Comparison of probabilistic Boolean network and dynamic Bayesian network approaches for inferring gene regulatory networks

School of Computing, University of Southern Mississippi, Hattiesburg, MS 39406, USA.
BMC Bioinformatics (Impact Factor: 2.67). 02/2007; 8 Suppl 7(Suppl 7):S13. DOI: 10.1186/1471-2105-8-S7-S13
Source: PubMed

ABSTRACT The regulation of gene expression is achieved through gene regulatory networks (GRNs) in which collections of genes interact with one another and other substances in a cell. In order to understand the underlying function of organisms, it is necessary to study the behavior of genes in a gene regulatory network context. Several computational approaches are available for modeling gene regulatory networks with different datasets. In order to optimize modeling of GRN, these approaches must be compared and evaluated in terms of accuracy and efficiency.
In this paper, two important computational approaches for modeling gene regulatory networks, probabilistic Boolean network methods and dynamic Bayesian network methods, are compared using a biological time-series dataset from the Drosophila Interaction Database to construct a Drosophila gene network. A subset of time points and gene samples from the whole dataset is used to evaluate the performance of these two approaches.
The comparison indicates that both approaches had good performance in modeling the gene regulatory networks. The accuracy in terms of recall and precision can be improved if a smaller subset of genes is selected for inferring GRNs. The accuracy of both approaches is dependent upon the number of selected genes and time points of gene samples. In all tested cases, DBN identified more gene interactions and gave better recall than PBN.

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Available from: Ping Gong, Aug 01, 2015
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    • "Currently, a lot of research is being devoted to introduce improvements in the working of these algorithms and enhance our understanding about gene interactions. Out of the statistical techniques currently adopted to model gene networks, dynamic Bayesian networks have received the most widespread attention [9], [10], [37]. State space models [11], [12], [24], [25], [32] and Kalman filter (EKF), which are specific instances of dynamic Bayesian networks, have also been employed to model gene regulatory networks [5], [22]. "
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    ABSTRACT: This paper considers the problem of learning the structure of gene regulatory networks from gene expression time series data. A more realistic scenario when the state space model representing a gene network evolves nonlinearly is considered while a linear model is assumed for the microarray data. To capture the nonlinearity, a particle filter-based state estimation algorithm is considered instead of the contemporary linear approximation-based approaches. The parameters characterizing the regulatory relations among various genes are estimated online using a Kalman filter. Since a particular gene interacts with a few other genes only, the parameter vector is expected to be sparse. The state estimates delivered by the particle filter and the observed microarray data are then subjected to a LASSO-based least squares regression operation which yields a parsimonious and efficient description of the regulatory network by setting the irrelevant coefficients to zero. The performance of the aforementioned algorithm is compared with the extended Kalman filter (EKF) and Unscented Kalman Filter (UKF) employing the Mean Square Error (MSE) as the fidelity criterion in recovering the parameters of gene regulatory networks from synthetic data and real biological data. Extensive computer simulations illustrate that the proposed particle filter-based network inference algorithm outperforms EKF and UKF, and therefore, it can serve as a natural framework for modeling gene regulatory networks with nonlinear and sparse structure.
    IEEE/ACM transactions on computational biology and bioinformatics / IEEE, ACM 02/2012; 9(4):1203-11. DOI:10.1109/TCBB.2012.32 · 1.54 Impact Factor
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    • "For the inference of a PBN, the Boolean functions, the predictor sets and the selection probabilities of the Boolean functions can be obtained using the methods proposed in [14] [21] [25]. Given a PBN, assuming the underlying Markov chain is irreducible, its longrun behavior is characterized by its stationary distribution. "
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    ABSTRACT: Modeling gene regulation is an important problem in genomic research. Boolean networks (BN) and its generalization probabilistic Boolean networks (PBNs) have been proposed to model genetic regulatory interactions. BN is a deterministic model while PBN is a stochastic model. In a PBN, on one hand, its stationary distribution gives important information about the long-run behavior of the network. On the other hand, one may be interested in system synthesis which requires the construction of networks from the observed stationary distribution. This results in an inverse problem which is ill-posed and challenging. Because there may be many networks or no network having the given properties and the size of the inverse problem is huge. In this paper, we consider the problem of constructing PBNs from a given stationary distribution and a set of given Boolean Networks (BNs). We first formulate the inverse problem as a constrained least squares problem. We then propose a heuristic method based on Conjugate Gradient (CG) algorithm, an iterative method, to solve the resulting least squares problem. We also introduce an estimation method for the parameters of the PBNs. Numerical examples are then given to demonstrate the effectiveness of the proposed methods.
    Information Sciences 07/2010; DOI:10.1016/j.ins.2010.03.014 · 3.89 Impact Factor
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    • "And in a recent work, [12] applying the various search methods to real microarray data from an independently known gene expression regulatory network confirms their failure. But, various researches still concentrate motivationally on this problem [8], [10], [9], [11], [1], [5]. For each work, the authors propose their own effective methods to improve the accuracy of the inference of gene regulation networks for a specific type of microarray experiments data. "
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    ABSTRACT: In this work, we reconstruct the gene regulation networks from the microarray experiments data by Bayesian networks approach. We use the evolutionary algorithm for the search-and-score based structure learning methods. The learned network is tested by the hypothesis testing with two populations of patient data, one with treatment (drugs), other without treatment. The answer of question "How does the treatment influence to gene regulation?" is expected.
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