Dynamic Bayesian networks in molecular plant science: inferring gene regulatory networks from multiple gene expression time series

Dondelinger, Frank; Husmeier, Dirk; Lèbre, Sophie

doi:10.1007/s10681-011-0538-3

Cited by 16 publications

(11 citation statements)

References 25 publications

(23 reference statements)

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“…there can be different gene networks regulating different phases of the cell cycle). Hererogeneous DBNs [ 81 - 83 ] model the presence of changepoints; that is, specific times when both the structure and the parameters of the network can vary. The area of non-homogeneous processes with CTBNs is yet to be explored.…”

Section: Discussionmentioning

confidence: 99%

Gene network inference using continuous time Bayesian networks: a comparative study and application to Th17 cell differentiation

et al. 2014

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BackgroundDynamic aspects of gene regulatory networks are typically investigated by measuring system variables at multiple time points. Current state-of-the-art computational approaches for reconstructing gene networks directly build on such data, making a strong assumption that the system evolves in a synchronous fashion at fixed points in time. However, nowadays omics data are being generated with increasing time course granularity. Thus, modellers now have the possibility to represent the system as evolving in continuous time and to improve the models’ expressiveness.ResultsContinuous time Bayesian networks are proposed as a new approach for gene network reconstruction from time course expression data. Their performance was compared to two state-of-the-art methods: dynamic Bayesian networks and Granger causality analysis. On simulated data, the methods comparison was carried out for networks of increasing size, for measurements taken at different time granularity densities and for measurements unevenly spaced over time. Continuous time Bayesian networks outperformed the other methods in terms of the accuracy of regulatory interactions learnt from data for all network sizes. Furthermore, their performance degraded smoothly as the size of the network increased. Continuous time Bayesian networks were significantly better than dynamic Bayesian networks for all time granularities tested and better than Granger causality for dense time series. Both continuous time Bayesian networks and Granger causality performed robustly for unevenly spaced time series, with no significant loss of performance compared to the evenly spaced case, while the same did not hold true for dynamic Bayesian networks. The comparison included the IRMA experimental datasets which confirmed the effectiveness of the proposed method. Continuous time Bayesian networks were then applied to elucidate the regulatory mechanisms controlling murine T helper 17 (Th17) cell differentiation and were found to be effective in discovering well-known regulatory mechanisms, as well as new plausible biological insights.ConclusionsContinuous time Bayesian networks were effective on networks of both small and large size and were particularly feasible when the measurements were not evenly distributed over time. Reconstruction of the murine Th17 cell differentiation network using continuous time Bayesian networks revealed several autocrine loops, suggesting that Th17 cells may be auto regulating their own differentiation process.

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Section: Discussionmentioning

confidence: 99%

Gene network inference using continuous time Bayesian networks: a comparative study and application to Th17 cell differentiation

et al. 2014

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“…The selected feature sets can be used for causal discovery and classification. Dondelinger et al [21] introduced a novel information sharing scheme to infer gene regulatory networks from multiple sources of gene expression data. They illustrate and test this method on a set of synthetic data, using three different measures to quantify the network reconstruction accuracy.…”

Section: Related Workmentioning

confidence: 99%

Inferring Weighted Directed Association Networks from Multivariate Time Series with the Small-Shuffle Symbolic Transfer Entropy Spectrum Method

Zhao

2016

Entropy

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Abstract:Complex network methodology is very useful for complex system exploration. However, the relationships among variables in complex systems are usually not clear. Therefore, inferring association networks among variables from their observed data has been a popular research topic. We propose a method, named small-shuffle symbolic transfer entropy spectrum (SSSTES), for inferring association networks from multivariate time series. The method can solve four problems for inferring association networks, i.e., strong correlation identification, correlation quantification, direction identification and temporal relation identification. The method can be divided into four layers. The first layer is the so-called data layer. Data input and processing are the things to do in this layer. In the second layer, we symbolize the model data, original data and shuffled data, from the previous layer and calculate circularly transfer entropy with different time lags for each pair of time series variables. Thirdly, we compose transfer entropy spectrums for pairwise time series with the previous layer's output, a list of transfer entropy matrix. We also identify the correlation level between variables in this layer. In the last layer, we build a weighted adjacency matrix, the value of each entry representing the correlation level between pairwise variables, and then get the weighted directed association network. Three sets of numerical simulated data from a linear system, a nonlinear system and a coupled Rossler system are used to show how the proposed approach works. Finally, we apply SSSTES to a real industrial system and get a better result than with two other methods.

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“…Joint probability distribution is used in the calculation of relationships in Bayesian network. Based on this modeling several standard methods are introduced (Yang et al 2011; Tan and Mohamad 2012; Dondelinger et al 2012). Gene network interactions are cyclic and non-linearly complex.…”

Section: Introductionmentioning

confidence: 99%

Immuno-hybrid algorithm: a novel hybrid approach for GRN reconstruction

Jereesh

Govindan

2016

3 Biotech

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Bio-inspired algorithms are widely used to optimize the model parameters of GRN. In this paper, focus is given to develop improvised versions of bio-inspired algorithm for the specific problem of reconstruction of gene regulatory network. The approach is applied to the data set that was developed by the DNA microarray technology through biological experiments in the lab. This paper introduced a novel hybrid method, which combines the clonal selection algorithm and BFGS Quasi-Newton algorithm. The proposed approach implemented for real world E. coli data set and identified most of the relations. The results are also compared with the existing methods and proven to be efficient.

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Dynamic Bayesian networks in molecular plant science: inferring gene regulatory networks from multiple gene expression time series

Cited by 16 publications

References 25 publications

Gene network inference using continuous time Bayesian networks: a comparative study and application to Th17 cell differentiation

Gene network inference using continuous time Bayesian networks: a comparative study and application to Th17 cell differentiation

Inferring Weighted Directed Association Networks from Multivariate Time Series with the Small-Shuffle Symbolic Transfer Entropy Spectrum Method

Immuno-hybrid algorithm: a novel hybrid approach for GRN reconstruction

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