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Edward R Dougherty - One of the best experts on this subject based on the ideXlab platform.

  • learning restricted Boolean Network model by time series data
    Eurasip Journal on Bioinformatics and Systems Biology, 2014
    Co-Authors: Hongjia Ouyang, Jie Fang, Liangzhong Shen, Edward R Dougherty
    Abstract:

    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 μ e , the normalized Hamming distance of state transition μ st , and the steady-state distribution distance μ ssd . Results show that the proposed algorithm outperforms the others according to both μ e and μ st , whereas its performance according to μ ssd is intermediate between best-fit and the three-rule algorithms. Thus, our new algorithm is more appropriate

  • Intervention in context-sensitive probabilistic Boolean Networks revisited
    EURASIP journal on bioinformatics & systems biology, 2009
    Co-Authors: B. Faryabi, Aniruddha Datta, Golnaz Vahedi, Jean-francois Chamberland, Edward R Dougherty
    Abstract:

    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.

  • GENSiPS - Quantification of data extraction noise in probabilistic Boolean Network modeling
    2009 IEEE International Workshop on Genomic Signal Processing and Statistics, 2009
    Co-Authors: Ranadip Pal, Aniruddha Datta, Edward R Dougherty
    Abstract:

    Probabilistic Boolean Networks have served as the main model for studying the application of optimal intervention strategies to favorably affect system dynamics. The errors originating in the data extraction or Network inference process prevent the accurate estimation of the state transition probabilities of the Network. The mathematical characterization of the uncertainties will enable us to analyze the performance of intervention strategies derived without considering the uncertainties and assist in the design of control policies robust to those uncertainties. In this paper, we will quantify the errors due to data extraction noise and discretization and their effects on the state transition and steady state probabilities of the probabilistic Boolean Network.

  • inference of a probabilistic Boolean Network from a single observed temporal sequence
    Eurasip Journal on Bioinformatics and Systems Biology, 2007
    Co-Authors: Stephen Marshall, Yufei Xiao, Edward R Dougherty
    Abstract:

    The inference of gene regulatory Networks is a key issue for genomic signal processing. This paper addresses the inference of probabilistic Boolean Networks (PBNs) from observed temporal sequences of Network states. Since a PBN is composed of a finite number of Boolean Networks, a basic observation is that the characteristics of a single Boolean Network without perturbation may be determined by its pairwise transitions. Because the Network function is fixed and there are no perturbations, a given state will always be followed by a unique state at the succeeding time point. Thus, a transition counting matrix compiled over a data sequence will be sparse and contain only one entry per line. If the Network also has perturbations, with small perturbation probability, then the transition counting matrix would have some insignificant nonzero entries replacing some (or all) of the zeros. If a data sequence is sufficiently long to adequately populate the matrix, then determination of the functions and inputs underlying the model is straightforward. The difficulty comes when the transition counting matrix consists of data derived from more than one Boolean Network. We address the PBN inference procedure in several steps: (1) separate the data sequence into "pure" subsequences corresponding to constituent Boolean Networks; (2) given a subsequence, infer a Boolean Network; and (3) infer the probabilities of perturbation, the probability of there being a switch between constituent Boolean Networks, and the selection probabilities governing which Network is to be selected given a switch. Capturing the full dynamic behavior of probabilistic Boolean Networks, be they binary or multivalued, will require the use of temporal data, and a great deal of it. This should not be surprising given the complexity of the model and the number of parameters, both transitional and static, that must be estimated. In addition to providing an inference algorithm, this paper demonstrates that the data requirement is much smaller if one does not wish to infer the switching, perturbation, and selection probabilities, and that constituent-Network connectivity can be discovered with decent accuracy for relatively small time-course sequences.

Tatsuya Akutsu - One of the best experts on this subject based on the ideXlab platform.

  • identification of the structure of a probabilistic Boolean Network from samples including frequencies of outcomes
    IEEE Transactions on Neural Networks, 2019
    Co-Authors: Tatsuya Akutsu, Avraham A Melkman
    Abstract:

    We study the problem of identifying the structure of a probabilistic Boolean Network (PBN), a probabilistic model of biological Networks, from a given set of samples. This problem can be regarded as an identification of a set of Boolean functions from samples. Existing studies on the identification of the structure of a PBN only use information on the occurrences of samples. In this paper, we also make use of the frequencies of occurrences of subtuples, information that is obtainable from the samples. We show that under this model, it is possible to identify a PBN from among a class of PBNs, for much broader classes of PBNs. In particular, we prove that, under a reasonable assumption, the structure of a PBN can be identified from among the class of PBNs that have at most three functions assigned to each node, but that identification may be impossible if four or more functions are assigned to each node. We also analyze the sample complexity for exactly identifying the structure of a PBN, and present an efficient algorithm for the identification of a PBN consisting of threshold functions from samples.

  • on the number of driver nodes for controlling a Boolean Network when the targets are restricted to attractors
    Journal of Theoretical Biology, 2019
    Co-Authors: Wai-ki Ching, Wenpin Hou, Peiying Ruan, Tatsuya Akutsu
    Abstract:

    Abstract It is known that many driver nodes are required to control complex biological Networks. Previous studies imply that O(N) driver nodes are required in both linear complex Network and Boolean Network models with N nodes if an arbitrary state is specified as the target. In order to cope with this intrinsic difficulty, we consider a special case of the control problem in which the targets are restricted to attractors. For this special case, we mathematically prove under the uniform distribution of states in basins that the expected number of driver nodes is only O ( log 2 N + log 2 M ) for controlling Boolean Networks, where M is the number of attractors. Since it is expected that M is not very large in many practical Networks, the new model requires a much smaller number of driver nodes. This result is based on discovery of novel relationships between control problems on Boolean Networks and the coupon collector’s problem, a well-known concept in combinatorics. We also provide lower bounds of the number of driver nodes as well as simulation results using artificial and realistic Network data, which support our theoretical findings.

  • on the number of driver nodes for controlling a Boolean Network to attractors
    bioRxiv, 2018
    Co-Authors: Wenpin Hou, Wai-ki Ching, Peiying Ruan, Tatsuya Akutsu
    Abstract:

    It is known that many driver nodes are required to control complex biological Networks. Previous studies imply that O(N) driver nodes are required in both linear complex Network and Boolean Network models with N nodes if an arbitrary state is specified as the target. In this paper, we mathematically prove under a reasonable assumption that the expected number of driver nodes is only O(log_2 N +log_2 M) for controlling Boolean Networks if the targets are restricted to attractors, where M is the number of attractors. Since it is expected that M is not very large in many practical Networks, this is a significant improvement. This result is based on discovery of novel relationships between control problems on Boolean Networks and the coupon collector9s problem, a well-known concept in combinatorics. We also provide lower bounds of the number of driver nodes as well as simulation results using artificial and realistic Network data, which support our theoretical findings.

  • exact identification of the structure of a probabilistic Boolean Network from samples
    IEEE ACM Transactions on Computational Biology and Bioinformatics, 2016
    Co-Authors: Xiaoqing Cheng, Wai-ki Ching, Tomoya Mori, Yushan Qiu, Tatsuya Akutsu
    Abstract:

    We study the number of samples required to uniquely determine the structure of a probabilistic Boolean Network (PBN), where PBNs are probabilistic extensions of Boolean Networks. We show via theoretical analysis and computational analysis that the structure of a PBN can be exactly identified with high probability from a relatively small number of samples for interesting classes of PBNs of bounded indegree. On the other hand, we also show that there exist classes of PBNs for which it is impossible to uniquely determine the structure of a PBN from samples.

  • finding a periodic attractor of a Boolean Network
    IEEE ACM Transactions on Computational Biology and Bioinformatics, 2012
    Co-Authors: Tatsuya Akutsu, Sven Kosub, Avraham A Melkman, Takeyuki Tamura
    Abstract:

    In this paper, we study the problem of finding a periodic attractor of a Boolean Network (BN), which arises in computational systems biology and is known to be NP-hard. Since a general case is quite hard to solve, we consider special but biologically important subclasses of BNs. For finding an attractor of period 2 of a BN consisting of n OR functions of positive literals, we present a polynomial time algorithm. For finding an attractor of period 2 of a BN consisting of n AND/OR functions of literals, we present an O(1.985^n) time algorithm. For finding an attractor of a fixed period of a BN consisting of n nested canalyzing functions and having constant treewidth w, we present an O(n^{2p(w+1)} poly(n)) time algorithm.

Jijayanagaram Venkatraj - One of the best experts on this subject based on the ideXlab platform.

  • Boolean Network model of oxidative stress response pathways
    2012 American Control Conference (ACC), 2012
    Co-Authors: Sriram Sridharan, Ritwik Layek, Aniruddha Datta, Jijayanagaram Venkatraj
    Abstract:

    Oxidative stress is a consequence of normal cellular metabolism. The effective functioning of the pathway responding to oxidative stress protects the cellular DNA against oxidative damage and its failure can induce aberrant cellular behaviour leading to diseases such as neurodegenerative disorders and cancer. Thus, understanding the normal signalling present in oxidative stress response pathways and determining possible signalling alterations leading to disease could provide us with useful pointers for therapeutic purposes. Using knowledge of oxidative stress response pathways from the literature, a Boolean Network model is developed whose simulated behaviour is consistent with earlier experimental observations from the literature.

  • ACC - Boolean Network model of oxidative stress response pathways
    2012 American Control Conference (ACC), 2012
    Co-Authors: Sriram Sridharan, Ritwik Layek, Aniruddha Datta, Jijayanagaram Venkatraj
    Abstract:

    Oxidative stress is a consequence of normal cellular metabolism. The effective functioning of the pathway responding to oxidative stress protects the cellular DNA against oxidative damage and its failure can induce aberrant cellular behaviour leading to diseases such as neurodegenerative disorders and cancer. Thus, understanding the normal signalling present in oxidative stress response pathways and determining possible signalling alterations leading to disease could provide us with useful pointers for therapeutic purposes. Using knowledge of oxidative stress response pathways from the literature, a Boolean Network model is developed whose simulated behaviour is consistent with earlier experimental observations from the literature.

Manuel Ruiz Marin - One of the best experts on this subject based on the ideXlab platform.

  • inference of time varying Networks through transfer entropy the case of a Boolean Network model
    Chaos, 2018
    Co-Authors: Maurizio Porfiri, Manuel Ruiz Marin
    Abstract:

    Inferring Network topologies from the time series of individual units is of paramount importance in the study of biological and social Networks. Despite considerable progress, our success in Network inference is largely limited to static Networks and autonomous node dynamics, which are often inadequate to describe complex systems. Here, we explore the possibility of reconstructing time-varying weighted topologies through the information-theoretic notion of transfer entropy. We focus on a Boolean Network model in which the weight of the links and the spontaneous activity periodically vary in time. For slowly-varying dynamics, we establish closed-form expressions for the stationary periodic distribution and transfer entropy between each pair of nodes. Our results indicate that the instantaneous weight of each link is mapped into a corresponding transfer entropy value, thereby affording the possibility of pinpointing the dominant weights at each time. However, comparing transfer entropy readings at different times may provide erroneous estimates of the strength of the links in time, due to a counterintuitive modulation of the information flow by the non-autonomous dynamics. In fact, this time variation should be used to scale transfer entropy values toward the correct inference of the time evolution of the Network weights. This study constitutes a necessary step toward a mathematically-principled use of transfer entropy to reconstruct time-varying Networks.Inferring Network topologies from the time series of individual units is of paramount importance in the study of biological and social Networks. Despite considerable progress, our success in Network inference is largely limited to static Networks and autonomous node dynamics, which are often inadequate to describe complex systems. Here, we explore the possibility of reconstructing time-varying weighted topologies through the information-theoretic notion of transfer entropy. We focus on a Boolean Network model in which the weight of the links and the spontaneous activity periodically vary in time. For slowly-varying dynamics, we establish closed-form expressions for the stationary periodic distribution and transfer entropy between each pair of nodes. Our results indicate that the instantaneous weight of each link is mapped into a corresponding transfer entropy value, thereby affording the possibility of pinpointing the dominant weights at each time. However, comparing transfer entropy readings at different...

  • Inference of time-varying Networks through transfer entropy, the case of a Boolean Network model.
    Chaos: An Interdisciplinary Journal of Nonlinear Science, 2018
    Co-Authors: Maurizio Porfiri, Manuel Ruiz Marin
    Abstract:

    Inferring Network topologies from the time series of individual units is of paramount importance in the study of biological and social Networks. Despite considerable progress, our success in Network inference is largely limited to static Networks and autonomous node dynamics, which are often inadequate to describe complex systems. Here, we explore the possibility of reconstructing time-varying weighted topologies through the information-theoretic notion of transfer entropy. We focus on a Boolean Network model in which the weight of the links and the spontaneous activity periodically vary in time. For slowly-varying dynamics, we establish closed-form expressions for the stationary periodic distribution and transfer entropy between each pair of nodes. Our results indicate that the instantaneous weight of each link is mapped into a corresponding transfer entropy value, thereby affording the possibility of pinpointing the dominant weights at each time. However, comparing transfer entropy readings at different times may provide erroneous estimates of the strength of the links in time, due to a counterintuitive modulation of the information flow by the non-autonomous dynamics. In fact, this time variation should be used to scale transfer entropy values toward the correct inference of the time evolution of the Network weights. This study constitutes a necessary step toward a mathematically-principled use of transfer entropy to reconstruct time-varying Networks.

Xingyuan Wang - One of the best experts on this subject based on the ideXlab platform.

  • image encryption algorithm based on the matrix semi tensor product with a compound secret key produced by a Boolean Network
    Information Sciences, 2020
    Co-Authors: Xingyuan Wang, Suo Gao
    Abstract:

    Abstract In this paper, a chaotic image encryption algorithm based on the matrix semi-tensor product (STP) with a compound secret key is designed. First, a new scrambling method is designed. The pixels of the initial plaintext image are randomly divided into four blocks. The pixels in each block are then subjected to different numbers of rounds of Arnold transformation, and the four blocks are combined to generate a scrambled image. Then, a compound secret key is designed. A set of pseudosecret keys is given and filtered through a synchronously updating Boolean Network to generate the real secret key. This secret key is used as the initial value of the mixed linear-nonlinear coupled map lattice (MLNCML) system to generate a chaotic sequence. Finally, the STP operation is applied to the chaotic sequences and the scrambled image to generate an encrypted image. Compared with other encryption algorithms, the algorithm proposed in this paper is more secure and effective, and it is also suitable for color image encryption.

  • image encryption algorithm for synchronously updating Boolean Networks based on matrix semi tensor product theory
    Information Sciences, 2020
    Co-Authors: Xingyuan Wang, Suo Gao
    Abstract:

    Abstract This paper studies chaotic image encryption technology and an application of matrix semi-tensor product theory, and a Boolean Network encryption algorithm for a synchronous update process is proposed. A 2D-LASM chaotic system is used to generate a random key stream. First, a Boolean Network is coded, and a Boolean matrix is generated. If necessary, the Boolean Network matrix is diffused in one round so that the Boolean matrix can be saved in the form of an image. Then, three random position scramblings are used to scramble the plaintext image. Finally, using a matrix semi-tensor product technique to generate an encrypted image in a second round of diffusion, a new Boolean Network can be generated by encoding the encrypted image. In secure communications, users can choose to implement an image encryption transmission or a Boolean Network encryption transmission according to their own needs. Compared with other algorithms, this algorithm exhibits good security characteristics.

  • Cluster synchronization of Boolean Network
    Communications in Nonlinear Science and Numerical Simulation, 2018
    Co-Authors: Hao Zhang, Xingyuan Wang
    Abstract:

    Abstract This paper studies the cluster synchronization of Boolean Network with open-loop control. Both synchronous Boolean Network and asynchronous Boolean Network are considered. Firstly, the basic cluster synchronization concept in synchronous Boolean Network is provided and necessary and sufficient conditions are derived. Secondly, with different update scheme, necessary and sufficient conditions for cluster synchronization to reference trajectories are provided and open-loop cycle controllers are designed. Finally, some numerical examples are provided and results show the efficiency of cluster synchronization methods in Boolean Network.

  • Synchronization of Asynchronous Switched Boolean Network
    IEEE ACM Transactions on Computational Biology and Bioinformatics, 2015
    Co-Authors: Hao Zhang, Xingyuan Wang, Xiaohui Lin
    Abstract:

    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.

  • Synchronization of Boolean Networks with different update schemes
    IEEE ACM transactions on computational biology and bioinformatics, 2014
    Co-Authors: Hao Zhang, Xingyuan Wang, Xiaohui Lin
    Abstract:

    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.