lights in understanding the underlying laws governing the structure of real online bipartite networks.
Distributed Bayesian Networks for User Modeling
DEFF Research Database (Denmark)
Tedesco, Roberto; Dolog, Peter; Nejdl, Wolfgang;
2006-01-01
The World Wide Web is a popular platform for providing eLearning applications to a wide spectrum of users. However – as users differ in their preferences, background, requirements, and goals – applications should provide personalization mechanisms. In the Web context, user models used...... by such adaptive applications are often partial fragments of an overall user model. The fragments have then to be collected and merged into a global user profile. In this paper we investigate and present algorithms able to cope with distributed, fragmented user models – based on Bayesian Networks – in the context...... of Web-based eLearning platforms. The scenario we are tackling assumes learners who use several systems over time, which are able to create partial Bayesian Networks for user models based on the local system context. In particular, we focus on how to merge these partial user models. Our merge mechanism...
Network Reconstruction with Realistic Models
Grzegorczyk, Marco; Aderhold, Andrej; Husmeier, Dirk
2015-01-01
We extend a recently proposed gradient-matching method for inferring interactions in complex systems described by differential equations in various respects: improved gradient inference, evaluation of the influence of the prior on kinetic parameters, comparative evaluation of two model selection paradigms: marginal likelihood versus DIC (divergence information criterion), comparative evaluation of different numerical procedures for computing the marginal likelihood, extension of the methodolo...
Delivery Time Reliability Model of Logistics Network
Directory of Open Access Journals (Sweden)
Liusan Wu
2013-01-01
Full Text Available Natural disasters like earthquake and flood will surely destroy the existing traffic network, usually accompanied by delivery delay or even network collapse. A logistics-network-related delivery time reliability model defined by a shortest-time entropy is proposed as a means to estimate the actual delivery time reliability. The less the entropy is, the stronger the delivery time reliability remains, and vice versa. The shortest delivery time is computed separately based on two different assumptions. If a path is concerned without capacity restriction, the shortest delivery time is positively related to the length of the shortest path, and if a path is concerned with capacity restriction, a minimax programming model is built to figure up the shortest delivery time. Finally, an example is utilized to confirm the validity and practicality of the proposed approach.
International Trade: a Reinforced Urn Network Model
Peluso, Stefano; Muliere, Pietro; Lomi, Alessandro
2016-01-01
We propose a unified modelling framework that theoretically justifies the main empirical regularities characterizing the international trade network. Each country is associated to a Polya urn whose composition controls the propensity of the country to trade with other countries. The urn composition is updated through the walk of the Reinforced Urn Process of Muliere et al. (2000). The model implies a local preferential attachment scheme and a power law right tail behaviour of bilateral trade flows. Different assumptions on the urns' reinforcement parameters account for local clustering, path-shortening and sparsity. Likelihood-based estimation approaches are facilitated by feasible likelihood analytical derivation in various network settings. A simulated example and the empirical results on the international trade network are discussed.
Bayesian Network Based XP Process Modelling
Directory of Open Access Journals (Sweden)
Mohamed Abouelela
2010-07-01
Full Text Available A Bayesian Network based mathematical model has been used for modelling Extreme Programmingsoftware development process. The model is capable of predicting the expected finish time and theexpected defect rate for each XP release. Therefore, it can be used to determine the success/failure of anyXP Project. The model takes into account the effect of three XP practices, namely: Pair Programming,Test Driven Development and Onsite Customer practices. The model’s predictions were validated againsttwo case studies. Results show the precision of our model especially in predicting the project finish time.
A Method to Design Synthetic Cell-Cycle Networks
Institute of Scientific and Technical Information of China (English)
MIAO Ke-Ke
2009-01-01
The interactions among proteins, DNA and RNA in an organism form elaborate cell-cycle networks which govern cell growth and proliferation. Understanding the common structure of ce11-cycle networks will be of great benefit to science research. Here, inspired by the importance of the cell-cycle regulatory network of yeast which has been studied intensively, we focus on small networks with 11 nodes, equivalent to that of the cell-cycle regulatory network used by Li et al. [Proc. Natl. Acad. Sci. USA 101(2004)4781] Using a Boolean model, we study the correlation between structure and function, and a possible common structure. It is found that cascade-like networks with a great number of interactions between nodes are stable. Based on these findings, we are able to construct synthetic networks that have the same functions as the cell-cycle regulatory network.
Dynamical Analysis of Protein Regulatory Network in Budding Yeast Nucleus
Institute of Scientific and Technical Information of China (English)
LI Fang-Ting; JIA Xun
2006-01-01
@@ Recent progresses in the protein regulatory network of budding yeast Saccharomyces cerevisiae have provided a global picture of its protein network for further dynamical research. We simplify and modularize the protein regulatory networks in yeast nucleus, and study the dynamical properties of the core 37-node network by a Boolean network model, especially the evolution steps and final fixed points. Our simulation results show that the number of fixed points N(k) for a given size of the attraction basin k obeys a power-law distribution N(k)∝k-2.024. The yeast network is more similar to a scale-free network than a random network in the above dynamical properties.
Keystone Business Models for Network Security Processors
Directory of Open Access Journals (Sweden)
Arthur Low
2013-07-01
Full Text Available Network security processors are critical components of high-performance systems built for cybersecurity. Development of a network security processor requires multi-domain experience in semiconductors and complex software security applications, and multiple iterations of both software and hardware implementations. Limited by the business models in use today, such an arduous task can be undertaken only by large incumbent companies and government organizations. Neither the “fabless semiconductor” models nor the silicon intellectual-property licensing (“IP-licensing” models allow small technology companies to successfully compete. This article describes an alternative approach that produces an ongoing stream of novel network security processors for niche markets through continuous innovation by both large and small companies. This approach, referred to here as the "business ecosystem model for network security processors", includes a flexible and reconfigurable technology platform, a “keystone” business model for the company that maintains the platform architecture, and an extended ecosystem of companies that both contribute and share in the value created by innovation. New opportunities for business model innovation by participating companies are made possible by the ecosystem model. This ecosystem model builds on: i the lessons learned from the experience of the first author as a senior integrated circuit architect for providers of public-key cryptography solutions and as the owner of a semiconductor startup, and ii the latest scholarly research on technology entrepreneurship, business models, platforms, and business ecosystems. This article will be of interest to all technology entrepreneurs, but it will be of particular interest to owners of small companies that provide security solutions and to specialized security professionals seeking to launch their own companies.
Kaushik, Aman Chandra; Sahi, Shakti
2015-01-01
Systems biology addresses challenges in the analysis of genomics data, especially for complex genes and protein interactions using Meta data approach on various signaling pathways. In this paper, we report systems biology and biological circuits approach to construct pathway and identify early gene and protein interactions for predicting GPR142 responses in Type 2 diabetes. The information regarding genes, proteins and other molecules involved in Type 2 diabetes were retrieved from literature...
A Model of Mental State Transition Network
Xiang, Hua; Jiang, Peilin; Xiao, Shuang; Ren, Fuji; Kuroiwa, Shingo
Emotion is one of the most essential and basic attributes of human intelligence. Current AI (Artificial Intelligence) research is concentrating on physical components of emotion, rarely is it carried out from the view of psychology directly(1). Study on the model of artificial psychology is the first step in the development of human-computer interaction. As affective computing remains unpredictable, creating a reasonable mental model becomes the primary task for building a hybrid system. A pragmatic mental model is also the fundament of some key topics such as recognition and synthesis of emotions. In this paper a Mental State Transition Network Model(2) is proposed to detect human emotions. By a series of psychological experiments, we present a new way to predict coming human's emotions depending on the various current emotional states under various stimuli. Besides, people in different genders and characters are taken into consideration in our investigation. According to the psychological experiments data derived from 200 questionnaires, a Mental State Transition Network Model for describing the transitions in distribution among the emotions and relationships between internal mental situations and external are concluded. Further more the coefficients of the mental transition network model were achieved. Comparing seven relative evaluating experiments, an average precision rate of 0.843 is achieved using a set of samples for the proposed model.
The Kuramoto model in complex networks
Rodrigues, Francisco A.; Peron, Thomas K. DM.; Ji, Peng; Kurths, Jürgen
2016-01-01
Synchronization of an ensemble of oscillators is an emergent phenomenon present in several complex systems, ranging from social and physical to biological and technological systems. The most successful approach to describe how coherent behavior emerges in these complex systems is given by the paradigmatic Kuramoto model. This model has been traditionally studied in complete graphs. However, besides being intrinsically dynamical, complex systems present very heterogeneous structure, which can be represented as complex networks. This report is dedicated to review main contributions in the field of synchronization in networks of Kuramoto oscillators. In particular, we provide an overview of the impact of network patterns on the local and global dynamics of coupled phase oscillators. We cover many relevant topics, which encompass a description of the most used analytical approaches and the analysis of several numerical results. Furthermore, we discuss recent developments on variations of the Kuramoto model in networks, including the presence of noise and inertia. The rich potential for applications is discussed for special fields in engineering, neuroscience, physics and Earth science. Finally, we conclude by discussing problems that remain open after the last decade of intensive research on the Kuramoto model and point out some promising directions for future research.
Neural Network Model of Memory Retrieval.
Recanatesi, Stefano; Katkov, Mikhail; Romani, Sandro; Tsodyks, Misha
2015-01-01
Human memory can store large amount of information. Nevertheless, recalling is often a challenging task. In a classical free recall paradigm, where participants are asked to repeat a briefly presented list of words, people make mistakes for lists as short as 5 words. We present a model for memory retrieval based on a Hopfield neural network where transition between items are determined by similarities in their long-term memory representations. Meanfield analysis of the model reveals stable states of the network corresponding (1) to single memory representations and (2) intersection between memory representations. We show that oscillating feedback inhibition in the presence of noise induces transitions between these states triggering the retrieval of different memories. The network dynamics qualitatively predicts the distribution of time intervals required to recall new memory items observed in experiments. It shows that items having larger number of neurons in their representation are statistically easier to recall and reveals possible bottlenecks in our ability of retrieving memories. Overall, we propose a neural network model of information retrieval broadly compatible with experimental observations and is consistent with our recent graphical model (Romani et al., 2013). PMID:26732491
Modelling crime linkage with Bayesian networks
J. de Zoete; M. Sjerps; D. Lagnado; N. Fenton
2015-01-01
When two or more crimes show specific similarities, such as a very distinct modus operandi, the probability that they were committed by the same offender becomes of interest. This probability depends on the degree of similarity and distinctiveness. We show how Bayesian networks can be used to model
Unified Model for Generation Complex Networks with Utility Preferential Attachment
Institute of Scientific and Technical Information of China (English)
WU Jian-Jun; GAO Zi-You; SUN Hui-Jun
2006-01-01
In this paper, based on the utility preferential attachment, we propose a new unified model to generate different network topologies such as scale-free, small-world and random networks. Moreover, a new network structure named super scale network is found, which has monopoly characteristic in our simulation experiments. Finally, the characteristics ofthis new network are given.
Hybrid simulation models of production networks
Kouikoglou, Vassilis S
2001-01-01
This book is concerned with a most important area of industrial production, that of analysis and optimization of production lines and networks using discrete-event models and simulation. The book introduces a novel approach that combines analytic models and discrete-event simulation. Unlike conventional piece-by-piece simulation, this method observes a reduced number of events between which the evolution of the system is tracked analytically. Using this hybrid approach, several models are developed for the analysis of production lines and networks. The hybrid approach combines speed and accuracy for exceptional analysis of most practical situations. A number of optimization problems, involving buffer design, workforce planning, and production control, are solved through the use of hybrid models.
Network Coding Capacity of Random Wireless Networks under a SINR Model
Kong, Zhenning; Aly, Salah A.; Soljanin, Emina; Yeh, Edmund M.; Klappenecker, Andreas
2008-01-01
Previous work on network coding capacity for random wired and wireless networks have focused on the case where the capacities of links in the network are independent. In this paper, we consider a more realistic model, where wireless networks are modelled by random geometric graphs with interference and noise. In this model, the capacities of links are not independent. By employing coupling and martingale methods, we show that, under mild conditions, the network coding capacity for random wire...
Modeling Multistandard Wireless Networks in OPNET
DEFF Research Database (Denmark)
Zakrzewska, Anna; Berger, Michael Stübert; Ruepp, Sarah Renée
2011-01-01
Future wireless communication is emerging towards one heterogeneous platform. In this new environment wireless access will be provided by multiple radio technologies that are cooperating and complementing one another. The paper investigates the possibilities of developing such a multistandard sys...... system using OPNET Modeler. A network model consisting of LTE interworking with WLAN and WiMAX is considered from the radio resource management perspective. In particular, implementing a joint packet scheduler across multiple systems is discussed more in detail....
XY model in small-world networks
Kim, Beom Jun; Hong, H.; Holme, Petter; Jeon, Gun Sang; Minnhagen, Petter; Choi, M. Y.
2001-01-01
The phase transition in the XY model on one-dimensional small-world networks is investigated by means of Monte-Carlo simulations. It is found that long-range order is present at finite temperatures, even for very small values of the rewiring probability, suggesting a finite-temperature transition for any nonzero rewiring probability. Nature of the phase transition is discussed in comparison with the globally-coupled XY model.
Constructing a fish metabolic network model
Li, S.; Pozhitkov, A.; R. Ryan; Manning, C; Brown-Peterson, N.; Brouwer, M
2010-01-01
We report the construction of a genome-wide fish metabolic network model, MetaFishNet, and its application to analyzing high throughput gene expression data. This model is a stepping stone to broader applications of fish systems biology, for example by guiding study design through comparison with human metabolism and the integration of multiple data types. MetaFishNet resources, including a pathway enrichment analysis tool, are accessible at http://metafishnet.appspot.com.
Serra, R; Villani, M; Graudenzi, A; Kauffman, S A
2007-06-01
In a previous study it was shown that a simple random Boolean network model, with two input connections per node, can describe with a good approximation (with the exception of the smallest avalanches) the distribution of perturbations in gene expression levels induced by the knock-out of single genes in Saccharomyces cerevisiae. Here we address the reason why such a simple model actually works: we present a theoretical study of the distribution of avalanches and show that, in the case of a Poissonian distribution of outgoing links, their distribution is determined by the value of the Derrida exponent. This explains why the simulations based on the simple model have been effective, in spite of the unrealistic hypothesis about the number of input connections per node. Moreover, we consider here the problem of the choice of an optimal threshold for binarizing continuous data, and we show that tuning its value provides an even better agreement between model and data, valuable also in the important case of the smallest avalanches. Finally, we also discuss the choice of an optimal value of the Derrida parameter in order to match the experimental distributions: our results indicate a value slightly below the critical value 1. PMID:17316697
Modelling dendritic ecological networks in space: anintegrated network perspective
Peterson, Erin E.; Ver Hoef, Jay M.; Isaak, Dan J.; Falke, Jeffrey A.; Fortin, Marie-Josée; Jordon, Chris E.; McNyset, Kristina; Monestiez, Pascal; Ruesch, Aaron S.; Sengupta, Aritra; Som, Nicholas; Steel, E. Ashley; Theobald, David M.; Torgersen, Christian E.; Wenger, Seth J.
2013-01-01
Dendritic ecological networks (DENs) are a unique form of ecological networks that exhibit a dendritic network topology (e.g. stream and cave networks or plant architecture). DENs have a dual spatial representation; as points within the network and as points in geographical space. Consequently, some analytical methods used to quantify relationships in other types of ecological networks, or in 2-D space, may be inadequate for studying the influence of structure and connectivity on ecological processes within DENs. We propose a conceptual taxonomy of network analysis methods that account for DEN characteristics to varying degrees and provide a synthesis of the different approaches within
On traffic modelling in GPRS networks
DEFF Research Database (Denmark)
Madsen, Tatiana Kozlova; Schwefel, Hans-Peter; Prasad, Ramjee;
2005-01-01
Optimal design and dimensioning of wireless data networks, such as GPRS, requires the knowledge of traffic characteristics of different data services. This paper presents an in-detail analysis of an IP-level traffic measurements taken in an operational GPRS network. The data measurements reported...... here are done at the Gi interface. The aim of this paper is to reveal some key statistics of GPRS data applications and to validate if the existing traffic models can adequately describe traffic volume and inter-arrival time distribution for different services. Additionally, we present a method of user...
Pointwise Approximation for the Iterated Boolean Sums of Bernstein Operators
Institute of Scientific and Technical Information of China (English)
HUO Xiao-yan; LI Cui-xiang; YAO Qiu-mei
2013-01-01
In this paper,with the help of modulus of smoothness ω2r(4)(f,t),we discuss the pointwise approximation properties for the iterated Boolean sums of Bernstein operator Bnn and obtain direct and inverse theorems when 1-1/r ≤ λ ≤ 1,r ∈ N.
Linear Strategy for Boolean Ring Based Theorem Proving
Institute of Scientific and Technical Information of China (English)
WU Jinzhao; LIU Zhuojun
2000-01-01
Two inference rules are discussed in boolean ring based theorem proving, and linear strategy is developed. It is shown that both of them are complete for linear strategy. Moreover, by introducing a partial ordering on atoms, pseudo O-linear and O-linear strategies are presented. The former is complete, the latter, however, is complete for clausal theorem proving.
Unlimited multistability and Boolean logic in microbial signalling
DEFF Research Database (Denmark)
Kothamachu, Varun B; Feliu, Elisenda; Cardelli, Luca;
2015-01-01
further prove that sharing of downstream components allows a system with n multi-domain hybrid HKs to attain 3n steady states. We find that such systems, when sensing distinct signals, can readily implement Boolean logic functions on these signals. Using two experimentally studied examples of two...
Boolean linear differential operators on elementary cellular automata
Martín Del Rey, Ángel
2014-12-01
In this paper, the notion of boolean linear differential operator (BLDO) on elementary cellular automata (ECA) is introduced and some of their more important properties are studied. Special attention is paid to those differential operators whose coefficients are the ECA with rule numbers 90 and 150.
Complexity of Identification and Dualization of Positive Boolean Functions
J.C. Bioch (Cor); T. Ibaraki
1995-01-01
textabstractWe consider in this paper the problem of identifying min T(f{hook}) and max F(f{hook}) of a positive (i.e., monotone) Boolean function f{hook}, by using membership queries only, where min T(f{hook}) (max F(f{hook})) denotes the set of minimal true vectors (maximal false vectors) of f{hoo
New Considerations for Spectral Classification of Boolean Switching Functions
Directory of Open Access Journals (Sweden)
J. E. Rice
2011-01-01
Full Text Available This paper presents some new considerations for spectral techniques for classification of Boolean functions. These considerations incorporate discussions of the feasibility of extending this classification technique beyond n=5. A new implementation is presented along with a basic analysis of the complexity of the problem. We also note a correction to results in this area that were reported in previous work.
Superatomic Boolean algebras constructed from strongly unbounded functions
Martinez, Juan Carlos
2010-01-01
Using Koszmider's strongly unbounded functions, we show the following consistency result: Suppose that $\\kappa,\\lambda$ are infinite cardinals such that $\\kappa^{+++} \\leq \\lambda$, $\\kappa^{_{{\\omega}_1}\\concatenation \\$ and $\\_{{\\omega}_2}\\concatenation \\$ can be cardinal sequences of superatomic Boolean algebras.
Constant-overhead secure computation of Boolean circuits using preprocessing
DEFF Research Database (Denmark)
Damgård, Ivan Bjerre; Zakarias, S.
2013-01-01
We present a protocol for securely computing a Boolean circuit C in presence of a dishonest and malicious majority. The protocol is unconditionally secure, assuming a preprocessing functionality that is not given the inputs. For a large number of players the work for each player is the same...
Graphical interpretation of Boolean operators for protein NMR assignments
Verdegem, Dries; Dijkstra, Klaas; Hanoulle, Xavier; Lippens, Guy
2008-01-01
We have developed a graphics based algorithm for semi-automated protein NMR assignments. Using the basic sequential triple resonance assignment strategy, the method is inspired by the Boolean operators as it applies "AND"-, "OR"- and "NOT"-like operations on planes pulled out of the classical three-
On the Prime Whales of a Boolean Algebra
Holland, Jason
2013-01-01
In this note, we introduce objects called prime whales and use them to represent a Boolean algebra as an algebra of sets in a way that is analogous to Stone's Representation Theorem. We also characterize the existence of prime whales in terms of the existence of prime ideals.
Boolean approaches to graph embeddings related to VLSI
Institute of Scientific and Technical Information of China (English)
刘彦佩
2001-01-01
This paper discusses the development of Boolean methods in some topics on graph em-beddings which are related to VLSI. They are mainly the general theory of graph embeddability, the orientabilities of a graph and the rectilinear layout of an electronic circuit.
Gavinsky, D; Kempe, J; Gavinsky, Dmitry; Kempe, Julia; Wolf, Ronald de
2006-01-01
We give an exponential separation between one-way quantum and classical communication complexity for a Boolean function. Earlier such a separation was known only for a relation. A very similar result was obtained earlier but independently by Kerenidis and Raz [KR06]. Our version of the result gives an example in the bounded storage model of cryptography, where the key is secure if the adversary has a certain amount of classical storage, but is completely insecure if he has a similar amount of quantum storage.
Neural Network Model of memory retrieval
Directory of Open Access Journals (Sweden)
Stefano eRecanatesi
2015-12-01
Full Text Available Human memory can store large amount of information. Nevertheless, recalling is often achallenging task. In a classical free recall paradigm, where participants are asked to repeat abriefly presented list of words, people make mistakes for lists as short as 5 words. We present amodel for memory retrieval based on a Hopfield neural network where transition between itemsare determined by similarities in their long-term memory representations. Meanfield analysis ofthe model reveals stable states of the network corresponding (1 to single memory representationsand (2 intersection between memory representations. We show that oscillating feedback inhibitionin the presence of noise induces transitions between these states triggering the retrieval ofdifferent memories. The network dynamics qualitatively predicts the distribution of time intervalsrequired to recall new memory items observed in experiments. It shows that items having largernumber of neurons in their representation are statistically easier to recall and reveals possiblebottlenecks in our ability of retrieving memories. Overall, we propose a neural network model ofinformation retrieval broadly compatible with experimental observations and is consistent with ourrecent graphical model (Romani et al., 2013.
Floral morphogenesis: stochastic explorations of a gene network epigenetic landscape.
Directory of Open Access Journals (Sweden)
Elena R Alvarez-Buylla
Full Text Available In contrast to the classical view of development as a preprogrammed and deterministic process, recent studies have demonstrated that stochastic perturbations of highly non-linear systems may underlie the emergence and stability of biological patterns. Herein, we address the question of whether noise contributes to the generation of the stereotypical temporal pattern in gene expression during flower development. We modeled the regulatory network of organ identity genes in the Arabidopsis thaliana flower as a stochastic system. This network has previously been shown to converge to ten fixed-point attractors, each with gene expression arrays that characterize inflorescence cells and primordial cells of sepals, petals, stamens, and carpels. The network used is binary, and the logical rules that govern its dynamics are grounded in experimental evidence. We introduced different levels of uncertainty in the updating rules of the network. Interestingly, for a level of noise of around 0.5-10%, the system exhibited a sequence of transitions among attractors that mimics the sequence of gene activation configurations observed in real flowers. We also implemented the gene regulatory network as a continuous system using the Glass model of differential equations, that can be considered as a first approximation of kinetic-reaction equations, but which are not necessarily equivalent to the Boolean model. Interestingly, the Glass dynamics recover a temporal sequence of attractors, that is qualitatively similar, although not identical, to that obtained using the Boolean model. Thus, time ordering in the emergence of cell-fate patterns is not an artifact of synchronous updating in the Boolean model. Therefore, our model provides a novel explanation for the emergence and robustness of the ubiquitous temporal pattern of floral organ specification. It also constitutes a new approach to understanding morphogenesis, providing predictions on the population dynamics of
A Networks Approach to Modeling Enzymatic Reactions.
Imhof, P
2016-01-01
Modeling enzymatic reactions is a demanding task due to the complexity of the system, the many degrees of freedom involved and the complex, chemical, and conformational transitions associated with the reaction. Consequently, enzymatic reactions are not determined by precisely one reaction pathway. Hence, it is beneficial to obtain a comprehensive picture of possible reaction paths and competing mechanisms. By combining individually generated intermediate states and chemical transition steps a network of such pathways can be constructed. Transition networks are a discretized representation of a potential energy landscape consisting of a multitude of reaction pathways connecting the end states of the reaction. The graph structure of the network allows an easy identification of the energetically most favorable pathways as well as a number of alternative routes.
Models for the Generation of Heterogeneous Complex Networks
Youssef, Bassant El Sayed
2015-01-01
Complex networks are composed of a large number of interacting nodes. Examples of complex networks include the topology of the Internet, connections between websites or web pages in the World Wide Web (WWW), and connections between participants in social networks.Due to their ubiquity, modeling complex networks is importantfor answering many research questions that cannot be answered without a mathematical model. For example, mathematical models of complex networks can be used to find the mo...
KSC Centralized Index Model in Complex Network
Directory of Open Access Journals (Sweden)
Jian Xu
2014-05-01
Full Text Available To dig potential spread nodes in a complex network mainly relies on using centralized indicators such as the node degree, closeness, betweenness and K-shell to evaluate spread node, which causes that the excavation accuracy is not high and adaptability not strong and induces other shortcomings, therefore this paper proposes KSC of centering indicator model. This model not only considers the internal attributes of nodes, but also takes the external attributes of nodes into account, and it finally conducts simulation experiments on propagation through the use of SIR model. The experimental results show that: The proposed algorithm is suitable for a variety of complex networks and it finds better, more promising and more influential dissemination nodes.
The noisy voter model on complex networks
Carro, Adrián; Miguel, Maxi San
2016-01-01
We propose a new analytical method to study stochastic, binary-state models on complex networks. Moving beyond the usual mean-field theories, this alternative approach is based on the introduction of an uncorrelated network approximation, allowing to deal with the network structure as parametric heterogeneity. As an illustration, we study the noisy voter model, a modification of the original voter model including random changes of state. The proposed method is able to unfold the dependence of the model not only on the mean degree (the mean-field prediction) but also on more complex averages over the degree distribution. In particular, we find that the degree heterogeneity ---variance of the underlying degree distribution--- has a strong influence on the location of the critical point of a noise-induced, finite-size transition occurring in the model, on the local ordering of the system, and on the functional form of its temporal correlations. Finally, we show how this latter point opens the possibility of infe...
Models and Algorithm for Stochastic Network Designs
Institute of Scientific and Technical Information of China (English)
Anthony Chen; Juyoung Kim; Seungjae Lee; Jaisung Choi
2009-01-01
The network design problem (NDP) is one of the most difficult and challenging problems in trans-portation. Traditional NDP models are often posed as a deterministic bilevel program assuming that all rele-vant inputs are known with certainty. This paper presents three stochastic models for designing transporta-tion networks with demand uncertainty. These three stochastic NDP models were formulated as the ex-pected value model, chance-constrained model, and dependent-chance model in a bilevel programming framework using different criteria to hedge against demand uncertainty. Solution procedures based on the traffic assignment algorithm, genetic algorithm, and Monte-Cado simulations were developed to solve these stochastic NDP models. The nonlinear and nonconvex nature of the bilevel program was handled by the genetic algorithm and traffic assignment algorithm, whereas the stochastic nature was addressed through simulations. Numerical experiments were conducted to evaluate the applicability of the stochastic NDP models and the solution procedure. Results from the three experiments show that the solution procedures are quite robust to different parameter settings.
Network Strategies in the Voter Model
Javarone, Marco Alberto
2013-01-01
We study a simple voter model with two competing parties. In particular, we represent the case of political elections, where people can choose to support one of the two competitors or to remain neutral. People interact in a social network and their opinion depends on those of their neighbors. Therefore, people may change opinion over time, i.e., they can support one competitor or none. The two competitors try to gain the people's consensus by interacting with their neighbors and also with other people. In particular, competitors define temporal connections, following a strategy, to interact with people they do not know, i.e., with all the people that are not their neighbors. We analyze the proposed model to investigate which network strategies are more advantageous, for the competitors, in order to gain the popular consensus. As result, we found that the best network strategy depends on the topology of the social network. Finally, we investigate how the charisma of competitors affects the outcomes of the prop...
Modelling conflicts with cluster dynamics on networks
Tadic, Bosiljka
2010-01-01
We introduce cluster dynamical models of conflicts in which only the largest cluster can be involved in an action. This mimics the situations in which an attack is planned by a central body, and the largest attack force is used. We study the model in its annealed random graph version, on a fixed network, and on a network evolving through the actions. The sizes of actions are distributed with a power-law tail, however, the exponent is non-universal and depends on the frequency of actions and sparseness of the available connections between units. Allowing the network reconstruction over time in a self-organized manner, e.g., by adding the links based on previous liaisons between units, we find that the power-law exponent depends on the evolution time of the network. Its lower limit is given by the universal value 5/2, derived analytically for the case of random fragmentation processes. In the temporal patterns behind the size of actions we find long-range correlations in the time series of number of clusters an...
Modeling Dynamic Evolution of Online Friendship Network
Institute of Scientific and Technical Information of China (English)
吴联仁; 闫强
2012-01-01
In this paper,we study the dynamic evolution of friendship network in SNS (Social Networking Site).Our analysis suggests that an individual joining a community depends not only on the number of friends he or she has within the community,but also on the friendship network generated by those friends.In addition,we propose a model which is based on two processes:first,connecting nearest neighbors;second,strength driven attachment mechanism.The model reflects two facts:first,in the social network it is a universal phenomenon that two nodes are connected when they have at least one common neighbor;second,new nodes connect more likely to nodes which have larger weights and interactions,a phenomenon called strength driven attachment (also called weight driven attachment).From the simulation results,we find that degree distribution P(k),strength distribution P(s),and degree-strength correlation are all consistent with empirical data.
Features and heterogeneities in growing network models
Ferretti, Luca; Yang, Bin; Marmorini, Giacomo; Bianconi, Ginestra
2011-01-01
Many complex networks from the World-Wide-Web to biological networks are growing taking into account the heterogeneous features of the nodes. The feature of a node might be a discrete quantity such as a classification of a URL document as personal page, thematic website, news, blog, search engine, social network, ect. or the classification of a gene in a functional module. Moreover the feature of a node can be a continuous variable such as the position of a node in the embedding space. In order to account for these properties, in this paper we provide a generalization of growing network models with preferential attachment that includes the effect of heterogeneous features of the nodes. The main effect of heterogeneity is the emergence of an "effective fitness" for each class of nodes, determining the rate at which nodes acquire new links. Beyond the degree distribution, in this paper we give a full characterization of the other relevant properties of the model. We evaluate the clustering coefficient and show ...
Performance modeling, loss networks, and statistical multiplexing
Mazumdar, Ravi
2009-01-01
This monograph presents a concise mathematical approach for modeling and analyzing the performance of communication networks with the aim of understanding the phenomenon of statistical multiplexing. The novelty of the monograph is the fresh approach and insights provided by a sample-path methodology for queueing models that highlights the important ideas of Palm distributions associated with traffic models and their role in performance measures. Also presented are recent ideas of large buffer, and many sources asymptotics that play an important role in understanding statistical multiplexing. I
Artificial Neural Network Model for Predicting Compressive
Directory of Open Access Journals (Sweden)
Salim T. Yousif
2013-05-01
Full Text Available Compressive strength of concrete is a commonly used criterion in evaluating concrete. Although testing of the compressive strength of concrete specimens is done routinely, it is performed on the 28th day after concrete placement. Therefore, strength estimation of concrete at early time is highly desirable. This study presents the effort in applying neural network-based system identification techniques to predict the compressive strength of concrete based on concrete mix proportions, maximum aggregate size (MAS, and slump of fresh concrete. Back-propagation neural networks model is successively developed, trained, and tested using actual data sets of concrete mix proportions gathered from literature. The test of the model by un-used data within the range of input parameters shows that the maximum absolute error for model is about 20% and 88% of the output results has absolute errors less than 10%. The parametric study shows that water/cement ratio (w/c is the most significant factor affecting the output of the model. The results showed that neural networks has strong potential as a feasible tool for predicting compressive strength of concrete.
Simulating Quantitative Cellular Responses Using Asynchronous Threshold Boolean Network Ensembles
With increasing knowledge about the potential mechanisms underlying cellular functions, it is becoming feasible to predict the response of biological systems to genetic and environmental perturbations. Due to the lack of homogeneity in living tissues it is difficult to estimate t...
Networks in Cell Biology = Modelling cell biology with networks
Buchanan, Mark; Caldarelli, Guido; De Los Rios, Paolo; Rao, Francesco; Vendruscolo, M.
2010-01-01
The science of complex biological networks is transforming research in areas ranging from evolutionary biology to medicine. This is the first book on the subject, providing a comprehensive introduction to complex network science and its biological applications. With contributions from key leaders in both network theory and modern cell biology, this book discusses the network science that is increasingly foundational for systems biology and the quantitative understanding of living systems. It ...
New Federated Collaborative Networked Organization Model (FCNOM
Directory of Open Access Journals (Sweden)
Morcous M. Yassa
2012-01-01
Full Text Available Formation of Collaborative Networked Organization (CNO usually comes upon expected business opportunities and needs huge of negotiation during its lifecycle, especially to increase the Dynamic Virtual Organization (DVO configuration automation. Decision makers need more comprehensive information about CNO system to support their decisions. Unfortunately, there is no single formal modeling, tool, approach or any comprehensive methodology that covers all perspectives. In spite of there are some approaches to model CNO have been existed, these approaches model the CNO either with respect to the technology, or business without considering organizational behavior, federation modeling, and external environments. The aim of this paper is to propose an integrated framework that combines the existed modeling perspectives, as well as, proposes new ones. Also, it provides clear CNO boundaries. By using this approach the view of CNO environment becomes clear and unified. Also, it minimizes the negotiations within CNO components during its life cycle, supports DVO configuration automation, as well as, helps decision making for DVO, and achieves harmonization between CNO partners. The proposed FCNOM utilizes CommonKADS methodology organization model for describing CNO components. Insurance Collaborative Network has been used as an example to proof the proposed FCNOM model.
Influence of Deterministic Attachments for Large Unifying Hybrid Network Model
Institute of Scientific and Technical Information of China (English)
无
2011-01-01
Large unifying hybrid network model (LUHPM) introduced the deterministic mixing ratio fd on the basis of the harmonious unification hybrid preferential model, to describe the influence of deterministic attachment to the network topology characteristics,
Modeling In-Network Aggregation in VANETs
Dietzel, Stefan; Kargl, Frank; Heijenk, Geert; Schaub, Florian
2011-01-01
The multitude of applications envisioned for vehicular ad hoc networks requires efficient communication and dissemination mechanisms to prevent network congestion. In-network data aggregation promises to reduce bandwidth requirements and enable scalability in large vehicular networks. However, most
Reliable Communication Models in Interdependent Critical Infrastructure Networks
Energy Technology Data Exchange (ETDEWEB)
Lee, Sangkeun (Matt) [ORNL; Chinthavali, Supriya [ORNL; Shankar, Mallikarjun [ORNL
2016-01-01
Modern critical infrastructure networks are becoming increasingly interdependent where the failures in one network may cascade to other dependent networks, causing severe widespread national-scale failures. A number of previous efforts have been made to analyze the resiliency and robustness of interdependent networks based on different models. However, communication network, which plays an important role in today's infrastructures to detect and handle failures, has attracted little attention in the interdependency studies, and no previous models have captured enough practical features in the critical infrastructure networks. In this paper, we study the interdependencies between communication network and other kinds of critical infrastructure networks with an aim to identify vulnerable components and design resilient communication networks. We propose several interdependency models that systematically capture various features and dynamics of failures spreading in critical infrastructure networks. We also discuss several research challenges in building reliable communication solutions to handle failures in these models.
Nonequilibrium Zaklan model on Apollonian Networks
Lima, F W S
2012-01-01
The Zaklan model had been proposed and studied recently using the equilibrium Ising model on Square Lattices (SL) by Zaklan et al (2008), near the critical temperature of the Ising model presenting a well-defined phase transition; but on normal and modified Apollonian networks (ANs), Andrade et al. (2005, 2009) studied the equilibrium Ising model. They showed the equilibrium Ising model not to present on ANs a phase transition of the type for the 2D Ising model. Here, within the context of agent-based Monte-Carlo simulations, we study the Zaklan model using the well-known majority-vote model (MVM) with noise and apply it to tax evasion on ANs, to show that differently from the Ising model the MVM on ANs presents a well defined phase transition. To control the tax evasion in the economics model proposed by Zaklan et al, MVM is applied in the neighborhood of the critical noise $q_{c}$ to the Zaklan model. Here we show that the Zaklan model is robust because this can be studied besides using equilibrium dynamics...
Electronic circuits modeling using artificial neural networks
Directory of Open Access Journals (Sweden)
Andrejević Miona V.
2003-01-01
Full Text Available In this paper artificial neural networks (ANN are applied to modeling of electronic circuits. ANNs are used for application of the black-box modeling concept in the time domain. Modeling process is described, so the topology of the ANN, the testing signal used for excitation, together with the complexity of ANN are considered. The procedure is first exemplified in modeling of resistive circuits. MOS transistor, as a four-terminal device, is modeled. Then nonlinear negative resistive characteristic is modeled in order to be used as a piece-wise linear resistor in Chua's circuit. Examples of modeling nonlinear dynamic circuits are given encompassing a variety of modeling problems. A nonlinear circuit containing quartz oscillator is considered for modeling. Verification of the concept is performed by verifying the ability of the model to generalize i.e. to create acceptable responses to excitations not used during training. Implementation of these models within a behavioral simulator is exemplified. Every model is implemented in realistic surrounding in order to show its interaction, and of course, its usage and purpose.